feat: centralize command execution timeout constant for sandbox providers

# Conflicts:
#	api/uv.lock
#	web/app/components/apps/__tests__/app-card.spec.tsx
#	web/app/components/apps/__tests__/list.spec.tsx
#	web/app/components/datasets/create/__tests__/index.spec.tsx
#	web/app/components/datasets/metadata/metadata-dataset/__tests__/dataset-metadata-drawer.spec.tsx
#	web/app/components/plugins/readme-panel/__tests__/index.spec.tsx
#	web/app/components/rag-pipeline/__tests__/index.spec.tsx
#	web/app/components/rag-pipeline/hooks/__tests__/index.spec.ts
#	web/eslint-suppressions.json

.devcontainer/post_create_command.sh

@@ -7,7 +7,7 @@ cd web && pnpm install
 pipx install uv
 
 echo "alias start-api=\"cd $WORKSPACE_ROOT/api && uv run python -m flask run --host 0.0.0.0 --port=5001 --debug\"" >> ~/.bashrc
-echo "alias start-worker=\"cd $WORKSPACE_ROOT/api && uv run python -m celery -A app.celery worker -P threads -c 1 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention\"" >> ~/.bashrc
+echo "alias start-worker=\"cd $WORKSPACE_ROOT/api && uv run python -m celery -A app.celery worker -P threads -c 1 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention,workflow_based_app_execution\"" >> ~/.bashrc
 echo "alias start-web=\"cd $WORKSPACE_ROOT/web && pnpm dev:inspect\"" >> ~/.bashrc
 echo "alias start-web-prod=\"cd $WORKSPACE_ROOT/web && pnpm build && pnpm start\"" >> ~/.bashrc
 echo "alias start-containers=\"cd $WORKSPACE_ROOT/docker && docker-compose -f docker-compose.middleware.yaml -p dify --env-file middleware.env up -d\"" >> ~/.bashrc

.github/workflows/build-push.yml

@@ -76,7 +76,9 @@ jobs:
         with:
           context: "{{defaultContext}}:${{ matrix.context }}"
           platforms: ${{ matrix.platform }}
-          build-args: COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
+          build-args: |
+            COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
+            ENABLE_PROD_SOURCEMAP=${{ matrix.context == 'web' && github.ref_name == 'deploy/dev' }}
           labels: ${{ steps.meta.outputs.labels }}
           outputs: type=image,name=${{ env[matrix.image_name_env] }},push-by-digest=true,name-canonical=true,push=true
           cache-from: type=gha,scope=${{ matrix.service_name }}

.gitignore

@@ -209,6 +209,7 @@ api/.vscode
 .history
 
 .idea/
+web/migration/
 
 # pnpm
 /.pnpm-store
@@ -221,6 +222,7 @@ mise.toml
 
 
 # AI Assistant
+.sisyphus/
 .roo/
 api/.env.backup
 /clickzetta

api/.env.example

@@ -33,6 +33,9 @@ TRIGGER_URL=http://localhost:5001
 # The time in seconds after the signature is rejected
 FILES_ACCESS_TIMEOUT=300
 
+# Collaboration mode toggle
+ENABLE_COLLABORATION_MODE=false
+
 # Access token expiration time in minutes
 ACCESS_TOKEN_EXPIRE_MINUTES=60
 
@@ -626,6 +629,11 @@ INNER_API_KEY_FOR_PLUGIN=QaHbTe77CtuXmsfyhR7+vRjI/+XbV1AaFy691iy+kGDv2Jvy0/eAh8Y
 MARKETPLACE_ENABLED=true
 MARKETPLACE_API_URL=https://marketplace.dify.ai
 
+# Creators Platform configuration
+CREATORS_PLATFORM_FEATURES_ENABLED=true
+CREATORS_PLATFORM_API_URL=https://creators.dify.ai
+CREATORS_PLATFORM_OAUTH_CLIENT_ID=
+
 # Endpoint configuration
 ENDPOINT_URL_TEMPLATE=http://localhost:5002/e/{hook_id}
 
@@ -721,6 +729,33 @@ SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_MAX_INTERVAL=200
 SANDBOX_EXPIRED_RECORDS_RETENTION_DAYS=30
 SANDBOX_EXPIRED_RECORDS_CLEAN_TASK_LOCK_TTL=90000
 
+# Sandbox Dify CLI configuration
+# Directory containing dify CLI binaries (dify-cli-<os>-<arch>). Defaults to api/bin when unset.
+SANDBOX_DIFY_CLI_ROOT=
+
+# CLI API URL for sandbox (dify-sandbox or e2b) to call back to Dify API.
+# This URL must be accessible from the sandbox environment.
+# For local development: use http://localhost:5001 or http://127.0.0.1:5001
+# For middleware docker stack (api on host): keep localhost/127.0.0.1 and use agentbox via 127.0.0.1:2222
+# For Docker deployment: use http://api:5001 (internal Docker network)
+# For external sandbox (e.g., e2b): use a publicly accessible URL
+CLI_API_URL=http://localhost:5001
+
+# Base URL for storage file ticket API endpoints (upload/download).
+# Used by sandbox containers (internal or external like e2b) that need an absolute,
+# routable address to reach the Dify API file endpoints.
+# Falls back to FILES_URL if not specified.
+# For local development: http://localhost:5001
+# For Docker deployment: http://api:5001
+FILES_API_URL=http://localhost:5001
+
+# Optional defaults for SSH sandbox provider setup (for manual config/CLI usage).
+# Middleware/local dev usually uses 127.0.0.1:2222; full docker deployment usually uses agentbox:22.
+SSH_SANDBOX_HOST=127.0.0.1
+SSH_SANDBOX_PORT=2222
+SSH_SANDBOX_USERNAME=agentbox
+SSH_SANDBOX_PASSWORD=agentbox
+SSH_SANDBOX_BASE_WORKING_PATH=/workspace/sandboxes
 
 # Redis URL used for PubSub between API and
 # celery worker

api/README.md

@@ -122,7 +122,7 @@ These commands assume you start from the repository root.
 
    ```bash
    cd api
-   uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q api_token,dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
+   uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q api_token,dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention,workflow_based_app_execution
    ```
 
 1. Optional: start Celery Beat (scheduled tasks, in a new terminal).

api/agent-notes/configs/feature/__init__.py.md

@@ -0,0 +1,9 @@
+Summary:
+Summary:
+- Application configuration definitions, including file access settings.
+
+Invariants:
+- File access settings drive signed URL expiration and base URLs.
+
+Tests:
+- Config parsing tests under tests/unit_tests/configs.

api/agent-notes/controllers/files/__init__.py.md

@@ -0,0 +1,9 @@
+Summary:
+- Registers file-related API namespaces and routes for files service.
+- Includes app-assets and sandbox archive proxy controllers.
+
+Invariants:
+- files_ns must include all file controller modules to register routes.
+
+Tests:
+- Coverage via controller unit tests and route registration smoke checks.

api/agent-notes/controllers/files/app_assets_download.py.md

@@ -0,0 +1,14 @@
+Summary:
+- App assets download proxy endpoint (signed URL verification, stream from storage).
+
+Invariants:
+- Validates AssetPath fields (UUIDs, asset_type allowlist).
+- Verifies tenant-scoped signature and expiration before reading storage.
+- URL uses expires_at/nonce/sign query params.
+
+Edge Cases:
+- Missing files return NotFound.
+- Invalid signature or expired link returns Forbidden.
+
+Tests:
+- Verify signature validation and invalid/expired cases.

api/agent-notes/controllers/files/app_assets_upload.py.md

@@ -0,0 +1,13 @@
+Summary:
+- App assets upload proxy endpoint (signed URL verification, upload to storage).
+
+Invariants:
+- Validates AssetPath fields (UUIDs, asset_type allowlist).
+- Verifies tenant-scoped signature and expiration before writing storage.
+- URL uses expires_at/nonce/sign query params.
+
+Edge Cases:
+- Invalid signature or expired link returns Forbidden.
+
+Tests:
+- Verify signature validation and invalid/expired cases.

api/agent-notes/controllers/files/sandbox_archive.py.md

@@ -0,0 +1,14 @@
+Summary:
+- Sandbox archive upload/download proxy endpoints (signed URL verification, stream to storage).
+
+Invariants:
+- Validates tenant_id and sandbox_id UUIDs.
+- Verifies tenant-scoped signature and expiration before storage access.
+- URL uses expires_at/nonce/sign query params.
+
+Edge Cases:
+- Missing archive returns NotFound.
+- Invalid signature or expired link returns Forbidden.
+
+Tests:
+- Add unit tests for signature validation if needed.

api/agent-notes/core/app_assets/builder/file_builder.py.md

@@ -0,0 +1,9 @@
+Summary:
+Summary:
+- Collects file assets and emits FileAsset entries with storage keys.
+
+Invariants:
+- Storage keys are derived via AppAssetStorage for draft files.
+
+Tests:
+- Covered by asset build pipeline tests.

api/agent-notes/core/app_assets/builder/skill_builder.py.md

@@ -0,0 +1,14 @@
+Summary:
+Summary:
+- Builds skill artifacts from markdown assets and uploads resolved outputs.
+
+Invariants:
+- Reads draft asset content via AppAssetStorage refs.
+- Writes resolved artifacts via AppAssetStorage refs.
+- FileAsset storage keys are derived via AppAssetStorage.
+
+Edge Cases:
+- Missing or invalid JSON content yields empty skill content/metadata.
+
+Tests:
+- Build pipeline unit tests covering compile/upload paths.

api/agent-notes/core/app_assets/converters.py.md

@@ -0,0 +1,9 @@
+Summary:
+Summary:
+- Converts AppAssetFileTree to FileAsset items for packaging.
+
+Invariants:
+- Storage keys for assets are derived via AppAssetStorage.
+
+Tests:
+- Used in packaging/service tests for asset bundles.

api/agent-notes/core/app_assets/packager/zip_packager.py.md

@@ -0,0 +1,14 @@
+# Zip Packager Notes
+
+## Purpose
+- Builds a ZIP archive of asset contents stored via the configured storage backend.
+
+## Key Decisions
+- Packaging writes assets into an in-memory zip buffer returned as bytes.
+- Asset fetch + zip writing are executed via a thread pool with a lock guarding `ZipFile` writes.
+
+## Edge Cases
+- ZIP writes are serialized by the lock; storage reads still run in parallel.
+
+## Tests/Verification
+- None yet.

api/agent-notes/core/app_assets/parser/asset_parser.py.md

@@ -0,0 +1,9 @@
+Summary:
+Summary:
+- Builds AssetItem entries for asset trees using AssetPath-derived storage keys.
+
+Invariants:
+- Uses AssetPath to compute draft storage keys.
+
+Tests:
+- Covered by asset parsing and packaging tests.

api/agent-notes/core/app_assets/storage.py.md

@@ -0,0 +1,20 @@
+Summary:
+- Defines AssetPath facade + typed asset path classes for app-asset storage access.
+- Maps asset paths to storage keys and generates presigned or signed-proxy URLs.
+- Signs proxy URLs using tenant private keys and enforces expiration.
+- Exposes app_asset_storage singleton for reuse.
+
+Invariants:
+- AssetPathBase fields (tenant_id/app_id/resource_id/node_id) must be UUIDs.
+- AssetPath.from_components enforces valid types and resolved node_id presence.
+- Storage keys are derived internally via AssetPathBase.get_storage_key; callers never supply raw paths.
+- AppAssetStorage.storage returns the cached presign wrapper (not the raw storage).
+
+Edge Cases:
+- Storage backends without presign support must fall back to signed proxy URLs.
+- Signed proxy verification enforces expiration and tenant-scoped signing keys.
+- Upload URLs also fall back to signed proxy endpoints when presign is unsupported.
+- load_or_none treats SilentStorage "File Not Found" bytes as missing.
+
+Tests:
+- Unit tests for ref validation, storage key mapping, and signed URL verification.

api/agent-notes/core/app_bundle/source_zip_extractor.py.md

@@ -0,0 +1,10 @@
+Summary:
+Summary:
+- Extracts asset files from a zip and persists them into app asset storage.
+
+Invariants:
+- Rejects path traversal/absolute/backslash paths.
+- Saves extracted files via AppAssetStorage draft refs.
+
+Tests:
+- Zip security edge cases and tree construction tests.

api/agent-notes/core/sandbox/initializer/app_assets_initializer.py.md

@@ -0,0 +1,9 @@
+Summary:
+Summary:
+- Downloads published app asset zip into sandbox and extracts it.
+
+Invariants:
+- Uses AppAssetStorage to generate download URLs for build zips (internal URL).
+
+Tests:
+- Sandbox initialization integration tests.

api/agent-notes/core/sandbox/initializer/draft_app_assets_initializer.py.md

@@ -0,0 +1,12 @@
+Summary:
+Summary:
+- Downloads draft/resolved assets into sandbox for draft execution.
+
+Invariants:
+- Uses AppAssetStorage to generate download URLs for draft/resolved refs (internal URL).
+
+Edge Cases:
+- No nodes -> returns early.
+
+Tests:
+- Sandbox draft initialization tests.

api/agent-notes/core/sandbox/sandbox.py.md

@@ -0,0 +1,9 @@
+Summary:
+- Sandbox lifecycle wrapper (ready/cancel/fail signals, mount/unmount, release).
+
+Invariants:
+- wait_ready raises with the original initialization error as the cause.
+- release always attempts unmount and environment release, logging failures.
+
+Tests:
+- Covered by sandbox lifecycle/unit tests and workflow execution error handling.

api/agent-notes/core/sandbox/security/__init__.py.md

@@ -0,0 +1,2 @@
+Summary:
+- Sandbox security helper modules.

api/agent-notes/core/sandbox/security/archive_signer.py.md

@@ -0,0 +1,13 @@
+Summary:
+- Generates and verifies signed URLs for sandbox archive upload/download.
+
+Invariants:
+- tenant_id and sandbox_id must be UUIDs.
+- Signatures are tenant-scoped and include operation, expiry, and nonce.
+
+Edge Cases:
+- Missing tenant private key raises ValueError.
+- Expired or tampered signatures are rejected.
+
+Tests:
+- Add unit tests if sandbox archive signature behavior expands.

api/agent-notes/core/sandbox/storage/archive_storage.py.md

@@ -0,0 +1,12 @@
+Summary:
+- Manages sandbox archive uploads/downloads for workspace persistence.
+
+Invariants:
+- Archive storage key is sandbox/<tenant_id>/<sandbox_id>.tar.gz.
+- Signed URLs are tenant-scoped and use external files URL.
+
+Edge Cases:
+- Missing archive skips mount.
+
+Tests:
+- Covered indirectly via sandbox integration tests.

api/agent-notes/core/skill/skill_manager.py.md

@@ -0,0 +1,9 @@
+Summary:
+Summary:
+- Loads/saves skill bundles to app asset storage.
+
+Invariants:
+- Skill bundles use AppAssetStorage refs and JSON serialization.
+
+Tests:
+- Covered by skill bundle build/load unit tests.

api/agent-notes/core/virtual_environment/providers/e2b_sandbox.py.md

@@ -0,0 +1,16 @@
+# E2B Sandbox Provider Notes
+
+## Purpose
+- Implements the E2B-backed `VirtualEnvironment` provider and bootstraps sandbox metadata, file I/O, and command execution.
+
+## Key Decisions
+- Sandbox metadata is gathered during `_construct_environment` using the E2B SDK before returning `Metadata`.
+- Architecture/OS detection uses a single `uname -m -s` call split by whitespace to reduce round-trips.
+- Command execution streams stdout/stderr through `QueueTransportReadCloser`; stdin is unsupported.
+
+## Edge Cases
+- `release_environment` raises when sandbox termination fails.
+- `execute_command` runs in a background thread; consumers must read stdout/stderr until EOF.
+
+## Tests/Verification
+- None yet. Add targeted service tests when behavior changes.

api/agent-notes/services/app_asset_service.py.md

@@ -0,0 +1,14 @@
+Summary:
+- App asset CRUD, publish/build pipeline, and presigned URL generation.
+
+Invariants:
+- Asset storage access goes through AppAssetStorage + AssetPath, using app_asset_storage singleton.
+- Tree operations require tenant/app scoping and lock for mutation.
+- Asset zips are packaged via raw storage with storage keys from AppAssetStorage.
+
+Edge Cases:
+- File nodes larger than preview limit are rejected.
+- Deletion runs asynchronously; storage failures are logged.
+
+Tests:
+- Unit tests for storage URL generation and publish/build flows.

api/agent-notes/services/app_bundle_service.py.md

@@ -0,0 +1,10 @@
+Summary:
+Summary:
+- Imports app bundles, including asset extraction into app asset storage.
+
+Invariants:
+- Asset imports respect zip security checks and tenant/app scoping.
+- Draft asset packaging uses AppAssetStorage for key mapping.
+
+Tests:
+- Bundle import unit tests and zip validation coverage.

api/agent-notes/tests/unit_tests/core/app_assets/test_storage.py.md

@@ -0,0 +1,6 @@
+Summary:
+Summary:
+- Unit tests for AppAssetStorage ref validation, key mapping, and signing.
+
+Tests:
+- Covers valid/invalid refs, signature verify, expiration handling, and proxy URL generation.

api/app.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import os
 import sys
 from typing import TYPE_CHECKING, cast
 
@@ -16,10 +17,15 @@ def is_db_command() -> bool:
 
 
 # create app
+flask_app = None
+socketio_app = None
+
 if is_db_command():
     from app_factory import create_migrations_app
 
     app = create_migrations_app()
+    socketio_app = app
+    flask_app = app
 else:
     # Gunicorn and Celery handle monkey patching automatically in production by
     # specifying the `gevent` worker class. Manual monkey patching is not required here.
@@ -30,8 +36,15 @@ else:
 
     from app_factory import create_app
 
-    app = create_app()
-    celery = cast("Celery", app.extensions["celery"])
+    socketio_app, flask_app = create_app()
+    app = flask_app
+    celery = cast("Celery", flask_app.extensions["celery"])
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=5001)
+    from gevent import pywsgi
+    from geventwebsocket.handler import WebSocketHandler  # type: ignore[reportMissingTypeStubs]
+
+    host = os.environ.get("HOST", "0.0.0.0")
+    port = int(os.environ.get("PORT", 5001))
+    server = pywsgi.WSGIServer((host, port), socketio_app, handler_class=WebSocketHandler)
+    server.serve_forever()

api/app_factory.py

@@ -1,6 +1,7 @@
 import logging
 import time
 
+import socketio  # type: ignore[reportMissingTypeStubs]
 from opentelemetry.trace import get_current_span
 from opentelemetry.trace.span import INVALID_SPAN_ID, INVALID_TRACE_ID
 
@@ -8,6 +9,7 @@ from configs import dify_config
 from contexts.wrapper import RecyclableContextVar
 from core.logging.context import init_request_context
 from dify_app import DifyApp
+from extensions.ext_socketio import sio
 
 logger = logging.getLogger(__name__)
 
@@ -60,14 +62,18 @@ def create_flask_app_with_configs() -> DifyApp:
     return dify_app
 
 
-def create_app() -> DifyApp:
+def create_app() -> tuple[socketio.WSGIApp, DifyApp]:
     start_time = time.perf_counter()
     app = create_flask_app_with_configs()
     initialize_extensions(app)
+
+    sio.app = app
+    socketio_app = socketio.WSGIApp(sio, app)
+
     end_time = time.perf_counter()
     if dify_config.DEBUG:
         logger.info("Finished create_app (%s ms)", round((end_time - start_time) * 1000, 2))
-    return app
+    return socketio_app, app
 
 
 def initialize_extensions(app: DifyApp):

api/commands.py

@@ -23,7 +23,8 @@ from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.index_processor.constant.built_in_field import BuiltInField
 from core.rag.models.document import ChildDocument, Document
-from core.tools.utils.system_oauth_encryption import encrypt_system_oauth_params
+from core.sandbox import SandboxBuilder, SandboxType
+from core.tools.utils.system_encryption import encrypt_system_params
 from events.app_event import app_was_created
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -1613,7 +1614,7 @@ def remove_orphaned_files_on_storage(force: bool):
             click.echo(click.style(f"- Scanning files on storage path {storage_path}", fg="white"))
             files = storage.scan(path=storage_path, files=True, directories=False)
             all_files_on_storage.extend(files)
-        except FileNotFoundError as e:
+        except FileNotFoundError:
             click.echo(click.style(f"  -> Skipping path {storage_path} as it does not exist.", fg="yellow"))
             continue
         except Exception as e:
@@ -1864,6 +1865,59 @@ def file_usage(
             click.echo(click.style(f"Use --offset {offset + limit} to see next page", fg="white"))
 
 
+@click.command("setup-sandbox-system-config", help="Setup system-level sandbox provider configuration.")
+@click.option(
+    "--provider-type", prompt=True, type=click.Choice(["e2b", "docker", "local", "ssh"]), help="Sandbox provider type"
+)
+@click.option("--config", prompt=True, help='Configuration JSON (e.g., {"api_key": "xxx"} for e2b)')
+def setup_sandbox_system_config(provider_type: str, config: str):
+    """
+    Setup system-level sandbox provider configuration.
+
+    Examples:
+        flask setup-sandbox-system-config --provider-type e2b --config '{"api_key": "e2b_xxx"}'
+        flask setup-sandbox-system-config --provider-type docker --config '{"docker_sock": "unix:///var/run/docker.sock"}'
+        flask setup-sandbox-system-config --provider-type local --config '{}'
+        flask setup-sandbox-system-config --provider-type ssh --config \
+        '{"ssh_host": "agentbox", "ssh_port": "22", "ssh_username": "agentbox", "ssh_password": "agentbox"}'
+    """
+    from models.sandbox import SandboxProviderSystemConfig
+
+    try:
+        click.echo(click.style(f"Validating config: {config}", fg="yellow"))
+        config_dict = TypeAdapter(dict[str, Any]).validate_json(config)
+        click.echo(click.style("Config validated successfully.", fg="green"))
+
+        click.echo(click.style(f"Validating config schema for provider type: {provider_type}", fg="yellow"))
+        SandboxBuilder.validate(SandboxType(provider_type), config_dict)
+        click.echo(click.style("Config schema validated successfully.", fg="green"))
+
+        click.echo(click.style("Encrypting config...", fg="yellow"))
+        click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
+        encrypted_config = encrypt_system_params(config_dict)
+        click.echo(click.style("Config encrypted successfully.", fg="green"))
+    except Exception as e:
+        click.echo(click.style(f"Error validating/encrypting config: {str(e)}", fg="red"))
+        return
+
+    deleted_count = db.session.query(SandboxProviderSystemConfig).filter_by(provider_type=provider_type).delete()
+    if deleted_count > 0:
+        click.echo(
+            click.style(
+                f"Deleted {deleted_count} existing system config for provider type: {provider_type}", fg="yellow"
+            )
+        )
+
+    system_config = SandboxProviderSystemConfig(
+        provider_type=provider_type,
+        encrypted_config=encrypted_config,
+    )
+    db.session.add(system_config)
+    db.session.commit()
+    click.echo(click.style(f"Sandbox system config setup successfully. id: {system_config.id}", fg="green"))
+    click.echo(click.style(f"Provider type: {provider_type}", fg="green"))
+
+
 @click.command("setup-system-tool-oauth-client", help="Setup system tool oauth client.")
 @click.option("--provider", prompt=True, help="Provider name")
 @click.option("--client-params", prompt=True, help="Client Params")
@@ -1883,7 +1937,7 @@ def setup_system_tool_oauth_client(provider, client_params):
 
         click.echo(click.style(f"Encrypting client params: {client_params}", fg="yellow"))
         click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        oauth_client_params = encrypt_system_oauth_params(client_params_dict)
+        oauth_client_params = encrypt_system_params(client_params_dict)
         click.echo(click.style("Client params encrypted successfully.", fg="green"))
     except Exception as e:
         click.echo(click.style(f"Error parsing client params: {str(e)}", fg="red"))
@@ -1932,7 +1986,7 @@ def setup_system_trigger_oauth_client(provider, client_params):
 
         click.echo(click.style(f"Encrypting client params: {client_params}", fg="yellow"))
         click.echo(click.style(f"Using SECRET_KEY: `{dify_config.SECRET_KEY}`", fg="yellow"))
-        oauth_client_params = encrypt_system_oauth_params(client_params_dict)
+        oauth_client_params = encrypt_system_params(client_params_dict)
         click.echo(click.style("Client params encrypted successfully.", fg="green"))
     except Exception as e:
         click.echo(click.style(f"Error parsing client params: {str(e)}", fg="red"))

api/configs/app_config.py

@@ -2,6 +2,7 @@ import logging
 from pathlib import Path
 from typing import Any
 
+from pydantic import Field
 from pydantic.fields import FieldInfo
 from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, SettingsConfigDict, TomlConfigSettingsSource
 
@@ -82,6 +83,17 @@ class DifyConfig(
         extra="ignore",
     )
 
+    SANDBOX_DIFY_CLI_ROOT: str | None = Field(
+        default=None,
+        description=(
+            "Filesystem directory containing dify CLI binaries named dify-cli-<os>-<arch>. "
+            "Defaults to api/bin when unset."
+        ),
+    )
+    DIFY_PORT: int = Field(
+        default=5001,
+        description="Port used by Dify to communicate with the host machine.",
+    )
     # Before adding any config,
     # please consider to arrange it in the proper config group of existed or added
     # for better readability and maintainability.

api/configs/feature/__init__.py

@@ -271,6 +271,17 @@ class PluginConfig(BaseSettings):
     )
 
 
+class CliApiConfig(BaseSettings):
+    """
+    Configuration for CLI API (for dify-cli to call back from external sandbox environments)
+    """
+
+    CLI_API_URL: str = Field(
+        description="CLI API URL for external sandbox (e.g., e2b) to call back.",
+        default="http://localhost:5001",
+    )
+
+
 class MarketplaceConfig(BaseSettings):
     """
     Configuration for marketplace
@@ -287,6 +298,27 @@ class MarketplaceConfig(BaseSettings):
     )
 
 
+class CreatorsPlatformConfig(BaseSettings):
+    """
+    Configuration for creators platform
+    """
+
+    CREATORS_PLATFORM_FEATURES_ENABLED: bool = Field(
+        description="Enable or disable creators platform features",
+        default=True,
+    )
+
+    CREATORS_PLATFORM_API_URL: HttpUrl = Field(
+        description="Creators Platform API URL",
+        default=HttpUrl("https://creators.dify.ai"),
+    )
+
+    CREATORS_PLATFORM_OAUTH_CLIENT_ID: str = Field(
+        description="OAuth client_id for the Creators Platform app registered in Dify",
+        default="",
+    )
+
+
 class EndpointConfig(BaseSettings):
     """
     Configuration for various application endpoints and URLs
@@ -341,6 +373,15 @@ class FileAccessConfig(BaseSettings):
         default="",
     )
 
+    FILES_API_URL: str = Field(
+        description="Base URL for storage file ticket API endpoints."
+        " Used by sandbox containers (internal or external like e2b) that need"
+        " an absolute, routable address to upload/download files via the API."
+        " Falls back to FILES_URL if not specified."
+        " For Docker deployments, set to http://api:5001.",
+        default="",
+    )
+
     FILES_ACCESS_TIMEOUT: int = Field(
         description="Expiration time in seconds for file access URLs",
         default=300,
@@ -1274,6 +1315,13 @@ class PositionConfig(BaseSettings):
         return {item.strip() for item in self.POSITION_TOOL_EXCLUDES.split(",") if item.strip() != ""}
 
 
+class CollaborationConfig(BaseSettings):
+    ENABLE_COLLABORATION_MODE: bool = Field(
+        description="Whether to enable collaboration mode features across the workspace",
+        default=False,
+    )
+
+
 class LoginConfig(BaseSettings):
     ENABLE_EMAIL_CODE_LOGIN: bool = Field(
         description="whether to enable email code login",
@@ -1375,7 +1423,9 @@ class FeatureConfig(
     TriggerConfig,
     AsyncWorkflowConfig,
     PluginConfig,
+    CliApiConfig,
     MarketplaceConfig,
+    CreatorsPlatformConfig,
     DataSetConfig,
     EndpointConfig,
     FileAccessConfig,
@@ -1399,6 +1449,7 @@ class FeatureConfig(
     WorkflowConfig,
     WorkflowNodeExecutionConfig,
     WorkspaceConfig,
+    CollaborationConfig,
     LoginConfig,
     AccountConfig,
     SwaggerUIConfig,

api/controllers/cli_api/__init__.py

@@ -0,0 +1,27 @@
+from flask import Blueprint
+from flask_restx import Namespace
+
+from libs.external_api import ExternalApi
+
+bp = Blueprint("cli_api", __name__, url_prefix="/cli/api")
+
+api = ExternalApi(
+    bp,
+    version="1.0",
+    title="CLI API",
+    description="APIs for Dify CLI to call back from external sandbox environments (e.g., e2b)",
+)
+
+# Create namespace
+cli_api_ns = Namespace("cli_api", description="CLI API operations", path="/")
+
+from .dify_cli import cli_api as _plugin
+
+api.add_namespace(cli_api_ns)
+
+__all__ = [
+    "_plugin",
+    "api",
+    "bp",
+    "cli_api_ns",
+]

api/controllers/cli_api/dify_cli/cli_api.py

@@ -0,0 +1,192 @@
+from flask import abort
+from flask_restx import Resource
+from pydantic import BaseModel
+
+from controllers.cli_api import cli_api_ns
+from controllers.cli_api.dify_cli.wraps import get_cli_user_tenant, plugin_data
+from controllers.cli_api.wraps import cli_api_only
+from controllers.console.wraps import setup_required
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.file.helpers import get_signed_file_url_for_plugin
+from core.plugin.backwards_invocation.app import PluginAppBackwardsInvocation
+from core.plugin.backwards_invocation.base import BaseBackwardsInvocationResponse
+from core.plugin.backwards_invocation.model import PluginModelBackwardsInvocation
+from core.plugin.backwards_invocation.tool import PluginToolBackwardsInvocation
+from core.plugin.entities.request import (
+    RequestInvokeApp,
+    RequestInvokeLLM,
+    RequestInvokeTool,
+    RequestRequestUploadFile,
+)
+from core.sandbox.bash.dify_cli import DifyCliToolConfig
+from core.session.cli_api import CliContext
+from core.skill.entities import ToolInvocationRequest
+from core.tools.entities.tool_entities import ToolProviderType
+from core.tools.tool_manager import ToolManager
+from libs.helper import length_prefixed_response
+from models.account import Account
+from models.model import EndUser, Tenant
+
+
+class FetchToolItem(BaseModel):
+    tool_type: str
+    tool_provider: str
+    tool_name: str
+    credential_id: str | None = None
+
+
+class FetchToolBatchRequest(BaseModel):
+    tools: list[FetchToolItem]
+
+
+@cli_api_ns.route("/invoke/llm")
+class CliInvokeLLMApi(Resource):
+    @cli_api_only
+    @get_cli_user_tenant
+    @setup_required
+    @plugin_data(payload_type=RequestInvokeLLM)
+    def post(
+        self,
+        user_model: Account | EndUser,
+        tenant_model: Tenant,
+        payload: RequestInvokeLLM,
+        cli_context: CliContext,
+    ):
+        def generator():
+            response = PluginModelBackwardsInvocation.invoke_llm(user_model.id, tenant_model, payload)
+            return PluginModelBackwardsInvocation.convert_to_event_stream(response)
+
+        return length_prefixed_response(0xF, generator())
+
+
+@cli_api_ns.route("/invoke/tool")
+class CliInvokeToolApi(Resource):
+    @cli_api_only
+    @get_cli_user_tenant
+    @setup_required
+    @plugin_data(payload_type=RequestInvokeTool)
+    def post(
+        self,
+        user_model: Account | EndUser,
+        tenant_model: Tenant,
+        payload: RequestInvokeTool,
+        cli_context: CliContext,
+    ):
+        tool_type = ToolProviderType.value_of(payload.tool_type)
+
+        request = ToolInvocationRequest(
+            tool_type=tool_type,
+            provider=payload.provider,
+            tool_name=payload.tool,
+            credential_id=payload.credential_id,
+        )
+        if cli_context.tool_access and not cli_context.tool_access.is_allowed(request):
+            abort(403, description=f"Access denied for tool: {payload.provider}/{payload.tool}")
+
+        def generator():
+            return PluginToolBackwardsInvocation.convert_to_event_stream(
+                PluginToolBackwardsInvocation.invoke_tool(
+                    tenant_id=tenant_model.id,
+                    user_id=user_model.id,
+                    tool_type=tool_type,
+                    provider=payload.provider,
+                    tool_name=payload.tool,
+                    tool_parameters=payload.tool_parameters,
+                    credential_id=payload.credential_id,
+                ),
+            )
+
+        return length_prefixed_response(0xF, generator())
+
+
+@cli_api_ns.route("/invoke/app")
+class CliInvokeAppApi(Resource):
+    @cli_api_only
+    @get_cli_user_tenant
+    @setup_required
+    @plugin_data(payload_type=RequestInvokeApp)
+    def post(
+        self,
+        user_model: Account | EndUser,
+        tenant_model: Tenant,
+        payload: RequestInvokeApp,
+        cli_context: CliContext,
+    ):
+        response = PluginAppBackwardsInvocation.invoke_app(
+            app_id=payload.app_id,
+            user_id=user_model.id,
+            tenant_id=tenant_model.id,
+            conversation_id=payload.conversation_id,
+            query=payload.query,
+            stream=payload.response_mode == "streaming",
+            inputs=payload.inputs,
+            files=payload.files,
+        )
+
+        return length_prefixed_response(0xF, PluginAppBackwardsInvocation.convert_to_event_stream(response))
+
+
+@cli_api_ns.route("/upload/file/request")
+class CliUploadFileRequestApi(Resource):
+    @cli_api_only
+    @get_cli_user_tenant
+    @setup_required
+    @plugin_data(payload_type=RequestRequestUploadFile)
+    def post(
+        self,
+        user_model: Account | EndUser,
+        tenant_model: Tenant,
+        payload: RequestRequestUploadFile,
+        cli_context: CliContext,
+    ):
+        url = get_signed_file_url_for_plugin(
+            filename=payload.filename,
+            mimetype=payload.mimetype,
+            tenant_id=tenant_model.id,
+            user_id=user_model.id,
+        )
+        return BaseBackwardsInvocationResponse(data={"url": url}).model_dump()
+
+
+@cli_api_ns.route("/fetch/tools/batch")
+class CliFetchToolsBatchApi(Resource):
+    @cli_api_only
+    @get_cli_user_tenant
+    @setup_required
+    @plugin_data(payload_type=FetchToolBatchRequest)
+    def post(
+        self,
+        user_model: Account | EndUser,
+        tenant_model: Tenant,
+        payload: FetchToolBatchRequest,
+        cli_context: CliContext,
+    ):
+        tools: list[dict] = []
+
+        for item in payload.tools:
+            provider_type = ToolProviderType.value_of(item.tool_type)
+
+            request = ToolInvocationRequest(
+                tool_type=provider_type,
+                provider=item.tool_provider,
+                tool_name=item.tool_name,
+                credential_id=item.credential_id,
+            )
+            if cli_context.tool_access and not cli_context.tool_access.is_allowed(request):
+                abort(403, description=f"Access denied for tool: {item.tool_provider}/{item.tool_name}")
+
+            try:
+                tool_runtime = ToolManager.get_tool_runtime(
+                    tenant_id=tenant_model.id,
+                    provider_type=provider_type,
+                    provider_id=item.tool_provider,
+                    tool_name=item.tool_name,
+                    invoke_from=InvokeFrom.AGENT,
+                    credential_id=item.credential_id,
+                )
+                tool_config = DifyCliToolConfig.create_from_tool(tool_runtime)
+                tools.append(tool_config.model_dump())
+            except Exception:
+                continue
+
+        return BaseBackwardsInvocationResponse(data={"tools": tools}).model_dump()

