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CAMEL-AI CAMEL

Open-source Python multi-agent framework for building agent societies, role-playing agents, stateful ChatAgent workflows, RAG agents, synthetic data generation, MCP-enabled use cases, and research-scale agent experiments.

by CAMEL-AI·added 2026-06-18·
CLI
HarnessCLI
Review first — review before installing

Open the source and read safety notes before installing.

Safety notes

  • CAMEL agents can coordinate multi-step tasks, call tools, use web/search integrations, connect to MCP examples, and run with provider credentials; review tool permissions before giving agents write access or account access.
  • Large-scale agent societies and role-playing workflows can generate high volumes of model calls, tool calls, logs, synthetic data, and intermediate artifacts; set budgets, rate limits, and stop conditions before long runs.
  • RAG, document, media, browser, communication, and data-tool extras may access local files, third-party APIs, vector stores, notebooks, or generated datasets; isolate experiments from production systems.
  • CAMEL examples include MCP-oriented use cases, but MCP does not make connected tools safe by default. Scope server permissions, credentials, filesystem access, and approval gates separately.
  • Do not treat generated code, generated datasets, citations, research summaries, or multi-agent decisions as verified until they have been reviewed against source data and policy requirements.

Privacy notes

  • Prompts, model responses, agent messages, tool arguments, tool outputs, retrieved documents, search results, logs, generated datasets, traces, and errors may include user or workspace data.
  • Model providers, search providers, MCP servers, vector stores, web tools, document parsers, browser tools, and observability integrations may receive data from CAMEL workflows.
  • Keep provider API keys, OAuth tokens, MCP server credentials, vector database URLs, generated logs, and synthetic datasets out of committed examples, screenshots, public issues, and shared notebooks.
  • If `CAMEL_MODEL_LOG_ENABLED` or other logging/tracing integrations are enabled, review request/response logs and model configuration logs before sharing or retaining them.

Prerequisites

  • Python 3.10 through 3.14 and an isolated Python environment managed with pip, uv, or another package manager.
  • A configured model provider such as OpenAI or another provider supported by the selected CAMEL model route.
  • Provider API keys, search credentials, vector database credentials, or tool-specific secrets stored outside source control.
  • Optional extras for web tools, document tools, RAG, model platforms, storage backends, dev tools, or research tools only when those integrations are required.
  • A review plan for any agent society, MCP server, browser/search tool, code tool, or account-writing tool attached to a CAMEL agent.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
25 minutes
Difficulty
intermediate
Tool listing metadata
Pricing
free
Disclosure
editorial
Application category
DeveloperApplication
Operating system
Cross-platform
Full copyable content
pip install camel-ai

About this resource

Overview

CAMEL is CAMEL-AI's open-source Python framework for building and studying multi-agent systems. Its core concepts include ChatAgent, agent societies, role-playing workflows, stateful memory, task automation, RAG agents, synthetic data generation, model integrations, and toolkits for web, documents, media, communication, data, research, storage, and development workflows.

It belongs in the directory because it is one of the clearest high-demand multi-agent framework gaps: the repo is active, the package is on PyPI, the project publishes official docs, and the README explicitly covers agent societies, RAG cookbooks, MCP-oriented use cases, and scalable agent research.

Install

Install the base Python package:

pip install camel-ai

For web-search examples and related tools, install the matching extra:

pip install "camel-ai[web_tools]"

The upstream pyproject.toml also defines optional extras for RAG, document tools, media tools, communication tools, data tools, research tools, development tools, model platforms, Hugging Face integrations, storage backends, and docs. Install only the extras needed by the workflow.

