Skip to main content
⌘K
All Categories
All ToolsLLM Inference EnginesLLM ModelsVector DatabasesAgent FrameworksChat InterfacesRAG & Document ProcessingSpeech to TextText to SpeechImage GenerationFine-tuning & TrainingMonitoring & ObservabilityPrivacy & SecurityEmbedding ModelsDeploymentAgent & Workflow AutomationVideo GenerationVision & MultimodalCode AssistantsData Utilities

CrewAI

Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

View on GitHub Official site
Agent Frameworks MIT Medium setup 51,567 stars

Overview

Plain English

Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

Technical

Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

Technical scorecard

License MIT
Commercial use Yes
OpenAI-compatible API No
REST API No
Fine-tuning support No
Quantization support No
Docker available No
GUI / no-code available No
Telemetry None
Offline after setup Yes

Data & Privacy

Does it send data online?

After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.

Does it store history?

Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.

License checks?

Commercial use is marked as allowed or likely allowed by the listed license.

Telemetry?

None

Last verified: May 17, 2026. Maintainer verification should be treated as directory guidance, not legal advice.

Setup & Installation

Medium

A developer can usually get this running with standard docs.

Prerequisites

Python, Docker, Bare Metal 

# Start with the official project documentation
# https://www.crewai.com/open-source

Hardware Requirements

RAM8 GB minimum / 16 GB recommended
Hardware tagsCPU Only
Model formatsNot specified
Primary languagePython

Works Well With

n8n Open-source workflow automation with 500+ integrations, native AI nodes, and MCP support for self-hosted AI orchestration. Ollama Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. llama.cpp LLM inference in C/C++. vLLM A high-throughput and memory-efficient inference and serving engine for LLMs.
Open suggested stack
Share on X Share on LinkedIn