CAMEL Multi-Agent Framework
A Python framework for multi-agent collaboration, simulation, and synthetic-data generation.
- Source repo
- camel-ai/camel
- Stars
- ★ 18k
- Last updated
- 4d ago
- License
- Apache-2.0
- Primary language
- Python
- FA score
- 61/100 · Some gaps
At a glance
- How it runs
- Works with
- Portable with changesOpenAI API
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A multi-agent research group studying emergent behavior can organize experiments with Agent Societies, stateful memory, and simulated environments.
- Not a fit if
- Teams wanting a ready-made UI or standalone service; you must integrate it into your own Python app
- Teams needing documented production deployment architecture and hardware specs
- Source review
- 61/100 · Some gaps 1 safety controls not found
What does this agent do, and when should you use it?
CAMEL is an open-source Python framework for building agents and agent societies, with an emphasis on large-scale multi-agent systems and agent scaling laws. Its listed modules include Agents, Agent Societies, Data Generation, Models, Tools, Memory, Storage, Retrievers, and Runtime. Developers can create a model through ModelFactory, run multi-turn work with ChatAgent, and attach callable tools such as SearchToolkit. The repository presents role-playing, Workforce, RAG, knowledge-graph, data-processing, and research-collaboration workflows, alongside CoT, Self-Instruct, and Source2Synth generation implementations. It is delivered as a library installed into an existing Python environment; the supplied material does not document a standalone hosted service, product CLI, or production deployment topology.
A developer installs camel-ai with pip, configures a model through ModelFactory.create, and then creates a ChatAgent. The documented example uses ModelPlatformType.OPENAI, ModelType.GPT_4O, and a model_config_dict, passing SearchToolkit().search_duckduckgo in the tools list; agent.step accepts a natural-language request and returns message content in response.msgs. The repository also identifies camel/datagen/cot_datagen.py, camel/datagen/self_instruct, camel/datagen/source2synth, camel/societies/role_playing.py, and camel/societies/workforce as implementations for generating training data or coordinating multi-agent tasks. Optional CAMEL_MODEL_LOG_ENABLED, CAMEL_MODEL_LOG_MODEL_CONFIG_ENABLED, and CAMEL_LOG_DIR settings write model requests and responses as UTF-8 JSON logs.
- A multi-agent research group studying emergent behavior can organize experiments with Agent Societies, stateful memory, and simulated environments.
- A model-development team that needs instruction or reasoning data can evaluate the CoT, Self-Instruct, or Source2Synth generation modules.
- A Python developer building an agent that searches the public web can follow the documented ChatAgent plus DuckDuckGo tool example.
- A hackathon organizer creating a collaborative judging workflow can use the documented Workforce judge-committee use case as a starting point.
- An internal-tools team working with codebases or video content can examine the Chat with GitHub, Chat with YouTube, and YouTube OCR use cases.
How do you install or deploy this agent?
Run: pip install camel-ai. For the documented DuckDuckGo example, run: pip install 'camel-ai[web_tools]' and set: export OPENAI_API_KEY='your_openai_api_key'. Alternatively, run cp .env.example .env and add keys to .env.
How do you use this agent?
Create a model and agent, then call agent.step: import ModelFactory from camel.models, ModelPlatformType and ModelType from camel.types, ChatAgent from camel.agents, and SearchToolkit from camel.toolkits. Create the model with ModelFactory.create(model_platform=ModelPlatformType.OPENAI, model_type=ModelType.GPT_4O, model_config_dict={"temperature": 0.0}), pass SearchToolkit().search_duckduckgo to ChatAgent(model=model, tools=[search_tool]), and call agent.step("What is CAMEL-AI?"). Read the result from response.msgs[0].content.
What are this agent's strengths and limitations?
- It brings agents, Agent Societies, memory, storage, retrieval, runtime, and benchmark-related components into one framework for multi-agent experimentation.
- It includes named data-generation paths for CoT, Self-Instruct, and Source2Synth rather than limiting the project to conversational orchestration.
