KI-Agenten
Langchain
The agent engineering platform
★ 147,4 Tsd. SterneMIT
Anleitung ansehen →CrewAI ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von crewAIInc (crewAIInc/crewAI auf GitHub). Es hat 59.319 Sterne auf GitHub und 8.628 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 3. Oktober 2026 auf GitHub aktualisiert.
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work…
Application frameworks, agent runtimes, or voice stacks — choose by whether you are building apps, automations, or realtime voice.
Python or TypeScript packages install with one command; most include quickstart templates.
Give the agent functions it can call and a memory layer so it stays coherent across sessions.
Launch the agent, watch its tool calls and reasoning traces, and iterate on prompts and tools.
Agents act on the world: APIs, databases, browsers, and shell commands.
Crews and graphs of specialized agents collaborating on complex tasks.
Short and long-term memory so agents improve across sessions.
Traces of every decision and tool call — essential for debugging.
Low-latency stacks for conversational voice agents.
Swap underlying LLMs without rewriting your agent logic.
Download the source and move into the project folder.
git clone https://github.com/crewAIInc/crewAI.git
cd crewAIIsolate dependencies so nothing clashes with your other projects.
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activateInstall the requirements, then follow the README's launch command (it varies per project).
pip install -r requirements.txtFrameworks build your own; some repos here ship ready-made agents you can run directly.
Tool-calling quality matters more than raw size — strong mid-size models work well.
It is an open-source project at crewAIInc/crewAI with 8,628 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Sandbox tool access, confirm destructive actions, and review traces — treat agents like junior employees with supervision.
Self-hosted frameworks are free; you pay only for model inference (local or API).
A quickstart template in the framework's docs — most get a first agent running in minutes.
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.