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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
Agentic RAG Bots
Real‑world retrieval agents for Discord, support, and knowledge applications