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berthos-agents

The Python side of Berth's agent framework: an Agent tool-use loop that works with any LLM provider, and Crew shapes for composing several agents. It mirrors @berthos/agents field for field, in snake_case. The import name is berth_agents.

Experimental and frozen: bug and security fixes only. It isn't published to PyPI; releases ship the sandbox only. Install it from a clone of the Berth repo:

pip install -e experimental/agents-python
import asyncio
from berth_agents import Agent, Computer, create_anthropic_provider

async def main():
    # started with: berth os up my-agent --apps=apps/filesystem --http-rpc
    computer = await Computer.connect("my-agent")
    agent = Agent(llm=create_anthropic_provider(), tools=computer.tools)
    result = await agent.run("write hello.txt with the text 'hi', then read it back")
    print(result.text)

asyncio.run(main())

What's in it

  • Agent: the tool-use loop, with checkpoint and resume, streaming, structured-output repair, guardrails, sessions and step tracing (including an OpenTelemetry tracer).
  • Providers: Anthropic, OpenAI, Google Gemini (and Vertex AI), Azure OpenAI, Amazon Bedrock and Ollama, plus create_fallback_provider() to chain them.
  • Crew: sequential, with_manager, parallel, loop_until, route and pipeline.
  • create_mcp_client_tools(): use any external MCP server's tools (stdio or Streamable HTTP).
  • Computer.connect(name): use a running berth os up --http-rpc sandbox's tools over HTTP. No Docker access needed from Python.

Limits

  • Python can't create a sandbox. There's no Computer.boot(); start one with berth os up --http-rpc and connect to it.
  • Computer.connect() reaches one app per sandbox: the one the HTTP bridge serves.
  • No Crew.networked, no HTTP server, no YAML-declared agents, no A2A, no retrieval or evals. Those are TypeScript-only.

Full reference: docs/agents-python-reference.md.

License

Apache-2.0. See LICENSE.