api/controllers/cli_api/dify_cli/wraps.py

@@ -0,0 +1,137 @@
+from collections.abc import Callable
+from functools import wraps
+from typing import ParamSpec, TypeVar
+
+from flask import current_app, g, request
+from flask_login import user_logged_in
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+from core.session.cli_api import CliApiSession, CliContext
+from extensions.ext_database import db
+from libs.login import current_user
+from models.account import Tenant
+from models.model import DefaultEndUserSessionID, EndUser
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+class TenantUserPayload(BaseModel):
+    tenant_id: str
+    user_id: str
+
+
+def get_user(tenant_id: str, user_id: str | None) -> EndUser:
+    """
+    Get current user
+
+    NOTE: user_id is not trusted, it could be maliciously set to any value.
+    As a result, it could only be considered as an end user id.
+    """
+    if not user_id:
+        user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
+    is_anonymous = user_id == DefaultEndUserSessionID.DEFAULT_SESSION_ID
+    try:
+        with Session(db.engine) as session:
+            user_model = None
+
+            if is_anonymous:
+                user_model = (
+                    session.query(EndUser)
+                    .where(
+                        EndUser.session_id == user_id,
+                        EndUser.tenant_id == tenant_id,
+                    )
+                    .first()
+                )
+            else:
+                user_model = (
+                    session.query(EndUser)
+                    .where(
+                        EndUser.id == user_id,
+                        EndUser.tenant_id == tenant_id,
+                    )
+                    .first()
+                )
+
+            if not user_model:
+                user_model = EndUser(
+                    tenant_id=tenant_id,
+                    type="service_api",
+                    is_anonymous=is_anonymous,
+                    session_id=user_id,
+                )
+                session.add(user_model)
+                session.commit()
+                session.refresh(user_model)
+
+    except Exception:
+        raise ValueError("user not found")
+
+    return user_model
+
+
+def get_cli_user_tenant(view_func: Callable[P, R]):
+    @wraps(view_func)
+    def decorated_view(*args: P.args, **kwargs: P.kwargs):
+        session: CliApiSession | None = getattr(g, "cli_api_session", None)
+        if session is None:
+            raise ValueError("session not found")
+
+        user_id = session.user_id
+        tenant_id = session.tenant_id
+        cli_context = CliContext.model_validate(session.context)
+
+        if not user_id:
+            user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
+
+        try:
+            tenant_model = (
+                db.session.query(Tenant)
+                .where(
+                    Tenant.id == tenant_id,
+                )
+                .first()
+            )
+        except Exception:
+            raise ValueError("tenant not found")
+
+        if not tenant_model:
+            raise ValueError("tenant not found")
+
+        kwargs["tenant_model"] = tenant_model
+        kwargs["user_model"] = get_user(tenant_id, user_id)
+        kwargs["cli_context"] = cli_context
+
+        current_app.login_manager._update_request_context_with_user(kwargs["user_model"])  # type: ignore
+        user_logged_in.send(current_app._get_current_object(), user=current_user)  # type: ignore
+
+        return view_func(*args, **kwargs)
+
+    return decorated_view
+
+
+def plugin_data(view: Callable[P, R] | None = None, *, payload_type: type[BaseModel]):
+    def decorator(view_func: Callable[P, R]):
+        @wraps(view_func)
+        def decorated_view(*args: P.args, **kwargs: P.kwargs):
+            try:
+                data = request.get_json()
+            except Exception:
+                raise ValueError("invalid json")
+
+            try:
+                payload = payload_type.model_validate(data)
+            except Exception as e:
+                raise ValueError(f"invalid payload: {str(e)}")
+
+            kwargs["payload"] = payload
+            return view_func(*args, **kwargs)
+
+        return decorated_view
+
+    if view is None:
+        return decorator
+    else:
+        return decorator(view)

api/controllers/cli_api/wraps.py

@@ -0,0 +1,56 @@
+import hashlib
+import hmac
+import time
+from collections.abc import Callable
+from functools import wraps
+from typing import ParamSpec, TypeVar
+
+from flask import abort, g, request
+
+from core.session.cli_api import CliApiSessionManager
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+SIGNATURE_TTL_SECONDS = 300
+
+
+def _verify_signature(session_secret: str, timestamp: str, body: bytes, signature: str) -> bool:
+    expected = hmac.new(
+        session_secret.encode(),
+        f"{timestamp}.".encode() + body,
+        hashlib.sha256,
+    ).hexdigest()
+    return hmac.compare_digest(f"sha256={expected}", signature)
+
+
+def cli_api_only(view: Callable[P, R]):
+    @wraps(view)
+    def decorated(*args: P.args, **kwargs: P.kwargs):
+        session_id = request.headers.get("X-Cli-Api-Session-Id")
+        timestamp = request.headers.get("X-Cli-Api-Timestamp")
+        signature = request.headers.get("X-Cli-Api-Signature")
+
+        if not session_id or not timestamp or not signature:
+            abort(401)
+
+        try:
+            ts = int(timestamp)
+            if abs(time.time() - ts) > SIGNATURE_TTL_SECONDS:
+                abort(401)
+        except ValueError:
+            abort(401)
+
+        session = CliApiSessionManager().get(session_id)
+        if not session:
+            abort(401)
+
+        body = request.get_data()
+        if not _verify_signature(session.secret, timestamp, body, signature):
+            abort(401)
+
+        g.cli_api_session = session
+
+        return view(*args, **kwargs)
+
+    return decorated

api/controllers/console/__init__.py

@@ -32,6 +32,7 @@ for module_name in RESOURCE_MODULES:
 
 # Ensure resource modules are imported so route decorators are evaluated.
 # Import other controllers
+# Sandbox file browser
 from . import (
     admin,
     apikey,
@@ -40,6 +41,7 @@ from . import (
     human_input_form,
     init_validate,
     ping,
+    sandbox_files,
     setup,
     spec,
     version,
@@ -51,6 +53,7 @@ from .app import (
     agent,
     annotation,
     app,
+    app_asset,
     audio,
     completion,
     conversation,
@@ -61,9 +64,11 @@ from .app import (
     model_config,
     ops_trace,
     site,
+    skills,
     statistic,
     workflow,
     workflow_app_log,
+    workflow_comment,
     workflow_draft_variable,
     workflow_run,
     workflow_statistic,
@@ -115,6 +120,7 @@ from .explore import (
     saved_message,
     trial,
 )
+from .socketio import workflow as socketio_workflow  # pyright: ignore[reportUnusedImport]
 
 # Import tag controllers
 from .tag import tags
@@ -129,6 +135,7 @@ from .workspace import (
     model_providers,
     models,
     plugin,
+    sandbox_providers,
     tool_providers,
     trigger_providers,
     workspace,
@@ -147,6 +154,7 @@ __all__ = [
     "api",
     "apikey",
     "app",
+    "app_asset",
     "audio",
     "banner",
     "billing",
@@ -196,9 +204,12 @@ __all__ = [
     "rag_pipeline_import",
     "rag_pipeline_workflow",
     "recommended_app",
+    "sandbox_files",
+    "sandbox_providers",
     "saved_message",
     "setup",
     "site",
+    "skills",
     "spec",
     "statistic",
     "tags",
@@ -209,6 +220,7 @@ __all__ = [
     "website",
     "workflow",
     "workflow_app_log",
+    "workflow_comment",
     "workflow_draft_variable",
     "workflow_run",
     "workflow_statistic",

api/controllers/console/app/app.py

@@ -1,6 +1,7 @@
 import logging
 import uuid
 from datetime import datetime
+from enum import StrEnum
 from typing import Any, Literal, TypeAlias
 
 from flask import request
@@ -31,6 +32,7 @@ from extensions.ext_database import db
 from libs.login import current_account_with_tenant, login_required
 from models import App, DatasetPermissionEnum, Workflow
 from models.model import IconType
+from models.workflow_features import WorkflowFeatures
 from services.app_dsl_service import AppDslService, ImportMode
 from services.app_service import AppService
 from services.enterprise.enterprise_service import EnterpriseService
@@ -58,6 +60,11 @@ register_enum_models(console_ns, IconType)
 _logger = logging.getLogger(__name__)
 
 
+class RuntimeType(StrEnum):
+    CLASSIC = "classic"
+    SANDBOXED = "sandboxed"
+
+
 class AppListQuery(BaseModel):
     page: int = Field(default=1, ge=1, le=99999, description="Page number (1-99999)")
     limit: int = Field(default=20, ge=1, le=100, description="Page size (1-100)")
@@ -122,6 +129,11 @@ class AppExportQuery(BaseModel):
     workflow_id: str | None = Field(default=None, description="Specific workflow ID to export")
 
 
+class AppExportBundleQuery(BaseModel):
+    include_secret: bool = Field(default=False, description="Include secrets in export")
+    workflow_id: str | None = Field(default=None, description="Specific workflow ID to export")
+
+
 class AppNamePayload(BaseModel):
     name: str = Field(..., min_length=1, description="Name to check")
 
@@ -347,6 +359,7 @@ class AppPartial(ResponseModel):
     create_user_name: str | None = None
     author_name: str | None = None
     has_draft_trigger: bool | None = None
+    runtime_type: RuntimeType = RuntimeType.CLASSIC
 
     @computed_field(return_type=str | None)  # type: ignore
     @property
@@ -496,6 +509,7 @@ class AppListApi(Resource):
             str(app.id) for app in app_pagination.items if app.mode in {"workflow", "advanced-chat"}
         ]
         draft_trigger_app_ids: set[str] = set()
+        sandbox_app_ids: set[str] = set()
         if workflow_capable_app_ids:
             draft_workflows = (
                 db.session.execute(
@@ -514,6 +528,10 @@ class AppListApi(Resource):
                 NodeType.TRIGGER_PLUGIN,
             }
             for workflow in draft_workflows:
+                # Check sandbox feature
+                if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
+                    sandbox_app_ids.add(str(workflow.app_id))
+
                 node_id = None
                 try:
                     for node_id, node_data in workflow.walk_nodes():
@@ -526,6 +544,7 @@ class AppListApi(Resource):
 
         for app in app_pagination.items:
             app.has_draft_trigger = str(app.id) in draft_trigger_app_ids
+            app.runtime_type = RuntimeType.SANDBOXED if str(app.id) in sandbox_app_ids else RuntimeType.CLASSIC
 
         pagination_model = AppPagination.model_validate(app_pagination, from_attributes=True)
         return pagination_model.model_dump(mode="json"), 200
@@ -694,6 +713,89 @@ class AppExportApi(Resource):
         return payload.model_dump(mode="json")
 
 
+@console_ns.route("/apps/<uuid:app_id>/export-bundle")
+class AppExportBundleApi(Resource):
+    @get_app_model
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @edit_permission_required
+    def get(self, app_model):
+        from services.app_bundle_service import AppBundleService
+
+        args = AppExportBundleQuery.model_validate(request.args.to_dict(flat=True))
+        current_user, _ = current_account_with_tenant()
+
+        result = AppBundleService.export_bundle(
+            app_model=app_model,
+            account_id=str(current_user.id),
+            include_secret=args.include_secret,
+            workflow_id=args.workflow_id,
+        )
+
+        return result.model_dump(mode="json")
+
+
+@console_ns.route("/apps/<uuid:app_id>/publish-to-creators-platform")
+class AppPublishToCreatorsPlatformApi(Resource):
+    @get_app_model
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @edit_permission_required
+    def post(self, app_model):
+        """Export the app DSL and upload to Creators Platform, returning a redirect URL.
+
+        Classic apps export as YAML; sandboxed apps export as ZIP bundle.
+        """
+        import httpx
+
+        from configs import dify_config
+        from core.helper.creators import get_redirect_url, upload_dsl
+        from services.app_bundle_service import AppBundleService
+        from services.workflow_service import WorkflowService
+
+        if not dify_config.CREATORS_PLATFORM_FEATURES_ENABLED:
+            return {"message": "Creators Platform is not enabled"}, 403
+
+        current_user, _ = current_account_with_tenant()
+
+        # Determine if the app is sandboxed by checking the draft workflow's sandbox feature
+        is_sandboxed = False
+        if app_model.mode in {"workflow", "advanced-chat"}:
+            draft_workflow = WorkflowService().get_draft_workflow(app_model)
+            if draft_workflow and draft_workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
+                is_sandboxed = True
+
+        if is_sandboxed:
+            # Sandboxed app: export as ZIP bundle
+            bundle_result = AppBundleService.export_bundle(
+                app_model=app_model,
+                account_id=str(current_user.id),
+                include_secret=False,
+            )
+            download_response = httpx.get(bundle_result.download_url, timeout=60, follow_redirects=True)
+            download_response.raise_for_status()
+            file_bytes = download_response.content
+            filename = bundle_result.filename
+        else:
+            # Classic app: export as YAML
+            dsl_content = AppDslService.export_dsl(
+                app_model=app_model,
+                include_secret=False,
+            )
+            file_bytes = dsl_content.encode("utf-8")
+            filename = f"{app_model.name}.yml"
+
+        # Upload to Creators Platform
+        claim_code = upload_dsl(file_bytes, filename=filename)
+
+        # Generate redirect URL (with optional OAuth code)
+        redirect_url = get_redirect_url(str(current_user.id), claim_code)
+
+        return {"redirect_url": redirect_url}
+
+
 @console_ns.route("/apps/<uuid:app_id>/name")
 class AppNameApi(Resource):
     @console_ns.doc("check_app_name")

api/controllers/console/app/app_asset.py

@@ -0,0 +1,321 @@
+from flask import request
+from flask_restx import Resource
+from pydantic import BaseModel, Field, field_validator
+
+from controllers.console import console_ns
+from controllers.console.app.error import (
+    AppAssetNodeNotFoundError,
+    AppAssetPathConflictError,
+)
+from controllers.console.app.wraps import get_app_model
+from controllers.console.wraps import account_initialization_required, setup_required
+from core.app.entities.app_asset_entities import BatchUploadNode
+from libs.login import current_account_with_tenant, login_required
+from models import App
+from models.model import AppMode
+from services.app_asset_service import AppAssetService
+from services.errors.app_asset import (
+    AppAssetNodeNotFoundError as ServiceNodeNotFoundError,
+)
+from services.errors.app_asset import (
+    AppAssetParentNotFoundError,
+)
+from services.errors.app_asset import (
+    AppAssetPathConflictError as ServicePathConflictError,
+)
+
+DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
+
+
+class CreateFolderPayload(BaseModel):
+    name: str = Field(..., min_length=1, max_length=255)
+    parent_id: str | None = None
+
+
+class CreateFilePayload(BaseModel):
+    name: str = Field(..., min_length=1, max_length=255)
+    parent_id: str | None = None
+
+    @field_validator("name", mode="before")
+    @classmethod
+    def strip_name(cls, v: str) -> str:
+        return v.strip() if isinstance(v, str) else v
+
+    @field_validator("parent_id", mode="before")
+    @classmethod
+    def empty_to_none(cls, v: str | None) -> str | None:
+        return v or None
+
+
+class GetUploadUrlPayload(BaseModel):
+    name: str = Field(..., min_length=1, max_length=255)
+    size: int = Field(..., ge=0)
+    parent_id: str | None = None
+
+    @field_validator("name", mode="before")
+    @classmethod
+    def strip_name(cls, v: str) -> str:
+        return v.strip() if isinstance(v, str) else v
+
+    @field_validator("parent_id", mode="before")
+    @classmethod
+    def empty_to_none(cls, v: str | None) -> str | None:
+        return v or None
+
+
+class BatchUploadPayload(BaseModel):
+    children: list[BatchUploadNode] = Field(..., min_length=1)
+
+
+class UpdateFileContentPayload(BaseModel):
+    content: str
+
+
+class RenameNodePayload(BaseModel):
+    name: str = Field(..., min_length=1, max_length=255)
+
+
+class MoveNodePayload(BaseModel):
+    parent_id: str | None = None
+
+
+class ReorderNodePayload(BaseModel):
+    after_node_id: str | None = Field(default=None, description="Place after this node, None for first position")
+
+
+def reg(cls: type[BaseModel]) -> None:
+    console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
+
+
+reg(CreateFolderPayload)
+reg(CreateFilePayload)
+reg(GetUploadUrlPayload)
+reg(BatchUploadNode)
+reg(BatchUploadPayload)
+reg(UpdateFileContentPayload)
+reg(RenameNodePayload)
+reg(MoveNodePayload)
+reg(ReorderNodePayload)
+
+
+@console_ns.route("/apps/<string:app_id>/assets/tree")
+class AppAssetTreeResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+        tree = AppAssetService.get_asset_tree(app_model, current_user.id)
+        return {"children": [view.model_dump() for view in tree.transform()]}
+
+
+@console_ns.route("/apps/<string:app_id>/assets/folders")
+class AppAssetFolderResource(Resource):
+    @console_ns.expect(console_ns.models[CreateFolderPayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+        payload = CreateFolderPayload.model_validate(console_ns.payload or {})
+
+        try:
+            node = AppAssetService.create_folder(app_model, current_user.id, payload.name, payload.parent_id)
+            return node.model_dump(), 201
+        except AppAssetParentNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/files/<string:node_id>")
+class AppAssetFileDetailResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        try:
+            content = AppAssetService.get_file_content(app_model, current_user.id, node_id)
+            return {"content": content.decode("utf-8", errors="replace")}
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+    @console_ns.expect(console_ns.models[UpdateFileContentPayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def put(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+
+        file = request.files.get("file")
+        if file:
+            content = file.read()
+        else:
+            payload = UpdateFileContentPayload.model_validate(console_ns.payload or {})
+            content = payload.content.encode("utf-8")
+
+        try:
+            node = AppAssetService.update_file_content(app_model, current_user.id, node_id, content)
+            return node.model_dump()
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>")
+class AppAssetNodeResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def delete(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        try:
+            AppAssetService.delete_node(app_model, current_user.id, node_id)
+            return {"result": "success"}, 200
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/rename")
+class AppAssetNodeRenameResource(Resource):
+    @console_ns.expect(console_ns.models[RenameNodePayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        payload = RenameNodePayload.model_validate(console_ns.payload or {})
+
+        try:
+            node = AppAssetService.rename_node(app_model, current_user.id, node_id, payload.name)
+            return node.model_dump()
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/move")
+class AppAssetNodeMoveResource(Resource):
+    @console_ns.expect(console_ns.models[MoveNodePayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        payload = MoveNodePayload.model_validate(console_ns.payload or {})
+
+        try:
+            node = AppAssetService.move_node(app_model, current_user.id, node_id, payload.parent_id)
+            return node.model_dump()
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except AppAssetParentNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/nodes/<string:node_id>/reorder")
+class AppAssetNodeReorderResource(Resource):
+    @console_ns.expect(console_ns.models[ReorderNodePayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        payload = ReorderNodePayload.model_validate(console_ns.payload or {})
+
+        try:
+            node = AppAssetService.reorder_node(app_model, current_user.id, node_id, payload.after_node_id)
+            return node.model_dump()
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/files/<string:node_id>/download-url")
+class AppAssetFileDownloadUrlResource(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App, node_id: str):
+        current_user, _ = current_account_with_tenant()
+        try:
+            download_url = AppAssetService.get_file_download_url(app_model, current_user.id, node_id)
+            return {"download_url": download_url}
+        except ServiceNodeNotFoundError:
+            raise AppAssetNodeNotFoundError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/files/upload")
+class AppAssetFileUploadUrlResource(Resource):
+    @console_ns.expect(console_ns.models[GetUploadUrlPayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+        payload = GetUploadUrlPayload.model_validate(console_ns.payload or {})
+
+        try:
+            node, upload_url = AppAssetService.get_file_upload_url(
+                app_model, current_user.id, payload.name, payload.size, payload.parent_id
+            )
+            return {"node": node.model_dump(), "upload_url": upload_url}, 201
+        except AppAssetParentNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()
+
+
+@console_ns.route("/apps/<string:app_id>/assets/batch-upload")
+class AppAssetBatchUploadResource(Resource):
+    @console_ns.expect(console_ns.models[BatchUploadPayload.__name__])
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        """
+        Create nodes from tree structure and return upload URLs.
+
+        Input:
+        {
+            "children": [
+                {"name": "folder1", "node_type": "folder", "children": [
+                    {"name": "file1.txt", "node_type": "file", "size": 1024}
+                ]},
+                {"name": "root.txt", "node_type": "file", "size": 512}
+            ]
+        }
+
+        Output:
+        {
+            "children": [
+                {"id": "xxx", "name": "folder1", "node_type": "folder", "children": [
+                    {"id": "yyy", "name": "file1.txt", "node_type": "file", "size": 1024, "upload_url": "..."}
+                ]},
+                {"id": "zzz", "name": "root.txt", "node_type": "file", "size": 512, "upload_url": "..."}
+            ]
+        }
+        """
+        current_user, _ = current_account_with_tenant()
+        payload = BatchUploadPayload.model_validate(console_ns.payload or {})
+
+        try:
+            result_children = AppAssetService.batch_create_from_tree(app_model, current_user.id, payload.children)
+            return {"children": [child.model_dump() for child in result_children]}, 201
+        except AppAssetParentNotFoundError:
+            raise AppAssetNodeNotFoundError()
+        except ServicePathConflictError:
+            raise AppAssetPathConflictError()

api/controllers/console/app/app_import.py

@@ -51,6 +51,14 @@ class AppImportPayload(BaseModel):
     app_id: str | None = Field(None)
 
 
+class AppImportBundleConfirmPayload(BaseModel):
+    name: str | None = None
+    description: str | None = None
+    icon_type: str | None = None
+    icon: str | None = None
+    icon_background: str | None = None
+
+
 console_ns.schema_model(
     AppImportPayload.__name__, AppImportPayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0)
 )
@@ -139,3 +147,68 @@ class AppImportCheckDependenciesApi(Resource):
             result = import_service.check_dependencies(app_model=app_model)
 
         return result.model_dump(mode="json"), 200
+
+
+@console_ns.route("/apps/imports-bundle/prepare")
+class AppImportBundlePrepareApi(Resource):
+    """Step 1: Get upload URL for bundle import."""
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @edit_permission_required
+    def post(self):
+        from services.app_bundle_service import AppBundleService
+
+        current_user, current_tenant_id = current_account_with_tenant()
+
+        result = AppBundleService.prepare_import(
+            tenant_id=current_tenant_id,
+            account_id=current_user.id,
+        )
+
+        return {"import_id": result.import_id, "upload_url": result.upload_url}, 200
+
+
+@console_ns.route("/apps/imports-bundle/<string:import_id>/confirm")
+class AppImportBundleConfirmApi(Resource):
+    """Step 2: Confirm bundle import after upload."""
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @marshal_with(app_import_model)
+    @cloud_edition_billing_resource_check("apps")
+    @edit_permission_required
+    def post(self, import_id: str):
+        from flask import request
+
+        from core.app.entities.app_bundle_entities import BundleFormatError
+        from services.app_bundle_service import AppBundleService
+
+        current_user, _ = current_account_with_tenant()
+
+        args = AppImportBundleConfirmPayload.model_validate(request.get_json() or {})
+
+        try:
+            result = AppBundleService.confirm_import(
+                import_id=import_id,
+                account=current_user,
+                name=args.name,
+                description=args.description,
+                icon_type=args.icon_type,
+                icon=args.icon,
+                icon_background=args.icon_background,
+            )
+        except BundleFormatError as e:
+            return {"error": str(e)}, 400
+
+        if result.app_id and FeatureService.get_system_features().webapp_auth.enabled:
+            EnterpriseService.WebAppAuth.update_app_access_mode(result.app_id, "private")
+
+        status = result.status
+        if status == ImportStatus.FAILED:
+            return result.model_dump(mode="json"), 400
+        elif status == ImportStatus.PENDING:
+            return result.model_dump(mode="json"), 202
+        return result.model_dump(mode="json"), 200

api/controllers/console/app/error.py

@@ -110,8 +110,6 @@ class TracingConfigCheckError(BaseHTTPException):
 
 
 class InvokeRateLimitError(BaseHTTPException):
-    """Raised when the Invoke returns rate limit error."""
-
     error_code = "rate_limit_error"
     description = "Rate Limit Error"
     code = 429
@@ -121,3 +119,21 @@ class NeedAddIdsError(BaseHTTPException):
     error_code = "need_add_ids"
     description = "Need to add ids."
     code = 400
+
+
+class AppAssetNodeNotFoundError(BaseHTTPException):
+    error_code = "app_asset_node_not_found"
+    description = "App asset node not found."
+    code = 404
+
+
+class AppAssetFileRequiredError(BaseHTTPException):
+    error_code = "app_asset_file_required"
+    description = "File is required."
+    code = 400
+
+
+class AppAssetPathConflictError(BaseHTTPException):
+    error_code = "app_asset_path_conflict"
+    description = "Path already exists."
+    code = 409

api/controllers/console/app/generator.py

@@ -1,4 +1,5 @@
 from collections.abc import Sequence
+from typing import Any
 
 from flask_restx import Resource
 from pydantic import BaseModel, Field
@@ -16,6 +17,11 @@ from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotIni
 from core.helper.code_executor.code_node_provider import CodeNodeProvider
 from core.helper.code_executor.javascript.javascript_code_provider import JavascriptCodeProvider
 from core.helper.code_executor.python3.python3_code_provider import Python3CodeProvider
+from core.llm_generator.context_models import (
+    AvailableVarPayload,
+    CodeContextPayload,
+    ParameterInfoPayload,
+)
 from core.llm_generator.entities import RuleCodeGeneratePayload, RuleGeneratePayload, RuleStructuredOutputPayload
 from core.llm_generator.llm_generator import LLMGenerator
 from core.model_runtime.errors.invoke import InvokeError
@@ -41,6 +47,34 @@ class InstructionTemplatePayload(BaseModel):
     type: str = Field(..., description="Instruction template type")
 
 
+class ContextGeneratePayload(BaseModel):
+    """Payload for generating extractor code node."""
+
+    language: str = Field(default="python3", description="Code language (python3/javascript)")
+    prompt_messages: list[dict[str, Any]] = Field(
+        ..., description="Multi-turn conversation history, last message is the current instruction"
+    )
+    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
+    available_vars: list[AvailableVarPayload] = Field(..., description="Available variables from upstream nodes")
+    parameter_info: ParameterInfoPayload = Field(..., description="Target parameter metadata from the frontend")
+    code_context: CodeContextPayload = Field(description="Existing code node context for incremental generation")
+
+
+class SuggestedQuestionsPayload(BaseModel):
+    """Payload for generating suggested questions."""
+
+    language: str = Field(
+        default="English", description="Language for generated questions (e.g. English, Chinese, Japanese)"
+    )
+    model_config_data: dict[str, Any] = Field(
+        default_factory=dict,
+        alias="model_config",
+        description="Model configuration (optional, uses system default if not provided)",
+    )
+    available_vars: list[AvailableVarPayload] = Field(..., description="Available variables from upstream nodes")
+    parameter_info: ParameterInfoPayload = Field(..., description="Target parameter metadata from the frontend")
+
+
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -50,6 +84,8 @@ reg(RuleCodeGeneratePayload)
 reg(RuleStructuredOutputPayload)
 reg(InstructionGeneratePayload)
 reg(InstructionTemplatePayload)
+reg(ContextGeneratePayload)
+reg(SuggestedQuestionsPayload)
 reg(ModelConfig)
 
 
@@ -263,3 +299,70 @@ class InstructionGenerationTemplateApi(Resource):
                 return {"data": INSTRUCTION_GENERATE_TEMPLATE_CODE}
             case _:
                 raise ValueError(f"Invalid type: {args.type}")
+
+
+@console_ns.route("/context-generate")
+class ContextGenerateApi(Resource):
+    @console_ns.doc("generate_with_context")
+    @console_ns.doc(description="Generate with multi-turn conversation context")
+    @console_ns.expect(console_ns.models[ContextGeneratePayload.__name__])
+    @console_ns.response(200, "Content generated successfully")
+    @console_ns.response(400, "Invalid request parameters or workflow not found")
+    @console_ns.response(402, "Provider quota exceeded")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        from core.llm_generator.utils import deserialize_prompt_messages
+
+        args = ContextGeneratePayload.model_validate(console_ns.payload)
+        _, current_tenant_id = current_account_with_tenant()
+
+        try:
+            return LLMGenerator.generate_with_context(
+                tenant_id=current_tenant_id,
+                language=args.language,
+                prompt_messages=deserialize_prompt_messages(args.prompt_messages),
+                model_config=args.model_config_data,
+                available_vars=args.available_vars,
+                parameter_info=args.parameter_info,
+                code_context=args.code_context,
+            )
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+
+
+@console_ns.route("/context-generate/suggested-questions")
+class SuggestedQuestionsApi(Resource):
+    @console_ns.doc("generate_suggested_questions")
+    @console_ns.doc(description="Generate suggested questions for context generation")
+    @console_ns.expect(console_ns.models[SuggestedQuestionsPayload.__name__])
+    @console_ns.response(200, "Questions generated successfully")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        args = SuggestedQuestionsPayload.model_validate(console_ns.payload)
+        _, current_tenant_id = current_account_with_tenant()
+        try:
+            return LLMGenerator.generate_suggested_questions(
+                tenant_id=current_tenant_id,
+                language=args.language,
+                available_vars=args.available_vars,
+                parameter_info=args.parameter_info,
+                model_config=args.model_config_data,
+            )
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)

api/controllers/console/app/message.py

@@ -212,6 +212,7 @@ message_detail_model = console_ns.model(
         "status": fields.String,
         "error": fields.String,
         "parent_message_id": fields.String,
+        "generation_detail": fields.Raw,
     },
 )
 

api/controllers/console/app/skills.py

@@ -0,0 +1,83 @@
+from flask_restx import Resource
+
+from controllers.console import console_ns
+from controllers.console.app.error import DraftWorkflowNotExist
+from controllers.console.app.wraps import get_app_model
+from controllers.console.wraps import account_initialization_required, current_account_with_tenant, setup_required
+from libs.login import login_required
+from models import App
+from models.model import AppMode
+from services.skill_service import SkillService
+from services.workflow_service import WorkflowService
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflows/draft/nodes/<string:node_id>/skills")
+class NodeSkillsApi(Resource):
+    """API for retrieving skill references for a specific workflow node."""
+
+    @console_ns.doc("get_node_skills")
+    @console_ns.doc(description="Get skill references for a specific node in the draft workflow")
+    @console_ns.doc(params={"app_id": "Application ID", "node_id": "Node ID"})
+    @console_ns.response(200, "Node skills retrieved successfully")
+    @console_ns.response(404, "Workflow or node not found")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App, node_id: str):
+        """
+        Get skill information for a specific node in the draft workflow.
+
+        Returns information about skill references in the node, including:
+        - skill_references: List of prompt messages marked as skills
+        - tool_references: Aggregated tool references from all skill prompts
+        - file_references: Aggregated file references from all skill prompts
+        """
+        current_user, _ = current_account_with_tenant()
+        workflow_service = WorkflowService()
+        workflow = workflow_service.get_draft_workflow(app_model=app_model)
+
+        if not workflow:
+            raise DraftWorkflowNotExist()
+
+        skill_info = SkillService.get_node_skill_info(
+            app=app_model,
+            workflow=workflow,
+            node_id=node_id,
+            user_id=current_user.id,
+        )
+        return skill_info.model_dump()
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflows/draft/skills")
+class WorkflowSkillsApi(Resource):
+    """API for retrieving all skill references in a workflow."""
+
+    @console_ns.doc("get_workflow_skills")
+    @console_ns.doc(description="Get all skill references in the draft workflow")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.response(200, "Workflow skills retrieved successfully")
+    @console_ns.response(404, "Workflow not found")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App):
+        """
+        Get skill information for all nodes in the draft workflow that have skill references.
+
+        Returns a list of nodes with their skill information.
+        """
+        current_user, _ = current_account_with_tenant()
+        workflow_service = WorkflowService()
+        workflow = workflow_service.get_draft_workflow(app_model=app_model)
+
+        if not workflow:
+            raise DraftWorkflowNotExist()
+
+        skills_info = SkillService.get_workflow_skills(
+            app=app_model,
+            workflow=workflow,
+            user_id=current_user.id,
+        )
+        return {"nodes": [info.model_dump() for info in skills_info]}

api/controllers/console/app/workflow.py

@@ -33,8 +33,10 @@ from core.trigger.debug.event_selectors import (
 from core.workflow.enums import NodeType
 from core.workflow.graph_engine.manager import GraphEngineManager
 from extensions.ext_database import db
+from extensions.ext_redis import redis_client
 from factories import file_factory, variable_factory
 from fields.member_fields import simple_account_fields
+from fields.online_user_fields import online_user_list_fields
 from fields.workflow_fields import workflow_fields, workflow_pagination_fields
 from libs import helper
 from libs.datetime_utils import naive_utc_now
@@ -43,9 +45,12 @@ from libs.login import current_account_with_tenant, login_required
 from models import App
 from models.model import AppMode
 from models.workflow import Workflow
+from repositories.workflow_collaboration_repository import WORKFLOW_ONLINE_USERS_PREFIX
 from services.app_generate_service import AppGenerateService
 from services.errors.app import WorkflowHashNotEqualError
 from services.errors.llm import InvokeRateLimitError
+from services.workflow.entities import NestedNodeGraphRequest, NestedNodeParameterSchema
+from services.workflow.nested_node_graph_service import NestedNodeGraphService
 from services.workflow_service import DraftWorkflowDeletionError, WorkflowInUseError, WorkflowService
 
 logger = logging.getLogger(__name__)
@@ -160,6 +165,14 @@ class WorkflowUpdatePayload(BaseModel):
     marked_comment: str | None = Field(default=None, max_length=100)
 
