---

name: camel-ai description: "Use CAMEL-AI to build, configure, tool, remember, evaluate, and troubleshoot multi-agent Python systems with self-contained routing across agents, models, tools, RAG, and evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

CAMEL-AI Repo Skill

Use this repo skill when a task mentions CAMEL-AI, camel-ai, the camel Python package, ChatAgent, RolePlaying, Workforce, CAMEL toolkits, CAMEL model backends, CAMEL memory/RAG, or CAMEL benchmark/datagen/evaluation workflows.

First Checks

1. Install the base package with pip install camel-ai for core agents, models, messages, tools, and package imports.
2. Install only the optional extras needed for the chosen workflow, such as camel-ai[model_platforms], camel-ai[rag], camel-ai[storage], camel-ai[document_tools], camel-ai[web_tools], or camel-ai[dev_tools].
3. Run scripts/inspect_camel_install.py for a no-network import, version, and route check.
4. Read references/troubleshooting.md before installing broad extras, debugging provider credentials, or running network/Docker/browser/benchmark workflows.

Route By Task

Task or signal	Read
Build a ChatAgent, system/user/assistant messages, task tree, role-playing society, or Workforce	sub-skills/agents-and-societies/SKILL.md
Configure model providers, ModelFactory, ModelManager, provider enums, local/OpenAI-compatible endpoints, structured output, audio, or multimodal models	sub-skills/models-and-configuration/SKILL.md
Attach FunctionTool, built-in toolkits, MCP/OpenAPI tools, code execution, interpreters, runtimes, browser/Docker/terminal tools, or CAMEL services	sub-skills/tools-runtimes-and-services/SKILL.md
Add memory, retrieval/RAG, embeddings, vector/key-value/object/graph stores, loaders, datahubs, or datasets	sub-skills/memory-rag-and-data/SKILL.md
Generate synthetic data, use data collectors, run verifiers/environments, or plan CAMEL benchmarks	sub-skills/datagen-evaluation-and-benchmarks/SKILL.md

Cross-Cutting References

• references/package-overview.md summarizes package surfaces, optional extras, and support modules such as prompts, personas, schemas, responses, caches, parsers, configs, and utilities.
• references/troubleshooting.md covers install/import failures, optional dependencies, credentials, data/config validation, and workflow-specific escalation.
• references/repo-provenance.md records the source commit, package version, evidence paths, and dirty-state baseline used to create this skill.
• references/repo-routing-metadata.json is structured metadata for the managed repo-skills-router import process.

Safety And Scope

• Start with credential-free inspection scripts and constructor/signature checks before calling models, services, datasets, browsers, Docker, or remote vector stores.
• Do not install camel-ai[all] by default in automated environments; select extras by workflow to avoid heavyweight or unsupported optional dependencies.
• Treat source examples and native tests as evidence; this skill is self-contained and does not require the original repository checkout at runtime.
• Keep API keys, service URLs with secrets, OAuth tokens, model credentials, and local data paths outside generated prompts and skill files.