# CAMEL review

> CAMEL is an open-source Python framework for building multi-agent systems with LLM-driven agents, tools, memory, and synthetic data generation.

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/camel/

## TL;DR

CAMEL is an open-source, modular Python framework for building multi-agent systems. It targets researchers and developers who study agent behavior, generate synthetic data, automate tasks, or simulate worlds. It includes agent societies with defined roles, stateful memory, interpreters, RAG pipelines, and support for benchmarks.

## CAMEL at a glance

- **Category:** [Agent frameworks](https://index.devtoolwatch.com/categories/agent-frameworks/)
- **Maintained by:** camel-ai.org (organization, [@camel-ai](https://github.com/camel-ai) on GitHub)
- **License:** Apache 2.0
- **Language:** Python, TypeScript, JavaScript
- **Repository:** [github.com/camel-ai/camel](https://github.com/camel-ai/camel)
- **Website:** [docs.camel-ai.org](https://docs.camel-ai.org/)
- **PyPI package:** [camel-ai](https://pypi.org/project/camel-ai/) 0.2.90
- **Latest release:** [v0.2.91a7](https://github.com/camel-ai/camel/releases/tag/v0.2.91a7) on GitHub, Sep 3, 2026
- **First published:** Mar 17, 2023

## What CAMEL does

- CAMEL creates LLM-driven agents that reason, plan, act, and call tools such as DuckDuckGo search.
- CAMEL composes societies of agents with defined roles, where coordinator layers delegate tasks and manage collaboration.
- CAMEL generates synthetic data using self-instruct, Chain-of-Thought, and Source2Synth pipelines with verifiers.
- CAMEL provides interpreters for Python, shell, and browser execution to evaluate code and automate work.
- CAMEL manages memory and storage for chat history, tool outputs, and learned knowledge across long interactions.
- CAMEL builds RAG pipelines that combine chunking, retrieval, and generation.
- CAMEL agents can act as an MCP client or server, and toolkits can be exposed as MCP servers.

### How it works

CAMEL is a Python library you install from PyPI and use in code. You create a model with ModelFactory, for example an OpenAI model, and pass it with tools to a ChatAgent. Then you call agent.step() with prompts. CAMEL supports multiple LLM backends, including Anthropic and DeepSeek models. It also connects to MCP. Optional environment variables enable JSON logs of model requests and responses.

## How to install CAMEL

**With pip**

```
pip install camel-ai
```

**With web tools**

```
pip install 'camel-ai[web_tools]'
```

**With all toolkits and interpreters**

```
pip install camel-ai[all]
```

### Requirements

- Python 3.10 or later, below 3.15.
- An API key for the model provider, such as OPENAI_API_KEY for the quick start example.

### First steps

1. Install the web tools package with pip install 'camel-ai[web_tools]'.
2. Set your OpenAI API key with export OPENAI_API_KEY='your_openai_api_key', or add it to a .env file.
3. Create a model with ModelFactory and a ChatAgent with SearchToolkit().search_duckduckgo as a tool.
4. Call agent.step() with a question and print response.msgs[0].content.

## Who CAMEL is for

- Researchers studying multi-agent behavior, emergent behaviors, and scaling laws of agents.
- Developers generating large-scale synthetic datasets with verifier loops.
- Teams building role-based agent societies for task automation.
- Projects simulating social networks or worlds with many agents.