 
+class WorkflowFeaturesPayload(BaseModel):
+    features: dict[str, Any] = Field(..., description="Workflow feature configuration")
+
+
+class WorkflowOnlineUsersQuery(BaseModel):
+    workflow_ids: str = Field(..., description="Comma-separated workflow IDs")
+
+
 class DraftWorkflowTriggerRunPayload(BaseModel):
     node_id: str
 
@@ -168,6 +181,15 @@ class DraftWorkflowTriggerRunAllPayload(BaseModel):
     node_ids: list[str]
 
 
+class NestedNodeGraphPayload(BaseModel):
+    """Request payload for generating nested node graph."""
+
+    parent_node_id: str = Field(description="ID of the parent node that uses the extracted value")
+    parameter_key: str = Field(description="Key of the parameter being extracted")
+    context_source: list[str] = Field(description="Variable selector for the context source")
+    parameter_schema: dict[str, Any] = Field(description="Schema of the parameter to extract")
+
+
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -183,8 +205,11 @@ reg(DefaultBlockConfigQuery)
 reg(ConvertToWorkflowPayload)
 reg(WorkflowListQuery)
 reg(WorkflowUpdatePayload)
+reg(WorkflowFeaturesPayload)
+reg(WorkflowOnlineUsersQuery)
 reg(DraftWorkflowTriggerRunPayload)
 reg(DraftWorkflowTriggerRunAllPayload)
+reg(NestedNodeGraphPayload)
 
 
 # TODO(QuantumGhost): Refactor existing node run API to handle file parameter parsing
@@ -827,13 +852,14 @@ class PublishedWorkflowApi(Resource):
         """
         Publish workflow
         """
+        from services.app_bundle_service import AppBundleService
+
         current_user, _ = current_account_with_tenant()
 
         args = PublishWorkflowPayload.model_validate(console_ns.payload or {})
 
-        workflow_service = WorkflowService()
         with Session(db.engine) as session:
-            workflow = workflow_service.publish_workflow(
+            workflow = AppBundleService.publish(
                 session=session,
                 app_model=app_model,
                 account=current_user,
@@ -944,6 +970,31 @@ class ConvertToWorkflowApi(Resource):
         }
 
 
+@console_ns.route("/apps/<uuid:app_id>/workflows/draft/features")
+class WorkflowFeaturesApi(Resource):
+    """Update draft workflow features."""
+
+    @console_ns.expect(console_ns.models[WorkflowFeaturesPayload.__name__])
+    @console_ns.doc("update_workflow_features")
+    @console_ns.doc(description="Update draft workflow features")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.response(200, "Workflow features updated successfully")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+
+        args = WorkflowFeaturesPayload.model_validate(console_ns.payload or {})
+        features = args.features
+
+        workflow_service = WorkflowService()
+        workflow_service.update_draft_workflow_features(app_model=app_model, features=features, account=current_user)
+
+        return {"result": "success"}
+
+
 @console_ns.route("/apps/<uuid:app_id>/workflows")
 class PublishedAllWorkflowApi(Resource):
     @console_ns.expect(console_ns.models[WorkflowListQuery.__name__])
@@ -1321,3 +1372,83 @@ class DraftWorkflowTriggerRunAllApi(Resource):
                     "status": "error",
                 }
             ), 400
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflows/draft/nested-node-graph")
+class NestedNodeGraphApi(Resource):
+    """
+    API for generating Nested Node LLM graph structures.
+
+    This endpoint creates a complete graph structure containing an LLM node
+    configured to extract values from list[PromptMessage] variables.
+    """
+
+    @console_ns.doc("generate_nested_node_graph")
+    @console_ns.doc(description="Generate a Nested Node LLM graph structure")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.expect(console_ns.models[NestedNodeGraphPayload.__name__])
+    @console_ns.response(200, "Nested node graph generated successfully")
+    @console_ns.response(400, "Invalid request parameters")
+    @console_ns.response(403, "Permission denied")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @edit_permission_required
+    def post(self, app_model: App):
+        """
+        Generate a Nested Node LLM graph structure.
+
+        Returns a complete graph structure containing a single LLM node
+        configured for extracting values from list[PromptMessage] context.
+        """
+
+        payload = NestedNodeGraphPayload.model_validate(console_ns.payload or {})
+
+        parameter_schema = NestedNodeParameterSchema(
+            name=payload.parameter_schema.get("name", payload.parameter_key),
+            type=payload.parameter_schema.get("type", "string"),
+            description=payload.parameter_schema.get("description", ""),
+        )
+
+        request = NestedNodeGraphRequest(
+            parent_node_id=payload.parent_node_id,
+            parameter_key=payload.parameter_key,
+            context_source=payload.context_source,
+            parameter_schema=parameter_schema,
+        )
+
+        with Session(db.engine) as session:
+            service = NestedNodeGraphService(session)
+            response = service.generate_nested_node_graph(tenant_id=app_model.tenant_id, request=request)
+
+        return response.model_dump()
+
+
+@console_ns.route("/apps/workflows/online-users")
+class WorkflowOnlineUsersApi(Resource):
+    @console_ns.expect(console_ns.models[WorkflowOnlineUsersQuery.__name__])
+    @console_ns.doc("get_workflow_online_users")
+    @console_ns.doc(description="Get workflow online users")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @marshal_with(online_user_list_fields)
+    def get(self):
+        args = WorkflowOnlineUsersQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore
+
+        workflow_ids = [workflow_id.strip() for workflow_id in args.workflow_ids.split(",") if workflow_id.strip()]
+
+        results = []
+        for workflow_id in workflow_ids:
+            users_json = redis_client.hgetall(f"{WORKFLOW_ONLINE_USERS_PREFIX}{workflow_id}")
+
+            users = []
+            for _, user_info_json in users_json.items():
+                try:
+                    users.append(json.loads(user_info_json))
+                except Exception:
+                    continue
+            results.append({"workflow_id": workflow_id, "users": users})
+
+        return {"data": results}

api/controllers/console/app/workflow_comment.py

@@ -0,0 +1,322 @@
+import logging
+
+from flask_restx import Resource, marshal_with
+from pydantic import BaseModel, Field, TypeAdapter
+
+from controllers.console import console_ns
+from controllers.console.app.wraps import get_app_model
+from controllers.console.wraps import account_initialization_required, setup_required
+from fields.member_fields import AccountWithRole
+from fields.workflow_comment_fields import (
+    workflow_comment_basic_fields,
+    workflow_comment_create_fields,
+    workflow_comment_detail_fields,
+    workflow_comment_reply_create_fields,
+    workflow_comment_reply_update_fields,
+    workflow_comment_resolve_fields,
+    workflow_comment_update_fields,
+)
+from libs.login import current_user, login_required
+from models import App
+from services.account_service import TenantService
+from services.workflow_comment_service import WorkflowCommentService
+
+logger = logging.getLogger(__name__)
+DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
+
+
+class WorkflowCommentCreatePayload(BaseModel):
+    position_x: float = Field(..., description="Comment X position")
+    position_y: float = Field(..., description="Comment Y position")
+    content: str = Field(..., description="Comment content")
+    mentioned_user_ids: list[str] = Field(default_factory=list, description="Mentioned user IDs")
+
+
+class WorkflowCommentUpdatePayload(BaseModel):
+    content: str = Field(..., description="Comment content")
+    position_x: float | None = Field(default=None, description="Comment X position")
+    position_y: float | None = Field(default=None, description="Comment Y position")
+    mentioned_user_ids: list[str] = Field(default_factory=list, description="Mentioned user IDs")
+
+
+class WorkflowCommentReplyCreatePayload(BaseModel):
+    content: str = Field(..., description="Reply content")
+    mentioned_user_ids: list[str] = Field(default_factory=list, description="Mentioned user IDs")
+
+
+class WorkflowCommentReplyUpdatePayload(BaseModel):
+    content: str = Field(..., description="Reply content")
+    mentioned_user_ids: list[str] = Field(default_factory=list, description="Mentioned user IDs")
+
+
+class WorkflowCommentMentionUsersResponse(BaseModel):
+    users: list[AccountWithRole] = Field(description="Mentionable users")
+
+
+for model in (
+    WorkflowCommentCreatePayload,
+    WorkflowCommentUpdatePayload,
+    WorkflowCommentReplyCreatePayload,
+    WorkflowCommentReplyUpdatePayload,
+):
+    console_ns.schema_model(model.__name__, model.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
+
+for model in (AccountWithRole, WorkflowCommentMentionUsersResponse):
+    console_ns.schema_model(model.__name__, model.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
+
+workflow_comment_basic_model = console_ns.model("WorkflowCommentBasic", workflow_comment_basic_fields)
+workflow_comment_detail_model = console_ns.model("WorkflowCommentDetail", workflow_comment_detail_fields)
+workflow_comment_create_model = console_ns.model("WorkflowCommentCreate", workflow_comment_create_fields)
+workflow_comment_update_model = console_ns.model("WorkflowCommentUpdate", workflow_comment_update_fields)
+workflow_comment_resolve_model = console_ns.model("WorkflowCommentResolve", workflow_comment_resolve_fields)
+workflow_comment_reply_create_model = console_ns.model(
+    "WorkflowCommentReplyCreate", workflow_comment_reply_create_fields
+)
+workflow_comment_reply_update_model = console_ns.model(
+    "WorkflowCommentReplyUpdate", workflow_comment_reply_update_fields
+)
+workflow_comment_mention_users_model = console_ns.models[WorkflowCommentMentionUsersResponse.__name__]
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflow/comments")
+class WorkflowCommentListApi(Resource):
+    """API for listing and creating workflow comments."""
+
+    @console_ns.doc("list_workflow_comments")
+    @console_ns.doc(description="Get all comments for a workflow")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.response(200, "Comments retrieved successfully", workflow_comment_basic_model)
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    @marshal_with(workflow_comment_basic_model, envelope="data")
+    def get(self, app_model: App):
+        """Get all comments for a workflow."""
+        comments = WorkflowCommentService.get_comments(tenant_id=current_user.current_tenant_id, app_id=app_model.id)
+
+        return comments
+
+    @console_ns.doc("create_workflow_comment")
+    @console_ns.doc(description="Create a new workflow comment")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.expect(console_ns.models[WorkflowCommentCreatePayload.__name__])
+    @console_ns.response(201, "Comment created successfully", workflow_comment_create_model)
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    @marshal_with(workflow_comment_create_model)
+    def post(self, app_model: App):
+        """Create a new workflow comment."""
+        payload = WorkflowCommentCreatePayload.model_validate(console_ns.payload or {})
+
+        result = WorkflowCommentService.create_comment(
+            tenant_id=current_user.current_tenant_id,
+            app_id=app_model.id,
+            created_by=current_user.id,
+            content=payload.content,
+            position_x=payload.position_x,
+            position_y=payload.position_y,
+            mentioned_user_ids=payload.mentioned_user_ids,
+        )
+
+        return result, 201
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflow/comments/<string:comment_id>")
+class WorkflowCommentDetailApi(Resource):
+    """API for managing individual workflow comments."""
+
+    @console_ns.doc("get_workflow_comment")
+    @console_ns.doc(description="Get a specific workflow comment")
+    @console_ns.doc(params={"app_id": "Application ID", "comment_id": "Comment ID"})
+    @console_ns.response(200, "Comment retrieved successfully", workflow_comment_detail_model)
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    @marshal_with(workflow_comment_detail_model)
+    def get(self, app_model: App, comment_id: str):
+        """Get a specific workflow comment."""
+        comment = WorkflowCommentService.get_comment(
+            tenant_id=current_user.current_tenant_id, app_id=app_model.id, comment_id=comment_id
+        )
+
+        return comment
+
+    @console_ns.doc("update_workflow_comment")
+    @console_ns.doc(description="Update a workflow comment")
+    @console_ns.doc(params={"app_id": "Application ID", "comment_id": "Comment ID"})
+    @console_ns.expect(console_ns.models[WorkflowCommentUpdatePayload.__name__])
+    @console_ns.response(200, "Comment updated successfully", workflow_comment_update_model)
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    @marshal_with(workflow_comment_update_model)
+    def put(self, app_model: App, comment_id: str):
+        """Update a workflow comment."""
+        payload = WorkflowCommentUpdatePayload.model_validate(console_ns.payload or {})
+
+        result = WorkflowCommentService.update_comment(
+            tenant_id=current_user.current_tenant_id,
+            app_id=app_model.id,
+            comment_id=comment_id,
+            user_id=current_user.id,
+            content=payload.content,
+            position_x=payload.position_x,
+            position_y=payload.position_y,
+            mentioned_user_ids=payload.mentioned_user_ids,
+        )
+
+        return result
+
+    @console_ns.doc("delete_workflow_comment")
+    @console_ns.doc(description="Delete a workflow comment")
+    @console_ns.doc(params={"app_id": "Application ID", "comment_id": "Comment ID"})
+    @console_ns.response(204, "Comment deleted successfully")
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    def delete(self, app_model: App, comment_id: str):
+        """Delete a workflow comment."""
+        WorkflowCommentService.delete_comment(
+            tenant_id=current_user.current_tenant_id,
+            app_id=app_model.id,
+            comment_id=comment_id,
+            user_id=current_user.id,
+        )
+
+        return {"result": "success"}, 204
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflow/comments/<string:comment_id>/resolve")
+class WorkflowCommentResolveApi(Resource):
+    """API for resolving and reopening workflow comments."""
+
+    @console_ns.doc("resolve_workflow_comment")
+    @console_ns.doc(description="Resolve a workflow comment")
+    @console_ns.doc(params={"app_id": "Application ID", "comment_id": "Comment ID"})
+    @console_ns.response(200, "Comment resolved successfully", workflow_comment_resolve_model)
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    @marshal_with(workflow_comment_resolve_model)
+    def post(self, app_model: App, comment_id: str):
+        """Resolve a workflow comment."""
+        comment = WorkflowCommentService.resolve_comment(
+            tenant_id=current_user.current_tenant_id,
+            app_id=app_model.id,
+            comment_id=comment_id,
+            user_id=current_user.id,
+        )
+
+        return comment
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflow/comments/<string:comment_id>/replies")
+class WorkflowCommentReplyApi(Resource):
+    """API for managing comment replies."""
+
+    @console_ns.doc("create_workflow_comment_reply")
+    @console_ns.doc(description="Add a reply to a workflow comment")
+    @console_ns.doc(params={"app_id": "Application ID", "comment_id": "Comment ID"})
+    @console_ns.expect(console_ns.models[WorkflowCommentReplyCreatePayload.__name__])
+    @console_ns.response(201, "Reply created successfully", workflow_comment_reply_create_model)
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    @marshal_with(workflow_comment_reply_create_model)
+    def post(self, app_model: App, comment_id: str):
+        """Add a reply to a workflow comment."""
+        # Validate comment access first
+        WorkflowCommentService.validate_comment_access(
+            comment_id=comment_id, tenant_id=current_user.current_tenant_id, app_id=app_model.id
+        )
+
+        payload = WorkflowCommentReplyCreatePayload.model_validate(console_ns.payload or {})
+
+        result = WorkflowCommentService.create_reply(
+            comment_id=comment_id,
+            content=payload.content,
+            created_by=current_user.id,
+            mentioned_user_ids=payload.mentioned_user_ids,
+        )
+
+        return result, 201
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflow/comments/<string:comment_id>/replies/<string:reply_id>")
+class WorkflowCommentReplyDetailApi(Resource):
+    """API for managing individual comment replies."""
+
+    @console_ns.doc("update_workflow_comment_reply")
+    @console_ns.doc(description="Update a comment reply")
+    @console_ns.doc(params={"app_id": "Application ID", "comment_id": "Comment ID", "reply_id": "Reply ID"})
+    @console_ns.expect(console_ns.models[WorkflowCommentReplyUpdatePayload.__name__])
+    @console_ns.response(200, "Reply updated successfully", workflow_comment_reply_update_model)
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    @marshal_with(workflow_comment_reply_update_model)
+    def put(self, app_model: App, comment_id: str, reply_id: str):
+        """Update a comment reply."""
+        # Validate comment access first
+        WorkflowCommentService.validate_comment_access(
+            comment_id=comment_id, tenant_id=current_user.current_tenant_id, app_id=app_model.id
+        )
+
+        payload = WorkflowCommentReplyUpdatePayload.model_validate(console_ns.payload or {})
+
+        reply = WorkflowCommentService.update_reply(
+            reply_id=reply_id,
+            user_id=current_user.id,
+            content=payload.content,
+            mentioned_user_ids=payload.mentioned_user_ids,
+        )
+
+        return reply
+
+    @console_ns.doc("delete_workflow_comment_reply")
+    @console_ns.doc(description="Delete a comment reply")
+    @console_ns.doc(params={"app_id": "Application ID", "comment_id": "Comment ID", "reply_id": "Reply ID"})
+    @console_ns.response(204, "Reply deleted successfully")
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    def delete(self, app_model: App, comment_id: str, reply_id: str):
+        """Delete a comment reply."""
+        # Validate comment access first
+        WorkflowCommentService.validate_comment_access(
+            comment_id=comment_id, tenant_id=current_user.current_tenant_id, app_id=app_model.id
+        )
+
+        WorkflowCommentService.delete_reply(reply_id=reply_id, user_id=current_user.id)
+
+        return {"result": "success"}, 204
+
+
+@console_ns.route("/apps/<uuid:app_id>/workflow/comments/mention-users")
+class WorkflowCommentMentionUsersApi(Resource):
+    """API for getting mentionable users for workflow comments."""
+
+    @console_ns.doc("workflow_comment_mention_users")
+    @console_ns.doc(description="Get all users in current tenant for mentions")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.response(200, "Mentionable users retrieved successfully", workflow_comment_mention_users_model)
+    @login_required
+    @setup_required
+    @account_initialization_required
+    @get_app_model()
+    def get(self, app_model: App):
+        """Get all users in current tenant for mentions."""
+        members = TenantService.get_tenant_members(current_user.current_tenant)
+        member_models = TypeAdapter(list[AccountWithRole]).validate_python(members, from_attributes=True)
+        response = WorkflowCommentMentionUsersResponse(users=member_models)
+        return response.model_dump(mode="json"), 200

api/controllers/console/app/workflow_draft_variable.py

@@ -17,15 +17,16 @@ from controllers.console.wraps import account_initialization_required, edit_perm
 from controllers.web.error import InvalidArgumentError, NotFoundError
 from core.file import helpers as file_helpers
 from core.variables.segment_group import SegmentGroup
-from core.variables.segments import ArrayFileSegment, FileSegment, Segment
+from core.variables.segments import ArrayFileSegment, ArrayPromptMessageSegment, FileSegment, Segment
 from core.variables.types import SegmentType
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID, SYSTEM_VARIABLE_NODE_ID
 from extensions.ext_database import db
+from factories import variable_factory
 from factories.file_factory import build_from_mapping, build_from_mappings
-from factories.variable_factory import build_segment_with_type
-from libs.login import login_required
+from libs.login import current_account_with_tenant, login_required
 from models import App, AppMode
 from models.workflow import WorkflowDraftVariable
+from services.sandbox.sandbox_service import SandboxService
 from services.workflow_draft_variable_service import WorkflowDraftVariableList, WorkflowDraftVariableService
 from services.workflow_service import WorkflowService
 
@@ -43,6 +44,16 @@ class WorkflowDraftVariableUpdatePayload(BaseModel):
     value: Any | None = Field(default=None, description="Variable value")
 
 
+class ConversationVariableUpdatePayload(BaseModel):
+    conversation_variables: list[dict[str, Any]] = Field(
+        ..., description="Conversation variables for the draft workflow"
+    )
+
+
+class EnvironmentVariableUpdatePayload(BaseModel):
+    environment_variables: list[dict[str, Any]] = Field(..., description="Environment variables for the draft workflow")
+
+
 console_ns.schema_model(
     WorkflowDraftVariableListQuery.__name__,
     WorkflowDraftVariableListQuery.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
@@ -51,6 +62,14 @@ console_ns.schema_model(
     WorkflowDraftVariableUpdatePayload.__name__,
     WorkflowDraftVariableUpdatePayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
 )
+console_ns.schema_model(
+    ConversationVariableUpdatePayload.__name__,
+    ConversationVariableUpdatePayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
+)
+console_ns.schema_model(
+    EnvironmentVariableUpdatePayload.__name__,
+    EnvironmentVariableUpdatePayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
+)
 
 
 def _convert_values_to_json_serializable_object(value: Segment):
@@ -58,6 +77,8 @@ def _convert_values_to_json_serializable_object(value: Segment):
         return value.value.model_dump()
     elif isinstance(value, ArrayFileSegment):
         return [i.model_dump() for i in value.value]
+    elif isinstance(value, ArrayPromptMessageSegment):
+        return value.to_object()
     elif isinstance(value, SegmentGroup):
         return [_convert_values_to_json_serializable_object(i) for i in value.value]
     else:
@@ -247,6 +268,8 @@ class WorkflowVariableCollectionApi(Resource):
     @console_ns.response(204, "Workflow variables deleted successfully")
     @_api_prerequisite
     def delete(self, app_model: App):
+        current_user, _ = current_account_with_tenant()
+        SandboxService.delete_draft_storage(app_model.tenant_id, app_model.id, current_user.id)
         draft_var_srv = WorkflowDraftVariableService(
             session=db.session(),
         )
@@ -383,7 +406,7 @@ class VariableApi(Resource):
                 if len(raw_value) > 0 and not isinstance(raw_value[0], dict):
                     raise InvalidArgumentError(description=f"expected dict for files[0], got {type(raw_value)}")
                 raw_value = build_from_mappings(mappings=raw_value, tenant_id=app_model.tenant_id)
-            new_value = build_segment_with_type(variable.value_type, raw_value)
+            new_value = variable_factory.build_segment_with_type(variable.value_type, raw_value)
         draft_var_srv.update_variable(variable, name=new_name, value=new_value)
         db.session.commit()
         return variable
@@ -476,6 +499,35 @@ class ConversationVariableCollectionApi(Resource):
         db.session.commit()
         return _get_variable_list(app_model, CONVERSATION_VARIABLE_NODE_ID)
 
+    @console_ns.expect(console_ns.models[ConversationVariableUpdatePayload.__name__])
+    @console_ns.doc("update_conversation_variables")
+    @console_ns.doc(description="Update conversation variables for workflow draft")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.response(200, "Conversation variables updated successfully")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @edit_permission_required
+    @get_app_model(mode=AppMode.ADVANCED_CHAT)
+    def post(self, app_model: App):
+        payload = ConversationVariableUpdatePayload.model_validate(console_ns.payload or {})
+
+        workflow_service = WorkflowService()
+
+        conversation_variables_list = payload.conversation_variables
+        conversation_variables = [
+            variable_factory.build_conversation_variable_from_mapping(obj) for obj in conversation_variables_list
+        ]
+
+        current_user, _ = current_account_with_tenant()
+        workflow_service.update_draft_workflow_conversation_variables(
+            app_model=app_model,
+            account=current_user,
+            conversation_variables=conversation_variables,
+        )
+
+        return {"result": "success"}
+
 
 @console_ns.route("/apps/<uuid:app_id>/workflows/draft/system-variables")
 class SystemVariableCollectionApi(Resource):
@@ -527,3 +579,32 @@ class EnvironmentVariableCollectionApi(Resource):
             )
 
         return {"items": env_vars_list}
+
+    @console_ns.expect(console_ns.models[EnvironmentVariableUpdatePayload.__name__])
+    @console_ns.doc("update_environment_variables")
+    @console_ns.doc(description="Update environment variables for workflow draft")
+    @console_ns.doc(params={"app_id": "Application ID"})
+    @console_ns.response(200, "Environment variables updated successfully")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @edit_permission_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        payload = EnvironmentVariableUpdatePayload.model_validate(console_ns.payload or {})
+        current_user, _ = current_account_with_tenant()
+
+        workflow_service = WorkflowService()
+
+        environment_variables_list = payload.environment_variables
+        environment_variables = [
+            variable_factory.build_environment_variable_from_mapping(obj) for obj in environment_variables_list
+        ]
+
+        workflow_service.update_draft_workflow_environment_variables(
+            app_model=app_model,
+            account=current_user,
+            environment_variables=environment_variables,
+        )
+
+        return {"result": "success"}

api/controllers/console/sandbox_files.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+from fastapi.encoders import jsonable_encoder
+from flask import request
+from flask_restx import Resource, fields
+from pydantic import BaseModel, Field
+
+from controllers.console import console_ns
+from controllers.console.wraps import account_initialization_required, setup_required
+from libs.login import current_account_with_tenant, login_required
+from services.sandbox.sandbox_file_service import SandboxFileService
+
+DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
+
+
+class SandboxFileListQuery(BaseModel):
+    path: str | None = Field(default=None, description="Workspace relative path")
+    recursive: bool = Field(default=False, description="List recursively")
+
+
+class SandboxFileDownloadRequest(BaseModel):
+    path: str = Field(..., description="Workspace relative file path")
+
+
+console_ns.schema_model(
+    SandboxFileListQuery.__name__,
+    SandboxFileListQuery.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
+)
+console_ns.schema_model(
+    SandboxFileDownloadRequest.__name__,
+    SandboxFileDownloadRequest.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
+)
+
+
+SANDBOX_FILE_NODE_FIELDS = {
+    "path": fields.String,
+    "is_dir": fields.Boolean,
+    "size": fields.Raw,
+    "mtime": fields.Raw,
+    "extension": fields.String,
+}
+
+
+SANDBOX_FILE_DOWNLOAD_TICKET_FIELDS = {
+    "download_url": fields.String,
+    "expires_in": fields.Integer,
+    "export_id": fields.String,
+}
+
+
+sandbox_file_node_model = console_ns.model("SandboxFileNode", SANDBOX_FILE_NODE_FIELDS)
+sandbox_file_download_ticket_model = console_ns.model("SandboxFileDownloadTicket", SANDBOX_FILE_DOWNLOAD_TICKET_FIELDS)
+
+
+@console_ns.route("/apps/<string:app_id>/sandbox/files")
+class SandboxFilesApi(Resource):
+    """List sandbox files for the current user.
+
+    The sandbox_id is derived from the current user's ID, as each user has
+    their own sandbox workspace per app.
+    """
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @console_ns.expect(console_ns.models[SandboxFileListQuery.__name__])
+    @console_ns.marshal_list_with(sandbox_file_node_model)
+    def get(self, app_id: str):
+        args = SandboxFileListQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore[arg-type]
+        account, tenant_id = current_account_with_tenant()
+        sandbox_id = account.id
+        return jsonable_encoder(
+            SandboxFileService.list_files(
+                tenant_id=tenant_id,
+                app_id=app_id,
+                sandbox_id=sandbox_id,
+                path=args.path,
+                recursive=args.recursive,
+            )
+        )
+
+
+@console_ns.route("/apps/<string:app_id>/sandbox/files/download")
+class SandboxFileDownloadApi(Resource):
+    """Download a sandbox file for the current user.
+
+    The sandbox_id is derived from the current user's ID, as each user has
+    their own sandbox workspace per app.
+    """
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @console_ns.expect(console_ns.models[SandboxFileDownloadRequest.__name__])
+    @console_ns.marshal_with(sandbox_file_download_ticket_model)
+    def post(self, app_id: str):
+        payload = SandboxFileDownloadRequest.model_validate(console_ns.payload or {})
+        account, tenant_id = current_account_with_tenant()
+        sandbox_id = account.id
+        res = SandboxFileService.download_file(
+            tenant_id=tenant_id, app_id=app_id, sandbox_id=sandbox_id, path=payload.path
+        )
+        return jsonable_encoder(res)

api/controllers/console/socketio/__init__.py

@@ -0,0 +1 @@
+

api/controllers/console/socketio/workflow.py

@@ -0,0 +1,119 @@
+import logging
+from collections.abc import Callable
+from typing import cast
+
+from flask import Request as FlaskRequest
+
+from extensions.ext_socketio import sio
+from libs.passport import PassportService
+from libs.token import extract_access_token
+from repositories.workflow_collaboration_repository import WorkflowCollaborationRepository
+from services.account_service import AccountService
+from services.workflow_collaboration_service import WorkflowCollaborationService
+
+repository = WorkflowCollaborationRepository()
+collaboration_service = WorkflowCollaborationService(repository, sio)
+
+
+def _sio_on(event: str) -> Callable[[Callable[..., object]], Callable[..., object]]:
+    return cast(Callable[[Callable[..., object]], Callable[..., object]], sio.on(event))
+
+
+@_sio_on("connect")
+def socket_connect(sid, environ, auth):
+    """
+    WebSocket connect event, do authentication here.
+    """
+    try:
+        request_environ = FlaskRequest(environ)
+        token = extract_access_token(request_environ)
+    except Exception:
+        logging.exception("Failed to extract token")
+        token = None
+
+    if not token:
+        logging.warning("Socket connect rejected: missing token (sid=%s)", sid)
+        return False
+
+    try:
+        decoded = PassportService().verify(token)
+        user_id = decoded.get("user_id")
+        if not user_id:
+            logging.warning("Socket connect rejected: missing user_id (sid=%s)", sid)
+            return False
+
+        with sio.app.app_context():
+            user = AccountService.load_logged_in_account(account_id=user_id)
+            if not user:
+                logging.warning("Socket connect rejected: user not found (user_id=%s, sid=%s)", user_id, sid)
+                return False
+            if not user.has_edit_permission:
+                logging.warning("Socket connect rejected: no edit permission (user_id=%s, sid=%s)", user_id, sid)
+                return False
+
+            collaboration_service.save_session(sid, user)
+            return True
+
+    except Exception:
+        logging.exception("Socket authentication failed")
+        return False
+
+
+@_sio_on("user_connect")
+def handle_user_connect(sid, data):
+    """
+    Handle user connect event. Each session (tab) is treated as an independent collaborator.
+    """
+    workflow_id = data.get("workflow_id")
+    if not workflow_id:
+        return {"msg": "workflow_id is required"}, 400
+
+    result = collaboration_service.register_session(workflow_id, sid)
+    if not result:
+        return {"msg": "unauthorized"}, 401
+
+    user_id, is_leader = result
+    return {"msg": "connected", "user_id": user_id, "sid": sid, "isLeader": is_leader}
+
+
+@_sio_on("disconnect")
+def handle_disconnect(sid):
+    """
+    Handle session disconnect event. Remove the specific session from online users.
+    """
+    collaboration_service.disconnect_session(sid)
+
+
+@_sio_on("collaboration_event")
+def handle_collaboration_event(sid, data):
+    """
+    Handle general collaboration events, include:
+    1. mouse_move
+    2. vars_and_features_update
+    3. sync_request (ask leader to update graph)
+    4. app_state_update
+    5. mcp_server_update
+    6. workflow_update
+    7. comments_update
+    8. node_panel_presence
+    9. skill_file_active
+    10. skill_sync_request
+    11. skill_resync_request
+    """
+    return collaboration_service.relay_collaboration_event(sid, data)
+
+
+@_sio_on("graph_event")
+def handle_graph_event(sid, data):
+    """
+    Handle graph events - simple broadcast relay.
+    """
+    return collaboration_service.relay_graph_event(sid, data)
+
+
+@_sio_on("skill_event")
+def handle_skill_event(sid, data):
+    """
+    Handle skill events - simple broadcast relay.
+    """
+    return collaboration_service.relay_skill_event(sid, data)

api/controllers/console/workspace/account.py

@@ -37,6 +37,7 @@ from controllers.console.wraps import (
     only_edition_cloud,
     setup_required,
 )
+from core.file import helpers as file_helpers
 from extensions.ext_database import db
 from fields.member_fields import Account as AccountResponse
 from libs.datetime_utils import naive_utc_now
@@ -74,6 +75,10 @@ class AccountAvatarPayload(BaseModel):
     avatar: str
 
 
+class AccountAvatarQuery(BaseModel):
+    avatar: str = Field(..., description="Avatar file ID")
+
+
 class AccountInterfaceLanguagePayload(BaseModel):
     interface_language: str
 
@@ -159,6 +164,7 @@ def reg(cls: type[BaseModel]):
 reg(AccountInitPayload)
 reg(AccountNamePayload)
 reg(AccountAvatarPayload)
+reg(AccountAvatarQuery)
 reg(AccountInterfaceLanguagePayload)
 reg(AccountInterfaceThemePayload)
 reg(AccountTimezonePayload)
@@ -268,6 +274,18 @@ class AccountNameApi(Resource):
 
