Create a new CrewAI project with standard scaffolding, including project structure and config files. After creation, configure API keys in the .env file.
MIT
649,985 tools. Updated 2026-10-09 16:50
"CrewAI" matching MCP tools:
- Look up how to use any CrewAI feature, tool, or pattern by querying the internal documentation RAG engine. Returns relevant snippets with source URIs.MIT
- Installs project dependencies for CrewAI projects by running crewai install and optionally adding extra pip/uv packages.MIT
- Retrieve the structure and core configs of a CrewAI project, including dependencies, YAML files, and source code, to inspect its current state.MIT
- Reset or check memory status for a CrewAI project. Use 'reset' to clear memories with specific flags, or 'status' to verify if memory is enabled.MIT
- Retrieve ranked knowledge snippets about MCP tool usage, response shapes, integration patterns, and code examples. Call before other tools to confirm parameter usage and avoid incorrect assumptions.MIT
Matching MCP Servers
- AlicenseBqualityCmaintenanceTransforms any compatible LLM or AI Assistant into a master orchestrator of CrewAI, providing tools to dynamically generate, edit, test, and execute multi-agent systems.14MIT
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- Lists available databases for querying, so you can discover the valid database_name before using SQL search tools.MIT
- Adds ioa-observe-sdk instrumentation to Python AI agent files, inserting Observe.init(), SDK imports, and decorators for LlamaIndex, LangGraph, CrewAI, and OpenAI SDK agents, while backing up the original file.Apache 2.0
- Statically analyze LLM-agent workflows to identify runaway budget risks and missing token caps before deployment. Get a worst-case budget summary with pass/fail verdict.MIT
- Converts natural-language questions into validated read-only SQL and executes them on a specified PostgreSQL database to return query results.MIT
- Replay a crew execution starting from a specific task to debug and retry failed tasks.MIT
- Search community artifacts, summon them to your collection, and trace their lineage of forks and dependencies.Apache 2.0
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- Audit multi-agent system communications to detect infinite delegation loops, privilege escalation, data leakage, and unauthorized handoffs across protocols like A2A, CrewAI, LangGraph, and AutoGen.Apache 2.0
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- Train AI agents using human-in-the-loop feedback to optimize prompts and improve multi-agent system performance.MIT
- Discover AI agents on the Beacon network. Filter by provider or capability to find specific agents.MIT
- Modify a CrewAI agent's configuration by adding tools, LLM models, or custom parameters directly in the crew.py file.MIT
- Generate an interactive HTML visualization of a Flow project, mapping states and transitions to clarify flow logic.MIT
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