The LLM Evaluation Framework
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
Creating realistic RL environments requires collaboration between researchers, engineers, and domain experts across many dimensions: artifacts, environments tools, dynamism of the environment, reproducibility, and more. There is no open source framework for building these environments effectively. Until now.
FactCircuit: an auditable claim-and-evidence verification loop with immutable provenance, staged checks, bounded retrieval, and reproducible evaluation.
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.
Trace-native CI/CD for AI agents: grade production traces, cluster failures, freeze them into replayable regression tests, and block PRs that regress. Self-hostable.
Hybrid rule-based and context-aware LLM evaluation system for Chinese-to-Japanese game localization QA.
AI Observability & Evaluation
Open-source benchmark for browser AI agents on daily tasks.
A clinical decision-evidence benchmark: pull the one fact an answer rests on, and see whether the assistant's action moves with the evidence. Built on HealthBench.
Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 14 runtimes: Claude Code, Codex CLI, Gemini CLI, OpenCode, Copilot, Kiro and more.
HAEnv — synthetic longitudinal patients with code-derived gold, for evaluating health agents over time
the LLM vulnerability scanner
A lightweight Python SDK and CLI for evaluating recursive self-improvement (RSI) methods across agent benchmarks.
🐢 Open-Source Evaluation & Testing library for LLM Agents
Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.
Learn harness engineering by building Claude Code from scratch. Free, open-source course: 8 articles, 6 videos, 1 codebase.
The platform for continuously improving AI agents.
RISE: Red-teaming via Iterative Strategy Evolution (NeurIPS 2026)
OpenClaw + 大模型驱动的完整电商客服自动化系统:覆盖淘宝、拼多多、抖音、快手、小红书、视频号,包含浏览器/手机 Worker、订单物流与售后工具、记忆、回执、恢复和 Eval。
Easily fine-tune (SFT/RL), evaluate, and deploy Qwen, Gemma, or any open agentic LLM/VLM!
OpenCompass is an LLM evaluation platform, supporting a wide range of models from OpenAI, Anthropic, Gemini, Qwen, GLM, DeepSeek, etc, across 100+ datasets covering knowledge, reasoning, coding, science, language, long-context, and safety.
One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks
Evaluation and Tracking for LLM Experiments and AI Agents
Agentic LLM Vulnerability Scanner / AI red teaming kit 🧪
[JMLR 2026] UQLM: A Python package for LLM hallucination detection using uncertainty quantification
The Continuous-Improvement Stack for Agents. Our environment data and evals power agent improvement and monitoring.
Open-source infrastructure that turns scattered SKILL.md files into curated, retrieval-ready agent-skill corpora—with retrieval and evaluation tooling included.
[ICML 2024] TrustLLM: LLM trustworthiness evaluation across truthfulness, safety, fairness, robustness, privacy and ethics. Python/CLI toolkit for local models, compatible APIs and AI agent orchestration.
[NeurIPS D&B '25] The one-stop repository for LLM unlearning
🔥🔥 Awesome Jev: open-source models, projects, benchmarks, and independent evaluations for Jev and System One models.
Evidence-first infrastructure for reproducible, isolated executable evaluation and online rewards.
Track 33,707+ AI benchmark, eval, dataset, and data-quality records from 40 public sources, with linked evidence and daily updates.
Compile real-world Claude Code and Codex trajectories into verified, tradable post-training assets.
Evolutionary multi-agent runtime that breeds, evaluates, and improves autonomous agents across reproducible epochs to converge on optimization of a goal.
Evaluator-neutral AI evaluation evidence, baseline regression gates, and JSON, JUnit, and SARIF reports for CI.
[EMNLP 2026] A multilingual benchmark for repository-level unit test generation, evaluating LLMs across 5 programming languages and real-world code under reproducible, Docker-based execution.
Deletion-first Agent Skill for removing test bloat, verification theater, and speculative fallbacks while preserving behavior.
Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.
Evaluate visual models on your own images, JSON Schema, and production constraints with LangGraph, Pareto analysis, and a no-key Replay demo.
Mini Program Engineering Skill Suite is an Agent Skill suite for evidence‑first mini‑program development. It helps agents bring WeChat or other mini‑program projects from vague intent to reliable engineering work: project intake, product specification, architecture, implementation, UI & device adaptation, debugging, verification, release readiness.
MCP gateway & OAuth MCP servers. LangGraph agent: memory, eval/tracking, PII guard, context trimming. Docker quick start for enterprise agents.
A benchmark for testing whether coding agents can resolve engineering tasks in scientific software
多智能体小说写作框架 | Multi-agent novel-writing framework — 每个角色都有自己的脑子,每一章都有质量门禁
PaperBench High-Difficulty and Expanded Tasks: paper reproduction beyond machine learning. 93 tasks in 12 research areas, run in domain-native software and graded by clean replay. 12 public tasks.
Typed judgment tools for MCP agents. TypeSafe's Jev model as verify, screen, find, classify, rerank, decide, compare, extract, review, gate, and score: the model judges, policy decides auto, review, or escalate.
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Learning to Evaluate Before Improving: Automatic Rubric Induction for Automatic Research Agents
Evidence-first game operations incident investigation agent with read-only MCP tools and reproducible evaluations.
Curated list of open-source LLMOps tools: tracing, evals, AI gateways, guardrails and prompt management. Sorted by stars, refreshed weekly.
PAST-Bench: Benchmarking the Foundations of Recursive Self-Improvement in Personal Agents
Argolink-focused LLM evaluation and regression workflow for GPT-6 Sol.
Independent, evidence-based map of when TypeSafe's Jev actually holds up vs. breaks down — real API-call receipts, not a leaderboard. 中文為主的雙語 repo。
Myrtia — agent-native red teaming for LLMs and AI agents: a deliberating council of attacker models (propose, cross-critique, Borda vote, chairman synthesis), 13 attack strategies, live turn-by-turn UI and ASR analytics. Open-source, built on IBM ARES.
RLCDAlignBench: 44 alignment-failure detection benchmarks and code for 'Just Ask Jev' (RLCD zero-shot detector of AI alignment failures)
pytest for AI Apps — Fast, free, offline testing and self-improving prompts for Python developers.
An LLM Benchmark evaluation platform for automated model testing and analysis. 面向大语言模型的 Benchmark 自动化评测平台,支持模型测试与结果分析