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What is CAMEL-AI?

CAMEL‑AI is an open‑source, modular framework for building intelligent multi‑agent systems. It provides the primitives to:
  • Create Agents that reason, plan, and act
  • Compose Societies of agents with defined roles
  • Integrate Interpreters for code execution and analysis
  • Manage Memory for long‑horizon context and learning
  • Orchestrate Retrieval‑Augmented Generation (RAG) pipelines
  • Generate Synthetic Data at scale with self‑instruct and verifier loops
  • Simulate Worlds and agent interactions in environments like social networks

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Core Components

  • Agents: Atomic reasoning units driven by LLMs, capable of tool calls and decision‑making
  • Societies: Coordinator layers that assign roles, delegate tasks, and manage collaboration
  • Interpreters: Execution backends (Python, shell, browsers) for live code evaluation and automation
  • Memory & Storage: Persistent context layers for chat history, tool outputs, and learned knowledge
  • RAG Pipelines: Combine chunking, retrieval, and generation for grounded, accurate responses
  • Synthetic Data Engines: Self‑instruct, Chain‑of‑Thought, and Source2Synth pipelines with verifiers
  • World Simulation: Platforms like Oasis for large‑scale multi‑agent social simulations
  • Task Automation: Benchmarks like CRAB for real‑world multi‑step software workflows

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Ecosystem Highlights

OASIS

Large‑scale social simulation environment: model Reddit, Twitter, and user interactions

CRAB Benchmark

Cross‑environment agent automation tasks across Ubuntu and Android platforms

Project Loong

Verifier‑driven synthetic data generation for domain‑specific QA at scale

OWL 🩉

OWL (Optimized Workforce Learning) is a multi-agent automation framework for real-world tasks. Built on CAMEL-AI, it enables dynamic agent collaboration using tools like browsers, code interpreters, and multimodal models.

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Ready to Get Started?

Quickstart

Spin up your first agent in under 5 minutes

Installation

pip install camel-ai[all] – all toolkits and interpreters included

Explore Cookbooks

Hands‑on examples: data gen, RAG, simulations, and more