Files
dify/api
Yansong Zhang d3d9f21cdf feat(api): wire sandbox into Agent V2 node execution pipeline
Integrate the ported sandbox system with Agent V2 node:

- Add DIFY_SANDBOX_CONTEXT_KEY to app_invoke_entities for passing
  sandbox through run_context without modifying graphon
- DifyNodeFactory._resolve_sandbox() extracts sandbox from run_context
  and passes it to AgentV2Node constructor
- AgentV2Node accepts optional sandbox parameter
- AgentV2ToolManager supports dual execution paths:
  - _invoke_tool_directly(): standard ToolEngine.generic_invoke (no sandbox)
  - _invoke_tool_in_sandbox(): delegates to SandboxBashSession.run_tool()
    which uses DifyCli to call back to Dify API from inside the sandbox
- Graceful fallback: if sandbox execution fails, logs warning and returns
  error message (does not crash the agent loop)

To enable sandbox for an Agent workflow:
1. Create a Sandbox via SandboxBuilder
2. Add it to run_context under DIFY_SANDBOX_CONTEXT_KEY
3. Agent V2 nodes will automatically use sandbox for tool execution

46 existing tests still pass.

Made-with: Cursor
2026-04-08 17:46:34 +08:00
..

Dify Backend API

Setup and Run

Important

In the v1.3.0 release, poetry has been replaced with uv as the package manager for Dify API backend service.

uv and pnpm are required to run the setup and development commands below.

The scripts resolve paths relative to their location, so you can run them from anywhere.

  1. Run setup (copies env files and installs dependencies).

    ./dev/setup
    
  2. Review api/.env, web/.env.local, and docker/middleware.env values (see the SECRET_KEY note below).

  3. Start middleware (PostgreSQL/Redis/Weaviate).

    ./dev/start-docker-compose
    
  4. Start backend (runs migrations first).

    ./dev/start-api
    
  5. Start Dify web service.

    ./dev/start-web
    

    ./dev/setup and ./dev/start-web install JavaScript dependencies through the repository root workspace, so you do not need a separate cd web && pnpm install step.

  6. Set up your application by visiting http://localhost:3000.

  7. Start the worker service (async and scheduler tasks, runs from api).

    ./dev/start-worker
    
  8. Optional: start Celery Beat (scheduled tasks).

    ./dev/start-beat
    

Environment notes

Important

When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the sites top-level domain (e.g., example.com). The frontend and backend must be under the same top-level domain in order to share authentication cookies.

  • Generate a SECRET_KEY in the .env file.

    bash for Linux

    sed -i "/^SECRET_KEY=/c\\SECRET_KEY=$(openssl rand -base64 42)" .env
    

    bash for Mac

    secret_key=$(openssl rand -base64 42)
    sed -i '' "/^SECRET_KEY=/c\\
    SECRET_KEY=${secret_key}" .env
    

Testing

  1. Install dependencies for both the backend and the test environment

    cd api
    uv sync --group dev
    
  2. Run the tests locally with mocked system environment variables in tool.pytest_env section in pyproject.toml, more can check Claude.md

    cd api
    uv run pytest                           # Run all tests
    uv run pytest tests/unit_tests/         # Unit tests only
    uv run pytest tests/integration_tests/  # Integration tests
    
    # Code quality
    ./dev/reformat               # Run all formatters and linters
    uv run ruff check --fix ./   # Fix linting issues
    uv run ruff format ./        # Format code
    uv run basedpyright .        # Type checking