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CAMEL-AI is an open-source multi-agent framework focused on finding the scaling laws of agents, with role-playing agents, a Workforce abstraction, and 60+ tool integrations.
CAMEL-AI is an open-source multi-agent framework focused on finding the scaling laws of agents, with role-playing agents, a Workforce abstraction, and 60+ tool integrations.
CAMEL (Communicative Agents for 'Mind' Exploration of Large Language Models) is an open-source multi-agent framework maintained by an academic-leaning community whose explicit research mission, written into the homepage tagline, is 'finding the scaling laws of agents.' The project ships under Apache-2.0 from camel-ai.org and the camel-ai/camel GitHub repository, with installation reduced to a single pip install camel-ai. The 2026 product is organized around four design principles — Evolvability (agents continuously evolve via data generation), Scalability, Statefulness, and Communication — and exposes a 'Workforce' abstraction that models real agent organizations with roles, hierarchies, and long-horizon tasks. The framework ships with 60+ tool integrations (Brave Search, GitHub, Gmail, Google Calendar/Drive/Maps/Scholar, Headless Browser, Hybrid Browser, IMAP, Jina Reranker, LinkedIn, MarkItDown, OpenBB, Slack, etc.) and adjacent open-source projects from the same community — OWL (a generalist multi-agent assistant), SETA (search agent), OASIS (social-simulation environment), CRAB (cross-platform agent benchmark), and LOONG (web-scale agent dataset) — giving researchers an unusually deep toolbox compared to single-purpose frameworks. There is no commercial product or pricing; CAMEL is free open source, with a Discord-based community and HuggingFace-style model and dataset distribution.
CAMEL stands out as the research-grade multi-agent framework that achieved top performance on the GAIA benchmark while remaining completely open-source. Best for teams exploring advanced agent behaviors, researchers studying agent societies, and developers who need deeper customization than business-focused alternatives provide.
Free (Apache-2.0)
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CAMEL continues its strong research cadence into 2026, with OWL ranking among the top performers on the GAIA benchmark for general multi-agent task automation. The Loong project for verifier-based long chain-of-thought synthesis was released as an arXiv preprint in September 2025, expanding the framework's role in producing reasoning training data. The team launched Eigent as a commercial platform offering managed deployment. The community continues to grow its 'HuggingFace-like' ecosystem for multi-agent systems, with active Discord engagement and a steady pipeline of new sub-projects exploring agent reinforcement learning and self-evolving environments.
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