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AFM Python Interpreter

The AFM Python Interpreter is the shared runtime for Python-based Agent-Flavored Markdown (AFM) implementations. It provides a modular, plugin-based architecture that supports multiple execution backends, with LangChain provided as the reference backend.

Features

  • Pluggable execution backends - Support for multiple LLM frameworks via the AgentRunner protocol (LangChain included as reference implementation)
  • Support for all interface types:
    • Console chat (interactive CLI)
    • Web chat (HTTP API + optional UI)
    • Webhook (WebSub-based event handling)
  • Multi-interface agents - run multiple interfaces simultaneously
  • MCP support for tools (Model Context Protocol)
  • Validation - dry-run mode to validate AFM definitions

Prerequisites

  • Python 3.11 or later.
  • uv for dependency management.
  • Docker (optional, for running via containers).

Quick Start

Via pip

pip install afm-cli
export OPENAI_API_KEY="your-api-key-here"
afm run path/to/agent.afm.md

Via uv (development)

export OPENAI_API_KEY="your-api-key-here"
uv run afm path/to/agent.afm.md

Configuration

Configuration via environment variables or CLI options:

  • OPENAI_API_KEY, ANTHROPIC_API_KEY, etc. (Required based on provider)
  • HTTP port can be set via -p or --port (default: 8085)

Running with Docker

The Docker image bundles the LangChain execution backend.

# Using the pre-built image
docker run -v $(pwd)/path/to/agent.afm.md:/app/agent.afm.md \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  -p 8085:8085 \
  ghcr.io/wso2/afm-langchain-interpreter:latest run /app/agent.afm.md

# Or build locally
docker build -t afm-langchain-interpreter .

Testing

uv run pytest

Project Structure

This is a uv workspace with three packages:

python-interpreter/
├── packages/
│   ├── afm-core/          # Core: parser, CLI, models, interfaces, protocols
│   │   ├── src/afm/
│   │   └── tests/
│   ├── afm-langchain/     # LangChain execution backend
│   │   ├── src/afm_langchain/
│   │   └── tests/
│   └── afm-cli/           # User-facing metapackage
├── Dockerfile             # Container build
├── pyproject.toml         # Workspace configuration
└── uv.lock                # Dependency lock file

License

Apache-2.0