Devtoolwatch BoardCAMEL review
CAMEL is an open-source Python framework for building multi-agent systems with LLM-driven agents, tools, memory, and synthetic data generation.
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.
- 18kGitHub stars
- +40new stars, week of Oct 25
- 15.3kdownloads a week, −6% vs 4-week avg
- #42in Agent frameworks this week
CAMEL at a glance
- Category
- Agent frameworks
- Maintained by
- camel-ai.org (organization, @camel-ai on GitHub)
- License
- Apache 2.0
- Language
- Python, TypeScript, JavaScript
- Repository
- github.com/camel-ai/camel
- Website
- docs.camel-ai.org
- PyPI package
- camel-ai 0.2.90
- Latest release
- 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 and start using CAMEL
With pip
pip install camel-aiWith 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
- Install the web tools package with pip install 'camel-ai[web_tools]'.
- Set your OpenAI API key with export OPENAI_API_KEY='your_openai_api_key', or add it to a .env file.
- Create a model with ModelFactory and a ChatAgent with SearchToolkit().search_duckduckgo as a tool.
- 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 to know
- 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.
Is CAMEL growing? Usage data
In the week of Oct 25–31, 2026, CAMEL ranked #367 on the Devtoolwatch Board and #42 in Agent frameworks, with a momentum score of 16 out of 100. Its main package had 15.3k downloads that week, −6% against its own 4-week average.
Show the weekly numbers
| Week of | New stars |
|---|---|
| Aug 9, 2026 | 36 |
| Aug 16, 2026 | 41 |
| Aug 23, 2026 | 32 |
| Aug 30, 2026 | 32 |
| Sep 6, 2026 | 40 |
| Sep 13, 2026 | 48 |
| Sep 20, 2026 | 42 |
| Sep 27, 2026 | 35 |
| Oct 4, 2026 | 59 |
| Oct 11, 2026 | 73 |
| Oct 18, 2026 | 50 |
| Oct 25, 2026 | 40 |
The Board ranks tools by four signals each week: new stars, new stars for the repo's size, added downloads and download growth beyond the market. How the ranking works.
CAMEL alternatives
Other AI agent frameworks on the Board in the week of Oct 25–31, 2026, by momentum:
| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | Harness²Harness² is a Python framework from Google Research for recursive agent harnessing, refining an agent's prompts, tools, memory and… | 21.7k | – |
| #2 | HyperFramesHyperFrames is an open-source framework that turns HTML, CSS, media, and seekable animations into deterministic MP4 videos, built for AI… | 76.6k | 1.5M |
| #3 | AgentScopeAgentScope is a Python agent framework with an SDK for building ReAct agents and an agent service backend with a web UI. | 35k | 51.4k |
| #4 | AG-UIAG-UI is an open, event-based protocol that standardizes how AI agents connect to user-facing frontend applications. | 16.9k | 4.6M |
| #5 | LangGraphLangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents in… | 44.2k | 18.5M |
| #6 | BifrostOpen-source, high-performance AI gateway that works as your LLM gateway, MCP gateway and agent gateway. One OpenAI-compatible API for… | 9.3k | – |
Recent CAMEL releases
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
CAMEL README badge
Maintainers can show the tool's weekly Board rank in their README:
[](https://index.devtoolwatch.com/tools/camel/)How this page is made: Devtoolwatch writes each review from the project's README, releases, package metadata and website, with an AI model, then checks every claim against the source it cites and drops what the sources don't support. The numbers come from Devtoolwatch's daily GitHub snapshots, GitHub's stargazer history, and the npm and PyPI download APIs. Spotted an error? Email [email protected].