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Introduction
CAMEL-AI is an open-source community for finding the scaling laws of agents for data generation, world simulation, and task automation.
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
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
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.