 @console_ns.route("/account/avatar")
 class AccountAvatarApi(Resource):
+    @console_ns.expect(console_ns.models[AccountAvatarQuery.__name__])
+    @console_ns.doc("get_account_avatar")
+    @console_ns.doc(description="Get account avatar url")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        args = AccountAvatarQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore
+
+        avatar_url = file_helpers.get_signed_file_url(args.avatar)
+        return {"avatar_url": avatar_url}
+
     @console_ns.expect(console_ns.models[AccountAvatarPayload.__name__])
     @setup_required
     @login_required

api/controllers/console/workspace/dsl.py

@@ -0,0 +1,67 @@
+import json
+
+import httpx
+import yaml
+from flask import request
+from flask_restx import Resource
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+from werkzeug.exceptions import Forbidden
+
+from controllers.console import console_ns
+from controllers.console.wraps import account_initialization_required, setup_required
+from core.plugin.impl.exc import PluginPermissionDeniedError
+from extensions.ext_database import db
+from libs.login import current_account_with_tenant, login_required
+from models.model import App
+from models.workflow import Workflow
+from services.app_dsl_service import AppDslService
+
+
+class DSLPredictRequest(BaseModel):
+    app_id: str
+    current_node_id: str
+
+
+@console_ns.route("/workspaces/current/dsl/predict")
+class DSLPredictApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        user, _ = current_account_with_tenant()
+        if not user.is_admin_or_owner:
+            raise Forbidden()
+
+        args = DSLPredictRequest.model_validate(request.get_json())
+
+        app_id: str = args.app_id
+        current_node_id: str = args.current_node_id
+
+        with Session(db.engine) as session:
+            app = session.query(App).filter_by(id=app_id).first()
+            workflow = session.query(Workflow).filter_by(app_id=app_id, version=Workflow.VERSION_DRAFT).first()
+
+        if not app:
+            raise ValueError("App not found")
+        if not workflow:
+            raise ValueError("Workflow not found")
+
+        try:
+            i = 0
+            for node_id, _ in workflow.walk_nodes():
+                if node_id == current_node_id:
+                    break
+                i += 1
+
+            dsl = yaml.safe_load(AppDslService.export_dsl(app_model=app))
+
+            response = httpx.post(
+                "http://spark-832c:8000/predict",
+                json={"graph_data": dsl, "source_node_index": i},
+            )
+            return {
+                "nodes": json.loads(response.json()),
+            }
+        except PluginPermissionDeniedError as e:
+            raise ValueError(e.description) from e

api/controllers/console/workspace/sandbox_providers.py

@@ -0,0 +1,104 @@
+import logging
+
+from flask import request
+from flask_restx import Resource, fields
+from pydantic import BaseModel
+
+from controllers.console import console_ns
+from controllers.console.wraps import account_initialization_required, setup_required
+from core.model_runtime.utils.encoders import jsonable_encoder
+from libs.login import current_account_with_tenant, login_required
+from services.sandbox.sandbox_provider_service import SandboxProviderService
+
+logger = logging.getLogger(__name__)
+
+
+class SandboxProviderConfigRequest(BaseModel):
+    config: dict
+    activate: bool = False
+
+
+class SandboxProviderActivateRequest(BaseModel):
+    type: str
+
+
+@console_ns.route("/workspaces/current/sandbox-providers")
+class SandboxProviderListApi(Resource):
+    @console_ns.doc("list_sandbox_providers")
+    @console_ns.doc(description="Get list of available sandbox providers with configuration status")
+    @console_ns.response(200, "Success", fields.List(fields.Raw(description="Sandbox provider information")))
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        _, current_tenant_id = current_account_with_tenant()
+        providers = SandboxProviderService.list_providers(current_tenant_id)
+        return jsonable_encoder([p.model_dump() for p in providers])
+
+
+@console_ns.route("/workspaces/current/sandbox-provider/<string:provider_type>/config")
+class SandboxProviderConfigApi(Resource):
+    @console_ns.doc("save_sandbox_provider_config")
+    @console_ns.doc(description="Save or update configuration for a sandbox provider")
+    @console_ns.response(200, "Success")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider_type: str):
+        _, current_tenant_id = current_account_with_tenant()
+        args = SandboxProviderConfigRequest.model_validate(request.get_json())
+
+        try:
+            result = SandboxProviderService.save_config(
+                tenant_id=current_tenant_id,
+                provider_type=provider_type,
+                config=args.config,
+                activate=args.activate,
+            )
+            return result
+        except ValueError as e:
+            return {"message": str(e)}, 400
+
+    @console_ns.doc("delete_sandbox_provider_config")
+    @console_ns.doc(description="Delete configuration for a sandbox provider")
+    @console_ns.response(200, "Success")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def delete(self, provider_type: str):
+        _, current_tenant_id = current_account_with_tenant()
+
+        try:
+            result = SandboxProviderService.delete_config(
+                tenant_id=current_tenant_id,
+                provider_type=provider_type,
+            )
+            return result
+        except ValueError as e:
+            return {"message": str(e)}, 400
+
+
+@console_ns.route("/workspaces/current/sandbox-provider/<string:provider_type>/activate")
+class SandboxProviderActivateApi(Resource):
+    """Activate a sandbox provider."""
+
+    @console_ns.doc("activate_sandbox_provider")
+    @console_ns.doc(description="Activate a sandbox provider for the current workspace")
+    @console_ns.response(200, "Success")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider_type: str):
+        """Activate a sandbox provider."""
+        _, current_tenant_id = current_account_with_tenant()
+
+        try:
+            args = SandboxProviderActivateRequest.model_validate(request.get_json())
+            result = SandboxProviderService.activate_provider(
+                tenant_id=current_tenant_id,
+                provider_type=provider_type,
+                type=args.type,
+            )
+            return result
+        except ValueError as e:
+            return {"message": str(e)}, 400

api/controllers/files/__init__.py

@@ -14,7 +14,12 @@ api = ExternalApi(
 
 files_ns = Namespace("files", description="File operations", path="/")
 
-from . import image_preview, tool_files, upload
+from . import (
+    image_preview,
+    storage_files,
+    tool_files,
+    upload,
+)
 
 api.add_namespace(files_ns)
 
@@ -23,6 +28,7 @@ __all__ = [
     "bp",
     "files_ns",
     "image_preview",
+    "storage_files",
     "tool_files",
     "upload",
 ]

api/controllers/files/storage_files.py

@@ -0,0 +1,80 @@
+"""Token-based file proxy controller for storage operations.
+
+This controller handles file download and upload operations using opaque UUID tokens.
+The token maps to the real storage key in Redis, so the actual storage path is never
+exposed in the URL.
+
+Routes:
+    GET  /files/storage-files/{token} - Download a file
+    PUT  /files/storage-files/{token} - Upload a file
+
+The operation type (download/upload) is determined by the ticket stored in Redis,
+not by the HTTP method. This ensures a download ticket cannot be used for upload
+and vice versa.
+"""
+
+from urllib.parse import quote
+
+from flask import Response, request
+from flask_restx import Resource
+from werkzeug.exceptions import Forbidden, NotFound, RequestEntityTooLarge
+
+from controllers.files import files_ns
+from extensions.ext_storage import storage
+from services.storage_ticket_service import StorageTicketService
+
+
+@files_ns.route("/storage-files/<string:token>")
+class StorageFilesApi(Resource):
+    """Handle file operations through token-based URLs."""
+
+    def get(self, token: str):
+        """Download a file using a token.
+
+        The ticket must have op="download", otherwise returns 403.
+        """
+        ticket = StorageTicketService.get_ticket(token)
+        if ticket is None:
+            raise Forbidden("Invalid or expired token")
+
+        if ticket.op != "download":
+            raise Forbidden("This token is not valid for download")
+
+        try:
+            generator = storage.load_stream(ticket.storage_key)
+        except FileNotFoundError:
+            raise NotFound("File not found")
+
+        filename = ticket.filename or ticket.storage_key.rsplit("/", 1)[-1]
+        encoded_filename = quote(filename)
+
+        return Response(
+            generator,
+            mimetype="application/octet-stream",
+            direct_passthrough=True,
+            headers={
+                "Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}",
+            },
+        )
+
+    def put(self, token: str):
+        """Upload a file using a token.
+
+        The ticket must have op="upload", otherwise returns 403.
+        If the request body exceeds max_bytes, returns 413.
+        """
+        ticket = StorageTicketService.get_ticket(token)
+        if ticket is None:
+            raise Forbidden("Invalid or expired token")
+
+        if ticket.op != "upload":
+            raise Forbidden("This token is not valid for upload")
+
+        content = request.get_data()
+
+        if ticket.max_bytes is not None and len(content) > ticket.max_bytes:
+            raise RequestEntityTooLarge(f"Upload exceeds maximum size of {ticket.max_bytes} bytes")
+
+        storage.save(ticket.storage_key, content)
+
+        return Response(status=204)

api/controllers/inner_api/plugin/plugin.py

@@ -448,3 +448,53 @@ class PluginFetchAppInfoApi(Resource):
         return BaseBackwardsInvocationResponse(
             data=PluginAppBackwardsInvocation.fetch_app_info(payload.app_id, tenant_model.id)
         ).model_dump()
+
+
+@inner_api_ns.route("/fetch/tools/list")
+class PluginFetchToolsListApi(Resource):
+    @get_user_tenant
+    @setup_required
+    @plugin_inner_api_only
+    @inner_api_ns.doc("plugin_fetch_tools_list")
+    @inner_api_ns.doc(description="Fetch all available tools through plugin interface")
+    @inner_api_ns.doc(
+        responses={
+            200: "Tools list retrieved successfully",
+            401: "Unauthorized - invalid API key",
+            404: "Service not available",
+        }
+    )
+    def post(self, user_model: Account | EndUser, tenant_model: Tenant):
+        from sqlalchemy.orm import Session
+
+        from extensions.ext_database import db
+        from services.tools.api_tools_manage_service import ApiToolManageService
+        from services.tools.builtin_tools_manage_service import BuiltinToolManageService
+        from services.tools.mcp_tools_manage_service import MCPToolManageService
+        from services.tools.workflow_tools_manage_service import WorkflowToolManageService
+
+        providers = []
+
+        # Get builtin tools
+        builtin_providers = BuiltinToolManageService.list_builtin_tools(user_model.id, tenant_model.id)
+        for provider in builtin_providers:
+            providers.append(provider.to_dict())
+
+        # Get API tools
+        api_providers = ApiToolManageService.list_api_tools(tenant_model.id)
+        for provider in api_providers:
+            providers.append(provider.to_dict())
+
+        # Get workflow tools
+        workflow_providers = WorkflowToolManageService.list_tenant_workflow_tools(user_model.id, tenant_model.id)
+        for provider in workflow_providers:
+            providers.append(provider.to_dict())
+
+        # Get MCP tools
+        with Session(db.engine) as session:
+            mcp_service = MCPToolManageService(session)
+            mcp_providers = mcp_service.list_providers(tenant_id=tenant_model.id, for_list=True)
+            for provider in mcp_providers:
+                providers.append(provider.to_dict())
+
+        return BaseBackwardsInvocationResponse(data={"providers": providers}).model_dump()

api/controllers/inner_api/plugin/wraps.py

@@ -75,7 +75,6 @@ def get_user_tenant(view_func: Callable[P, R]):
     @wraps(view_func)
     def decorated_view(*args: P.args, **kwargs: P.kwargs):
         payload = TenantUserPayload.model_validate(request.get_json(silent=True) or {})
-
         user_id = payload.user_id
         tenant_id = payload.tenant_id
 

api/controllers/inner_api/wraps.py

@@ -5,14 +5,15 @@ from hashlib import sha1
 from hmac import new as hmac_new
 from typing import ParamSpec, TypeVar
 
-P = ParamSpec("P")
-R = TypeVar("R")
 from flask import abort, request
 
 from configs import dify_config
 from extensions.ext_database import db
 from models.model import EndUser
 
+P = ParamSpec("P")
+R = TypeVar("R")
+
 
 def billing_inner_api_only(view: Callable[P, R]):
     @wraps(view)
@@ -88,11 +89,11 @@ def plugin_inner_api_only(view: Callable[P, R]):
         if not dify_config.PLUGIN_DAEMON_KEY:
             abort(404)
 
-        # get header 'X-Inner-Api-Key'
+        # validate using inner api key
         inner_api_key = request.headers.get("X-Inner-Api-Key")
-        if not inner_api_key or inner_api_key != dify_config.INNER_API_KEY_FOR_PLUGIN:
-            abort(404)
+        if inner_api_key and inner_api_key == dify_config.INNER_API_KEY_FOR_PLUGIN:
+            return view(*args, **kwargs)
 
-        return view(*args, **kwargs)
+        abort(401)
 
     return decorated

api/core/agent/agent_app_runner.py

@@ -0,0 +1,380 @@
+import logging
+from collections.abc import Generator
+from copy import deepcopy
+from typing import Any
+
+from core.agent.base_agent_runner import BaseAgentRunner
+from core.agent.entities import AgentEntity, AgentLog, AgentResult
+from core.agent.patterns.strategy_factory import StrategyFactory
+from core.app.apps.base_app_queue_manager import PublishFrom
+from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
+from core.file import file_manager
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageContentType,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
+from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
+from core.tools.__base.tool import Tool
+from core.tools.entities.tool_entities import ToolInvokeMeta
+from core.tools.tool_engine import ToolEngine
+from models.model import Message
+
+logger = logging.getLogger(__name__)
+
+
+class AgentAppRunner(BaseAgentRunner):
+    def _create_tool_invoke_hook(self, message: Message):
+        """
+        Create a tool invoke hook that uses ToolEngine.agent_invoke.
+        This hook handles file creation and returns proper meta information.
+        """
+        # Get trace manager from app generate entity
+        trace_manager = self.application_generate_entity.trace_manager
+
+        def tool_invoke_hook(
+            tool: Tool, tool_args: dict[str, Any], tool_name: str
+        ) -> tuple[str, list[str], ToolInvokeMeta]:
+            """Hook that uses agent_invoke for proper file and meta handling."""
+            tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
+                tool=tool,
+                tool_parameters=tool_args,
+                user_id=self.user_id,
+                tenant_id=self.tenant_id,
+                message=message,
+                invoke_from=self.application_generate_entity.invoke_from,
+                agent_tool_callback=self.agent_callback,
+                trace_manager=trace_manager,
+                app_id=self.application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                conversation_id=self.conversation.id,
+            )
+
+            # Publish files and track IDs
+            for message_file_id in message_files:
+                self.queue_manager.publish(
+                    QueueMessageFileEvent(message_file_id=message_file_id),
+                    PublishFrom.APPLICATION_MANAGER,
+                )
+                self._current_message_file_ids.append(message_file_id)
+
+            return tool_invoke_response, message_files, tool_invoke_meta
+
+        return tool_invoke_hook
+
+    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
+        """
+        Run Agent application
+        """
+        self.query = query
+        app_generate_entity = self.application_generate_entity
+
+        app_config = self.app_config
+        assert app_config is not None, "app_config is required"
+        assert app_config.agent is not None, "app_config.agent is required"
+
+        # convert tools into ModelRuntime Tool format
+        tool_instances, _ = self._init_prompt_tools()
+
+        assert app_config.agent
+
+        # Create tool invoke hook for agent_invoke
+        tool_invoke_hook = self._create_tool_invoke_hook(message)
+
+        # Get instruction for ReAct strategy
+        instruction = self.app_config.prompt_template.simple_prompt_template or ""
+
+        # Use factory to create appropriate strategy
+        strategy = StrategyFactory.create_strategy(
+            model_features=self.model_features,
+            model_instance=self.model_instance,
+            tools=list(tool_instances.values()),
+            files=list(self.files),
+            max_iterations=app_config.agent.max_iteration,
+            context=self.build_execution_context(),
+            agent_strategy=self.config.strategy,
+            tool_invoke_hook=tool_invoke_hook,
+            instruction=instruction,
+        )
+
+        # Initialize state variables
+        current_agent_thought_id = None
+        has_published_thought = False
+        current_tool_name: str | None = None
+        self._current_message_file_ids: list[str] = []
+
+        # organize prompt messages
+        prompt_messages = self._organize_prompt_messages()
+
+        # Run strategy
+        generator = strategy.run(
+            prompt_messages=prompt_messages,
+            model_parameters=app_generate_entity.model_conf.parameters,
+            stop=app_generate_entity.model_conf.stop,
+            stream=True,
+        )
+
+        # Consume generator and collect result
+        result: AgentResult | None = None
+        try:
+            while True:
+                try:
+                    output = next(generator)
+                except StopIteration as e:
+                    # Generator finished, get the return value
+                    result = e.value
+                    break
+
+                if isinstance(output, LLMResultChunk):
+                    # Handle LLM chunk
+                    if current_agent_thought_id and not has_published_thought:
+                        self.queue_manager.publish(
+                            QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                            PublishFrom.APPLICATION_MANAGER,
+                        )
+                        has_published_thought = True
+
+                    yield output
+
+                elif isinstance(output, AgentLog):
+                    # Handle Agent Log using log_type for type-safe dispatch
+                    if output.status == AgentLog.LogStatus.START:
+                        if output.log_type == AgentLog.LogType.ROUND:
+                            # Start of a new round
+                            message_file_ids: list[str] = []
+                            current_agent_thought_id = self.create_agent_thought(
+                                message_id=message.id,
+                                message="",
+                                tool_name="",
+                                tool_input="",
+                                messages_ids=message_file_ids,
+                            )
+                            has_published_thought = False
+
+                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Tool call start - extract data from structured fields
+                            current_tool_name = output.data.get("tool_name", "")
+                            tool_input = output.data.get("tool_args", {})
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=current_tool_name,
+                                tool_input=tool_input,
+                                thought=None,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=None,
+                                messages_ids=[],
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                    elif output.status == AgentLog.LogStatus.SUCCESS:
+                        if output.log_type == AgentLog.LogType.THOUGHT:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            thought_text = output.data.get("thought")
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=thought_text,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=None,
+                                messages_ids=[],
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                        elif output.log_type == AgentLog.LogType.TOOL_CALL:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Tool call finished
+                            tool_output = output.data.get("output")
+                            # Get meta from strategy output (now properly populated)
+                            tool_meta = output.data.get("meta")
+
+                            # Wrap tool_meta with tool_name as key (required by agent_service)
+                            if tool_meta and current_tool_name:
+                                tool_meta = {current_tool_name: tool_meta}
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=None,
+                                observation=tool_output,
+                                tool_invoke_meta=tool_meta,
+                                answer=None,
+                                messages_ids=self._current_message_file_ids,
+                            )
+                            # Clear message file ids after saving
+                            self._current_message_file_ids = []
+                            current_tool_name = None
+
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+                        elif output.log_type == AgentLog.LogType.ROUND:
+                            if current_agent_thought_id is None:
+                                continue
+
+                            # Round finished - save LLM usage and answer
+                            llm_usage = output.metadata.get(AgentLog.LogMetadata.LLM_USAGE)
+                            llm_result = output.data.get("llm_result")
+                            final_answer = output.data.get("final_answer")
+
+                            self.save_agent_thought(
+                                agent_thought_id=current_agent_thought_id,
+                                tool_name=None,
+                                tool_input=None,
+                                thought=llm_result,
+                                observation=None,
+                                tool_invoke_meta=None,
+                                answer=final_answer,
+                                messages_ids=[],
+                                llm_usage=llm_usage,
+                            )
+                            self.queue_manager.publish(
+                                QueueAgentThoughtEvent(agent_thought_id=current_agent_thought_id),
+                                PublishFrom.APPLICATION_MANAGER,
+                            )
+
+        except Exception:
+            # Re-raise any other exceptions
+            raise
+
+        # Process final result
+        if isinstance(result, AgentResult):
+            final_answer = result.text
+            usage = result.usage or LLMUsage.empty_usage()
+
+            # Publish end event
+            self.queue_manager.publish(
+                QueueMessageEndEvent(
+                    llm_result=LLMResult(
+                        model=self.model_instance.model,
+                        prompt_messages=prompt_messages,
+                        message=AssistantPromptMessage(content=final_answer),
+                        usage=usage,
+                        system_fingerprint="",
+                    )
+                ),
+                PublishFrom.APPLICATION_MANAGER,
+            )
+
+    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Initialize system message
+        """
+        if not prompt_template:
+            return prompt_messages or []
+
+        prompt_messages = prompt_messages or []
+
+        if prompt_messages and isinstance(prompt_messages[0], SystemPromptMessage):
+            prompt_messages[0] = SystemPromptMessage(content=prompt_template)
+            return prompt_messages
+
+        if not prompt_messages:
+            return [SystemPromptMessage(content=prompt_template)]
+
+        prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
+        return prompt_messages
+
+    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Organize user query
+        """
+        if self.files:
+            # get image detail config
+            image_detail_config = (
+                self.application_generate_entity.file_upload_config.image_config.detail
+                if (
+                    self.application_generate_entity.file_upload_config
+                    and self.application_generate_entity.file_upload_config.image_config
+                )
+                else None
+            )
+            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+            for file in self.files:
+                prompt_message_contents.append(
+                    file_manager.to_prompt_message_content(
+                        file,
+                        image_detail_config=image_detail_config,
+                    )
+                )
+            prompt_message_contents.append(TextPromptMessageContent(data=query))
+
+            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+        else:
+            prompt_messages.append(UserPromptMessage(content=query))
+
+        return prompt_messages
+
+    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        As for now, gpt supports both fc and vision at the first iteration.
+        We need to remove the image messages from the prompt messages at the first iteration.
+        """
+        prompt_messages = deepcopy(prompt_messages)
+
+        for prompt_message in prompt_messages:
+            if isinstance(prompt_message, UserPromptMessage):
+                if isinstance(prompt_message.content, list):
+                    prompt_message.content = "\n".join(
+                        [
+                            content.data
+                            if content.type == PromptMessageContentType.TEXT
+                            else "[image]"
+                            if content.type == PromptMessageContentType.IMAGE
+                            else "[file]"
+                            for content in prompt_message.content
+                        ]
+                    )
+
+        return prompt_messages
+
+    def _organize_prompt_messages(self):
+        # For ReAct strategy, use the agent prompt template
+        if self.config.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT and self.config.prompt:
+            prompt_template = self.config.prompt.first_prompt
+        else:
+            prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
+
+        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
+        query_prompt_messages = self._organize_user_query(self.query or "", [])
+
+        self.history_prompt_messages = AgentHistoryPromptTransform(
+            model_config=self.model_config,
+            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
+            history_messages=self.history_prompt_messages,
+            memory=self.memory,
+        ).get_prompt()
+
+        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
+        if len(self._current_thoughts) != 0:
+            # clear messages after the first iteration
+            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
+        return prompt_messages

api/core/agent/base_agent_runner.py

@@ -6,7 +6,7 @@ from typing import Union, cast
 
 from sqlalchemy import select
 
-from core.agent.entities import AgentEntity, AgentToolEntity
+from core.agent.entities import AgentEntity, AgentToolEntity, ExecutionContext
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager
@@ -116,9 +116,20 @@ class BaseAgentRunner(AppRunner):
         features = model_schema.features if model_schema and model_schema.features else []
         self.stream_tool_call = ModelFeature.STREAM_TOOL_CALL in features
         self.files = application_generate_entity.files if ModelFeature.VISION in features else []
+        self.model_features = features
         self.query: str | None = ""
         self._current_thoughts: list[PromptMessage] = []
 
+    def build_execution_context(self) -> ExecutionContext:
+        """Build execution context."""
+        return ExecutionContext(
+            user_id=self.user_id,
+            app_id=self.app_config.app_id,
+            conversation_id=self.conversation.id,
+            message_id=self.message.id,
+            tenant_id=self.tenant_id,
+        )
+
     def _repack_app_generate_entity(
         self, app_generate_entity: AgentChatAppGenerateEntity
     ) -> AgentChatAppGenerateEntity:

api/core/agent/cot_agent_runner.py

@@ -1,437 +0,0 @@
-import json
-import logging
-from abc import ABC, abstractmethod
-from collections.abc import Generator, Mapping, Sequence
-from typing import Any
-
-from core.agent.base_agent_runner import BaseAgentRunner
-from core.agent.entities import AgentScratchpadUnit
-from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
-from core.app.apps.base_app_queue_manager import PublishFrom
-from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    PromptMessageTool,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-from core.ops.ops_trace_manager import TraceQueueManager
-from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
-from core.tools.__base.tool import Tool
-from core.tools.entities.tool_entities import ToolInvokeMeta
-from core.tools.tool_engine import ToolEngine
-from core.workflow.nodes.agent.exc import AgentMaxIterationError
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class CotAgentRunner(BaseAgentRunner, ABC):
-    _is_first_iteration = True
-    _ignore_observation_providers = ["wenxin"]
-    _historic_prompt_messages: list[PromptMessage]
-    _agent_scratchpad: list[AgentScratchpadUnit]
-    _instruction: str
-    _query: str
-    _prompt_messages_tools: Sequence[PromptMessageTool]
-
-    def run(
-        self,
-        message: Message,
-        query: str,
-        inputs: Mapping[str, str],
-    ) -> Generator:
-        """
-        Run Cot agent application
-        """
-
-        app_generate_entity = self.application_generate_entity
-        self._repack_app_generate_entity(app_generate_entity)
-        self._init_react_state(query)
-
-        trace_manager = app_generate_entity.trace_manager
-
-        # check model mode
-        if "Observation" not in app_generate_entity.model_conf.stop:
-            if app_generate_entity.model_conf.provider not in self._ignore_observation_providers:
-                app_generate_entity.model_conf.stop.append("Observation")
-
-        app_config = self.app_config
-        assert app_config.agent
-
-        # init instruction
-        inputs = inputs or {}
-        instruction = app_config.prompt_template.simple_prompt_template or ""
-        self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
-
-        iteration_step = 1
-        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
-
-        # convert tools into ModelRuntime Tool format
-        tool_instances, prompt_messages_tools = self._init_prompt_tools()
-        self._prompt_messages_tools = prompt_messages_tools
-
-        function_call_state = True
-        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
-        final_answer = ""
-        prompt_messages: list = []  # Initialize prompt_messages
-        agent_thought_id = ""  # Initialize agent_thought_id
-
-        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
-            if not final_llm_usage_dict["usage"]:
-                final_llm_usage_dict["usage"] = usage
-            else:
-                llm_usage = final_llm_usage_dict["usage"]
-                llm_usage.prompt_tokens += usage.prompt_tokens
-                llm_usage.completion_tokens += usage.completion_tokens
-                llm_usage.total_tokens += usage.total_tokens
-                llm_usage.prompt_price += usage.prompt_price
-                llm_usage.completion_price += usage.completion_price
-                llm_usage.total_price += usage.total_price
-
-        model_instance = self.model_instance
-
-        while function_call_state and iteration_step <= max_iteration_steps:
-            # continue to run until there is not any tool call
-            function_call_state = False
-
-            if iteration_step == max_iteration_steps:
-                # the last iteration, remove all tools
-                self._prompt_messages_tools = []
-
-            message_file_ids: list[str] = []
-
-            agent_thought_id = self.create_agent_thought(
-                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
-            )
-
-            if iteration_step > 1:
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            # recalc llm max tokens
-            prompt_messages = self._organize_prompt_messages()
-            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
-            # invoke model
-            chunks = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters=app_generate_entity.model_conf.parameters,
-                tools=[],
-                stop=app_generate_entity.model_conf.stop,
-                stream=True,
-                user=self.user_id,
-                callbacks=[],
-            )
-
-            usage_dict: dict[str, LLMUsage | None] = {}
-            react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks, usage_dict)
-            scratchpad = AgentScratchpadUnit(
-                agent_response="",
-                thought="",
-                action_str="",
-                observation="",
-                action=None,
-            )
-
-            # publish agent thought if it's first iteration
-            if iteration_step == 1:
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            for chunk in react_chunks:
-                if isinstance(chunk, AgentScratchpadUnit.Action):
-                    action = chunk
-                    # detect action
-                    assert scratchpad.agent_response is not None
-                    scratchpad.agent_response += json.dumps(chunk.model_dump())
-                    scratchpad.action_str = json.dumps(chunk.model_dump())
-                    scratchpad.action = action
-                else:
-                    assert scratchpad.agent_response is not None
-                    scratchpad.agent_response += chunk
-                    assert scratchpad.thought is not None
-                    scratchpad.thought += chunk
-                    yield LLMResultChunk(
-                        model=self.model_config.model,
-                        prompt_messages=prompt_messages,
-                        system_fingerprint="",
-                        delta=LLMResultChunkDelta(index=0, message=AssistantPromptMessage(content=chunk), usage=None),
-                    )
-
-            assert scratchpad.thought is not None
-            scratchpad.thought = scratchpad.thought.strip() or "I am thinking about how to help you"
-            self._agent_scratchpad.append(scratchpad)
-
-            # Check if max iteration is reached and model still wants to call tools
-            if iteration_step == max_iteration_steps and scratchpad.action:
-                if scratchpad.action.action_name.lower() != "final answer":
-                    raise AgentMaxIterationError(app_config.agent.max_iteration)
-
-            # get llm usage
-            if "usage" in usage_dict:
-                if usage_dict["usage"] is not None:
-                    increase_usage(llm_usage, usage_dict["usage"])
-            else:
-                usage_dict["usage"] = LLMUsage.empty_usage()
-
-            self.save_agent_thought(
-                agent_thought_id=agent_thought_id,
-                tool_name=(scratchpad.action.action_name if scratchpad.action and not scratchpad.is_final() else ""),
-                tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
-                tool_invoke_meta={},
-                thought=scratchpad.thought or "",
-                observation="",
-                answer=scratchpad.agent_response or "",
-                messages_ids=[],
-                llm_usage=usage_dict["usage"],
-            )
-
-            if not scratchpad.is_final():
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            if not scratchpad.action:
-                # failed to extract action, return final answer directly
-                final_answer = ""
-            else:
-                if scratchpad.action.action_name.lower() == "final answer":
-                    # action is final answer, return final answer directly
-                    try:
-                        if isinstance(scratchpad.action.action_input, dict):
-                            final_answer = json.dumps(scratchpad.action.action_input, ensure_ascii=False)
-                        elif isinstance(scratchpad.action.action_input, str):
-                            final_answer = scratchpad.action.action_input
-                        else:
-                            final_answer = f"{scratchpad.action.action_input}"
-                    except TypeError:
-                        final_answer = f"{scratchpad.action.action_input}"
-                else:
-                    function_call_state = True
-                    # action is tool call, invoke tool
-                    tool_invoke_response, tool_invoke_meta = self._handle_invoke_action(
-                        action=scratchpad.action,
-                        tool_instances=tool_instances,
-                        message_file_ids=message_file_ids,
-                        trace_manager=trace_manager,
-                    )
-                    scratchpad.observation = tool_invoke_response
-                    scratchpad.agent_response = tool_invoke_response
-
-                    self.save_agent_thought(
-                        agent_thought_id=agent_thought_id,
-                        tool_name=scratchpad.action.action_name,
-                        tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
-                        thought=scratchpad.thought or "",
-                        observation={scratchpad.action.action_name: tool_invoke_response},
-                        tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
-                        answer=scratchpad.agent_response,
-                        messages_ids=message_file_ids,
-                        llm_usage=usage_dict["usage"],
-                    )
-
-                    self.queue_manager.publish(
-                        QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                    )
-
-                # update prompt tool message
-                for prompt_tool in self._prompt_messages_tools:
-                    self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
-
-            iteration_step += 1
-
-        yield LLMResultChunk(
-            model=model_instance.model,
-            prompt_messages=prompt_messages,
-            delta=LLMResultChunkDelta(
-                index=0, message=AssistantPromptMessage(content=final_answer), usage=llm_usage["usage"]
-            ),
-            system_fingerprint="",
-        )
-
-        # save agent thought
-        self.save_agent_thought(
-            agent_thought_id=agent_thought_id,
-            tool_name="",
-            tool_input={},
-            tool_invoke_meta={},
-            thought=final_answer,
-            observation={},
-            answer=final_answer,
-            messages_ids=[],
-        )
-        # publish end event
-        self.queue_manager.publish(
-            QueueMessageEndEvent(
-                llm_result=LLMResult(
-                    model=model_instance.model,
-                    prompt_messages=prompt_messages,
-                    message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
-                    system_fingerprint="",
-                )
-            ),
-            PublishFrom.APPLICATION_MANAGER,
-        )
-
-    def _handle_invoke_action(
-        self,
-        action: AgentScratchpadUnit.Action,
-        tool_instances: Mapping[str, Tool],
-        message_file_ids: list[str],
-        trace_manager: TraceQueueManager | None = None,
-    ) -> tuple[str, ToolInvokeMeta]:
-        """
-        handle invoke action
-        :param action: action
-        :param tool_instances: tool instances
-        :param message_file_ids: message file ids
-        :param trace_manager: trace manager
-        :return: observation, meta
-        """
-        # action is tool call, invoke tool
-        tool_call_name = action.action_name
-        tool_call_args = action.action_input
-        tool_instance = tool_instances.get(tool_call_name)
-
-        if not tool_instance:
-            answer = f"there is not a tool named {tool_call_name}"
-            return answer, ToolInvokeMeta.error_instance(answer)
-
-        if isinstance(tool_call_args, str):
-            try:
-                tool_call_args = json.loads(tool_call_args)
-            except json.JSONDecodeError:
-                pass
-
-        # invoke tool
-        tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
-            tool=tool_instance,
-            tool_parameters=tool_call_args,
-            user_id=self.user_id,
-            tenant_id=self.tenant_id,
-            message=self.message,
-            invoke_from=self.application_generate_entity.invoke_from,
-            agent_tool_callback=self.agent_callback,
-            trace_manager=trace_manager,
-        )
-
-        # publish files
-        for message_file_id in message_files:
-            # publish message file
-            self.queue_manager.publish(
-                QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
-            )
-            # add message file ids
-            message_file_ids.append(message_file_id)
-
-        return tool_invoke_response, tool_invoke_meta
-
-    def _convert_dict_to_action(self, action: dict) -> AgentScratchpadUnit.Action:
-        """
-        convert dict to action
-        """
-        return AgentScratchpadUnit.Action(action_name=action["action"], action_input=action["action_input"])
-
-    def _fill_in_inputs_from_external_data_tools(self, instruction: str, inputs: Mapping[str, Any]) -> str:
-        """
-        fill in inputs from external data tools
-        """
-        for key, value in inputs.items():
-            try:
-                instruction = instruction.replace(f"{{{{{key}}}}}", str(value))
-            except Exception:
-                continue
-
-        return instruction
-
-    def _init_react_state(self, query):
-        """
-        init agent scratchpad
-        """
-        self._query = query
-        self._agent_scratchpad = []
-        self._historic_prompt_messages = self._organize_historic_prompt_messages()
-
-    @abstractmethod
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        organize prompt messages
-        """
-
-    def _format_assistant_message(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
-        """
-        format assistant message
-        """
-        message = ""
-        for scratchpad in agent_scratchpad:
-            if scratchpad.is_final():
-                message += f"Final Answer: {scratchpad.agent_response}"
-            else:
-                message += f"Thought: {scratchpad.thought}\n\n"
-                if scratchpad.action_str:
-                    message += f"Action: {scratchpad.action_str}\n\n"
-                if scratchpad.observation:
-                    message += f"Observation: {scratchpad.observation}\n\n"
-
-        return message
-
-    def _organize_historic_prompt_messages(
-        self, current_session_messages: list[PromptMessage] | None = None
-    ) -> list[PromptMessage]:
-        """
-        organize historic prompt messages
-        """
-        result: list[PromptMessage] = []
-        scratchpads: list[AgentScratchpadUnit] = []
-        current_scratchpad: AgentScratchpadUnit | None = None
-
-        for message in self.history_prompt_messages:
-            if isinstance(message, AssistantPromptMessage):
-                if not current_scratchpad:
-                    assert isinstance(message.content, str)
-                    current_scratchpad = AgentScratchpadUnit(
-                        agent_response=message.content,
-                        thought=message.content or "I am thinking about how to help you",
-                        action_str="",
-                        action=None,
-                        observation=None,
-                    )
-                    scratchpads.append(current_scratchpad)
-                if message.tool_calls:
-                    try:
-                        current_scratchpad.action = AgentScratchpadUnit.Action(
-                            action_name=message.tool_calls[0].function.name,
-                            action_input=json.loads(message.tool_calls[0].function.arguments),
-                        )
-                        current_scratchpad.action_str = json.dumps(current_scratchpad.action.to_dict())
-                    except Exception:
-                        logger.exception("Failed to parse tool call from assistant message")
-            elif isinstance(message, ToolPromptMessage):
-                if current_scratchpad:
-                    assert isinstance(message.content, str)
-                    current_scratchpad.observation = message.content
-                else:
-                    raise NotImplementedError("expected str type")
-            elif isinstance(message, UserPromptMessage):
-                if scratchpads:
-                    result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
-                    scratchpads = []
-                    current_scratchpad = None
-
-                result.append(message)
-
-        if scratchpads:
-            result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
-
-        historic_prompts = AgentHistoryPromptTransform(
-            model_config=self.model_config,
-            prompt_messages=current_session_messages or [],
-            history_messages=result,
-            memory=self.memory,
-        ).get_prompt()
-        return historic_prompts