Agent Capabilities

Area CAMEL Coverage
Agent runtime ChatAgent, agent messages, role-playing, societies, workforce patterns, and multi-step task automation
Multi-agent systems Agent societies, role-playing examples, workforce use cases, collaborative research assistants, and judging workflows
Tools and integrations Web tools, document tools, media tools, communication tools, data tools, research tools, dev tools, storage backends, and model platforms
RAG RAG, Graph RAG, dynamic knowledge graph examples, PDF/document parsing, and vector-store integrations through optional extras
MCP relevance mcp is a core dependency in the current project metadata, and the README lists MCP-oriented use cases such as ACI MCP, Cloudflare MCP CAMEL, and Airbnb MCP
Research workflows Synthetic data generation, benchmarks, role simulations, large-scale agent experiments, and cited research artifacts
Logging Optional request/response and model-configuration logging through CAMEL environment variables

MCP Fit

CAMEL is not listed here as a standalone MCP server. It is a multi-agent framework that can sit above MCP tools and servers in agent workflows. That is useful for teams testing how Claude-adjacent agents coordinate across external tools, but it also raises the normal MCP trust questions: what each server can read, what it can write, which credentials it receives, and whether an operator approves high-impact actions.

Use Cases

  • Build Python ChatAgent workflows with model and tool integrations.
  • Prototype role-playing agent societies for task automation and research.
  • Run multi-agent RAG, Graph RAG, and document-processing experiments.
  • Evaluate synthetic data generation and benchmark-oriented agent behavior.
  • Connect agent workflows to web/search tools, vector stores, communication tools, model platforms, or MCP-oriented examples.
  • Study scaling behavior, stateful memory, emergent coordination, and multi-agent failure modes.

Source Review

Verified on 2026-06-18:

  • GitHub reports camel-ai/camel as an Apache-2.0 repository with active development, 17,000+ stars, and a latest release v0.2.90.
  • The upstream README describes CAMEL as an open-source community and framework for studying agents at scale, with agents, tasks, prompts, models, simulated environments, agent societies, data generation, task automation, and world simulation.
  • The README documents pip install camel-ai, a ChatAgent quick start, optional camel-ai[web_tools], OPENAI_API_KEY, model request/response logging variables, official docs, cookbooks, RAG examples, agent society examples, and MCP-oriented use cases.
  • pyproject.toml declares the camel-ai package, Python >=3.10,<3.15, Apache-2.0 licensing, an mcp>=1.3.0 dependency, and optional extras for RAG, web tools, document tools, media tools, communication tools, data tools, research tools, dev tools, model platforms, Hugging Face, storage, and docs.
  • PyPI resolves package metadata for camel-ai version 0.2.90.

Safety and Privacy

CAMEL is powerful because it combines model calls, agent memory, tool use, multi-agent coordination, and optional integrations. Treat each agent run as an automation surface. Keep credentials scoped, cap long-running experiments, review tool permissions, and inspect generated outputs before they affect repositories, accounts, infrastructure, users, or datasets.

Logging and observability are especially sensitive in multi-agent systems. Prompts, intermediate messages, retrieved documents, generated code, tool arguments, model responses, and synthetic data can leak private context when shared in notebooks, examples, issue reports, dashboards, or retained logs.

Duplicate Check

Checked current content/tools/, content/mcp/, content/agents/, content/skills/, guides, collections, open pull requests, and repository-wide content for camel-ai/camel, CAMEL-AI, CAMEL multi-agent framework, camel-ai, ChatAgent, agent societies, CAMEL MCP, ACI MCP, Cloudflare MCP CAMEL, Airbnb MCP, and communicative agents. Existing entries cover adjacent agent frameworks such as LangGraph, CrewAI, AutoGen, Agno, Mastra, Pydantic AI, Smolagents, Letta, LiveKit Agents, and mcp-agent, but no dedicated CAMEL tools entry, exact source URL duplicate, target file, or open duplicate PR was found.

#camel-ai#multi-agent#ai-agents#agent-framework#python#mcp#rag#synthetic-data

Source citations

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How it compares

CAMEL-AI CAMEL side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

FieldCAMEL-AI CAMEL

Open-source Python multi-agent framework for building agent societies, role-playing agents, stateful ChatAgent workflows, RAG agents, synthetic data generation, MCP-enabled use cases, and research-scale agent experiments.

Open dossier 		AG2 Agent Framework

Open-source Python AgentOS and multi-agent framework, evolved from AutoGen, for building conversable agents, group chats, swarms, human-in-the-loop workflows, tool use, RAG, code execution, and provider-backed agent systems.

