init0

  AgentU uses two operators for workflows: >> chains steps, & runs parallel. That's it.

  def search(topic: str) -> str:
      return f"Results for {topic}"

  # Agent auto-detects available model, connects to authenticated MCP server
  agent = Agent("researcher").with_tools([search]).with_mcp([
      {"url": "http://localhost:3000", "headers": {"Authorization": "Bearer token123"}}
  ])

  # Memory
  agent.remember("User wants technical depth", importance=0.9)

  # Parallel then sequential: & runs parallel, >> chains
  workflow = (
      agent("AI") & agent("ML") & agent("LLMs")
      >> agent(lambda prev: f"Compare: {prev}")
  )

  # Execute workflow
  result = asyncio.run(workflow.run())

  # REST API with auto-generated Swagger docs
  serve(agent, port=8000)

  Features:
  - Auto-detects Ollama models (also works with OpenAI, vLLM, LM Studio)
  - Memory with importance weights, SQLite backend
  - MCP integration with auth support
  - One-line REST API with Swagger docs
  - Python functions are tools, no decorators needed

  Using it for automated code review, parallel data enrichment, research synthesis.

  pip install agentu

  GitHub: https://github.com/hemanth/agentu

  Open to feedback.

    import OpenAI from 'openai'

    const openai = new OpenAI({
      baseURL: 'http://localhost:11434/v1',
      apiKey: 'ollama', // required but unused
    })

    const chatCompletion = await 
      openai.chat.completions.create({
      model: 'llama2',
      messages: [{ role: 'user', content: 'Why is the sky blue?' }],
    })

    console.log(completion.choices[0].message.content)

    return new NotFoundError(status, error, message, headers);
                   ^
    NotFoundError: 404 404 page not found