api/core/agent/cot_chat_agent_runner.py

@@ -1,118 +0,0 @@
-import json
-
-from core.agent.cot_agent_runner import CotAgentRunner
-from core.file import file_manager
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from core.model_runtime.utils.encoders import jsonable_encoder
-
-
-class CotChatAgentRunner(CotAgentRunner):
-    def _organize_system_prompt(self) -> SystemPromptMessage:
-        """
-        Organize system prompt
-        """
-        assert self.app_config.agent
-        assert self.app_config.agent.prompt
-
-        prompt_entity = self.app_config.agent.prompt
-        if not prompt_entity:
-            raise ValueError("Agent prompt configuration is not set")
-        first_prompt = prompt_entity.first_prompt
-
-        system_prompt = (
-            first_prompt.replace("{{instruction}}", self._instruction)
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
-            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
-        )
-
-        return SystemPromptMessage(content=system_prompt)
-
-    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Organize user query
-        """
-        if self.files:
-            # get image detail config
-            image_detail_config = (
-                self.application_generate_entity.file_upload_config.image_config.detail
-                if (
-                    self.application_generate_entity.file_upload_config
-                    and self.application_generate_entity.file_upload_config.image_config
-                )
-                else None
-            )
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-
-            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
-            for file in self.files:
-                prompt_message_contents.append(
-                    file_manager.to_prompt_message_content(
-                        file,
-                        image_detail_config=image_detail_config,
-                    )
-                )
-            prompt_message_contents.append(TextPromptMessageContent(data=query))
-
-            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-        else:
-            prompt_messages.append(UserPromptMessage(content=query))
-
-        return prompt_messages
-
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        Organize
-        """
-        # organize system prompt
-        system_message = self._organize_system_prompt()
-
-        # organize current assistant messages
-        agent_scratchpad = self._agent_scratchpad
-        if not agent_scratchpad:
-            assistant_messages = []
-        else:
-            assistant_message = AssistantPromptMessage(content="")
-            assistant_message.content = ""  # FIXME: type check tell mypy that assistant_message.content is str
-            for unit in agent_scratchpad:
-                if unit.is_final():
-                    assert isinstance(assistant_message.content, str)
-                    assistant_message.content += f"Final Answer: {unit.agent_response}"
-                else:
-                    assert isinstance(assistant_message.content, str)
-                    assistant_message.content += f"Thought: {unit.thought}\n\n"
-                    if unit.action_str:
-                        assistant_message.content += f"Action: {unit.action_str}\n\n"
-                    if unit.observation:
-                        assistant_message.content += f"Observation: {unit.observation}\n\n"
-
-            assistant_messages = [assistant_message]
-
-        # query messages
-        query_messages = self._organize_user_query(self._query, [])
-
-        if assistant_messages:
-            # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages(
-                [system_message, *query_messages, *assistant_messages, UserPromptMessage(content="continue")]
-            )
-            messages = [
-                system_message,
-                *historic_messages,
-                *query_messages,
-                *assistant_messages,
-                UserPromptMessage(content="continue"),
-            ]
-        else:
-            # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages([system_message, *query_messages])
-            messages = [system_message, *historic_messages, *query_messages]
-
-        # join all messages
-        return messages

api/core/agent/cot_completion_agent_runner.py

@@ -1,87 +0,0 @@
-import json
-
-from core.agent.cot_agent_runner import CotAgentRunner
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.model_runtime.utils.encoders import jsonable_encoder
-
-
-class CotCompletionAgentRunner(CotAgentRunner):
-    def _organize_instruction_prompt(self) -> str:
-        """
-        Organize instruction prompt
-        """
-        if self.app_config.agent is None:
-            raise ValueError("Agent configuration is not set")
-        prompt_entity = self.app_config.agent.prompt
-        if prompt_entity is None:
-            raise ValueError("prompt entity is not set")
-        first_prompt = prompt_entity.first_prompt
-
-        system_prompt = (
-            first_prompt.replace("{{instruction}}", self._instruction)
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
-            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
-        )
-
-        return system_prompt
-
-    def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] | None = None) -> str:
-        """
-        Organize historic prompt
-        """
-        historic_prompt_messages = self._organize_historic_prompt_messages(current_session_messages)
-        historic_prompt = ""
-
-        for message in historic_prompt_messages:
-            if isinstance(message, UserPromptMessage):
-                historic_prompt += f"Question: {message.content}\n\n"
-            elif isinstance(message, AssistantPromptMessage):
-                if isinstance(message.content, str):
-                    historic_prompt += message.content + "\n\n"
-                elif isinstance(message.content, list):
-                    for content in message.content:
-                        if not isinstance(content, TextPromptMessageContent):
-                            continue
-                        historic_prompt += content.data
-
-        return historic_prompt
-
-    def _organize_prompt_messages(self) -> list[PromptMessage]:
-        """
-        Organize prompt messages
-        """
-        # organize system prompt
-        system_prompt = self._organize_instruction_prompt()
-
-        # organize historic prompt messages
-        historic_prompt = self._organize_historic_prompt()
-
-        # organize current assistant messages
-        agent_scratchpad = self._agent_scratchpad
-        assistant_prompt = ""
-        for unit in agent_scratchpad or []:
-            if unit.is_final():
-                assistant_prompt += f"Final Answer: {unit.agent_response}"
-            else:
-                assistant_prompt += f"Thought: {unit.thought}\n\n"
-                if unit.action_str:
-                    assistant_prompt += f"Action: {unit.action_str}\n\n"
-                if unit.observation:
-                    assistant_prompt += f"Observation: {unit.observation}\n\n"
-
-        # query messages
-        query_prompt = f"Question: {self._query}"
-
-        # join all messages
-        prompt = (
-            system_prompt.replace("{{historic_messages}}", historic_prompt)
-            .replace("{{agent_scratchpad}}", assistant_prompt)
-            .replace("{{query}}", query_prompt)
-        )
-
-        return [UserPromptMessage(content=prompt)]

api/core/agent/entities.py

@@ -1,3 +1,5 @@
+import uuid
+from collections.abc import Mapping
 from enum import StrEnum
 from typing import Any, Union
 
@@ -92,3 +94,96 @@ class AgentInvokeMessage(ToolInvokeMessage):
     """
 
     pass
+
+
+class ExecutionContext(BaseModel):
+    """Execution context containing trace and audit information.
+
+    This context carries all the IDs and metadata that are not part of
+    the core business logic but needed for tracing, auditing, and
+    correlation purposes.
+    """
+
+    user_id: str | None = None
+    app_id: str | None = None
+    conversation_id: str | None = None
+    message_id: str | None = None
+    tenant_id: str | None = None
+
+    @classmethod
+    def create_minimal(cls, user_id: str | None = None) -> "ExecutionContext":
+        """Create a minimal context with only essential fields."""
+        return cls(user_id=user_id)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for passing to legacy code."""
+        return {
+            "user_id": self.user_id,
+            "app_id": self.app_id,
+            "conversation_id": self.conversation_id,
+            "message_id": self.message_id,
+            "tenant_id": self.tenant_id,
+        }
+
+    def with_updates(self, **kwargs) -> "ExecutionContext":
+        """Create a new context with updated fields."""
+        data = self.to_dict()
+        data.update(kwargs)
+
+        return ExecutionContext(
+            user_id=data.get("user_id"),
+            app_id=data.get("app_id"),
+            conversation_id=data.get("conversation_id"),
+            message_id=data.get("message_id"),
+            tenant_id=data.get("tenant_id"),
+        )
+
+
+class AgentLog(BaseModel):
+    """
+    Agent Log.
+    """
+
+    class LogType(StrEnum):
+        """Type of agent log entry."""
+
+        ROUND = "round"  # A complete iteration round
+        THOUGHT = "thought"  # LLM thinking/reasoning
+        TOOL_CALL = "tool_call"  # Tool invocation
+
+    class LogMetadata(StrEnum):
+        STARTED_AT = "started_at"
+        FINISHED_AT = "finished_at"
+        ELAPSED_TIME = "elapsed_time"
+        TOTAL_PRICE = "total_price"
+        TOTAL_TOKENS = "total_tokens"
+        PROVIDER = "provider"
+        CURRENCY = "currency"
+        LLM_USAGE = "llm_usage"
+        ICON = "icon"
+        ICON_DARK = "icon_dark"
+
+    class LogStatus(StrEnum):
+        START = "start"
+        ERROR = "error"
+        SUCCESS = "success"
+
+    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="The id of the log")
+    label: str = Field(..., description="The label of the log")
+    log_type: LogType = Field(..., description="The type of the log")
+    parent_id: str | None = Field(default=None, description="Leave empty for root log")
+    error: str | None = Field(default=None, description="The error message")
+    status: LogStatus = Field(..., description="The status of the log")
+    data: Mapping[str, Any] = Field(..., description="Detailed log data")
+    metadata: Mapping[LogMetadata, Any] = Field(default={}, description="The metadata of the log")
+
+
+class AgentResult(BaseModel):
+    """
+    Agent execution result.
+    """
+
+    text: str = Field(default="", description="The generated text")
+    files: list[Any] = Field(default_factory=list, description="Files produced during execution")
+    usage: Any | None = Field(default=None, description="LLM usage statistics")
+    finish_reason: str | None = Field(default=None, description="Reason for completion")

api/core/agent/fc_agent_runner.py

@@ -1,468 +0,0 @@
-import json
-import logging
-from collections.abc import Generator
-from copy import deepcopy
-from typing import Any, Union
-
-from core.agent.base_agent_runner import BaseAgentRunner
-from core.app.apps.base_app_queue_manager import PublishFrom
-from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
-from core.file import file_manager
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    LLMResult,
-    LLMResultChunk,
-    LLMResultChunkDelta,
-    LLMUsage,
-    PromptMessage,
-    PromptMessageContentType,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
-from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
-from core.tools.entities.tool_entities import ToolInvokeMeta
-from core.tools.tool_engine import ToolEngine
-from core.workflow.nodes.agent.exc import AgentMaxIterationError
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class FunctionCallAgentRunner(BaseAgentRunner):
-    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
-        """
-        Run FunctionCall agent application
-        """
-        self.query = query
-        app_generate_entity = self.application_generate_entity
-
-        app_config = self.app_config
-        assert app_config is not None, "app_config is required"
-        assert app_config.agent is not None, "app_config.agent is required"
-
-        # convert tools into ModelRuntime Tool format
-        tool_instances, prompt_messages_tools = self._init_prompt_tools()
-
-        assert app_config.agent
-
-        iteration_step = 1
-        max_iteration_steps = min(app_config.agent.max_iteration, 99) + 1
-
-        # continue to run until there is not any tool call
-        function_call_state = True
-        llm_usage: dict[str, LLMUsage | None] = {"usage": None}
-        final_answer = ""
-        prompt_messages: list = []  # Initialize prompt_messages
-
-        # get tracing instance
-        trace_manager = app_generate_entity.trace_manager
-
-        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage | None], usage: LLMUsage):
-            if not final_llm_usage_dict["usage"]:
-                final_llm_usage_dict["usage"] = usage
-            else:
-                llm_usage = final_llm_usage_dict["usage"]
-                llm_usage.prompt_tokens += usage.prompt_tokens
-                llm_usage.completion_tokens += usage.completion_tokens
-                llm_usage.total_tokens += usage.total_tokens
-                llm_usage.prompt_price += usage.prompt_price
-                llm_usage.completion_price += usage.completion_price
-                llm_usage.total_price += usage.total_price
-
-        model_instance = self.model_instance
-
-        while function_call_state and iteration_step <= max_iteration_steps:
-            function_call_state = False
-
-            if iteration_step == max_iteration_steps:
-                # the last iteration, remove all tools
-                prompt_messages_tools = []
-
-            message_file_ids: list[str] = []
-            agent_thought_id = self.create_agent_thought(
-                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
-            )
-
-            # recalc llm max tokens
-            prompt_messages = self._organize_prompt_messages()
-            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
-            # invoke model
-            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters=app_generate_entity.model_conf.parameters,
-                tools=prompt_messages_tools,
-                stop=app_generate_entity.model_conf.stop,
-                stream=self.stream_tool_call,
-                user=self.user_id,
-                callbacks=[],
-            )
-
-            tool_calls: list[tuple[str, str, dict[str, Any]]] = []
-
-            # save full response
-            response = ""
-
-            # save tool call names and inputs
-            tool_call_names = ""
-            tool_call_inputs = ""
-
-            current_llm_usage = None
-
-            if isinstance(chunks, Generator):
-                is_first_chunk = True
-                for chunk in chunks:
-                    if is_first_chunk:
-                        self.queue_manager.publish(
-                            QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                        )
-                        is_first_chunk = False
-                    # check if there is any tool call
-                    if self.check_tool_calls(chunk):
-                        function_call_state = True
-                        tool_calls.extend(self.extract_tool_calls(chunk) or [])
-                        tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
-                        try:
-                            tool_call_inputs = json.dumps(
-                                {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
-                            )
-                        except TypeError:
-                            # fallback: force ASCII to handle non-serializable objects
-                            tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
-
-                    if chunk.delta.message and chunk.delta.message.content:
-                        if isinstance(chunk.delta.message.content, list):
-                            for content in chunk.delta.message.content:
-                                response += content.data
-                        else:
-                            response += str(chunk.delta.message.content)
-
-                    if chunk.delta.usage:
-                        increase_usage(llm_usage, chunk.delta.usage)
-                        current_llm_usage = chunk.delta.usage
-
-                    yield chunk
-            else:
-                result = chunks
-                # check if there is any tool call
-                if self.check_blocking_tool_calls(result):
-                    function_call_state = True
-                    tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
-                    tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
-                    try:
-                        tool_call_inputs = json.dumps(
-                            {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
-                        )
-                    except TypeError:
-                        # fallback: force ASCII to handle non-serializable objects
-                        tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
-
-                if result.usage:
-                    increase_usage(llm_usage, result.usage)
-                    current_llm_usage = result.usage
-
-                if result.message and result.message.content:
-                    if isinstance(result.message.content, list):
-                        for content in result.message.content:
-                            response += content.data
-                    else:
-                        response += str(result.message.content)
-
-                if not result.message.content:
-                    result.message.content = ""
-
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-                yield LLMResultChunk(
-                    model=model_instance.model,
-                    prompt_messages=result.prompt_messages,
-                    system_fingerprint=result.system_fingerprint,
-                    delta=LLMResultChunkDelta(
-                        index=0,
-                        message=result.message,
-                        usage=result.usage,
-                    ),
-                )
-
-            assistant_message = AssistantPromptMessage(content=response, tool_calls=[])
-            if tool_calls:
-                assistant_message.tool_calls = [
-                    AssistantPromptMessage.ToolCall(
-                        id=tool_call[0],
-                        type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name=tool_call[1], arguments=json.dumps(tool_call[2], ensure_ascii=False)
-                        ),
-                    )
-                    for tool_call in tool_calls
-                ]
-
-            self._current_thoughts.append(assistant_message)
-
-            # save thought
-            self.save_agent_thought(
-                agent_thought_id=agent_thought_id,
-                tool_name=tool_call_names,
-                tool_input=tool_call_inputs,
-                thought=response,
-                tool_invoke_meta=None,
-                observation=None,
-                answer=response,
-                messages_ids=[],
-                llm_usage=current_llm_usage,
-            )
-            self.queue_manager.publish(
-                QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-            )
-
-            final_answer += response + "\n"
-
-            # Check if max iteration is reached and model still wants to call tools
-            if iteration_step == max_iteration_steps and tool_calls:
-                raise AgentMaxIterationError(app_config.agent.max_iteration)
-
-            # call tools
-            tool_responses = []
-            for tool_call_id, tool_call_name, tool_call_args in tool_calls:
-                tool_instance = tool_instances.get(tool_call_name)
-                if not tool_instance:
-                    tool_response = {
-                        "tool_call_id": tool_call_id,
-                        "tool_call_name": tool_call_name,
-                        "tool_response": f"there is not a tool named {tool_call_name}",
-                        "meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict(),
-                    }
-                else:
-                    # invoke tool
-                    tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
-                        tool=tool_instance,
-                        tool_parameters=tool_call_args,
-                        user_id=self.user_id,
-                        tenant_id=self.tenant_id,
-                        message=self.message,
-                        invoke_from=self.application_generate_entity.invoke_from,
-                        agent_tool_callback=self.agent_callback,
-                        trace_manager=trace_manager,
-                        app_id=self.application_generate_entity.app_config.app_id,
-                        message_id=self.message.id,
-                        conversation_id=self.conversation.id,
-                    )
-                    # publish files
-                    for message_file_id in message_files:
-                        # publish message file
-                        self.queue_manager.publish(
-                            QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
-                        )
-                        # add message file ids
-                        message_file_ids.append(message_file_id)
-
-                    tool_response = {
-                        "tool_call_id": tool_call_id,
-                        "tool_call_name": tool_call_name,
-                        "tool_response": tool_invoke_response,
-                        "meta": tool_invoke_meta.to_dict(),
-                    }
-
-                tool_responses.append(tool_response)
-                if tool_response["tool_response"] is not None:
-                    self._current_thoughts.append(
-                        ToolPromptMessage(
-                            content=str(tool_response["tool_response"]),
-                            tool_call_id=tool_call_id,
-                            name=tool_call_name,
-                        )
-                    )
-
-            if len(tool_responses) > 0:
-                # save agent thought
-                self.save_agent_thought(
-                    agent_thought_id=agent_thought_id,
-                    tool_name="",
-                    tool_input="",
-                    thought="",
-                    tool_invoke_meta={
-                        tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
-                    },
-                    observation={
-                        tool_response["tool_call_name"]: tool_response["tool_response"]
-                        for tool_response in tool_responses
-                    },
-                    answer="",
-                    messages_ids=message_file_ids,
-                )
-                self.queue_manager.publish(
-                    QueueAgentThoughtEvent(agent_thought_id=agent_thought_id), PublishFrom.APPLICATION_MANAGER
-                )
-
-            # update prompt tool
-            for prompt_tool in prompt_messages_tools:
-                self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
-
-            iteration_step += 1
-
-        # publish end event
-        self.queue_manager.publish(
-            QueueMessageEndEvent(
-                llm_result=LLMResult(
-                    model=model_instance.model,
-                    prompt_messages=prompt_messages,
-                    message=AssistantPromptMessage(content=final_answer),
-                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
-                    system_fingerprint="",
-                )
-            ),
-            PublishFrom.APPLICATION_MANAGER,
-        )
-
-    def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
-        """
-        Check if there is any tool call in llm result chunk
-        """
-        if llm_result_chunk.delta.message.tool_calls:
-            return True
-        return False
-
-    def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
-        """
-        Check if there is any blocking tool call in llm result
-        """
-        if llm_result.message.tool_calls:
-            return True
-        return False
-
-    def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
-        """
-        Extract tool calls from llm result chunk
-
-        Returns:
-            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
-        """
-        tool_calls = []
-        for prompt_message in llm_result_chunk.delta.message.tool_calls:
-            args = {}
-            if prompt_message.function.arguments != "":
-                args = json.loads(prompt_message.function.arguments)
-
-            tool_calls.append(
-                (
-                    prompt_message.id,
-                    prompt_message.function.name,
-                    args,
-                )
-            )
-
-        return tool_calls
-
-    def extract_blocking_tool_calls(self, llm_result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
-        """
-        Extract blocking tool calls from llm result
-
-        Returns:
-            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
-        """
-        tool_calls = []
-        for prompt_message in llm_result.message.tool_calls:
-            args = {}
-            if prompt_message.function.arguments != "":
-                args = json.loads(prompt_message.function.arguments)
-
-            tool_calls.append(
-                (
-                    prompt_message.id,
-                    prompt_message.function.name,
-                    args,
-                )
-            )
-
-        return tool_calls
-
-    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Initialize system message
-        """
-        if not prompt_messages and prompt_template:
-            return [
-                SystemPromptMessage(content=prompt_template),
-            ]
-
-        if prompt_messages and not isinstance(prompt_messages[0], SystemPromptMessage) and prompt_template:
-            prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
-
-        return prompt_messages or []
-
-    def _organize_user_query(self, query: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Organize user query
-        """
-        if self.files:
-            # get image detail config
-            image_detail_config = (
-                self.application_generate_entity.file_upload_config.image_config.detail
-                if (
-                    self.application_generate_entity.file_upload_config
-                    and self.application_generate_entity.file_upload_config.image_config
-                )
-                else None
-            )
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-
-            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
-            for file in self.files:
-                prompt_message_contents.append(
-                    file_manager.to_prompt_message_content(
-                        file,
-                        image_detail_config=image_detail_config,
-                    )
-                )
-            prompt_message_contents.append(TextPromptMessageContent(data=query))
-
-            prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-        else:
-            prompt_messages.append(UserPromptMessage(content=query))
-
-        return prompt_messages
-
-    def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        As for now, gpt supports both fc and vision at the first iteration.
-        We need to remove the image messages from the prompt messages at the first iteration.
-        """
-        prompt_messages = deepcopy(prompt_messages)
-
-        for prompt_message in prompt_messages:
-            if isinstance(prompt_message, UserPromptMessage):
-                if isinstance(prompt_message.content, list):
-                    prompt_message.content = "\n".join(
-                        [
-                            content.data
-                            if content.type == PromptMessageContentType.TEXT
-                            else "[image]"
-                            if content.type == PromptMessageContentType.IMAGE
-                            else "[file]"
-                            for content in prompt_message.content
-                        ]
-                    )
-
-        return prompt_messages
-
-    def _organize_prompt_messages(self):
-        prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
-        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
-        query_prompt_messages = self._organize_user_query(self.query or "", [])
-
-        self.history_prompt_messages = AgentHistoryPromptTransform(
-            model_config=self.model_config,
-            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
-            history_messages=self.history_prompt_messages,
-            memory=self.memory,
-        ).get_prompt()
-
-        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
-        if len(self._current_thoughts) != 0:
-            # clear messages after the first iteration
-            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
-        return prompt_messages

api/core/agent/patterns/README.md

@@ -0,0 +1,55 @@
+# Agent Patterns
+
+A unified agent pattern module that powers both Agent V2 workflow nodes and agent applications. Strategies share a common execution contract while adapting to model capabilities and tool availability.
+
+## Overview
+
+The module applies a strategy pattern around LLM/tool orchestration. `StrategyFactory` auto-selects the best implementation based on model features or an explicit agent strategy, and each strategy streams logs and usage consistently.
+
+## Key Features
+
+- **Dual strategies**
+  - `FunctionCallStrategy`: uses native LLM function/tool calling when the model exposes `TOOL_CALL`, `MULTI_TOOL_CALL`, or `STREAM_TOOL_CALL`.
+  - `ReActStrategy`: ReAct (reasoning + acting) flow driven by `CotAgentOutputParser`, used when function calling is unavailable or explicitly requested.
+- **Explicit or auto selection**
+  - `StrategyFactory.create_strategy` prefers an explicit `AgentEntity.Strategy` (FUNCTION_CALLING or CHAIN_OF_THOUGHT).
+  - Otherwise it falls back to function calling when tool-call features exist, or ReAct when they do not.
+- **Unified execution contract**
+  - `AgentPattern.run` yields streaming `AgentLog` entries and `LLMResultChunk` data, returning an `AgentResult` with text, files, usage, and `finish_reason`.
+  - Iterations are configurable and hard-capped at 99 rounds; the last round forces a final answer by withholding tools.
+- **Tool handling and hooks**
+  - Tools convert to `PromptMessageTool` objects before invocation.
+  - Optional `tool_invoke_hook` lets callers override tool execution (e.g., agent apps) while workflow runs use `ToolEngine.generic_invoke`.
+  - Tool outputs support text, links, JSON, variables, blobs, retriever resources, and file attachments; `target=="self"` files are reloaded into model context, others are returned as outputs.
+- **File-aware arguments**
+  - Tool args accept `[File: <id>]` or `[Files: <id1, id2>]` placeholders that resolve to `File` objects before invocation, enabling models to reference uploaded files safely.
+- **ReAct prompt shaping**
+  - System prompts replace `{{instruction}}`, `{{tools}}`, and `{{tool_names}}` placeholders.
+  - Adds `Observation` to stop sequences and appends scratchpad text so the model sees prior Thought/Action/Observation history.
+- **Observability and accounting**
+  - Standardized `AgentLog` entries for rounds, model thoughts, and tool calls, including usage aggregation (`LLMUsage`) across streaming and non-streaming paths.
+
+## Architecture
+
+```
+agent/patterns/
+├── base.py              # Shared utilities: logging, usage, tool invocation, file handling
+├── function_call.py     # Native function-calling loop with tool execution
+├── react.py             # ReAct loop with CoT parsing and scratchpad wiring
+└── strategy_factory.py  # Strategy selection by model features or explicit override
+```
+
+## Usage
+
+- For auto-selection:
+  - Call `StrategyFactory.create_strategy(model_features, model_instance, context, tools, files, ...)` and run the returned strategy with prompt messages and model params.
+- For explicit behavior:
+  - Pass `agent_strategy=AgentEntity.Strategy.FUNCTION_CALLING` to force native calls (falls back to ReAct if unsupported), or `CHAIN_OF_THOUGHT` to force ReAct.
+- Both strategies stream chunks and logs; collect the generator output until it returns an `AgentResult`.
+
+## Integration Points
+
+- **Model runtime**: delegates to `ModelInstance.invoke_llm` for both streaming and non-streaming calls.
+- **Tool system**: defaults to `ToolEngine.generic_invoke`, with `tool_invoke_hook` for custom callers.
+- **Files**: flows through `File` objects for tool inputs/outputs and model-context attachments.
+- **Execution context**: `ExecutionContext` fields (user/app/conversation/message) propagate to tool invocations and logging.

api/core/agent/patterns/__init__.py

@@ -0,0 +1,19 @@
+"""Agent patterns module.
+
+This module provides different strategies for agent execution:
+- FunctionCallStrategy: Uses native function/tool calling
+- ReActStrategy: Uses ReAct (Reasoning + Acting) approach
+- StrategyFactory: Factory for creating strategies based on model features
+"""
+
+from .base import AgentPattern
+from .function_call import FunctionCallStrategy
+from .react import ReActStrategy
+from .strategy_factory import StrategyFactory
+
+__all__ = [
+    "AgentPattern",
+    "FunctionCallStrategy",
+    "ReActStrategy",
+    "StrategyFactory",
+]

