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Multi-Agent Systems

Camel

Camel is a public, open-source Python framework for building and studying multi-agent AI systems. Its documented focus includes cooperative agent interactions, agent societies and natural-language collaboration between LLM-based agents.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Camel — OpenViST project profile in Multi-Agent Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Camel when you need a Python foundation for experimenting with interacting AI agents, cooperative workflows or simulated agent societies.

Who it is for

  • Developers building multi-agent AI applications
  • Researchers studying cooperative AI and agent societies
  • Teams prototyping communication and coordination between AI agents

Documented uses

  • Build a cooperative multi-agent research environment
  • Create simulated AI-society experiments
  • Prototype multi-agent communication and coordination systems
  • Develop LLM-based agents for natural-language collaboration
Before you choose

Requirements & limits

Camel is publicly available under the Apache-2.0 license. The documented technical platforms include Linux, macOS, Windows, CLI, server and self-hosted environments.

  • Python is the primary language.
  • The framework is focused on multi-agent systems rather than single-agent use cases.
  • Its scope includes cooperative interactions, agent societies and natural-language collaboration.
  • The repository is public and licensed under Apache-2.0.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install CAMEL and create a tool-using ChatAgent

Install CAMEL, configure an OpenAI key, and run the documented ChatAgent search example.

Before you start

  • A terminal with Python package installation support.
  • An OpenAI API key.
  • CAMEL installed from PyPI.
  • The CAMEL web-tools package installed for DuckDuckGo search.

Steps

  1. Install CAMEL with the command below.

    Documented command
    pip install camel-ai

  2. Install the web-tools package with the command below.

    Documented command
    pip install 'camel-ai[web_tools]'

  3. Set OPENAI_API_KEY to your OpenAI API key, or copy .env.example to .env and add the keys there.

  4. Create a ChatAgent with ModelFactory, ModelPlatformType.OPENAI, ModelType.GPT_4O, and SearchToolkit().search_duckduckgo, then call agent.step(...) with the documented questions and print each response.

  5. Optionally enable request and response logging with the documented CAMEL_MODEL_LOG_ENABLED, CAMEL_MODEL_LOG_MODEL_CONFIG_ENABLED, and CAMEL_LOG_DIR environment variables.

Expected outcome

A configured CAMEL ChatAgent can perform the documented search queries and print its responses; optional JSON request and response logs are written to the configured log directory.

Guide limitations

  • The documented example uses OpenAI GPT-4O and requires an OpenAI API key.
  • The example demonstrates one ChatAgent with DuckDuckGo search, not a complete multi-agent coordination workflow.
  • The source does not document the expected response beyond the example comments or verify execution in a particular environment.
Documentation 1 ↗Documentation 2 ↗
COMPARE OPTIONS

Compare options for Agent orchestration

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
CamelBuild a cooperative multi-agent research environmentCLI, Linux, macOS, Self-hostedCamel is publicly available under the Apache-2.0 license. The documented technical platforms include Linux, macOS, Windows, CLI, server and self-hosted environments.
MassgenRun multiple AI agents against the same user task.CLIThe repository documents installation through pip or uv and requires Python 3.11 or later. Setup can configure API keys for OpenAI, Anthropic, Google and xAI; credentials are needed for the selected model backends.
Langchain Multi Agents BoilerplateRoute incoming football questions to model and agent workflows according to task complexity.WebThe repository is public and specifies an MIT license. Running the application requires OpenAI and Groq API keys and a Redis server.
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
camel-ai/camel · Official homepage title · retained in its source language
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