KI-Agenten
deepteam
Open-source red-teaming framework that stress-tests LLMs and AI agents.
★ 3,0 Tsd. SterneApache-2.0
Anleitung ansehen →deepeval ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von confident-ai (confident-ai/deepeval auf GitHub). Es hat 18.695 Sterne auf GitHub und 2.032 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 7. Oktober 2026 auf GitHub aktualisiert.
Unit tests for LLMs — evaluate before you ship.
Pick one concrete task so you can judge whether the project fits.
Clone the repo and check the prerequisites listed in the README.
Install what it needs and configure credentials or model access.
Run a minimal example end to end before wiring it into your stack.
A library of evaluation metrics for model outputs.
Integrates with pytest-style workflows.
Catch quality drops between model versions.
Maintained by confident-ai with docs and cloud options.
Apache-2.0 licensed.
Designed to run in automated pipelines.
Install DeepEval from PyPI.
pip install deepevalDefine metrics over your model's outputs in a test file.
# see README for metric examples
# test_example.pyExecute the evaluation suite.
deepeval test run test_example.pyIt is maintained by confident-ai at confident-ai/deepeval, with 2,032 forks.
The open-source framework is free under Apache-2.0; a cloud tier also exists.
Core evals run locally; some metrics may call models through your own API keys.
Compare with Ragas, LangSmith evals, and others on metrics coverage and workflow fit.
Install the package, write a test file with your metrics, and run it.
Open-source red-teaming framework that stress-tests LLMs and AI agents.
Put guardrails on LLMs: validate outputs before they reach users.
Open-source LLM observability: monitor, evaluate, and experiment.
A rigorous benchmark for LLMs acting as agents.
Chinese-language course on AI basics: GPU architecture, CUDA, LLMs, and agents.
JADX plugin + MCP server that lets LLMs analyze Android APKs live — decompile, review, find vulnerabilities
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls deepeval nicht das Richtige ist.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die deepeval bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
Agent Zero AI framework
Microsoft's trainer for AI agents - reinforcement learning for almost any agent framework
Open source agentic operating system
A self-healing harness that connects an LLM directly to your real browser
Devika - the open-source agentic software engineer that plans, browses and writes code
Build customer-facing AI agents that actually follow your rules
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.