api/core/agent/patterns/base.py

@@ -0,0 +1,502 @@
+"""Base class for agent strategies."""
+
+from __future__ import annotations
+
+import json
+import re
+import time
+from abc import ABC, abstractmethod
+from collections.abc import Callable, Generator
+from typing import TYPE_CHECKING, Any
+
+from core.agent.entities import AgentLog, AgentResult, ExecutionContext
+from core.file import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.message_entities import TextPromptMessageContent
+from core.tools.entities.tool_entities import ToolInvokeMessage, ToolInvokeMeta
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+# Type alias for tool invoke hook
+# Returns: (response_content, message_file_ids, tool_invoke_meta)
+ToolInvokeHook = Callable[["Tool", dict[str, Any], str], tuple[str, list[str], ToolInvokeMeta]]
+
+
+class AgentPattern(ABC):
+    """Base class for agent execution strategies."""
+
+    def __init__(
+        self,
+        model_instance: ModelInstance,
+        tools: list[Tool],
+        context: ExecutionContext,
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        files: list[File] = [],
+        tool_invoke_hook: ToolInvokeHook | None = None,
+    ):
+        """Initialize the agent strategy."""
+        self.model_instance = model_instance
+        self.tools = tools
+        self.context = context
+        self.max_iterations = min(max_iterations, 99)  # Cap at 99 iterations
+        self.workflow_call_depth = workflow_call_depth
+        self.files: list[File] = files
+        self.tool_invoke_hook = tool_invoke_hook
+
+    @abstractmethod
+    def run(
+        self, prompt_messages: list[PromptMessage], model_parameters: dict[str, Any], stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the agent strategy."""
+        pass
+
+    def _accumulate_usage(self, total_usage: dict[str, Any], delta_usage: LLMUsage) -> None:
+        """Accumulate LLM usage statistics."""
+        if not total_usage.get("usage"):
+            # Create a copy to avoid modifying the original
+            total_usage["usage"] = LLMUsage(
+                prompt_tokens=delta_usage.prompt_tokens,
+                prompt_unit_price=delta_usage.prompt_unit_price,
+                prompt_price_unit=delta_usage.prompt_price_unit,
+                prompt_price=delta_usage.prompt_price,
+                completion_tokens=delta_usage.completion_tokens,
+                completion_unit_price=delta_usage.completion_unit_price,
+                completion_price_unit=delta_usage.completion_price_unit,
+                completion_price=delta_usage.completion_price,
+                total_tokens=delta_usage.total_tokens,
+                total_price=delta_usage.total_price,
+                currency=delta_usage.currency,
+                latency=delta_usage.latency,
+            )
+        else:
+            current: LLMUsage = total_usage["usage"]
+            current.prompt_tokens += delta_usage.prompt_tokens
+            current.completion_tokens += delta_usage.completion_tokens
+            current.total_tokens += delta_usage.total_tokens
+            current.prompt_price += delta_usage.prompt_price
+            current.completion_price += delta_usage.completion_price
+            current.total_price += delta_usage.total_price
+
+    def _extract_content(self, content: Any) -> str:
+        """Extract text content from message content."""
+        if isinstance(content, list):
+            # Content items are PromptMessageContentUnionTypes
+            text_parts = []
+            for c in content:
+                # Check if it's a TextPromptMessageContent (which has data attribute)
+                if isinstance(c, TextPromptMessageContent):
+                    text_parts.append(c.data)
+            return "".join(text_parts)
+        return str(content)
+
+    def _has_tool_calls(self, chunk: LLMResultChunk) -> bool:
+        """Check if chunk contains tool calls."""
+        # LLMResultChunk always has delta attribute
+        return bool(chunk.delta.message and chunk.delta.message.tool_calls)
+
+    def _has_tool_calls_result(self, result: LLMResult) -> bool:
+        """Check if result contains tool calls (non-streaming)."""
+        # LLMResult always has message attribute
+        return bool(result.message and result.message.tool_calls)
+
+    def _extract_tool_calls(self, chunk: LLMResultChunk) -> list[tuple[str, str, dict[str, Any]]]:
+        """Extract tool calls from streaming chunk."""
+        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+        if chunk.delta.message and chunk.delta.message.tool_calls:
+            for tool_call in chunk.delta.message.tool_calls:
+                if tool_call.function:
+                    try:
+                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
+                    except json.JSONDecodeError:
+                        args = {}
+                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
+        return tool_calls
+
+    def _extract_tool_calls_result(self, result: LLMResult) -> list[tuple[str, str, dict[str, Any]]]:
+        """Extract tool calls from non-streaming result."""
+        tool_calls = []
+        if result.message and result.message.tool_calls:
+            for tool_call in result.message.tool_calls:
+                if tool_call.function:
+                    try:
+                        args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
+                    except json.JSONDecodeError:
+                        args = {}
+                    tool_calls.append((tool_call.id or "", tool_call.function.name, args))
+        return tool_calls
+
+    def _extract_text_from_message(self, message: PromptMessage) -> str:
+        """Extract text content from a prompt message."""
+        # PromptMessage always has content attribute
+        content = message.content
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            # Extract text from content list
+            text_parts = []
+            for item in content:
+                if isinstance(item, TextPromptMessageContent):
+                    text_parts.append(item.data)
+            return " ".join(text_parts)
+        return ""
+
+    def _get_tool_metadata(self, tool_instance: Tool) -> dict[AgentLog.LogMetadata, Any]:
+        """Get metadata for a tool including provider and icon info."""
+        from core.tools.tool_manager import ToolManager
+
+        metadata: dict[AgentLog.LogMetadata, Any] = {}
+        if tool_instance.entity and tool_instance.entity.identity:
+            identity = tool_instance.entity.identity
+            if identity.provider:
+                metadata[AgentLog.LogMetadata.PROVIDER] = identity.provider
+
+            # Get icon using ToolManager for proper URL generation
+            tenant_id = self.context.tenant_id
+            if tenant_id and identity.provider:
+                try:
+                    provider_type = tool_instance.tool_provider_type()
+                    icon = ToolManager.get_tool_icon(tenant_id, provider_type, identity.provider)
+                    if isinstance(icon, str):
+                        metadata[AgentLog.LogMetadata.ICON] = icon
+                    elif isinstance(icon, dict):
+                        # Handle icon dict with background/content or light/dark variants
+                        metadata[AgentLog.LogMetadata.ICON] = icon
+                except Exception:
+                    # Fallback to identity.icon if ToolManager fails
+                    if identity.icon:
+                        metadata[AgentLog.LogMetadata.ICON] = identity.icon
+            elif identity.icon:
+                metadata[AgentLog.LogMetadata.ICON] = identity.icon
+        return metadata
+
+    def _create_log(
+        self,
+        label: str,
+        log_type: AgentLog.LogType,
+        status: AgentLog.LogStatus,
+        data: dict[str, Any] | None = None,
+        parent_id: str | None = None,
+        extra_metadata: dict[AgentLog.LogMetadata, Any] | None = None,
+    ) -> AgentLog:
+        """Create a new AgentLog with standard metadata."""
+        metadata: dict[AgentLog.LogMetadata, Any] = {
+            AgentLog.LogMetadata.STARTED_AT: time.perf_counter(),
+        }
+        if extra_metadata:
+            metadata.update(extra_metadata)
+
+        return AgentLog(
+            label=label,
+            log_type=log_type,
+            status=status,
+            data=data or {},
+            parent_id=parent_id,
+            metadata=metadata,
+        )
+
+    def _finish_log(
+        self,
+        log: AgentLog,
+        data: dict[str, Any] | None = None,
+        usage: LLMUsage | None = None,
+    ) -> AgentLog:
+        """Finish an AgentLog by updating its status and metadata."""
+        log.status = AgentLog.LogStatus.SUCCESS
+
+        if data is not None:
+            log.data = data
+
+        # Calculate elapsed time
+        started_at = log.metadata.get(AgentLog.LogMetadata.STARTED_AT, time.perf_counter())
+        finished_at = time.perf_counter()
+
+        # Update metadata
+        log.metadata = {
+            **log.metadata,
+            AgentLog.LogMetadata.FINISHED_AT: finished_at,
+            # Calculate elapsed time in seconds
+            AgentLog.LogMetadata.ELAPSED_TIME: round(finished_at - started_at, 4),
+        }
+
+        # Add usage information if provided
+        if usage:
+            log.metadata.update(
+                {
+                    AgentLog.LogMetadata.TOTAL_PRICE: usage.total_price,
+                    AgentLog.LogMetadata.CURRENCY: usage.currency,
+                    AgentLog.LogMetadata.TOTAL_TOKENS: usage.total_tokens,
+                    AgentLog.LogMetadata.LLM_USAGE: usage,
+                }
+            )
+
+        return log
+
+    def _replace_file_references(self, tool_args: dict[str, Any]) -> dict[str, Any]:
+        """
+        Replace file references in tool arguments with actual File objects.
+
+        Args:
+            tool_args: Dictionary of tool arguments
+
+        Returns:
+            Updated tool arguments with file references replaced
+        """
+        # Process each argument in the dictionary
+        processed_args: dict[str, Any] = {}
+        for key, value in tool_args.items():
+            processed_args[key] = self._process_file_reference(value)
+        return processed_args
+
+    def _process_file_reference(self, data: Any) -> Any:
+        """
+        Recursively process data to replace file references.
+        Supports both single file [File: file_id] and multiple files [Files: file_id1, file_id2, ...].
+
+        Args:
+            data: The data to process (can be dict, list, str, or other types)
+
+        Returns:
+            Processed data with file references replaced
+        """
+        single_file_pattern = re.compile(r"^\[File:\s*([^\]]+)\]$")
+        multiple_files_pattern = re.compile(r"^\[Files:\s*([^\]]+)\]$")
+
+        if isinstance(data, dict):
+            # Process dictionary recursively
+            return {key: self._process_file_reference(value) for key, value in data.items()}
+        elif isinstance(data, list):
+            # Process list recursively
+            return [self._process_file_reference(item) for item in data]
+        elif isinstance(data, str):
+            # Check for single file pattern [File: file_id]
+            single_match = single_file_pattern.match(data.strip())
+            if single_match:
+                file_id = single_match.group(1).strip()
+                # Find the file in self.files
+                for file in self.files:
+                    if file.id and str(file.id) == file_id:
+                        return file
+                # If file not found, return original value
+                return data
+
+            # Check for multiple files pattern [Files: file_id1, file_id2, ...]
+            multiple_match = multiple_files_pattern.match(data.strip())
+            if multiple_match:
+                file_ids_str = multiple_match.group(1).strip()
+                # Split by comma and strip whitespace
+                file_ids = [fid.strip() for fid in file_ids_str.split(",")]
+
+                # Find all matching files
+                matched_files: list[File] = []
+                for file_id in file_ids:
+                    for file in self.files:
+                        if file.id and str(file.id) == file_id:
+                            matched_files.append(file)
+                            break
+
+                # Return list of files if any were found, otherwise return original
+                return matched_files or data
+
+            return data
+        else:
+            # Return other types as-is
+            return data
+
+    def _create_text_chunk(self, text: str, prompt_messages: list[PromptMessage]) -> LLMResultChunk:
+        """Create a text chunk for streaming."""
+        return LLMResultChunk(
+            model=self.model_instance.model,
+            prompt_messages=prompt_messages,
+            delta=LLMResultChunkDelta(
+                index=0,
+                message=AssistantPromptMessage(content=text),
+                usage=None,
+            ),
+            system_fingerprint="",
+        )
+
+    def _invoke_tool(
+        self,
+        tool_instance: Tool,
+        tool_args: dict[str, Any],
+        tool_name: str,
+    ) -> tuple[str, list[File], ToolInvokeMeta | None]:
+        """
+        Invoke a tool and collect its response.
+
+        Args:
+            tool_instance: The tool instance to invoke
+            tool_args: Tool arguments
+            tool_name: Name of the tool
+
+        Returns:
+            Tuple of (response_content, tool_files, tool_invoke_meta)
+        """
+        # Process tool_args to replace file references with actual File objects
+        tool_args = self._replace_file_references(tool_args)
+
+        # If a tool invoke hook is set, use it instead of generic_invoke
+        if self.tool_invoke_hook:
+            response_content, _, tool_invoke_meta = self.tool_invoke_hook(tool_instance, tool_args, tool_name)
+            # Note: message_file_ids are stored in DB, we don't convert them to File objects here
+            # The caller (AgentAppRunner) handles file publishing
+            return response_content, [], tool_invoke_meta
+
+        # Default: use generic_invoke for workflow scenarios
+        # Import here to avoid circular import
+        from core.tools.tool_engine import DifyWorkflowCallbackHandler, ToolEngine
+
+        tool_response = ToolEngine().generic_invoke(
+            tool=tool_instance,
+            tool_parameters=tool_args,
+            user_id=self.context.user_id or "",
+            workflow_tool_callback=DifyWorkflowCallbackHandler(),
+            workflow_call_depth=self.workflow_call_depth,
+            app_id=self.context.app_id,
+            conversation_id=self.context.conversation_id,
+            message_id=self.context.message_id,
+        )
+
+        # Collect response and files
+        response_content = ""
+        tool_files: list[File] = []
+
+        for response in tool_response:
+            if response.type == ToolInvokeMessage.MessageType.TEXT:
+                assert isinstance(response.message, ToolInvokeMessage.TextMessage)
+                response_content += response.message.text
+
+            elif response.type == ToolInvokeMessage.MessageType.LINK:
+                # Handle link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Link: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.IMAGE:
+                # Handle image URL messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Image: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK:
+                # Handle image link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    response_content += f"[Image: {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.BINARY_LINK:
+                # Handle binary file link messages
+                if isinstance(response.message, ToolInvokeMessage.TextMessage):
+                    filename = response.meta.get("filename", "file") if response.meta else "file"
+                    response_content += f"[File: {filename} - {response.message.text}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.JSON:
+                # Handle JSON messages
+                if isinstance(response.message, ToolInvokeMessage.JsonMessage):
+                    response_content += json.dumps(response.message.json_object, ensure_ascii=False, indent=2)
+
+            elif response.type == ToolInvokeMessage.MessageType.BLOB:
+                # Handle blob messages - convert to text representation
+                if isinstance(response.message, ToolInvokeMessage.BlobMessage):
+                    mime_type = (
+                        response.meta.get("mime_type", "application/octet-stream")
+                        if response.meta
+                        else "application/octet-stream"
+                    )
+                    size = len(response.message.blob)
+                    response_content += f"[Binary data: {mime_type}, size: {size} bytes]"
+
+            elif response.type == ToolInvokeMessage.MessageType.VARIABLE:
+                # Handle variable messages
+                if isinstance(response.message, ToolInvokeMessage.VariableMessage):
+                    var_name = response.message.variable_name
+                    var_value = response.message.variable_value
+                    if isinstance(var_value, str):
+                        response_content += var_value
+                    else:
+                        response_content += f"[Variable {var_name}: {json.dumps(var_value, ensure_ascii=False)}]"
+
+            elif response.type == ToolInvokeMessage.MessageType.BLOB_CHUNK:
+                # Handle blob chunk messages - these are parts of a larger blob
+                if isinstance(response.message, ToolInvokeMessage.BlobChunkMessage):
+                    response_content += f"[Blob chunk {response.message.sequence}: {len(response.message.blob)} bytes]"
+
+            elif response.type == ToolInvokeMessage.MessageType.RETRIEVER_RESOURCES:
+                # Handle retriever resources messages
+                if isinstance(response.message, ToolInvokeMessage.RetrieverResourceMessage):
+                    response_content += response.message.context
+
+            elif response.type == ToolInvokeMessage.MessageType.FILE:
+                # Extract file from meta
+                if response.meta and "file" in response.meta:
+                    file = response.meta["file"]
+                    if isinstance(file, File):
+                        # Check if file is for model or tool output
+                        if response.meta.get("target") == "self":
+                            # File is for model - add to files for next prompt
+                            self.files.append(file)
+                            response_content += f"File '{file.filename}' has been loaded into your context."
+                        else:
+                            # File is tool output
+                            tool_files.append(file)
+
+        return response_content, tool_files, None
+
+    def _validate_tool_args(self, tool_instance: Tool, tool_args: dict[str, Any]) -> str | None:
+        """Validate tool arguments against the tool's required parameters.
+
+        Checks that all required LLM-facing parameters are present and non-empty
+        before actual execution, preventing wasted tool invocations when the model
+        generates calls with missing arguments (e.g. empty ``{}``).
+
+        Returns:
+            Error message if validation fails, None if all required parameters are satisfied.
+        """
+        prompt_tool = tool_instance.to_prompt_message_tool()
+        required_params: list[str] = prompt_tool.parameters.get("required", [])
+
+        if not required_params:
+            return None
+
+        missing = [
+            p for p in required_params
+            if p not in tool_args
+            or tool_args[p] is None
+            or (isinstance(tool_args[p], str) and not tool_args[p].strip())
+        ]
+
+        if not missing:
+            return None
+
+        return (
+            f"Missing required parameter(s): {', '.join(missing)}. "
+            f"Please provide all required parameters before calling this tool."
+        )
+
+    def _find_tool_by_name(self, tool_name: str) -> Tool | None:
+        """Find a tool instance by its name."""
+        for tool in self.tools:
+            if tool.entity.identity.name == tool_name:
+                return tool
+        return None
+
+    def _convert_tools_to_prompt_format(self) -> list[PromptMessageTool]:
+        """Convert tools to prompt message format."""
+        prompt_tools: list[PromptMessageTool] = []
+        for tool in self.tools:
+            prompt_tools.append(tool.to_prompt_message_tool())
+        return prompt_tools
+
+    def _update_usage_with_empty(self, llm_usage: dict[str, Any]) -> None:
+        """Initialize usage tracking with empty usage if not set."""
+        if "usage" not in llm_usage or llm_usage["usage"] is None:
+            llm_usage["usage"] = LLMUsage.empty_usage()

api/core/agent/patterns/function_call.py

@@ -0,0 +1,358 @@
+"""Function Call strategy implementation.
+
+Implements the Function Call agent pattern where the LLM uses native tool-calling
+capability to invoke tools. Includes pre-execution parameter validation that
+intercepts invalid calls (e.g. empty arguments) before they reach tool backends,
+and avoids counting purely-invalid rounds against the iteration budget.
+"""
+
+import json
+import logging
+from collections.abc import Generator
+from typing import Any, Union
+
+from core.agent.entities import AgentLog, AgentResult
+from core.file import File
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    LLMUsage,
+    PromptMessage,
+    PromptMessageTool,
+    ToolPromptMessage,
+)
+from core.tools.entities.tool_entities import ToolInvokeMeta
+
+from .base import AgentPattern
+
+logger = logging.getLogger(__name__)
+
+
+class FunctionCallStrategy(AgentPattern):
+    """Function Call strategy using model's native tool calling capability."""
+
+    def run(
+        self, prompt_messages: list[PromptMessage], model_parameters: dict[str, Any], stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the function call agent strategy."""
+        # Convert tools to prompt format
+        prompt_tools: list[PromptMessageTool] = self._convert_tools_to_prompt_format()
+
+        # Initialize tracking
+        iteration_step: int = 1
+        max_iterations: int = self.max_iterations + 1
+        function_call_state: bool = True
+        total_usage: dict[str, LLMUsage | None] = {"usage": None}
+        messages: list[PromptMessage] = list(prompt_messages)  # Create mutable copy
+        final_text: str = ""
+        finish_reason: str | None = None
+        output_files: list[File] = []  # Track files produced by tools
+        # Consecutive rounds where ALL tool calls failed parameter validation.
+        # When this happens the round is "free" (iteration_step not incremented)
+        # up to a safety cap to prevent infinite loops.
+        consecutive_validation_failures: int = 0
+        max_validation_retries: int = 3
+
+        while function_call_state and iteration_step <= max_iterations:
+            function_call_state = False
+            round_log = self._create_log(
+                label=f"ROUND {iteration_step}",
+                log_type=AgentLog.LogType.ROUND,
+                status=AgentLog.LogStatus.START,
+                data={},
+            )
+            yield round_log
+            # On last iteration, remove tools to force final answer
+            current_tools: list[PromptMessageTool] = [] if iteration_step == max_iterations else prompt_tools
+            model_log = self._create_log(
+                label=f"{self.model_instance.model} Thought",
+                log_type=AgentLog.LogType.THOUGHT,
+                status=AgentLog.LogStatus.START,
+                data={},
+                parent_id=round_log.id,
+                extra_metadata={
+                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
+                },
+            )
+            yield model_log
+
+            # Track usage for this round only
+            round_usage: dict[str, LLMUsage | None] = {"usage": None}
+
+            # Invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
+                prompt_messages=messages,
+                model_parameters=model_parameters,
+                tools=current_tools,
+                stop=stop,
+                stream=stream,
+                user=self.context.user_id,
+                callbacks=[],
+            )
+
+            # Process response
+            tool_calls, response_content, chunk_finish_reason = yield from self._handle_chunks(
+                chunks, round_usage, model_log
+            )
+            messages.append(self._create_assistant_message(response_content, tool_calls))
+
+            # Accumulate to total usage
+            round_usage_value = round_usage.get("usage")
+            if round_usage_value:
+                self._accumulate_usage(total_usage, round_usage_value)
+
+            # Update final text if no tool calls (this is likely the final answer)
+            if not tool_calls:
+                final_text = response_content
+
+            # Update finish reason
+            if chunk_finish_reason:
+                finish_reason = chunk_finish_reason
+
+            # Process tool calls
+            tool_outputs: dict[str, str] = {}
+            all_validation_errors: bool = True
+            if tool_calls:
+                function_call_state = True
+                # Execute tools (with pre-execution parameter validation)
+                for tool_call_id, tool_name, tool_args in tool_calls:
+                    tool_response, tool_files, _, is_validation_error = yield from self._handle_tool_call(
+                        tool_name, tool_args, tool_call_id, messages, round_log
+                    )
+                    tool_outputs[tool_name] = tool_response
+                    output_files.extend(tool_files)
+                    if not is_validation_error:
+                        all_validation_errors = False
+            else:
+                all_validation_errors = False
+
+            yield self._finish_log(
+                round_log,
+                data={
+                    "llm_result": response_content,
+                    "tool_calls": [
+                        {"name": tc[1], "args": tc[2], "output": tool_outputs.get(tc[1], "")} for tc in tool_calls
+                    ]
+                    if tool_calls
+                    else [],
+                    "final_answer": final_text if not function_call_state else None,
+                },
+                usage=round_usage.get("usage"),
+            )
+
+            # Skip iteration counter when every tool call in this round failed validation,
+            # giving the model a free retry — but cap retries to prevent infinite loops.
+            if tool_calls and all_validation_errors:
+                consecutive_validation_failures += 1
+                if consecutive_validation_failures >= max_validation_retries:
+                    logger.warning(
+                        "Agent hit %d consecutive validation-only rounds, forcing iteration increment",
+                        consecutive_validation_failures,
+                    )
+                    iteration_step += 1
+                    consecutive_validation_failures = 0
+                else:
+                    logger.info(
+                        "All tool calls failed validation (attempt %d/%d), not counting iteration",
+                        consecutive_validation_failures,
+                        max_validation_retries,
+                    )
+            else:
+                consecutive_validation_failures = 0
+                iteration_step += 1
+
+        # Return final result
+        from core.agent.entities import AgentResult
+
+        return AgentResult(
+            text=final_text,
+            files=output_files,
+            usage=total_usage.get("usage") or LLMUsage.empty_usage(),
+            finish_reason=finish_reason,
+        )
+
+    def _handle_chunks(
+        self,
+        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
+        llm_usage: dict[str, LLMUsage | None],
+        start_log: AgentLog,
+    ) -> Generator[
+        LLMResultChunk | AgentLog,
+        None,
+        tuple[list[tuple[str, str, dict[str, Any]]], str, str | None],
+    ]:
+        """Handle LLM response chunks and extract tool calls and content.
+
+        Returns a tuple of (tool_calls, response_content, finish_reason).
+        """
+        tool_calls: list[tuple[str, str, dict[str, Any]]] = []
+        response_content: str = ""
+        finish_reason: str | None = None
+        if isinstance(chunks, Generator):
+            # Streaming response
+            for chunk in chunks:
+                # Extract tool calls
+                if self._has_tool_calls(chunk):
+                    tool_calls.extend(self._extract_tool_calls(chunk))
+
+                # Extract content
+                if chunk.delta.message and chunk.delta.message.content:
+                    response_content += self._extract_content(chunk.delta.message.content)
+
+                # Track usage
+                if chunk.delta.usage:
+                    self._accumulate_usage(llm_usage, chunk.delta.usage)
+
+                # Capture finish reason
+                if chunk.delta.finish_reason:
+                    finish_reason = chunk.delta.finish_reason
+
+                yield chunk
+        else:
+            # Non-streaming response
+            result: LLMResult = chunks
+
+            if self._has_tool_calls_result(result):
+                tool_calls.extend(self._extract_tool_calls_result(result))
+
+            if result.message and result.message.content:
+                response_content += self._extract_content(result.message.content)
+
+            if result.usage:
+                self._accumulate_usage(llm_usage, result.usage)
+
+            # Convert to streaming format
+            yield LLMResultChunk(
+                model=result.model,
+                prompt_messages=result.prompt_messages,
+                delta=LLMResultChunkDelta(index=0, message=result.message, usage=result.usage),
+            )
+        yield self._finish_log(
+            start_log,
+            data={
+                "result": response_content,
+            },
+            usage=llm_usage.get("usage"),
+        )
+        return tool_calls, response_content, finish_reason
+
+    def _create_assistant_message(
+        self, content: str, tool_calls: list[tuple[str, str, dict[str, Any]]] | None = None
+    ) -> AssistantPromptMessage:
+        """Create assistant message with tool calls."""
+        if tool_calls is None:
+            return AssistantPromptMessage(content=content)
+        return AssistantPromptMessage(
+            content=content or "",
+            tool_calls=[
+                AssistantPromptMessage.ToolCall(
+                    id=tc[0],
+                    type="function",
+                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tc[1], arguments=json.dumps(tc[2])),
+                )
+                for tc in tool_calls
+            ],
+        )
+
+    def _handle_tool_call(
+        self,
+        tool_name: str,
+        tool_args: dict[str, Any],
+        tool_call_id: str,
+        messages: list[PromptMessage],
+        round_log: AgentLog,
+    ) -> Generator[AgentLog, None, tuple[str, list[File], ToolInvokeMeta | None, bool]]:
+        """Handle a single tool call and return response with files, meta, and validation status.
+
+        Validates required parameters before execution. When validation fails the tool
+        is never invoked — a synthetic error is fed back to the model so it can self-correct
+        without consuming a real iteration.
+
+        Returns:
+            (response_content, tool_files, tool_invoke_meta, is_validation_error).
+            ``is_validation_error`` is True when the call was rejected due to missing
+            required parameters, allowing the caller to skip the iteration counter.
+        """
+        # Find tool
+        tool_instance = self._find_tool_by_name(tool_name)
+        if not tool_instance:
+            raise ValueError(f"Tool {tool_name} not found")
+
+        # Get tool metadata (provider, icon, etc.)
+        tool_metadata = self._get_tool_metadata(tool_instance)
+
+        # Create tool call log
+        tool_call_log = self._create_log(
+            label=f"CALL {tool_name}",
+            log_type=AgentLog.LogType.TOOL_CALL,
+            status=AgentLog.LogStatus.START,
+            data={
+                "tool_call_id": tool_call_id,
+                "tool_name": tool_name,
+                "tool_args": tool_args,
+            },
+            parent_id=round_log.id,
+            extra_metadata=tool_metadata,
+        )
+        yield tool_call_log
+
+        # Validate required parameters before execution to avoid wasted invocations
+        validation_error = self._validate_tool_args(tool_instance, tool_args)
+        if validation_error:
+            tool_call_log.status = AgentLog.LogStatus.ERROR
+            tool_call_log.error = validation_error
+            tool_call_log.data = {**tool_call_log.data, "error": validation_error}
+            yield tool_call_log
+
+            messages.append(
+                ToolPromptMessage(content=validation_error, tool_call_id=tool_call_id, name=tool_name)
+            )
+            return validation_error, [], None, True
+
+        # Invoke tool using base class method with error handling
+        try:
+            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args, tool_name)
+
+            yield self._finish_log(
+                tool_call_log,
+                data={
+                    **tool_call_log.data,
+                    "output": response_content,
+                    "files": len(tool_files),
+                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
+                },
+            )
+            final_content = response_content or "Tool executed successfully"
+            # Add tool response to messages
+            messages.append(
+                ToolPromptMessage(
+                    content=final_content,
+                    tool_call_id=tool_call_id,
+                    name=tool_name,
+                )
+            )
+            return response_content, tool_files, tool_invoke_meta, False
+        except Exception as e:
+            # Tool invocation failed, yield error log
+            error_message = str(e)
+            tool_call_log.status = AgentLog.LogStatus.ERROR
+            tool_call_log.error = error_message
+            tool_call_log.data = {
+                **tool_call_log.data,
+                "error": error_message,
+            }
+            yield tool_call_log
+
+            # Add error message to conversation
+            error_content = f"Tool execution failed: {error_message}"
+            messages.append(
+                ToolPromptMessage(
+                    content=error_content,
+                    tool_call_id=tool_call_id,
+                    name=tool_name,
+                )
+            )
+            return error_content, [], None, False

api/core/agent/patterns/react.py

@@ -0,0 +1,415 @@
+"""ReAct strategy implementation."""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Generator
+from typing import TYPE_CHECKING, Any, Union
+
+from core.agent.entities import AgentLog, AgentResult, AgentScratchpadUnit, ExecutionContext
+from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
+from core.file import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities import (
+    AssistantPromptMessage,
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    PromptMessage,
+    SystemPromptMessage,
+)
+
+from .base import AgentPattern, ToolInvokeHook
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+
+class ReActStrategy(AgentPattern):
+    """ReAct strategy using reasoning and acting approach."""
+
+    def __init__(
+        self,
+        model_instance: ModelInstance,
+        tools: list[Tool],
+        context: ExecutionContext,
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        files: list[File] = [],
+        tool_invoke_hook: ToolInvokeHook | None = None,
+        instruction: str = "",
+    ):
+        """Initialize the ReAct strategy with instruction support."""
+        super().__init__(
+            model_instance=model_instance,
+            tools=tools,
+            context=context,
+            max_iterations=max_iterations,
+            workflow_call_depth=workflow_call_depth,
+            files=files,
+            tool_invoke_hook=tool_invoke_hook,
+        )
+        self.instruction = instruction
+
+    def run(
+        self, prompt_messages: list[PromptMessage], model_parameters: dict[str, Any], stop: list[str] = [],
+        stream: bool = True,
+    ) -> Generator[LLMResultChunk | AgentLog, None, AgentResult]:
+        """Execute the ReAct agent strategy."""
+        # Initialize tracking
+        agent_scratchpad: list[AgentScratchpadUnit] = []
+        iteration_step: int = 1
+        max_iterations: int = self.max_iterations + 1
+        react_state: bool = True
+        total_usage: dict[str, Any] = {"usage": None}
+        output_files: list[File] = []  # Track files produced by tools
+        final_text: str = ""
+        finish_reason: str | None = None
+
+        # Add "Observation" to stop sequences
+        if "Observation" not in stop:
+            stop = stop.copy()
+            stop.append("Observation")
+
+        while react_state and iteration_step <= max_iterations:
+            react_state = False
+            round_log = self._create_log(
+                label=f"ROUND {iteration_step}",
+                log_type=AgentLog.LogType.ROUND,
+                status=AgentLog.LogStatus.START,
+                data={},
+            )
+            yield round_log
+
+            # Build prompt with/without tools based on iteration
+            include_tools = iteration_step < max_iterations
+            current_messages = self._build_prompt_with_react_format(
+                prompt_messages, agent_scratchpad, include_tools, self.instruction
+            )
+
+            model_log = self._create_log(
+                label=f"{self.model_instance.model} Thought",
+                log_type=AgentLog.LogType.THOUGHT,
+                status=AgentLog.LogStatus.START,
+                data={},
+                parent_id=round_log.id,
+                extra_metadata={
+                    AgentLog.LogMetadata.PROVIDER: self.model_instance.provider,
+                },
+            )
+            yield model_log
+
+            # Track usage for this round only
+            round_usage: dict[str, Any] = {"usage": None}
+
+            # Use current messages directly (files are handled by base class if needed)
+            messages_to_use = current_messages
+
+            # Invoke model
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
+                prompt_messages=messages_to_use,
+                model_parameters=model_parameters,
+                stop=stop,
+                stream=stream,
+                user=self.context.user_id or "",
+                callbacks=[],
+            )
+
+            # Process response
+            scratchpad, chunk_finish_reason = yield from self._handle_chunks(
+                chunks, round_usage, model_log, current_messages
+            )
+            agent_scratchpad.append(scratchpad)
+
+            # Accumulate to total usage
+            round_usage_value = round_usage.get("usage")
+            if round_usage_value:
+                self._accumulate_usage(total_usage, round_usage_value)
+
+            # Update finish reason
+            if chunk_finish_reason:
+                finish_reason = chunk_finish_reason
+
+            # Check if we have an action to execute
+            if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
+                react_state = True
+                # Execute tool
+                observation, tool_files = yield from self._handle_tool_call(
+                    scratchpad.action, current_messages, round_log
+                )
+                scratchpad.observation = observation
+                # Track files produced by tools
+                output_files.extend(tool_files)
+
+                # Add observation to scratchpad for display
+                yield self._create_text_chunk(f"\nObservation: {observation}\n", current_messages)
+            else:
+                # Extract final answer
+                if scratchpad.action and scratchpad.action.action_input:
+                    final_answer = scratchpad.action.action_input
+                    if isinstance(final_answer, dict):
+                        final_answer = json.dumps(final_answer, ensure_ascii=False)
+                    final_text = str(final_answer)
+                elif scratchpad.thought:
+                    # If no action but we have thought, use thought as final answer
+                    final_text = scratchpad.thought
+
+            yield self._finish_log(
+                round_log,
+                data={
+                    "thought": scratchpad.thought,
+                    "action": scratchpad.action_str if scratchpad.action else None,
+                    "observation": scratchpad.observation or None,
+                    "final_answer": final_text if not react_state else None,
+                },
+                usage=round_usage.get("usage"),
+            )
+            iteration_step += 1
+
+        # Return final result
+
+        from core.agent.entities import AgentResult
+
+        return AgentResult(
+            text=final_text, files=output_files, usage=total_usage.get("usage"), finish_reason=finish_reason
+        )
+
+    def _build_prompt_with_react_format(
+        self,
+        original_messages: list[PromptMessage],
+        agent_scratchpad: list[AgentScratchpadUnit],
+        include_tools: bool = True,
+        instruction: str = "",
+    ) -> list[PromptMessage]:
+        """Build prompt messages with ReAct format."""
+        # Copy messages to avoid modifying original
+        messages = list(original_messages)
+
+        # Find and update the system prompt that should already exist
+        system_prompt_found = False
+        for i, msg in enumerate(messages):
+            if isinstance(msg, SystemPromptMessage):
+                system_prompt_found = True
+                # The system prompt from frontend already has the template, just replace placeholders
+
+                # Format tools
+                tools_str = ""
+                tool_names = []
+                if include_tools and self.tools:
+                    # Convert tools to prompt message tools format
+                    prompt_tools = [tool.to_prompt_message_tool() for tool in self.tools]
+                    tool_names = [tool.name for tool in prompt_tools]
+
+                    # Format tools as JSON for comprehensive information
+                    from core.model_runtime.utils.encoders import jsonable_encoder
+
+                    tools_str = json.dumps(jsonable_encoder(prompt_tools), indent=2)
+                    tool_names_str = ", ".join(f'"{name}"' for name in tool_names)
+                else:
+                    tools_str = "No tools available"
+                    tool_names_str = ""
+
+                # Replace placeholders in the existing system prompt
+                updated_content = msg.content
+                assert isinstance(updated_content, str)
+                updated_content = updated_content.replace("{{instruction}}", instruction)
+                updated_content = updated_content.replace("{{tools}}", tools_str)
+                updated_content = updated_content.replace("{{tool_names}}", tool_names_str)
+
+                # Create new SystemPromptMessage with updated content
+                messages[i] = SystemPromptMessage(content=updated_content)
+                break
+
+        # If no system prompt found, that's unexpected but add scratchpad anyway
+        if not system_prompt_found:
+            # This shouldn't happen if frontend is working correctly
+            pass
+
+        # Format agent scratchpad
+        scratchpad_str = ""
+        if agent_scratchpad:
+            scratchpad_parts: list[str] = []
+            for unit in agent_scratchpad:
+                if unit.thought:
+                    scratchpad_parts.append(f"Thought: {unit.thought}")
+                if unit.action_str:
+                    scratchpad_parts.append(f"Action:\n```\n{unit.action_str}\n```")
+                if unit.observation:
+                    scratchpad_parts.append(f"Observation: {unit.observation}")
+            scratchpad_str = "\n".join(scratchpad_parts)
+
+        # If there's a scratchpad, append it to the last message
+        if scratchpad_str:
+            messages.append(AssistantPromptMessage(content=scratchpad_str))
+
+        return messages
+
+    def _handle_chunks(
+        self,
+        chunks: Union[Generator[LLMResultChunk, None, None], LLMResult],
+        llm_usage: dict[str, Any],
+        model_log: AgentLog,
+        current_messages: list[PromptMessage],
+    ) -> Generator[
+        LLMResultChunk | AgentLog,
+        None,
+        tuple[AgentScratchpadUnit, str | None],
+    ]:
+        """Handle LLM response chunks and extract action/thought.
+
+        Returns a tuple of (scratchpad_unit, finish_reason).
+        """
+        usage_dict: dict[str, Any] = {}
+
+        # Convert non-streaming to streaming format if needed
+        if isinstance(chunks, LLMResult):
+            # Create a generator from the LLMResult
+            def result_to_chunks() -> Generator[LLMResultChunk, None, None]:
+                yield LLMResultChunk(
+                    model=chunks.model,
+                    prompt_messages=chunks.prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=chunks.message,
+                        usage=chunks.usage,
+                        finish_reason=None,  # LLMResult doesn't have finish_reason, only streaming chunks do
+                    ),
+                    system_fingerprint=chunks.system_fingerprint or "",
+                )
+
+            streaming_chunks = result_to_chunks()
+        else:
+            streaming_chunks = chunks
+
+        react_chunks = CotAgentOutputParser.handle_react_stream_output(streaming_chunks, usage_dict)
+
+        # Initialize scratchpad unit
+        scratchpad = AgentScratchpadUnit(
+            agent_response="",
+            thought="",
+            action_str="",
+            observation="",
+            action=None,
+        )
+
+        finish_reason: str | None = None
+
+        # Process chunks
+        for chunk in react_chunks:
+            if isinstance(chunk, AgentScratchpadUnit.Action):
+                # Action detected
+                action_str = json.dumps(chunk.model_dump())
+                scratchpad.agent_response = (scratchpad.agent_response or "") + action_str
+                scratchpad.action_str = action_str
+                scratchpad.action = chunk
+
+                yield self._create_text_chunk(json.dumps(chunk.model_dump()), current_messages)
+            else:
+                # Text chunk
+                chunk_text = str(chunk)
+                scratchpad.agent_response = (scratchpad.agent_response or "") + chunk_text
+                scratchpad.thought = (scratchpad.thought or "") + chunk_text
+
+                yield self._create_text_chunk(chunk_text, current_messages)
+
+        # Update usage
+        if usage_dict.get("usage"):
+            if llm_usage.get("usage"):
+                self._accumulate_usage(llm_usage, usage_dict["usage"])
+            else:
+                llm_usage["usage"] = usage_dict["usage"]
+
+        # Clean up thought
+        scratchpad.thought = (scratchpad.thought or "").strip() or "I am thinking about how to help you"
+
+        # Finish model log
+        yield self._finish_log(
+            model_log,
+            data={
+                "thought": scratchpad.thought,
+                "action": scratchpad.action_str if scratchpad.action else None,
+            },
+            usage=llm_usage.get("usage"),
+        )
+
+        return scratchpad, finish_reason
+
+    def _handle_tool_call(
+        self,
+        action: AgentScratchpadUnit.Action,
+        prompt_messages: list[PromptMessage],
+        round_log: AgentLog,
+    ) -> Generator[AgentLog, None, tuple[str, list[File]]]:
+        """Handle tool call and return observation with files."""
+        tool_name = action.action_name
+        tool_args: dict[str, Any] | str = action.action_input
+
+        # Find tool instance first to get metadata
+        tool_instance = self._find_tool_by_name(tool_name)
+        tool_metadata = self._get_tool_metadata(tool_instance) if tool_instance else {}
+
+        # Start tool log with tool metadata
+        tool_log = self._create_log(
+            label=f"CALL {tool_name}",
+            log_type=AgentLog.LogType.TOOL_CALL,
+            status=AgentLog.LogStatus.START,
+            data={
+                "tool_name": tool_name,
+                "tool_args": tool_args,
+            },
+            parent_id=round_log.id,
+            extra_metadata=tool_metadata,
+        )
+        yield tool_log
+
+        if not tool_instance:
+            # Finish tool log with error
+            yield self._finish_log(
+                tool_log,
+                data={
+                    **tool_log.data,
+                    "error": f"Tool {tool_name} not found",
+                },
+            )
+            return f"Tool {tool_name} not found", []
+
+        # Ensure tool_args is a dict
+        tool_args_dict: dict[str, Any]
+        if isinstance(tool_args, str):
+            try:
+                tool_args_dict = json.loads(tool_args)
+            except json.JSONDecodeError:
+                tool_args_dict = {"input": tool_args}
+        elif not isinstance(tool_args, dict):
+            tool_args_dict = {"input": str(tool_args)}
+        else:
+            tool_args_dict = tool_args
+
+        # Invoke tool using base class method with error handling
+        try:
+            response_content, tool_files, tool_invoke_meta = self._invoke_tool(tool_instance, tool_args_dict, tool_name)
+
+            # Finish tool log
+            yield self._finish_log(
+                tool_log,
+                data={
+                    **tool_log.data,
+                    "output": response_content,
+                    "files": len(tool_files),
+                    "meta": tool_invoke_meta.to_dict() if tool_invoke_meta else None,
+                },
+            )
+
+            return response_content or "Tool executed successfully", tool_files
+        except Exception as e:
+            # Tool invocation failed, yield error log
+            error_message = str(e)
+            tool_log.status = AgentLog.LogStatus.ERROR
+            tool_log.error = error_message
+            tool_log.data = {
+                **tool_log.data,
+                "error": error_message,
+            }
+            yield tool_log
+
+            return f"Tool execution failed: {error_message}", []

