camel-ai
Communicative Agents for AI Society Study
Decision gist · record as of 2026-08-14
Yes. CAMEL is actively maintained, has low install friction, carries a permissive Apache-2.0 license, and offers a mature framework for multi-agent research and task automation. It is suitable for both academic research and production use if your application fits the multi-agent paradigm. No known security vulnerabilities. The main consideration is whether you need multi-agent orchestration; if you're building single-agent LLM applications, a lighter library may be more appropriate.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (and below 3.15); an OpenAI API key or compatible LLM provider is needed for agent operation.
- Low friction installation via PyPI.
- The package is actively maintained with a recent release and has 14 runtime dependencies including standard libraries (pydantic, httpx, openai).
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for both research and production applications.
last release 2026-03-22 (145 days) · last repo commit 2026-08-14 · 17,587 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 97,050 downloads/mo, #13,175 on PyPI
Alternatives
Verify before relying
pip install camel-ai
export OPENAI_API_KEY='your_key'
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType
from camel.agents import ChatAgent
model = ModelFactory.create(
model_platform=ModelPlatformType.OPENAI,
model_type=ModelType.GPT_4O,
model_config_dict={"temperature": 0.0}
)
agent = ChatAgent(model=model)
response = agent.step("Your query here")- Whether the framework can truly simulate up to 1M agents as claimed in the description without significant performance degradation.
- Specific benchmarks or datasets supported for agent evaluation and reproducibility.
- Real-world production deployments or case studies beyond the research context.
What it is and what it does
CAMEL is an open-source framework for building multi-agent systems where agents communicate, collaborate, and interact with environments to solve tasks. It provides abstractions for agent roles, tasks, models, and simulated environments, with built-in support for large language models (via openai and other providers), tool integration, and stateful memory across interactions.
The framework is designed for research into agent scaling laws and emergent behaviors, but also supports practical applications like task automation, synthetic data generation, and world simulation. It handles agent communication through websockets and mcp, integrates with external tools via SearchToolkit and similar modules, and includes utilities for prompt engineering, schema validation (jsonschema, pydantic), and logging. The 14 runtime dependencies cover LLM integration, HTTP communication, image processing, system monitoring, and configuration management.
Use it for
- Build multi-agent conversations where agents with different roles collaborate to solve complex problems or generate synthetic datasets.
- Automate repetitive tasks by defining agent workflows that use LLMs and external tools like web search to gather information and take action.
- Simulate large-scale agent systems to study emergent behaviors, communication patterns, and scaling laws in multi-agent environments.
- Generate structured synthetic data at scale by orchestrating agents to produce training datasets for machine learning models.
- Research cooperative AI and agent communication protocols by experimenting with different agent types, prompts, and interaction patterns.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
CAMEL is actively maintained, has low install friction, carries a permissive Apache-2.0 license, and offers a mature framework for multi-agent research and task automation. It is suitable for both academic research and production use if your application fits the multi-agent paradigm. No known security vulnerabilities. The main consideration is whether you need multi-agent orchestration; if you're building single-agent LLM applications, a lighter library may be more appropriate.
Install
camel-ai on PyPI
Before you install
Low friction installation via PyPI. The package is actively maintained with a recent release and has 14 runtime dependencies including standard libraries (pydantic, httpx, openai). The project shows strong community engagement with 17587 GitHub stars and active development.
Requires Python 3.10 or later (and below 3.15); an OpenAI API key or compatible LLM provider is needed for agent operation.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for both research and production applications.
Quickstart
pip install camel-ai
export OPENAI_API_KEY='your_key'
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType
from camel.agents import ChatAgent
model = ModelFactory.create(
model_platform=ModelPlatformType.OPENAI,
model_type=ModelType.GPT_4O,
model_config_dict={"temperature": 0.0}
)
agent = ChatAgent(model=model)
response = agent.step("Your query here")Verify before relying
- Whether the framework can truly simulate up to 1M agents as claimed in the description without significant performance degradation.
- Specific benchmarks or datasets supported for agent evaluation and reproducibility.
- Real-world production deployments or case studies beyond the research context.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 14 packagesastorcoloramadocstring-parsergoogle-search-resultshttpxjsonschemamcpopenaipillowpsutilpydanticpyyamltiktokenwebsockets |
| Maintenance | Actively maintained 145 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 97,050 / month, #13,175 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: camel_ai-0.2.90-py3-none-any.whl
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