Open dossier 		MetaGPT

Open-source Python multi-agent framework that assigns product manager, architect, project manager, engineer, and other software-company roles to LLM agents for natural-language programming, repo generation, data interpretation, research, debate, and custom agent workflows.

Open dossier 		AgentScope

Apache-2.0 Python framework for building visible, controllable, production AI agents and multi-agent services with event streaming, permission controls, workspaces, sandbox backends, middleware, MCP support, Mem0 memory, agent teams, and multi-tenant multi-session serving.

Open dossier
Trust
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
Categorytoolstoolstoolstools
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorCAMEL-AIAG2FoundationAgentsAgentScope
Added2026-06-182026-06-182026-06-182026-06-18
Platforms
CLI
CLI
CLI
CLI
Source repo————
Safety notes✓CAMEL agents can coordinate multi-step tasks, call tools, use web/search integrations, connect to MCP examples, and run with provider credentials; review tool permissions before giving agents write access or account access. Large-scale agent societies and role-playing workflows can generate high volumes of model calls, tool calls, logs, synthetic data, and intermediate artifacts; set budgets, rate limits, and stop conditions before long runs. RAG, document, media, browser, communication, and data-tool extras may access local files, third-party APIs, vector stores, notebooks, or generated datasets; isolate experiments from production systems. CAMEL examples include MCP-oriented use cases, but MCP does not make connected tools safe by default. Scope server permissions, credentials, filesystem access, and approval gates separately. Do not treat generated code, generated datasets, citations, research summaries, or multi-agent decisions as verified until they have been reviewed against source data and policy requirements.✓AG2 agents can converse, call tools, execute code, use retrieval systems, run browser workflows, and coordinate group chats; require explicit permissions and approval gates for high-impact actions. The upstream install docs and examples commonly involve provider credentials; keep API keys, config files, notebooks, and `.env` files out of commits and support tickets. Code execution, Docker, Jupyter, browser-use, and RAG extras can touch local files, network services, notebooks, databases, and external websites; scope them tightly before granting agent access. Multi-agent conversations can continue through nested chats, swarms, group chats, and custom reply handlers; define termination, escalation, retry, and human takeover behavior. Track the release roadmap before upgrading because deprecations and the v1.0 transition can change which APIs should be used for new work.✓MetaGPT can generate full repositories under a workspace from one-line requirements. Review generated code, dependencies, licenses, prompts, and build scripts before running or publishing anything. The framework coordinates multiple LLM roles and can call code, web, RAG, browser, email, GitHub, and provider integrations through its dependencies and optional extras; scope credentials and tools per workflow. Generated requirements, API designs, architecture documents, diagrams, and code can be plausible but wrong. Treat them as drafts until tested against source requirements and local constraints. Data Interpreter and notebook-style workflows may execute code, create plots, read files, and emit artifacts; run them in an isolated environment for untrusted data. Long multi-agent runs can consume significant model tokens and external API quota, so set cost ceilings, timeouts, and stopping criteria before production use.✓AgentScope examples can give agents Bash, file-read, file-write, edit, search, MCP, and custom tools. Scope tool permissions and approval rules before connecting a real project or account. The README demonstrates permission control, including bypass mode. Do not use bypass-style behavior on production systems, sensitive files, paid APIs, cloud resources, or unreviewed tool chains without compensating controls. Workspace support can run tools and code through local, Docker, or E2B backends; review filesystem mounts, network access, secrets, resource limits, and cleanup behavior. Agent teams, background tasks, and multi-session services can continue work after the initial request; define cancellation, timeout, wakeup, escalation, and audit behavior. Mem0 memory, Redis-backed sessions, MCP configuration, OpenTelemetry, FastAPI services, and model-provider integrations all need version pinning, credential isolation, and security review before production use.
