Docs

Overview

Python framework for building and orchestrating multiple autonomous AI agents that collaborate on complex tasks. Agents assume defined roles and work together through a task-based architecture, enabling multi-step problem solving without manual intervention between steps.

Best for

Best for
Python developers building multi-agent systems for research, automation, or knowledge work tasks

Use cases

• Automating multi-stage workflows requiring different specialized agent roles
• Building research or analysis systems where agents gather, process, and synthesize information
• Creating customer service systems with agents handling triage, resolution, and escalation

Notes

Python framework for building and orchestrating multiple autonomous AI agents that collaborate on complex tasks. Agents assume defined roles and work together through a task-based architecture, enabling multi-step problem solving without manual intervention between steps.

52,610 stars on GitHub. Last updated 2026-06-01. Licensed MIT.

Use cases

• Automating multi-stage workflows requiring different specialized agent roles
• Building research or analysis systems where agents gather, process, and synthesize information
• Creating customer service systems with agents handling triage, resolution, and escalation

Pros

• Strong community adoption (52k+ stars) with active development and examples
• Python-native, integrates with existing Python tooling and LLM APIs
• Built-in task orchestration reduces boilerplate for agent coordination

Cons

• Requires careful prompt engineering and role definition to avoid agent conflicts or loops
• Performance and cost scale with number of agents and task complexity
• Limited built-in observability for debugging agent interactions

Indexed from awesome-ai-agents and enriched against its public facts.