langgraph-swarm-py
by langchain-ai
Compose and run swarm-style multi-agent graphs for complex delegation
What It Does
Orchestrates multi-agent workflows using LangGraph primitives and a swarm-style coordination model. It wires agents, message passing, and routing logic so teams can compose cooperative agent networks with reusable graph components. Notable features include pluggable agent nodes, support for common LLM providers, and tooling for building complex delegation patterns. For routing and coordination, it leverages the Dynamic Task Routing Pattern and the Blackboard Pattern.
Key Benefits
When to Use
Teams prototyping or running multi-agent applications who need a graph-based orchestration layer and integrations with popular LLM providers can benefit from adopting a Planning Pattern approach to structure workflows, aligning with Planning Pattern.
How It's Used
- Compose specialist agents into a directed graph that delegates subtasks between nodes
- Simulate and inspect multi-agent delegation flows to find failure modes
- Integrate LLM providers into coordinated agent pipelines for complex tasks