- The documentation supplies a complete minimum call path from ModelFactory and ChatAgent to SearchToolkit and agent.step.
- It supports request/response JSON logging and explicitly notes preservation of multilingual text such as Chinese, Japanese, and Arabic.
- The first runnable example depends on an OpenAI API key; the supplied material does not state model-call costs, rate limits, or failure-handling behavior.
- Although the material says it can use various LLM backends, only the OpenAI configuration is shown in concrete code.
- Support for systems of up to one million agents is presented as a design goal, but the supplied material provides no hardware requirements, deployment architecture, or reproducible benchmark results.
- No standalone service, web UI, or stable CLI deployment path is shown, so adopters need to integrate it into their own Python application.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| CAMEL Multi-Agent Framework This agent | 61 · Some gaps | Library / SDKFree + model costs | ★ 18k | 4d ago | Python | OpenAI API |
| Agentic RAG Survey Guide | Insufficient evidence | Library / SDK | ★ 1.7k | 1y ago | — | — |
| LatentMAS | 42 · Major gaps | CLIFree | ★ 1.2k | 4mo ago | Python | — |
| AgentFlow | 31 · Major gaps | CLIFree + model costs | ★ 2.1k | 9mo ago | Python | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: project provides SECURITY.md with vulnerability reporting and dependency security tools (Dependabot, CodeQL), but lacks specific implementation details for least privilege, user confirmation, data flow transparency, and sensitive data handling. External effects: README shows tool integrations (e.g., search, RAG) but no explicit permission boundaries. Rollback not mentioned. Source attribution: author is CAMEL-AI.org, unverified. Deductions: lack of concrete security mechanism documentation and implementation evidence.
Evidence shows: test files exist (e.g., apps/agents/test/test_agents.py), but no test results or coverage. Dependencies listed in pyproject.toml, but availability not verified. Failure messages: README lacks error handling guidance. Deductions: tests not executed, dependency availability unverified, failure message documentation insufficient.
Evidence shows: README details multiple use cases (data generation, task automation, world simulation) with rich examples and documentation links. Capability boundaries: modules and tools listed, but no explicit limits. Trigger precision: example code shows specific usage. Environment fit: supports Python 3.10-3.14, but no OS restrictions mentioned. Deductions: capability boundaries and trigger precision documentation not detailed.
Evidence shows: README well-structured with installation, examples, documentation links. Install notes detailed (pip install camel-ai). Naming stable, version in pyproject.toml. Examples and FAQ abundant. Known limitations not explicitly listed. License Apache-2.0. Changelog not provided. Maintenance responsibility: contribution guide and community channels. Deductions: missing known limitations and changelog.
Evidence shows: output usability: examples show how to get responses. Marginal value high: multi-agent framework with various tools. Cost-benefit: open-source free, but many dependencies. Deductions: output usability not detailed, cost-benefit not quantified.
Evidence shows: claims in README (e.g., support for million agents) lack specific evidence. Cross-source verification: external links (e.g., paper, docs) but not directly verified. Fact-inference separation: README mixes facts and promotional language. Deductions: claims lack traceable evidence, facts and inferences not clearly separated.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Unverified publisher identity: author is CAMEL-AI.org, but not verified by FollowAgents enterprise registry; treat as unknown.
- Insufficient security mechanism documentation: SECURITY.md exists, but lacks specific implementation details for least privilege, user confirmation, data flow transparency; evaluate cautiously.
- Many dependencies unverified: pyproject.toml lists numerous dependencies, but their security and availability are not verified; potential supply chain risk.
- Claims lack evidence: performance claims in README (e.g., support for million agents) lack specific evidence; need independent verification.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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v0.2.91a7 Pre-release
Release notes excerpt
What's Changed * release: 0.2.91a7 with dependency compatibility fixes by @fengju0213 in https://github.com/camel-ai/camel/pull/4313 Full Changelog: https://github.com/camel-ai/camel/compare/v0.2.91a6...v0.2.91a7View on GitHub ↗