Optimize prompts, code, and more with AI-powered Reflective Optimization
🧪 评测·观测·安全
模型评测、提示词测试、可观测性与护栏红队
Superagent protects your AI applications against prompt injections, data leaks, and harmful outputs. Embed safety directly into your app and prove compliance to your customers.
非线智能 NoneLinear - ReLE评测:中文AI大模型能力评测(持续更新):目前已囊括374个大模型,覆盖chatgpt、gpt-5.4、谷歌gemini-3.1-pro、Claude-4.6、文心ERNIE-X1.1、ERNIE-5.0、qwen3.6-max、qwen3.6-plus、百川、讯飞星火、商汤senseChat等商用模型, 以及step3.5-flash、kimi-k2.6、ernie4.5、MiniMax-M
AI Agent Governance Toolkit — Policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for autonomous AI agents. Covers 10/10 OWASP Agentic Top 10.
开源 LLM 可观测平台,一行代码接入监控与缓存。
Framework for evaluating and improving agents
SWE-bench: Can Language Models Resolve Real-world Github Issues?
🐢 Open-Source Evaluation & Testing library for LLM Agents
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI
Model interpretability and understanding for PyTorch
AutoRAG: Now your agent can find anything in your computer. It gets smarter if you are using it frequently.
Open-Source Agent Observability & Evals for AI Platform teams. Trace, test, route and govern every agent & coding assistant in the company.
Agenta is a workspace where you and your team build agents and automations.
Open-source observability for AI agents. Find where your agents fail, dispatch your coding agent to fix it, and verify the fix against real traces.
The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.
大型多模态模型的评测套件。
Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks
Set of tools to assess and improve LLM security.
AdalFlow: The library to build & auto-optimize LLM applications.
A library for mechanistic interpretability of GPT-style language models
评估与追踪 LLM 应用实验。
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents
🕹️ Open-source, developer-first LLMOps platform designed to streamline prompt design, version management, instant delivery, collaboration, troubleshooting, observability and more.