### Limitations

- CAMEL is a Python library only. The PyPI package lists no command-line scripts.
- CAMEL's recent v0.2.91 releases are pre-releases. The latest stable PyPI version listed is 0.2.90.
- CAMEL's quick start example requires a paid-provider API key such as OpenAI's to run.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #367 overall, #42 in Agent frameworks, momentum score 16
- Downloads that week: 15.3k (−6% vs 4-week average)
- New GitHub stars per week (last 12 weeks): Aug 9: 36; Aug 16: 41; Aug 23: 32; Aug 30: 32; Sep 6: 40; Sep 13: 48; Sep 20: 42; Sep 27: 35; Oct 4: 59; Oct 11: 73; Oct 18: 50; Oct 25: 40

## CAMEL alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [Harness²](https://index.devtoolwatch.com/tools/google-research-harness2/) | 21.7k | – |
| #2 | [HyperFrames](https://index.devtoolwatch.com/tools/heygen-com-hyperframes/) | 76.6k | 1.5M |
| #3 | [AgentScope](https://index.devtoolwatch.com/tools/agentscope-ai-agentscope/) | 35k | 51.4k |
| #4 | [AG-UI](https://index.devtoolwatch.com/tools/ag-ui/) | 16.9k | 4.6M |
| #5 | [LangGraph](https://index.devtoolwatch.com/tools/langgraph/) | 44.2k | 18.5M |
| #6 | [Bifrost](https://index.devtoolwatch.com/tools/maximhq-bifrost/) | 9.3k | – |

## CAMEL FAQ

### Is CAMEL free and open source?

Yes. CAMEL's source code is on GitHub (camel-ai/camel) under the Apache 2.0 license, so you can use it for free.

### How do I install CAMEL?

Run `pip install camel-ai`. The camel-ai package needs Python 3.10 or newer, below 3.15. The install section above has the other options from the project's README.

### Is CAMEL still maintained?

Yes. The camel-ai/camel repository was last updated on Oct 19, 2026, and the latest release, v0.2.91a7 on GitHub, came out on Sep 3, 2026.

### How popular is CAMEL?

CAMEL has 18k GitHub stars as of Nov 1, 2026. It gained 40 new stars in the week of Oct 25–31, 2026, against a 12-week average of 44 a week. Its main package had 15.3k downloads that week, −6% against its own 4-week average. That made it #42 among AI agent frameworks on the Devtoolwatch Board.

### What are the best alternatives to CAMEL?

Other AI agent frameworks on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: Harness² (#1), HyperFrames (#2), AgentScope (#3), AG-UI (#4), LangGraph (#5). The alternatives table above has their numbers.

### What can you build with CAMEL?

CAMEL targets three areas: data generation, task automation, and world simulation. Its modules cover agents, agent societies, tools, memory, storage, interpreters, retrievers, benchmarks, and human-in-the-loop oversight.

### Which LLMs does CAMEL support?

CAMEL supports various LLMs as backends through its Models module. The quick start uses OpenAI GPT-4o. Recent releases added Anthropic thinking support, new Claude models, DeepSeek V4 thinking tool calls, and GPT 5.5.

### Does CAMEL work with MCP?

Yes. CAMEL's documentation covers CAMEL agents as an MCP client, a CAMEL agent as an MCP server, toolkits as MCP servers, and the CAMEL-AI MCPHub.

### How large can CAMEL agent systems get?

CAMEL's README says the framework can simulate up to 1M agents. It describes the framework as designed for coordination, communication, and resource management at that scale.

### Can CAMEL log model requests?

Yes. Set CAMEL_MODEL_LOG_ENABLED=true to write request and response JSON logs. CAMEL_LOG_DIR sets the log directory, which defaults to camel_logs.

## Sources

1. [camel-ai/camel on GitHub](https://github.com/camel-ai/camel)
2. [CAMEL README](https://github.com/camel-ai/camel/blob/master/README.md)
3. [camel-ai on PyPI](https://pypi.org/project/camel-ai/)
4. [CAMEL website](https://docs.camel-ai.org/)
5. [Release v0.2.91a7 (Sep 3, 2026)](https://github.com/camel-ai/camel/releases/tag/v0.2.91a7)
6. [Release v0.2.91a6 (Sep 3, 2026)](https://github.com/camel-ai/camel/releases/tag/v0.2.91a6)
7. [Release v0.2.91a5 (Jul 13, 2026)](https://github.com/camel-ai/camel/releases/tag/v0.2.91a5)
8. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)