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camel
by camel-ai
Framework for building and studying multi-agent AI societies
Python
Updated Feb 12, 2026
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Summary
Implements a Model Context Protocol (MCP) Pattern multi-agent framework for building, running, and evaluating AI societies. Uses configurable agent roles, message passing, and scripted interaction patterns to study emergent behaviors and scale agent coordination. Includes tool- and LLM-agnostic adapters so you can plug different model backends or custom policies, using Agent Registry Pattern to manage agent identities and capabilities.
Key Benefits
As agents become more autonomous, understanding how they interact and fail is crucial for trust and governance. CAMEL provides a playground to reproduce multi-agent scenarios, observe interaction patterns, and collect structured traces that feed into reputation and evaluation systems. Until now many multi-agent studies were ad-hoc; CAMEL standardizes interaction patterns useful for agent-to-agent evaluation and pre-production testing. Integrate human-centric checks with Human-in-the-Loop workflows to improve oversight.
Ideal For
Researchers and engineers prototyping Hierarchical Multi-Agent Pattern workflows who need repeatable interaction patterns and traceable agent behaviors.
Applications
- Reproducing multi-agent interaction experiments to analyze failure modes and delegation patterns
- Collecting structured conversation traces for agent track record and reputation analysis
- Prototyping role-based agent systems that swap model backends (OpenAI, Hugging Face, Anthropic)
- Stress-testing coordination strategies before deploying agents into production workflows
Works With
openaihuggingfaceanthropic
Topics
agentai-societiesartificial-intelligencecommunicative-aicooperative-aideep-learninglarge-language-modelsmulti-agent-systemsnatural-language-processing
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Keywords
multi-agent trustagent-to-agent evaluationmulti-agent orchestrationagent-evaluation