api/core/agent/patterns/strategy_factory.py

@@ -0,0 +1,108 @@
+"""Strategy factory for creating agent strategies."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from core.agent.entities import AgentEntity, ExecutionContext
+from core.file.models import File
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.model_entities import ModelFeature
+
+from .base import AgentPattern, ToolInvokeHook
+from .function_call import FunctionCallStrategy
+from .react import ReActStrategy
+
+if TYPE_CHECKING:
+    from core.tools.__base.tool import Tool
+
+
+class StrategyFactory:
+    """Factory for creating agent strategies based on model features."""
+
+    # Tool calling related features
+    TOOL_CALL_FEATURES = {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL}
+
+    @staticmethod
+    def create_strategy(
+        model_features: list[ModelFeature],
+        model_instance: ModelInstance,
+        context: ExecutionContext,
+        tools: list[Tool],
+        files: list[File],
+        max_iterations: int = 10,
+        workflow_call_depth: int = 0,
+        agent_strategy: AgentEntity.Strategy | None = None,
+        tool_invoke_hook: ToolInvokeHook | None = None,
+        instruction: str = "",
+    ) -> AgentPattern:
+        """
+        Create an appropriate strategy based on model features.
+
+        Args:
+            model_features: List of model features/capabilities
+            model_instance: Model instance to use
+            context: Execution context containing trace/audit information
+            tools: Available tools
+            files: Available files
+            max_iterations: Maximum iterations for the strategy
+            workflow_call_depth: Depth of workflow calls
+            agent_strategy: Optional explicit strategy override
+            tool_invoke_hook: Optional hook for custom tool invocation (e.g., agent_invoke)
+            instruction: Optional instruction for ReAct strategy
+
+        Returns:
+            AgentStrategy instance
+        """
+
+        # If explicit strategy is provided and it's Function Calling, try to use it if supported
+        if agent_strategy == AgentEntity.Strategy.FUNCTION_CALLING:
+            if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
+                return FunctionCallStrategy(
+                    model_instance=model_instance,
+                    context=context,
+                    tools=tools,
+                    files=files,
+                    max_iterations=max_iterations,
+                    workflow_call_depth=workflow_call_depth,
+                    tool_invoke_hook=tool_invoke_hook,
+                )
+            # Fallback to ReAct if FC is requested but not supported
+
+        # If explicit strategy is Chain of Thought (ReAct)
+        if agent_strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
+            return ReActStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+                instruction=instruction,
+            )
+
+        # Default auto-selection logic
+        if set(model_features) & StrategyFactory.TOOL_CALL_FEATURES:
+            # Model supports native function calling
+            return FunctionCallStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+            )
+        else:
+            # Use ReAct strategy for models without function calling
+            return ReActStrategy(
+                model_instance=model_instance,
+                context=context,
+                tools=tools,
+                files=files,
+                max_iterations=max_iterations,
+                workflow_call_depth=workflow_call_depth,
+                tool_invoke_hook=tool_invoke_hook,
+                instruction=instruction,
+            )

api/core/app/apps/advanced_chat/app_generator.py

@@ -15,6 +15,7 @@ from sqlalchemy.orm import Session, sessionmaker
 import contexts
 from configs import dify_config
 from constants import UUID_NIL
+from core.app.layers.sandbox_layer import SandboxLayer
 
 if TYPE_CHECKING:
     from controllers.console.app.workflow import LoopNodeRunPayload
@@ -35,6 +36,7 @@ from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
 from core.repositories import DifyCoreRepositoryFactory
+from core.sandbox import Sandbox
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.repositories.draft_variable_repository import (
     DraftVariableSaverFactory,
@@ -49,7 +51,10 @@ from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, Conversation, EndUser, Message, Workflow, WorkflowNodeExecutionTriggeredFrom
 from models.base import Base
 from models.enums import WorkflowRunTriggeredFrom
+from models.workflow_features import WorkflowFeatures
 from services.conversation_service import ConversationService
+from services.sandbox.sandbox_provider_service import SandboxProviderService
+from services.sandbox.sandbox_service import SandboxService
 from services.workflow_draft_variable_service import (
     DraftVarLoader,
     WorkflowDraftVariableService,
@@ -495,6 +500,30 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                     state_owner_user_id=pause_state_config.state_owner_user_id,
                 )
             )
+        sandbox: Sandbox | None = None
+        if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
+            sandbox_provider = SandboxProviderService.get_sandbox_provider(
+                application_generate_entity.app_config.tenant_id
+            )
+            try:
+                if workflow.version == Workflow.VERSION_DRAFT:
+                    sandbox = SandboxService.create_draft(
+                        tenant_id=application_generate_entity.app_config.tenant_id,
+                        app_id=application_generate_entity.app_config.app_id,
+                        user_id=user.id,
+                        sandbox_provider=sandbox_provider,
+                    )
+                else:
+                    sandbox = SandboxService.create(
+                        tenant_id=application_generate_entity.app_config.tenant_id,
+                        app_id=application_generate_entity.app_config.app_id,
+                        user_id=user.id,
+                        sandbox_id=conversation.id,
+                        sandbox_provider=sandbox_provider,
+                    )
+                graph_layers.append(SandboxLayer(sandbox))
+            except ValueError as e:
+                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
 
         # new thread with request context and contextvars
         context = contextvars.copy_context()
@@ -513,6 +542,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 "workflow_node_execution_repository": workflow_node_execution_repository,
                 "graph_engine_layers": tuple(graph_layers),
                 "graph_runtime_state": graph_runtime_state,
+                "sandbox": sandbox,
             },
         )
 
@@ -560,6 +590,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
         graph_runtime_state: GraphRuntimeState | None = None,
+        sandbox: Sandbox | None = None,
     ):
         """
         Generate worker in a new thread.
@@ -618,6 +649,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 workflow_execution_repository=workflow_execution_repository,
                 workflow_node_execution_repository=workflow_node_execution_repository,
                 graph_engine_layers=graph_engine_layers,
+                sandbox=sandbox,
                 graph_runtime_state=graph_runtime_state,
             )
 

api/core/app/apps/advanced_chat/app_runner.py

@@ -25,6 +25,7 @@ from core.app.workflow.layers.persistence import PersistenceWorkflowInfo, Workfl
 from core.db.session_factory import session_factory
 from core.moderation.base import ModerationError
 from core.moderation.input_moderation import InputModeration
+from core.sandbox import Sandbox
 from core.variables.variables import Variable
 from core.workflow.enums import WorkflowType
 from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
@@ -66,6 +67,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
+        sandbox: Sandbox | None = None,
         graph_runtime_state: GraphRuntimeState | None = None,
     ):
         super().__init__(
@@ -83,6 +85,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         self._app = app
         self._workflow_execution_repository = workflow_execution_repository
         self._workflow_node_execution_repository = workflow_node_execution_repository
+        self._sandbox = sandbox
         self._resume_graph_runtime_state = graph_runtime_state
 
     @trace_span(WorkflowAppRunnerHandler)
@@ -116,6 +119,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
 
         if resume_state is not None:
             graph_runtime_state = resume_state
+            graph_runtime_state.set_sandbox(self._sandbox)
             variable_pool = graph_runtime_state.variable_pool
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,
@@ -172,6 +176,8 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
 
             # init graph
             graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.time())
+            graph_runtime_state.set_sandbox(self._sandbox)
+
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,
                 graph_runtime_state=graph_runtime_state,

api/core/app/apps/advanced_chat/generate_response_converter.py

@@ -82,7 +82,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -110,7 +110,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -120,6 +120,6 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -4,6 +4,7 @@ import re
 import time
 from collections.abc import Callable, Generator, Mapping
 from contextlib import contextmanager
+from dataclasses import dataclass, field
 from threading import Thread
 from typing import Any, Union
 
@@ -19,6 +20,7 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
 )
 from core.app.entities.queue_entities import (
+    ChunkType,
     MessageQueueMessage,
     QueueAdvancedChatMessageEndEvent,
     QueueAgentLogEvent,
@@ -75,7 +77,7 @@ from core.workflow.runtime import GraphRuntimeState
 from core.workflow.system_variable import SystemVariable
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models import Account, Conversation, EndUser, Message, MessageFile
+from models import Account, Conversation, EndUser, LLMGenerationDetail, Message, MessageFile
 from models.enums import CreatorUserRole, MessageStatus
 from models.execution_extra_content import HumanInputContent
 from models.workflow import Workflow
@@ -83,6 +85,127 @@ from models.workflow import Workflow
 logger = logging.getLogger(__name__)
 
 
+@dataclass
+class StreamEventBuffer:
+    """
+    Buffer for recording stream events in order to reconstruct the generation sequence.
+    Records the exact order of text chunks, thoughts, and tool calls as they stream.
+    """
+
+    # Accumulated reasoning content (each thought block is a separate element)
+    reasoning_content: list[str] = field(default_factory=list)
+    # Current reasoning buffer (accumulates until we see a different event type)
+    _current_reasoning: str = ""
+    # Tool calls with their details
+    tool_calls: list[dict] = field(default_factory=list)
+    # Tool call ID to index mapping for updating results
+    _tool_call_id_map: dict[str, int] = field(default_factory=dict)
+    # Sequence of events in stream order
+    sequence: list[dict] = field(default_factory=list)
+    # Current position in answer text
+    _content_position: int = 0
+    # Track last event type to detect transitions
+    _last_event_type: str | None = None
+
+    def _flush_current_reasoning(self) -> None:
+        """Flush accumulated reasoning to the list and add to sequence."""
+        if self._current_reasoning.strip():
+            self.reasoning_content.append(self._current_reasoning.strip())
+            self.sequence.append({"type": "reasoning", "index": len(self.reasoning_content) - 1})
+            self._current_reasoning = ""
+
+    def record_text_chunk(self, text: str) -> None:
+        """Record a text chunk event."""
+        if not text:
+            return
+
+        # Flush any pending reasoning first
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+        text_len = len(text)
+        start_pos = self._content_position
+
+        # If last event was also content, extend it; otherwise create new
+        if self.sequence and self.sequence[-1].get("type") == "content":
+            self.sequence[-1]["end"] = start_pos + text_len
+        else:
+            self.sequence.append({"type": "content", "start": start_pos, "end": start_pos + text_len})
+
+        self._content_position += text_len
+        self._last_event_type = "content"
+
+    def record_thought_chunk(self, text: str) -> None:
+        """Record a thought/reasoning chunk event."""
+        if not text:
+            return
+
+        # Accumulate thought content
+        self._current_reasoning += text
+        self._last_event_type = "thought"
+
+    def record_tool_call(
+        self,
+        tool_call_id: str,
+        tool_name: str,
+        tool_arguments: str,
+        tool_icon: str | dict | None = None,
+        tool_icon_dark: str | dict | None = None,
+    ) -> None:
+        """Record a tool call event."""
+        if not tool_call_id:
+            return
+
+        # Flush any pending reasoning first
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+        # Check if this tool call already exists (we might get multiple chunks)
+        if tool_call_id in self._tool_call_id_map:
+            idx = self._tool_call_id_map[tool_call_id]
+            # Update arguments if provided
+            if tool_arguments:
+                self.tool_calls[idx]["arguments"] = tool_arguments
+        else:
+            # New tool call
+            tool_call = {
+                "id": tool_call_id or "",
+                "name": tool_name or "",
+                "arguments": tool_arguments or "",
+                "result": "",
+                "elapsed_time": None,
+                "icon": tool_icon,
+                "icon_dark": tool_icon_dark,
+            }
+            self.tool_calls.append(tool_call)
+            idx = len(self.tool_calls) - 1
+            self._tool_call_id_map[tool_call_id] = idx
+            self.sequence.append({"type": "tool_call", "index": idx})
+
+        self._last_event_type = "tool_call"
+
+    def record_tool_result(self, tool_call_id: str, result: str, tool_elapsed_time: float | None = None) -> None:
+        """Record a tool result event (update existing tool call)."""
+        if not tool_call_id:
+            return
+        if tool_call_id in self._tool_call_id_map:
+            idx = self._tool_call_id_map[tool_call_id]
+            self.tool_calls[idx]["result"] = result
+            self.tool_calls[idx]["elapsed_time"] = tool_elapsed_time
+            # Remove from map after result is recorded, so that subsequent calls
+            # with the same tool_call_id are treated as new tool calls
+            del self._tool_call_id_map[tool_call_id]
+
+    def finalize(self) -> None:
+        """Finalize the buffer, flushing any pending data."""
+        if self._last_event_type == "thought":
+            self._flush_current_reasoning()
+
+    def has_data(self) -> bool:
+        """Check if there's any meaningful data recorded."""
+        return bool(self.reasoning_content or self.tool_calls or self.sequence)
+
+
 class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
     """
     AdvancedChatAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
@@ -152,6 +275,8 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         self._workflow_run_id: str = ""
         self._draft_var_saver_factory = draft_var_saver_factory
         self._graph_runtime_state: GraphRuntimeState | None = None
+        # Stream event buffer for recording generation sequence
+        self._stream_buffer = StreamEventBuffer()
         self._message_saved_on_pause = False
         self._seed_graph_runtime_state_from_queue_manager()
 
@@ -397,7 +522,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         queue_message: Union[WorkflowQueueMessage, MessageQueueMessage] | None = None,
         **kwargs,
     ) -> Generator[StreamResponse, None, None]:
-        """Handle text chunk events."""
+        """Handle text chunk events and record to stream buffer for sequence reconstruction."""
         delta_text = event.text
         if delta_text is None:
             return
@@ -419,9 +544,53 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        self._task_state.answer += delta_text
+        tool_call = event.tool_call
+        tool_result = event.tool_result
+        tool_payload = tool_call or tool_result
+        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else ""
+        tool_name = tool_payload.name if tool_payload and tool_payload.name else ""
+        tool_arguments = tool_call.arguments if tool_call and tool_call.arguments else ""
+        tool_files = tool_result.files if tool_result else []
+        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
+        tool_icon = tool_payload.icon if tool_payload else None
+        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
+        # Record stream event based on chunk type
+        chunk_type = event.chunk_type or ChunkType.TEXT
+        match chunk_type:
+            case ChunkType.TEXT:
+                self._stream_buffer.record_text_chunk(delta_text)
+                self._task_state.answer += delta_text
+            case ChunkType.THOUGHT:
+                # Reasoning should not be part of final answer text
+                self._stream_buffer.record_thought_chunk(delta_text)
+            case ChunkType.TOOL_CALL:
+                self._stream_buffer.record_tool_call(
+                    tool_call_id=tool_call_id,
+                    tool_name=tool_name,
+                    tool_arguments=tool_arguments,
+                    tool_icon=tool_icon,
+                    tool_icon_dark=tool_icon_dark,
+                )
+            case ChunkType.TOOL_RESULT:
+                self._stream_buffer.record_tool_result(
+                    tool_call_id=tool_call_id,
+                    result=delta_text,
+                    tool_elapsed_time=tool_elapsed_time,
+                )
+            case _:
+                pass
         yield self._message_cycle_manager.message_to_stream_response(
-            answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
+            answer=delta_text,
+            message_id=self._message_id,
+            from_variable_selector=event.from_variable_selector,
+            chunk_type=event.chunk_type.value if event.chunk_type else None,
+            tool_call_id=tool_call_id or None,
+            tool_name=tool_name or None,
+            tool_arguments=tool_arguments or None,
+            tool_files=tool_files,
+            tool_elapsed_time=tool_elapsed_time,
+            tool_icon=tool_icon,
+            tool_icon_dark=tool_icon_dark,
         )
 
     def _handle_iteration_start_event(
@@ -889,6 +1058,7 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
 
         # If there are assistant files, remove markdown image links from answer
         answer_text = self._task_state.answer
+        answer_text = self._strip_think_blocks(answer_text)
         if self._recorded_files:
             # Remove markdown image links since we're storing files separately
             answer_text = re.sub(r"!\[.*?\]\(.*?\)", "", answer_text).strip()
@@ -940,6 +1110,54 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         ]
         session.add_all(message_files)
 
+        # Save generation detail (reasoning/tool calls/sequence) from stream buffer
+        self._save_generation_detail(session=session, message=message)
+
+    @staticmethod
+    def _strip_think_blocks(text: str) -> str:
+        """Remove <think>...</think> blocks (including their content) from text."""
+        if not text or "<think" not in text.lower():
+            return text
+
+        clean_text = re.sub(r"<think[^>]*>.*?</think>", "", text, flags=re.IGNORECASE | re.DOTALL)
+        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
+        return clean_text
+
+    def _save_generation_detail(self, *, session: Session, message: Message) -> None:
+        """
+        Save LLM generation detail for Chatflow using stream event buffer.
+        The buffer records the exact order of events as they streamed,
+        allowing accurate reconstruction of the generation sequence.
+        """
+        # Finalize the stream buffer to flush any pending data
+        self._stream_buffer.finalize()
+
+        # Only save if there's meaningful data
+        if not self._stream_buffer.has_data():
+            return
+
+        reasoning_content = self._stream_buffer.reasoning_content
+        tool_calls = self._stream_buffer.tool_calls
+        sequence = self._stream_buffer.sequence
+
+        # Check if generation detail already exists for this message
+        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
+
+        if existing:
+            existing.reasoning_content = json.dumps(reasoning_content) if reasoning_content else None
+            existing.tool_calls = json.dumps(tool_calls) if tool_calls else None
+            existing.sequence = json.dumps(sequence) if sequence else None
+        else:
+            generation_detail = LLMGenerationDetail(
+                tenant_id=self._application_generate_entity.app_config.tenant_id,
+                app_id=self._application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                reasoning_content=json.dumps(reasoning_content) if reasoning_content else None,
+                tool_calls=json.dumps(tool_calls) if tool_calls else None,
+                sequence=json.dumps(sequence) if sequence else None,
+            )
+            session.add(generation_detail)
+
     def _seed_graph_runtime_state_from_queue_manager(self) -> None:
         """Bootstrap the cached runtime state from the queue manager when present."""
         candidate = self._base_task_pipeline.queue_manager.graph_runtime_state

api/core/app/apps/agent_chat/app_runner.py

@@ -3,10 +3,8 @@ from typing import cast
 
 from sqlalchemy import select
 
-from core.agent.cot_chat_agent_runner import CotChatAgentRunner
-from core.agent.cot_completion_agent_runner import CotCompletionAgentRunner
+from core.agent.agent_app_runner import AgentAppRunner
 from core.agent.entities import AgentEntity
-from core.agent.fc_agent_runner import FunctionCallAgentRunner
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.apps.base_app_runner import AppRunner
@@ -14,8 +12,7 @@ from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity
 from core.app.entities.queue_entities import QueueAnnotationReplyEvent
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
-from core.model_runtime.entities.llm_entities import LLMMode
-from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
+from core.model_runtime.entities.model_entities import ModelFeature
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.moderation.base import ModerationError
 from extensions.ext_database import db
@@ -194,22 +191,7 @@ class AgentChatAppRunner(AppRunner):
             raise ValueError("Message not found")
         db.session.close()
 
-        runner_cls: type[FunctionCallAgentRunner] | type[CotChatAgentRunner] | type[CotCompletionAgentRunner]
-        # start agent runner
-        if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
-            # check LLM mode
-            if model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT:
-                runner_cls = CotChatAgentRunner
-            elif model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.COMPLETION:
-                runner_cls = CotCompletionAgentRunner
-            else:
-                raise ValueError(f"Invalid LLM mode: {model_schema.model_properties.get(ModelPropertyKey.MODE)}")
-        elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
-            runner_cls = FunctionCallAgentRunner
-        else:
-            raise ValueError(f"Invalid agent strategy: {agent_entity.strategy}")
-
-        runner = runner_cls(
+        runner = AgentAppRunner(
             tenant_id=app_config.tenant_id,
             application_generate_entity=application_generate_entity,
             conversation=conversation_result,

api/core/app/apps/agent_chat/generate_response_converter.py

@@ -81,7 +81,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

api/core/app/apps/chat/generate_response_converter.py

@@ -81,7 +81,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

api/core/app/apps/common/workflow_response_converter.py

@@ -86,6 +86,8 @@ class _NodeSnapshot:
     """Empty string means the node is not executing inside an iteration."""
     loop_id: str = ""
     """Empty string means the node is not executing inside a loop."""
+    parent_node_id: str = ""
+    """Empty string means the node is not an nested node (extractor node)."""
 
 
 class WorkflowResponseConverter:
@@ -147,6 +149,7 @@ class WorkflowResponseConverter:
             start_at=event.start_at,
             iteration_id=event.in_iteration_id or "",
             loop_id=event.in_loop_id or "",
+            parent_node_id=event.in_parent_node_id or "",
         )
         node_execution_id = NodeExecutionId(event.node_execution_id)
         self._node_snapshots[node_execution_id] = snapshot
@@ -459,6 +462,7 @@ class WorkflowResponseConverter:
                 created_at=int(snapshot.start_at.timestamp()),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                parent_node_id=event.in_parent_node_id,
                 agent_strategy=event.agent_strategy,
             ),
         )
@@ -545,6 +549,7 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                parent_node_id=event.in_parent_node_id,
             ),
         )
 
@@ -594,6 +599,7 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
+                parent_node_id=event.in_parent_node_id,
                 retry_index=event.retry_index,
             ),
         )
@@ -844,7 +850,7 @@ class WorkflowResponseConverter:
             task_id=task_id,
             data=AgentLogStreamResponse.Data(
                 node_execution_id=event.node_execution_id,
-                id=event.id,
+                message_id=event.id,
                 parent_id=event.parent_id,
                 label=event.label,
                 error=event.error,

api/core/app/apps/completion/generate_response_converter.py

@@ -79,7 +79,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -106,7 +106,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
                 metadata = sub_stream_response_dict.get("metadata", {})
                 if not isinstance(metadata, dict):
                     metadata = {}
@@ -116,6 +116,6 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
 
             yield response_chunk

api/core/app/apps/pipeline/generate_response_converter.py

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(cast(dict, data))
             else:
-                response_chunk.update(sub_stream_response.model_dump())
+                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(cast(dict, sub_stream_response.to_ignore_detail_dict()))
             else:
-                response_chunk.update(sub_stream_response.model_dump())
+                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
             yield response_chunk

api/core/app/apps/workflow/app_generator.py

@@ -26,11 +26,13 @@ from core.app.apps.workflow.generate_task_pipeline import WorkflowAppGenerateTas
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import WorkflowAppBlockingResponse, WorkflowAppStreamResponse
 from core.app.layers.pause_state_persist_layer import PauseStateLayerConfig, PauseStatePersistenceLayer
+from core.app.layers.sandbox_layer import SandboxLayer
 from core.db.session_factory import session_factory
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.repositories import DifyCoreRepositoryFactory
+from core.sandbox.sandbox import Sandbox
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
@@ -44,6 +46,9 @@ from models.account import Account
 from models.enums import WorkflowRunTriggeredFrom
 from models.model import App, EndUser
 from models.workflow import Workflow, WorkflowNodeExecutionTriggeredFrom
+from models.workflow_features import WorkflowFeatures
+from services.sandbox.sandbox_provider_service import SandboxProviderService
+from services.sandbox.sandbox_service import SandboxService
 from services.workflow_draft_variable_service import DraftVarLoader, WorkflowDraftVariableService
 
 if TYPE_CHECKING:
@@ -311,6 +316,31 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 )
             )
 
+        sandbox: Sandbox | None = None
+        if workflow.get_feature(WorkflowFeatures.SANDBOX).enabled:
+            sandbox_provider = SandboxProviderService.get_sandbox_provider(
+                application_generate_entity.app_config.tenant_id
+            )
+            try:
+                if workflow.version == Workflow.VERSION_DRAFT:
+                    sandbox = SandboxService.create_draft(
+                        tenant_id=application_generate_entity.app_config.tenant_id,
+                        app_id=application_generate_entity.app_config.app_id,
+                        user_id=user.id,
+                        sandbox_provider=sandbox_provider,
+                    )
+                else:
+                    sandbox = SandboxService.create(
+                        tenant_id=application_generate_entity.app_config.tenant_id,
+                        app_id=application_generate_entity.app_config.app_id,
+                        user_id=user.id,
+                        sandbox_id=application_generate_entity.workflow_execution_id,
+                        sandbox_provider=sandbox_provider,
+                    )
+                graph_layers.append(SandboxLayer(sandbox=sandbox))
+            except ValueError as e:
+                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
+
         # new thread with request context and contextvars
         context = contextvars.copy_context()
 
@@ -330,6 +360,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 "workflow_node_execution_repository": workflow_node_execution_repository,
                 "graph_engine_layers": tuple(graph_layers),
                 "graph_runtime_state": graph_runtime_state,
+                "sandbox": sandbox,
             },
         )
 
@@ -528,6 +559,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         root_node_id: str | None = None,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
         graph_runtime_state: GraphRuntimeState | None = None,
+        sandbox: Sandbox | None = None,
     ) -> None:
         """
         Generate worker in a new thread.
@@ -573,6 +605,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 workflow_node_execution_repository=workflow_node_execution_repository,
                 root_node_id=root_node_id,
                 graph_engine_layers=graph_engine_layers,
+                sandbox=sandbox,
                 graph_runtime_state=graph_runtime_state,
             )
 

api/core/app/apps/workflow/app_runner.py

@@ -8,6 +8,7 @@ from core.app.apps.workflow.app_config_manager import WorkflowAppConfig
 from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.workflow.layers.persistence import PersistenceWorkflowInfo, WorkflowPersistenceLayer
+from core.sandbox import Sandbox
 from core.workflow.enums import WorkflowType
 from core.workflow.graph_engine.command_channels.redis_channel import RedisChannel
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
@@ -42,6 +43,7 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         workflow_execution_repository: WorkflowExecutionRepository,
         workflow_node_execution_repository: WorkflowNodeExecutionRepository,
         graph_engine_layers: Sequence[GraphEngineLayer] = (),
+        sandbox: Sandbox | None = None,
         graph_runtime_state: GraphRuntimeState | None = None,
     ):
         super().__init__(
@@ -56,6 +58,7 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
         self._root_node_id = root_node_id
         self._workflow_execution_repository = workflow_execution_repository
         self._workflow_node_execution_repository = workflow_node_execution_repository
+        self._sandbox = sandbox
         self._resume_graph_runtime_state = graph_runtime_state
 
     @trace_span(WorkflowAppRunnerHandler)
@@ -75,6 +78,7 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
 
         if resume_state is not None:
             graph_runtime_state = resume_state
+            graph_runtime_state.set_sandbox(self._sandbox)
             variable_pool = graph_runtime_state.variable_pool
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,
@@ -112,6 +116,9 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
             )
 
             graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
+            graph_runtime_state.set_sandbox(self._sandbox)
+
+            # init graph
             graph = self._init_graph(
                 graph_config=self._workflow.graph_dict,
                 graph_runtime_state=graph_runtime_state,

api/core/app/apps/workflow/generate_response_converter.py

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json"))
+                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
             yield response_chunk

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -13,6 +13,7 @@ from core.app.apps.common.workflow_response_converter import WorkflowResponseCon
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.queue_entities import (
     AppQueueEvent,
+    ChunkType,
     MessageQueueMessage,
     QueueAgentLogEvent,
     QueueErrorEvent,
@@ -524,11 +525,33 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         if delta_text is None:
             return
 