Privacy notes✓Prompts, model responses, agent messages, tool arguments, tool outputs, retrieved documents, search results, logs, generated datasets, traces, and errors may include user or workspace data. Model providers, search providers, MCP servers, vector stores, web tools, document parsers, browser tools, and observability integrations may receive data from CAMEL workflows. Keep provider API keys, OAuth tokens, MCP server credentials, vector database URLs, generated logs, and synthetic datasets out of committed examples, screenshots, public issues, and shared notebooks. If `CAMEL_MODEL_LOG_ENABLED` or other logging/tracing integrations are enabled, review request/response logs and model configuration logs before sharing or retaining them.✓Prompts, messages, tool arguments, tool outputs, code snippets, notebook state, retrieved documents, vector-store contents, provider responses, traces, and execution logs may contain sensitive user or workspace data. Do not expose secrets, API keys, private file paths, customer records, internal documents, database rows, or raw exceptions through agent messages, logs, notebooks, screenshots, or public examples. Provider extras and retrieval integrations can route data through OpenAI, Anthropic, Google, AWS, local model servers, databases, vector stores, browser automation, or other third-party services. If AG2 is used for code execution or browser automation, define which files, domains, credentials, downloads, screenshots, and logs can be read or retained.✓Requirements, prompts, role messages, generated code, diagrams, documents, repo files, notebook outputs, model responses, logs, and traces may contain private product or workspace data. Configured LLM providers, browser/search tools, RAG/vector services, GitHub integrations, email/IMAP tools, cloud providers, and generated workspaces may receive or retain workflow data. Do not commit `~/.metagpt/config2.yaml`, provider keys, local model URLs, generated repos with secrets, workspace logs, notebook outputs, or customer requirements. If teams share MetaGPT outputs, strip private prompts, internal system names, customer data, generated credentials, and non-public architecture details first.✓AgentScope workflows can process prompts, model responses, tool arguments, tool outputs, workspace files, code, credentials accidentally present in context, event streams, web UI state, logs, traces, memory records, session state, and tenant metadata. Long-term memory through Mem0 and multi-session service storage can persist user facts, intermediate outputs, retrieved context, and tool results beyond a single conversation. Docker, E2B, MCP servers, model providers, Redis, OpenTelemetry exporters, FastAPI deployments, and web UI integrations may send or store data outside the local Python process depending on configuration. Do not expose private prompts, API keys, unpublished code, customer data, tenant identifiers, session transcripts, or workspace artifacts in public issues, examples, screenshots, logs, or generated reports.
Prerequisites
  • Python 3.10 through 3.14 and an isolated Python environment managed with pip, uv, or another package manager.
  • A configured model provider such as OpenAI or another provider supported by the selected CAMEL model route.
  • Provider API keys, search credentials, vector database credentials, or tool-specific secrets stored outside source control.
  • Optional extras for web tools, document tools, RAG, model platforms, storage backends, dev tools, or research tools only when those integrations are required.
  • Python 3.10 or newer and a Python environment managed with pip, uv, or another package manager.
  • Model provider credentials for the selected provider extra, such as OpenAI, Anthropic, Gemini, Bedrock, Mistral, Ollama, Groq, xAI, or another supported route.
  • A secrets strategy for provider keys, AG2 config files, `.env` files, notebooks, and example `OAI_CONFIG_LIST`-style credentials.
  • A reviewed execution boundary for code execution, Docker, Jupyter, browser-use, RAG, retrieval, database, and external tool extras.
  • Python 3.9 through 3.11 and an isolated Python environment.
  • Node.js and pnpm for workflows that render diagrams or use MetaGPT's documented software-company generation path.
  • LLM provider configuration in `~/.metagpt/config2.yaml`, such as OpenAI, Azure, Ollama, Groq, or another supported provider route.
  • API keys, base URLs, local model endpoints, and generated workspace paths kept outside source control.
  • Python 3.11 or newer and an isolated Python environment managed with pip, uv, or another package manager.
  • Model provider credentials for the selected model backend, such as DashScope, OpenAI-compatible APIs, Anthropic, Gemini, Ollama, xAI, or another supported route.
  • A permission policy for tools such as Bash, Grep, Glob, Read, Write, Edit, MCP tools, custom functions, and long-running background tasks.
  • A workspace isolation decision for local, Docker, E2B, or other sandbox backends before running code or file tools.
Install
pip install camel-ai
pip install 'ag2[openai]'
pip install --upgrade metagpt
pip install agentscope
Config————
Citations
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