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swarms
by kyegomez
Enterprise-ready multi-agent orchestration with delegation and observability
Python
Updated Feb 12, 2026
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Summary
Orchestrates large-scale multi-agent workflows for production systems. Uses a swarm director and configurable agent roles to route tasks, manage delegation, and aggregate results across specialists. Includes primitives for trees-of-thought, retry/failover strategies, and scalable async execution. See the Planning Pattern.
The Value Proposition
As agents get more autonomous, coordinating who does what and tracking outcomes becomes essential for trust and reliability. Swarms makes delegation explicit and observable, so teams can instrument agent interactions and detect failure modes. That visibility is a practical foundation for building agent track records, continuous evaluation, and governance around multi-agent systems. This is reinforced by approaches like the Agent Registry Pattern to keep track of agents and their roles.
Ideal For
Teams building production multi-agent systems that need structured delegation, failover, and visibility into agent interactions. This environment benefits from clear coordination patterns such as the Handoff Pattern to manage task transfer and failover.
Real-World Examples
- Coordinate specialist agents to break down and solve complex tasks
- Implement delegation, retry, and failover policies for reliable agent pipelines
- Log and observe agent interactions to build agent track records and detect failure modes
- Run scalable async multi-agent workflows integrated with LangChain or Hugging Face models
Works With
langchainhuggingfacegpt-4gpt4allchatgptprompt-toolkit
Topics
agentic-aiagentic-workflowagentsaiartificial-intelligencechatgptgpt4gpt4allhuggingfacelangchain+8 more
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Keywords
multi-agent orchestrationagent delegationmulti-agent trustagent reliability