Ask CrewAI Docs
Answer CrewAI questions by looking up the official documentation at docs.crewai.com.
When to Use This Skill
Use this skill when:
- The user asks about a CrewAI feature, parameter, or behavior not covered in detail by the other skills
- You need to verify current API syntax, method signatures, or configuration options
- The user hits an error and needs troubleshooting guidance from official docs
- The question is about a newer or less common CrewAI feature (e.g., telemetry, testing, CLI commands, deployment, enterprise features)
- You're unsure whether your knowledge is current — the docs reflect the latest published state
Do NOT use this skill when the question is clearly answered by one of the other skills (getting-started, design-agent, design-task). Those skills contain curated, opinionated guidance. This skill is for filling gaps and verifying details.
How to Query the Docs
Step 1: Fetch the docs index
The CrewAI docs site publishes an llms.txt file — a structured index of every documentation page with descriptions. Fetch it first to find the right page:
WebFetch: https://docs.crewai.com/llms.txtThis returns a categorized list of all doc pages in the format:
- [Page Title](https://docs.crewai.com/path/to/page): "Description of what the page covers"Categories include:
- API Reference — REST endpoints (kickoff, status, resume, inputs)
- Concepts — agents, crews, tasks, tools, flows, memory, knowledge, LLMs, processes, training, testing
- Enterprise — RBAC, SSO, automations, traces, deployment, triggers, integrations
- Tools Library — 40+ tools organized by category (AI/ML, automation, cloud, database, files, search, web scraping)
- MCP Integration — MCP server setup, transports, DSL, security
- Examples & Cookbooks — practical implementations
- Learning Paths — tutorials and advanced topics
- Observability — monitoring integrations
Step 2: Fetch the relevant page
Once you identify the right page from the index, fetch its content:
WebFetch: https://docs.crewai.com/<path-from-index>Step 3: Synthesize and cite
Combine what you find from the docs with context from the other skills to give a clear, actionable response. Always include the docs URL so the user can read further.
Workflow Summary
- Understand the user's question — what specific CrewAI concept, API, or behavior are they asking about?
- Fetch
llms.txt— scan the index to find the most relevant page(s) - Fetch the page(s) — retrieve the actual documentation content
- Synthesize the answer — combine docs content with context from other skills
- Cite the source — include the docs URL in your response
For an Even Better Experience
Users who frequently query CrewAI docs can configure the CrewAI docs MCP server in their coding agent for richer, structured search:
https://docs.crewai.com/mcpThis is optional — the llms.txt workflow above works without any setup.
Examples of Good Use Cases
| User Question | Why This Skill |
|---|---|
"What parameters does Crew() accept?" | Specific API reference — docs are authoritative |
| "How do I set up telemetry in CrewAI?" | Niche feature not covered in other skills |
"What's the difference between Process.sequential and Process.hierarchical?" | Detailed comparison best sourced from docs |
"I'm getting ValidationError when using output_pydantic" | Troubleshooting — docs may have known issues or caveats |
| "How do I deploy a CrewAI flow to production?" | Deployment guidance lives in docs, not in design skills |
"What CLI commands does crewai support?" | CLI reference is a docs concern |
| "How do I configure memory for a crew?" | Detailed config options beyond what design-agent covers |
| "What tools are available for web scraping?" | Tools library reference |
| "How do I set up SSO for CrewAI enterprise?" | Enterprise features live in docs |
Related Skills
- getting-started — project scaffolding, choosing abstractions, Flow architecture
- design-agent — agent Role-Goal-Backstory, parameter tuning, tools, memory & knowledge
- design-task — task descriptions, expected_output, guardrails, structured output, dependencies