SwarmCore: Multi-Agent Orchestration for Python

SwarmCore is a Python library for composing AI agents into workflows. You define agents, wire them together with >> (sequential) and | (parallel), and run them.

Why

Most agent frameworks are heavy. They want you to buy into a whole ecosystem — custom runtimes, proprietary tool formats, YAML configs. I wanted something that feels like writing normal Python.

How it works

Agents are functions with instructions and a model. You compose them with operators:

researcher() >> writer()                            # sequential
(analyst() | writer()) >> editor()                  # parallel then sequential
researcher() >> (analyst() | writer()) >> editor()  # mixed

Each agent in a flow receives context from prior steps automatically. The previous step’s full output is passed through, and earlier steps are summarized to keep context windows manageable. Agents can call expand_context to retrieve any prior agent’s full output on demand.

Built-in agent factories

SwarmCore ships with pre-built factories for common roles — researcher, analyst, writer, editor, summarizer — each with sensible defaults. Zero config for prototyping, full override for production.

from swarmcore import researcher, analyst, writer

flow = researcher() >> analyst() >> writer()

Any model

It works with any LiteLLM-compatible provider. Anthropic, OpenAI, Gemini, Groq, Ollama — whatever you have an API key for. Gemini’s free tier works out of the box.

Tools

Tools are plain Python functions. Type hints and docstrings become the tool schema automatically.

def search_web(query: str) -> str:
    """Search the web for information."""
    return results

agent = Agent(name="researcher", instructions="...", tools=[search_web])

The repo is at github.com/MatchaOnMuffins/swarmcore and it’s on PyPI.