+        tool_call = event.tool_call
+        tool_result = event.tool_result
+        tool_payload = tool_call or tool_result
+        tool_call_id = tool_payload.id if tool_payload and tool_payload.id else None
+        tool_name = tool_payload.name if tool_payload and tool_payload.name else None
+        tool_arguments = tool_call.arguments if tool_call else None
+        tool_elapsed_time = tool_result.elapsed_time if tool_result else None
+        tool_files = tool_result.files if tool_result else []
+        tool_icon = tool_payload.icon if tool_payload else None
+        tool_icon_dark = tool_payload.icon_dark if tool_payload else None
+
         # only publish tts message at text chunk streaming
         if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-        yield self._text_chunk_to_stream_response(delta_text, from_variable_selector=event.from_variable_selector)
+        yield self._text_chunk_to_stream_response(
+            text=delta_text,
+            from_variable_selector=event.from_variable_selector,
+            chunk_type=event.chunk_type,
+            tool_call_id=tool_call_id,
+            tool_name=tool_name,
+            tool_arguments=tool_arguments,
+            tool_files=tool_files,
+            tool_elapsed_time=tool_elapsed_time,
+            tool_icon=tool_icon,
+            tool_icon_dark=tool_icon_dark,
+        )
 
     def _handle_agent_log_event(self, event: QueueAgentLogEvent, **kwargs) -> Generator[StreamResponse, None, None]:
         """Handle agent log events."""
@@ -713,16 +736,61 @@ class WorkflowAppGenerateTaskPipeline(GraphRuntimeStateSupport):
         session.add(workflow_app_log)
 
     def _text_chunk_to_stream_response(
-        self, text: str, from_variable_selector: list[str] | None = None
+        self,
+        text: str,
+        from_variable_selector: list[str] | None = None,
+        chunk_type: ChunkType | None = None,
+        tool_call_id: str | None = None,
+        tool_name: str | None = None,
+        tool_arguments: str | None = None,
+        tool_files: list[str] | None = None,
+        tool_error: str | None = None,
+        tool_elapsed_time: float | None = None,
+        tool_icon: str | dict | None = None,
+        tool_icon_dark: str | dict | None = None,
     ) -> TextChunkStreamResponse:
         """
         Handle completed event.
         :param text: text
         :return:
         """
+        from core.app.entities.task_entities import ChunkType as ResponseChunkType
+
+        response_chunk_type = ResponseChunkType(chunk_type.value) if chunk_type else ResponseChunkType.TEXT
+
+        data = TextChunkStreamResponse.Data(
+            text=text,
+            from_variable_selector=from_variable_selector,
+            chunk_type=response_chunk_type,
+        )
+
+        if response_chunk_type == ResponseChunkType.TOOL_CALL:
+            data = data.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+        elif response_chunk_type == ResponseChunkType.TOOL_RESULT:
+            data = data.model_copy(
+                update={
+                    "tool_call_id": tool_call_id,
+                    "tool_name": tool_name,
+                    "tool_arguments": tool_arguments,
+                    "tool_files": tool_files,
+                    "tool_error": tool_error,
+                    "tool_elapsed_time": tool_elapsed_time,
+                    "tool_icon": tool_icon,
+                    "tool_icon_dark": tool_icon_dark,
+                }
+            )
+
         response = TextChunkStreamResponse(
             task_id=self._application_generate_entity.task_id,
-            data=TextChunkStreamResponse.Data(text=text, from_variable_selector=from_variable_selector),
+            data=data,
         )
 
         return response

api/core/app/apps/workflow_app_runner.py

@@ -396,6 +396,7 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_parent_node_id=event.in_parent_node_id,
                     inputs=inputs,
                     process_data=process_data,
                     outputs=outputs,
@@ -416,6 +417,7 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_parent_node_id=event.in_parent_node_id,
                     agent_strategy=event.agent_strategy,
                     provider_type=event.provider_type,
                     provider_id=event.provider_id,
@@ -439,6 +441,7 @@ class WorkflowBasedAppRunner:
                     execution_metadata=execution_metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunFailedEvent):
@@ -455,6 +458,7 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunExceptionEvent):
@@ -471,15 +475,25 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunStreamChunkEvent):
+            from core.app.entities.queue_entities import ChunkType as QueueChunkType
+
+            if event.is_final and not event.chunk:
+                return
+
             self._publish_event(
                 QueueTextChunkEvent(
                     text=event.chunk,
                     from_variable_selector=list(event.selector),
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    chunk_type=QueueChunkType(event.chunk_type.value),
+                    tool_call=event.tool_call,
+                    tool_result=event.tool_result,
+                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunRetrieverResourceEvent):
@@ -488,6 +502,7 @@ class WorkflowBasedAppRunner:
                     retriever_resources=event.retriever_resources,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
+                    in_parent_node_id=event.in_parent_node_id,
                 )
             )
         elif isinstance(event, NodeRunAgentLogEvent):

api/core/app/entities/app_asset_entities.py

@@ -0,0 +1,349 @@
+from __future__ import annotations
+
+import os
+from collections import defaultdict
+from collections.abc import Generator
+from enum import StrEnum
+
+from pydantic import BaseModel, Field
+
+
+class AssetNodeType(StrEnum):
+    FILE = "file"
+    FOLDER = "folder"
+
+
+class AppAssetNode(BaseModel):
+    id: str = Field(description="Unique identifier for the node")
+    node_type: AssetNodeType = Field(description="Type of node: file or folder")
+    name: str = Field(description="Name of the file or folder")
+    parent_id: str | None = Field(default=None, description="Parent folder ID, None for root level")
+    order: int = Field(default=0, description="Sort order within parent folder, lower values first")
+    extension: str = Field(default="", description="File extension without dot, empty for folders")
+    size: int = Field(default=0, description="File size in bytes, 0 for folders")
+
+    @classmethod
+    def create_folder(cls, node_id: str, name: str, parent_id: str | None = None) -> AppAssetNode:
+        return cls(id=node_id, node_type=AssetNodeType.FOLDER, name=name, parent_id=parent_id)
+
+    @classmethod
+    def create_file(cls, node_id: str, name: str, parent_id: str | None = None, size: int = 0) -> AppAssetNode:
+        return cls(
+            id=node_id,
+            node_type=AssetNodeType.FILE,
+            name=name,
+            parent_id=parent_id,
+            extension=name.rsplit(".", 1)[-1] if "." in name else "",
+            size=size,
+        )
+
+
+class AppAssetNodeView(BaseModel):
+    id: str = Field(description="Unique identifier for the node")
+    node_type: str = Field(description="Type of node: 'file' or 'folder'")
+    name: str = Field(description="Name of the file or folder")
+    path: str = Field(description="Full path from root, e.g. '/folder/file.txt'")
+    extension: str = Field(default="", description="File extension without dot")
+    size: int = Field(default=0, description="File size in bytes")
+    children: list[AppAssetNodeView] = Field(default_factory=list, description="Child nodes for folders")
+
+
+class BatchUploadNode(BaseModel):
+    """Structure for batch upload_url tree nodes, used for both input and output."""
+
+    name: str
+    node_type: AssetNodeType
+    size: int = 0
+    children: list[BatchUploadNode] = []
+    id: str | None = None
+    upload_url: str | None = None
+
+    def to_app_asset_nodes(self, parent_id: str | None = None) -> list[AppAssetNode]:
+        """
+        Generate IDs when missing and convert to AppAssetNode list.
+        Mutates self to set id field when it is not set.
+        """
+        from uuid import uuid4
+
+        self.id = self.id or str(uuid4())
+        nodes: list[AppAssetNode] = []
+
+        if self.node_type == AssetNodeType.FOLDER:
+            nodes.append(AppAssetNode.create_folder(self.id, self.name, parent_id))
+            for child in self.children:
+                nodes.extend(child.to_app_asset_nodes(self.id))
+        else:
+            nodes.append(AppAssetNode.create_file(self.id, self.name, parent_id, self.size))
+
+        return nodes
+
+
+class TreeNodeNotFoundError(Exception):
+    """Tree internal: node not found"""
+
+    pass
+
+
+class TreeParentNotFoundError(Exception):
+    """Tree internal: parent folder not found"""
+
+    pass
+
+
+class TreePathConflictError(Exception):
+    """Tree internal: path already exists"""
+
+    pass
+
+
+class AppAssetFileTree(BaseModel):
+    """
+    File tree structure for app assets using adjacency list pattern.
+
+    Design:
+    - Storage: Flat list with parent_id references (adjacency list)
+    - Path: Computed dynamically via get_path(), not stored
+    - Order: Integer field for user-defined sorting within each folder
+    - API response: transform() builds nested tree with computed paths
+
+    Why adjacency list over nested tree or materialized path:
+    - Simpler CRUD: move/rename only updates one node's parent_id
+    - No path cascade: renaming parent doesn't require updating all descendants
+    - JSON-friendly: flat list serializes cleanly to database JSON column
+    - Trade-off: path lookup is O(depth), acceptable for typical file trees
+    """
+
+    nodes: list[AppAssetNode] = Field(default_factory=list, description="Flat list of all nodes in the tree")
+
+    def ensure_unique_name(
+        self,
+        parent_id: str | None,
+        name: str,
+        *,
+        is_file: bool,
+        extra_taken: set[str] | None = None,
+    ) -> str:
+        """
+        Return a sibling-unique name by appending numeric suffixes when needed.
+
+        The suffix format is " <n>" (e.g. "report 1", "report 2"). For files,
+        the suffix is inserted before the extension.
+        """
+        taken = extra_taken or set()
+        if not self.has_child_named(parent_id, name) and name not in taken:
+            return name
+        suffix_index = 1
+        while True:
+            candidate = self._apply_name_suffix(name, suffix_index, is_file=is_file)
+            if not self.has_child_named(parent_id, candidate) and candidate not in taken:
+                return candidate
+            suffix_index += 1
+
+    @staticmethod
+    def _apply_name_suffix(name: str, suffix_index: int, *, is_file: bool) -> str:
+        if not is_file:
+            return f"{name} {suffix_index}"
+        stem, extension = os.path.splitext(name)
+        return f"{stem} {suffix_index}{extension}"
+
+    def get(self, node_id: str) -> AppAssetNode | None:
+        return next((n for n in self.nodes if n.id == node_id), None)
+
+    def get_children(self, parent_id: str | None) -> list[AppAssetNode]:
+        return [n for n in self.nodes if n.parent_id == parent_id]
+
+    def has_child_named(self, parent_id: str | None, name: str) -> bool:
+        return any(n.name == name and n.parent_id == parent_id for n in self.nodes)
+
+    def get_path(self, node_id: str) -> str:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+        parts: list[str] = []
+        current: AppAssetNode | None = node
+        while current:
+            parts.append(current.name)
+            current = self.get(current.parent_id) if current.parent_id else None
+        return "/".join(reversed(parts))
+
+    def relative_path(self, a: AppAssetNode, b: AppAssetNode) -> str:
+        """
+        Calculate relative path from node a to node b for Markdown references.
+        Path is computed from a's parent directory (where the file resides).
+
+        Examples:
+            /foo/a.md -> /foo/b.md         => ./b.md
+            /foo/a.md -> /foo/sub/b.md     => ./sub/b.md
+            /foo/sub/a.md -> /foo/b.md     => ../b.md
+            /foo/sub/deep/a.md -> /foo/b.md => ../../b.md
+        """
+
+        def get_ancestor_ids(node_id: str | None) -> list[str]:
+            chain: list[str] = []
+            current_id = node_id
+            while current_id:
+                chain.append(current_id)
+                node = self.get(current_id)
+                current_id = node.parent_id if node else None
+            return chain
+
+        a_dir_ancestors = get_ancestor_ids(a.parent_id)
+        b_ancestors = [b.id] + get_ancestor_ids(b.parent_id)
+        a_dir_set = set(a_dir_ancestors)
+
+        lca_id: str | None = None
+        lca_index_in_b = -1
+        for idx, ancestor_id in enumerate(b_ancestors):
+            if ancestor_id in a_dir_set or (a.parent_id is None and b_ancestors[idx:] == []):
+                lca_id = ancestor_id
+                lca_index_in_b = idx
+                break
+
+        if a.parent_id is None:
+            steps_up = 0
+            lca_index_in_b = len(b_ancestors)
+        elif lca_id is None:
+            steps_up = len(a_dir_ancestors)
+            lca_index_in_b = len(b_ancestors)
+        else:
+            steps_up = 0
+            for ancestor_id in a_dir_ancestors:
+                if ancestor_id == lca_id:
+                    break
+                steps_up += 1
+
+        path_down: list[str] = []
+        for i in range(lca_index_in_b - 1, -1, -1):
+            node = self.get(b_ancestors[i])
+            if node:
+                path_down.append(node.name)
+
+        if steps_up == 0:
+            return "./" + "/".join(path_down)
+
+        parts: list[str] = [".."] * steps_up + path_down
+        return "/".join(parts)
+
+    def get_descendant_ids(self, node_id: str) -> list[str]:
+        result: list[str] = []
+        stack = [node_id]
+        while stack:
+            current_id = stack.pop()
+            for child in self.nodes:
+                if child.parent_id == current_id:
+                    result.append(child.id)
+                    stack.append(child.id)
+        return result
+
+    def add(self, node: AppAssetNode) -> AppAssetNode:
+        if self.get(node.id):
+            raise TreePathConflictError(node.id)
+        if self.has_child_named(node.parent_id, node.name):
+            raise TreePathConflictError(node.name)
+        if node.parent_id:
+            parent = self.get(node.parent_id)
+            if not parent or parent.node_type != AssetNodeType.FOLDER:
+                raise TreeParentNotFoundError(node.parent_id)
+        siblings = self.get_children(node.parent_id)
+        node.order = max((s.order for s in siblings), default=-1) + 1
+        self.nodes.append(node)
+        return node
+
+    def update(self, node_id: str, size: int) -> AppAssetNode:
+        node = self.get(node_id)
+        if not node or node.node_type != AssetNodeType.FILE:
+            raise TreeNodeNotFoundError(node_id)
+        node.size = size
+        return node
+
+    def rename(self, node_id: str, new_name: str) -> AppAssetNode:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+        if node.name != new_name and self.has_child_named(node.parent_id, new_name):
+            raise TreePathConflictError(new_name)
+        node.name = new_name
+        if node.node_type == AssetNodeType.FILE:
+            node.extension = new_name.rsplit(".", 1)[-1] if "." in new_name else ""
+        return node
+
+    def move(self, node_id: str, new_parent_id: str | None) -> AppAssetNode:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+        if new_parent_id:
+            parent = self.get(new_parent_id)
+            if not parent or parent.node_type != AssetNodeType.FOLDER:
+                raise TreeParentNotFoundError(new_parent_id)
+        if self.has_child_named(new_parent_id, node.name):
+            raise TreePathConflictError(node.name)
+        node.parent_id = new_parent_id
+        siblings = self.get_children(new_parent_id)
+        node.order = max((s.order for s in siblings if s.id != node_id), default=-1) + 1
+        return node
+
+    def reorder(self, node_id: str, after_node_id: str | None) -> AppAssetNode:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+
+        siblings = sorted(self.get_children(node.parent_id), key=lambda x: x.order)
+        siblings = [s for s in siblings if s.id != node_id]
+
+        if after_node_id is None:
+            insert_idx = 0
+        else:
+            after_node = self.get(after_node_id)
+            if not after_node or after_node.parent_id != node.parent_id:
+                raise TreeNodeNotFoundError(after_node_id)
+            insert_idx = next((i for i, s in enumerate(siblings) if s.id == after_node_id), -1) + 1
+
+        siblings.insert(insert_idx, node)
+        for idx, sibling in enumerate(siblings):
+            sibling.order = idx
+
+        return node
+
+    def remove(self, node_id: str) -> list[str]:
+        node = self.get(node_id)
+        if not node:
+            raise TreeNodeNotFoundError(node_id)
+        ids_to_remove = [node_id] + self.get_descendant_ids(node_id)
+        self.nodes = [n for n in self.nodes if n.id not in ids_to_remove]
+        return ids_to_remove
+
+    def walk_files(self) -> Generator[AppAssetNode, None, None]:
+        return (n for n in self.nodes if n.node_type == AssetNodeType.FILE)
+
+    def transform(self) -> list[AppAssetNodeView]:
+        by_parent: dict[str | None, list[AppAssetNode]] = defaultdict(list)
+        for n in self.nodes:
+            by_parent[n.parent_id].append(n)
+
+        for children in by_parent.values():
+            children.sort(key=lambda x: x.order)
+
+        paths: dict[str, str] = {}
+        tree_views: dict[str, AppAssetNodeView] = {}
+
+        def build_view(node: AppAssetNode, parent_path: str) -> None:
+            path = f"{parent_path}/{node.name}"
+            paths[node.id] = path
+            child_views: list[AppAssetNodeView] = []
+            for child in by_parent.get(node.id, []):
+                build_view(child, path)
+                child_views.append(tree_views[child.id])
+            tree_views[node.id] = AppAssetNodeView(
+                id=node.id,
+                node_type=node.node_type.value,
+                name=node.name,
+                path=path,
+                extension=node.extension,
+                size=node.size,
+                children=child_views,
+            )
+
+        for root_node in by_parent.get(None, []):
+            build_view(root_node, "")
+
+        return [tree_views[n.id] for n in by_parent.get(None, [])]

api/core/app/entities/app_bundle_entities.py

@@ -0,0 +1,96 @@
+from __future__ import annotations
+
+import re
+from datetime import UTC, datetime
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from core.app.entities.app_asset_entities import AppAssetFileTree
+
+# Constants
+BUNDLE_DSL_FILENAME_PATTERN = re.compile(r"^[^/]+\.ya?ml$")
+BUNDLE_MAX_SIZE = 50 * 1024 * 1024  # 50MB
+MANIFEST_FILENAME = "manifest.json"
+MANIFEST_SCHEMA_VERSION = "1.0"
+
+
+# Exceptions
+class BundleFormatError(Exception):
+    """Raised when bundle format is invalid."""
+
+    pass
+
+
+class ZipSecurityError(Exception):
+    """Raised when zip file contains security violations."""
+
+    pass
+
+
+# Manifest DTOs
+class ManifestFileEntry(BaseModel):
+    """Maps node_id to file path in the bundle."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    node_id: str
+    path: str
+
+
+class ManifestIntegrity(BaseModel):
+    """Basic integrity check fields."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    file_count: int
+
+
+class ManifestAppAssets(BaseModel):
+    """App assets section containing the full tree."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    tree: AppAssetFileTree
+
+
+class BundleManifest(BaseModel):
+    """
+    Bundle manifest for app asset import/export.
+
+    Schema version 1.0:
+    - dsl_filename: DSL file name in bundle root (e.g. "my_app.yml")
+    - tree: Full AppAssetFileTree (files + folders) for 100% restoration including node IDs
+    - files: Explicit node_id -> path mapping for file nodes only
+    - integrity: Basic file_count validation
+    """
+
+    model_config = ConfigDict(extra="forbid")
+
+    schema_version: str = Field(default=MANIFEST_SCHEMA_VERSION)
+    generated_at: datetime = Field(default_factory=lambda: datetime.now(tz=UTC))
+    dsl_filename: str = Field(description="DSL file name in bundle root")
+    app_assets: ManifestAppAssets
+    files: list[ManifestFileEntry]
+    integrity: ManifestIntegrity
+
+    @property
+    def assets_prefix(self) -> str:
+        """Assets directory name (DSL filename without extension)."""
+        return self.dsl_filename.rsplit(".", 1)[0]
+
+    @classmethod
+    def from_tree(cls, tree: AppAssetFileTree, dsl_filename: str) -> BundleManifest:
+        """Build manifest from an AppAssetFileTree."""
+        files = [ManifestFileEntry(node_id=n.id, path=tree.get_path(n.id)) for n in tree.walk_files()]
+        return cls(
+            dsl_filename=dsl_filename,
+            app_assets=ManifestAppAssets(tree=tree),
+            files=files,
+            integrity=ManifestIntegrity(file_count=len(files)),
+        )
+
+
+# Export result
+class BundleExportResult(BaseModel):
+    download_url: str = Field(description="Temporary download URL for the ZIP")
+    filename: str = Field(description="Suggested filename for the ZIP")

api/core/app/entities/app_invoke_entities.py

@@ -36,6 +36,9 @@ class InvokeFrom(StrEnum):
     # this is used for plugin trigger and webhook trigger.
     TRIGGER = "trigger"
 
+    # AGENT indicates that this invocation is from an agent.
+    AGENT = "agent"
+
     # EXPLORE indicates that this invocation is from
     # the workflow (or chatflow) explore page.
     EXPLORE = "explore"

api/core/app/entities/llm_generation_entities.py

@@ -0,0 +1,72 @@
+"""
+LLM Generation Detail entities.
+
+Defines the structure for storing and transmitting LLM generation details
+including reasoning content, tool calls, and their sequence.
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class ContentSegment(BaseModel):
+    """Represents a content segment in the generation sequence."""
+
+    type: Literal["content"] = "content"
+    start: int = Field(..., description="Start position in the text")
+    end: int = Field(..., description="End position in the text")
+
+
+class ReasoningSegment(BaseModel):
+    """Represents a reasoning segment in the generation sequence."""
+
+    type: Literal["reasoning"] = "reasoning"
+    index: int = Field(..., description="Index into reasoning_content array")
+
+
+class ToolCallSegment(BaseModel):
+    """Represents a tool call segment in the generation sequence."""
+
+    type: Literal["tool_call"] = "tool_call"
+    index: int = Field(..., description="Index into tool_calls array")
+
+
+SequenceSegment = ContentSegment | ReasoningSegment | ToolCallSegment
+
+
+class ToolCallDetail(BaseModel):
+    """Represents a tool call with its arguments and result."""
+
+    id: str = Field(default="", description="Unique identifier for the tool call")
+    name: str = Field(..., description="Name of the tool")
+    arguments: str = Field(default="", description="JSON string of tool arguments")
+    result: str = Field(default="", description="Result from the tool execution")
+    elapsed_time: float | None = Field(default=None, description="Elapsed time in seconds")
+    icon: str | dict | None = Field(default=None, description="Icon of the tool")
+    icon_dark: str | dict | None = Field(default=None, description="Dark theme icon of the tool")
+
+
+class LLMGenerationDetailData(BaseModel):
+    """
+    Domain model for LLM generation detail.
+
+    Contains the structured data for reasoning content, tool calls,
+    and their display sequence.
+    """
+
+    reasoning_content: list[str] = Field(default_factory=list, description="List of reasoning segments")
+    tool_calls: list[ToolCallDetail] = Field(default_factory=list, description="List of tool call details")
+    sequence: list[SequenceSegment] = Field(default_factory=list, description="Display order of segments")
+
+    def is_empty(self) -> bool:
+        """Check if there's any meaningful generation detail."""
+        return not self.reasoning_content and not self.tool_calls
+
+    def to_response_dict(self) -> dict:
+        """Convert to dictionary for API response."""
+        return {
+            "reasoning_content": self.reasoning_content,
+            "tool_calls": [tc.model_dump() for tc in self.tool_calls],
+            "sequence": [seg.model_dump() for seg in self.sequence],
+        }

api/core/app/entities/queue_entities.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities import AgentNodeStrategyInit
+from core.workflow.entities import AgentNodeStrategyInit, ToolCall, ToolResult
 from core.workflow.entities.pause_reason import PauseReason
 from core.workflow.entities.workflow_start_reason import WorkflowStartReason
 from core.workflow.enums import WorkflowNodeExecutionMetadataKey
@@ -182,6 +182,17 @@ class QueueLoopCompletedEvent(AppQueueEvent):
     error: str | None = None
 
 
+class ChunkType(StrEnum):
+    """Stream chunk type for LLM-related events."""
+
+    TEXT = "text"  # Normal text streaming
+    TOOL_CALL = "tool_call"  # Tool call arguments streaming
+    TOOL_RESULT = "tool_result"  # Tool execution result
+    THOUGHT = "thought"  # Agent thinking process (ReAct)
+    THOUGHT_START = "thought_start"  # Agent thought start
+    THOUGHT_END = "thought_end"  # Agent thought end
+
+
 class QueueTextChunkEvent(AppQueueEvent):
     """
     QueueTextChunkEvent entity
@@ -195,6 +206,18 @@ class QueueTextChunkEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_parent_node_id: str | None = None
+    """parent node id if this is an extractor node event"""
+
+    # Extended fields for Agent/Tool streaming
+    chunk_type: ChunkType = ChunkType.TEXT
+    """type of the chunk"""
+
+    # Tool streaming payloads
+    tool_call: ToolCall | None = None
+    """structured tool call info"""
+    tool_result: ToolResult | None = None
+    """structured tool result info"""
 
 
 class QueueAgentMessageEvent(AppQueueEvent):
@@ -234,6 +257,8 @@ class QueueRetrieverResourcesEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_parent_node_id: str | None = None
+    """parent node id if this is an extractor node event"""
 
 
 class QueueAnnotationReplyEvent(AppQueueEvent):
@@ -313,6 +338,8 @@ class QueueNodeStartedEvent(AppQueueEvent):
     node_run_index: int = 1  # FIXME(-LAN-): may not used
     in_iteration_id: str | None = None
     in_loop_id: str | None = None
+    in_parent_node_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
     agent_strategy: AgentNodeStrategyInit | None = None
 
@@ -335,6 +362,8 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_parent_node_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -390,6 +419,8 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_parent_node_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -414,6 +445,8 @@ class QueueNodeFailedEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
+    in_parent_node_id: str | None = None
+    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)

api/core/app/entities/task_entities.py

@@ -119,6 +119,38 @@ class MessageStreamResponse(StreamResponse):
     answer: str
     from_variable_selector: list[str] | None = None
 
+    # Extended fields for Agent/Tool streaming (imported at runtime to avoid circular import)
+    chunk_type: str | None = None
+    """type of the chunk: text, tool_call, tool_result, thought"""
+
+    # Tool call fields (when chunk_type == "tool_call")
+    tool_call_id: str | None = None
+    """unique identifier for this tool call"""
+    tool_name: str | None = None
+    """name of the tool being called"""
+    tool_arguments: str | None = None
+    """accumulated tool arguments JSON"""
+
+    # Tool result fields (when chunk_type == "tool_result")
+    tool_files: list[str] | None = None
+    """file IDs produced by tool"""
+    tool_error: str | None = None
+    """error message if tool failed"""
+    tool_elapsed_time: float | None = None
+    """elapsed time spent executing the tool"""
+    tool_icon: str | dict | None = None
+    """icon of the tool"""
+    tool_icon_dark: str | dict | None = None
+    """dark theme icon of the tool"""
+
+    def model_dump(self, *args, **kwargs) -> dict[str, object]:
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump(*args, **kwargs)
+
+    def model_dump_json(self, *args, **kwargs) -> str:
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump_json(*args, **kwargs)
+
 
 class MessageAudioStreamResponse(StreamResponse):
     """
@@ -349,6 +381,7 @@ class NodeStartStreamResponse(StreamResponse):
         extras: dict[str, object] = Field(default_factory=dict)
         iteration_id: str | None = None
         loop_id: str | None = None
+        parent_node_id: str | None = None
         agent_strategy: AgentNodeStrategyInit | None = None
 
     event: StreamEvent = StreamEvent.NODE_STARTED
@@ -372,6 +405,7 @@ class NodeStartStreamResponse(StreamResponse):
                 "extras": {},
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "parent_node_id": self.data.parent_node_id,
             },
         }
 
@@ -407,6 +441,7 @@ class NodeFinishStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
+        parent_node_id: str | None = None
 
     event: StreamEvent = StreamEvent.NODE_FINISHED
     workflow_run_id: str
@@ -436,6 +471,7 @@ class NodeFinishStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "parent_node_id": self.data.parent_node_id,
             },
         }
 
@@ -471,6 +507,7 @@ class NodeRetryStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
+        parent_node_id: str | None = None
         retry_index: int = 0
 
     event: StreamEvent = StreamEvent.NODE_RETRY
@@ -501,6 +538,7 @@ class NodeRetryStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
+                "parent_node_id": self.data.parent_node_id,
                 "retry_index": self.data.retry_index,
             },
         }
@@ -669,6 +707,17 @@ class LoopNodeCompletedStreamResponse(StreamResponse):
     data: Data
 
 
+class ChunkType(StrEnum):
+    """Stream chunk type for LLM-related events."""
+
+    TEXT = "text"  # Normal text streaming
+    TOOL_CALL = "tool_call"  # Tool call arguments streaming
+    TOOL_RESULT = "tool_result"  # Tool execution result
+    THOUGHT = "thought"  # Agent thinking process (ReAct)
+    THOUGHT_START = "thought_start"  # Agent thought start
+    THOUGHT_END = "thought_end"  # Agent thought end
+
+
 class TextChunkStreamResponse(StreamResponse):
     """
     TextChunkStreamResponse entity
@@ -682,6 +731,36 @@ class TextChunkStreamResponse(StreamResponse):
         text: str
         from_variable_selector: list[str] | None = None
 
+        # Extended fields for Agent/Tool streaming
+        chunk_type: ChunkType = ChunkType.TEXT
+        """type of the chunk"""
+
+        # Tool call fields (when chunk_type == TOOL_CALL)
+        tool_call_id: str | None = None
+        """unique identifier for this tool call"""
+        tool_name: str | None = None
+        """name of the tool being called"""
+        tool_arguments: str | None = None
+        """accumulated tool arguments JSON"""
+
+        # Tool result fields (when chunk_type == TOOL_RESULT)
+        tool_files: list[str] | None = None
+        """file IDs produced by tool"""
+        tool_error: str | None = None
+        """error message if tool failed"""
+
+        # Tool elapsed time fields (when chunk_type == TOOL_RESULT)
+        tool_elapsed_time: float | None = None
+        """elapsed time spent executing the tool"""
+
+        def model_dump(self, *args, **kwargs) -> dict[str, object]:
+            kwargs.setdefault("exclude_none", True)
+            return super().model_dump(*args, **kwargs)
+
+        def model_dump_json(self, *args, **kwargs) -> str:
+            kwargs.setdefault("exclude_none", True)
+            return super().model_dump_json(*args, **kwargs)
+
     event: StreamEvent = StreamEvent.TEXT_CHUNK
     data: Data
 
@@ -830,7 +909,7 @@ class AgentLogStreamResponse(StreamResponse):
         """
 
         node_execution_id: str
-        id: str
+        message_id: str
         label: str
         parent_id: str | None = None
         error: str | None = None

api/core/app/layers/sandbox_layer.py

@@ -0,0 +1,22 @@
+import logging
+
+from core.sandbox import Sandbox
+from core.workflow.graph_engine.layers.base import GraphEngineLayer
+from core.workflow.graph_events.base import GraphEngineEvent
+
+logger = logging.getLogger(__name__)
+
+
+class SandboxLayer(GraphEngineLayer):
+    def __init__(self, sandbox: Sandbox) -> None:
+        super().__init__()
+        self._sandbox = sandbox
+
+    def on_graph_start(self) -> None:
+        pass
+
+    def on_event(self, event: GraphEngineEvent) -> None:
+        pass
+
+    def on_graph_end(self, error: Exception | None) -> None:
+        self._sandbox.release()

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -1,4 +1,5 @@
 import logging
+import re
 import time
 from collections.abc import Generator
 from threading import Thread
@@ -62,7 +63,15 @@ from core.tools.signature import sign_tool_file
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models.model import AppMode, Conversation, Message, MessageAgentThought, MessageFile, UploadFile
+from models.model import (
+    AppMode,
+    Conversation,
+    LLMGenerationDetail,
+    Message,
+    MessageAgentThought,
+    MessageFile,
+    UploadFile,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -72,6 +81,8 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
     EasyUIBasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
+    _THINK_PATTERN = re.compile(r"<think[^>]*>(.*?)</think>", re.IGNORECASE | re.DOTALL)
+
     _task_state: EasyUITaskState
     _application_generate_entity: Union[ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity]
     _precomputed_event_type: StreamEvent | None = None
@@ -418,11 +429,136 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
                 )
             )
 
+        # Save LLM generation detail if there's reasoning_content
+        self._save_generation_detail(session=session, message=message, llm_result=llm_result)
+
         message_was_created.send(
             message,
             application_generate_entity=self._application_generate_entity,
         )
 
+    def _save_generation_detail(self, *, session: Session, message: Message, llm_result: LLMResult) -> None:
+        """
+        Save LLM generation detail for Completion/Chat/Agent-Chat applications.
+        For Agent-Chat, also merges MessageAgentThought records.
+        """
+        import json
+
+        reasoning_list: list[str] = []
+        tool_calls_list: list[dict] = []
+        sequence: list[dict] = []
+        answer = message.answer or ""
+
+        # Check if this is Agent-Chat mode by looking for agent thoughts
+        agent_thoughts = (
+            session.query(MessageAgentThought)
+            .filter_by(message_id=message.id)
+            .order_by(MessageAgentThought.position.asc())
+            .all()
+        )
+
+        if agent_thoughts:
+            # Agent-Chat mode: merge MessageAgentThought records
+            content_pos = 0
+            cleaned_answer_parts: list[str] = []
+            for thought in agent_thoughts:
+                # Add thought/reasoning
+                if thought.thought:
+                    reasoning_text = thought.thought
+                    if "<think" in reasoning_text.lower():
+                        clean_text, extracted_reasoning = self._split_reasoning_from_answer(reasoning_text)
+                        if extracted_reasoning:
+                            reasoning_text = extracted_reasoning
+                            thought.thought = clean_text or extracted_reasoning
+                    reasoning_list.append(reasoning_text)
+                    sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
+
+                # Add tool calls
+                if thought.tool:
+                    tool_calls_list.append(
+                        {
+                            "name": thought.tool,
+                            "arguments": thought.tool_input or "",
+                            "result": thought.observation or "",
+                        }
+                    )
+                    sequence.append({"type": "tool_call", "index": len(tool_calls_list) - 1})
+
+                # Add answer content if present
+                if thought.answer:
+                    content_text = thought.answer
+                    if "<think" in content_text.lower():
+                        clean_answer, extracted_reasoning = self._split_reasoning_from_answer(content_text)
+                        if extracted_reasoning:
+                            reasoning_list.append(extracted_reasoning)
+                            sequence.append({"type": "reasoning", "index": len(reasoning_list) - 1})
+                        content_text = clean_answer
+                        thought.answer = clean_answer or content_text
+
+                    if content_text:
+                        start = content_pos
+                        end = content_pos + len(content_text)
+                        sequence.append({"type": "content", "start": start, "end": end})
+                        content_pos = end
+                        cleaned_answer_parts.append(content_text)
+
+            if cleaned_answer_parts:
+                merged_answer = "".join(cleaned_answer_parts)
+                message.answer = merged_answer
+                llm_result.message.content = merged_answer
+        else:
+            # Completion/Chat mode: use reasoning_content from llm_result
+            reasoning_content = llm_result.reasoning_content
+            if not reasoning_content and answer:
+                # Extract reasoning from <think> blocks and clean the final answer
+                clean_answer, reasoning_content = self._split_reasoning_from_answer(answer)
+                if reasoning_content:
+                    answer = clean_answer
+                    llm_result.message.content = clean_answer
+                    llm_result.reasoning_content = reasoning_content
+                    message.answer = clean_answer
+            if reasoning_content:
+                reasoning_list = [reasoning_content]
+                # Content comes first, then reasoning
+                if answer:
+                    sequence.append({"type": "content", "start": 0, "end": len(answer)})
+                sequence.append({"type": "reasoning", "index": 0})
+
+        # Only save if there's meaningful generation detail
+        if not reasoning_list and not tool_calls_list:
+            return
+
+        # Check if generation detail already exists
+        existing = session.query(LLMGenerationDetail).filter_by(message_id=message.id).first()
+
+        if existing:
+            existing.reasoning_content = json.dumps(reasoning_list) if reasoning_list else None
+            existing.tool_calls = json.dumps(tool_calls_list) if tool_calls_list else None
+            existing.sequence = json.dumps(sequence) if sequence else None
+        else:
+            generation_detail = LLMGenerationDetail(
+                tenant_id=self._application_generate_entity.app_config.tenant_id,
+                app_id=self._application_generate_entity.app_config.app_id,
+                message_id=message.id,
+                reasoning_content=json.dumps(reasoning_list) if reasoning_list else None,
+                tool_calls=json.dumps(tool_calls_list) if tool_calls_list else None,
+                sequence=json.dumps(sequence) if sequence else None,
+            )
+            session.add(generation_detail)
+
+    @classmethod
+    def _split_reasoning_from_answer(cls, text: str) -> tuple[str, str]:
+        """
+        Extract reasoning segments from <think> blocks and return (clean_text, reasoning).
+        """
+        matches = cls._THINK_PATTERN.findall(text)
+        reasoning_content = "\n".join(match.strip() for match in matches) if matches else ""
+
+        clean_text = cls._THINK_PATTERN.sub("", text)
+        clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
+
+        return clean_text, reasoning_content or ""
+
     def _handle_stop(self, event: QueueStopEvent):
         """
         Handle stop.