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Future AGI, LLM ve AI agent uygulamalarını değerlendirme, izleme, simüle etme ve koruma işlerini self-host edilebilen tek bir platformda birleştiriyor.

Neden yükseliyor · Konu, agent uygulamalarında evaluation ve observability ihtiyacının artmasıyla örtüşüyor; 157 günlük repo yaşına karşılık 2.081 yıldız ve son 28 günde 597 commit geliştirme temposuna işaret ediyor. Farklı yazar sayısı 6; bu veriler ilginin nedenini tek başına kanıtlamaz ve yıldızlar benimseme ya da kalite ölçüsü değildir.

neatroots: see, check and replay your AI agent's runs on your machine while you build it. Local-first, by Neatlogs.

Agent skills for the Agentic SDLC: team lifecycle (team-boot, team-learn, team-init, team-repair), software factory, evals, CDR lifecycle wi

Build agentic systems. Run them with confidence. Orchestrate agents, automate business processes, inspect every execution, and keep humans i

Run your real agent with and without your skills, MCPs, and rules. See which ones actually help, and what they cost in tokens. Supports Clau

FailproofAI, farklı AI agent harness’lerindeki çalıştırmaları izleyen ve tehlikeli tool çağrılarını yürütülmeden önce policy ile engelleyebilen bir araçtır.

Neden yükseliyor · 175 günlük bir repo için 5.166 yıldız ve son 28 günde 84 commit ile 8 farklı yazar, hem belirgin ilgiye hem de yakın dönem geliştirme faaliyetine işaret ediyor. Çok sayıda harness için ortak observability ve enforcement sunma iddiası, agent araçlarının çeşitlendiği dönemde dikkat çekici; yıldızlar tek başına benimseme veya kalite kanıtı değil.

Same model, different wrapper: a from-scratch benchmark comparing coding-agent harnesses (codex, pi, opencode, cursor, devin) and open model

jev-calibrate, Jev sorularının etiketli örneklerde karar vermeye uygunluğunu ölçen bir TypeScript CLI aracıdır.

Neden yükseliyor · Kesin yükseliş nedeni mevcut verilerden anlaşılamıyor. Repo 6 günlük, 32 yıldızlı ve son 28 günde 11 commit almış; ancak farklı yazar sayısının 1 olması aktivitenin tek geliştiricide toplandığını gösteriyor.

My Pi coding agent setup: secret redaction, LSP, searchable context, recall, telemetry, observability, and team mode. Toggle with /extension

Open-source skills for Claude Code and Codex, with an MCP server, a PyPI package and Suede Awesome Everything, a directory that scores AI ag

Local LLM cost-tracking proxy for OpenAI, Anthropic, Gemini, and pinned OpenRouter calls with token usage, failure, and billing-integrity re

Benchmark, evaluate, and optimize skills to ensure reliable performance across all LLMs

A composable framework for reproducible evaluation of long-running agents.

AI evaluators CLI for your AI apps and Agents - Dynatrace AI Observability

Open benchmark for Claude Code SEO skills — real headless execution against fixture sites with planted-defect answer keys. Deterministic sco

Benchmark coding agents and models on your own repository with private Harbor-formatted evals built from its merged pull requests.

CI for your coding-agent setup (CLAUDE.md, skills, hooks): pinned baselines, a canary on every Claude Code release, model and version matrix

Skills for your skills. Create, test, audit, package, and visualize Agent Skill libraries—locally.

Catch AI agents that say "done" when the task isn't. Checks agent-trace/v1 traces with claim-evidence rules, LLM judges and a trained classi

Type-aware, evidence-backed delivery contract for AI coding harnesses. Different agents in. One predictable human handoff out.

Blind A/B testing for AI agent skills, .cursorrules, CLAUDE.md, and system prompts. Detects placebo and harmful instructions.

Diff your AI agent's behavior between two runs. See exactly which tool calls, args, costs and outputs changed when you swap models or edit p

Portable YAML evals for AI agents and model harnesses.

Developer and product skills for Claude Code, Cursor and Codex: 1-file AI evals and Chrome extension store assets.

Evidence-grounded evaluation for AI agents — verifies each claim against the agent's real tool outputs (constrained, evidence-grounded model

Red Team AI Benchmark: Evaluating LLMs for authorized offensive-security tasks

Benchmark of AI coding agents building web apps and deploying them to Cloudish, DigitalOcean, Render and Vercel, measuring success, time, to

How do you KNOW if a 'skill' is any good - deterministically? This runs controlled skill-vs-no-skill experiments. It doesn't just ask, “Did

Open-source control plane for governed ModelOps — fine-tune, evaluate, promote, and serve models as cost-capped, policy-gated, audited comma

What can Jev actually do? Reproducible experiments and research reports exploring its capabilities and limits.

Does the model pick the right tool? Tool-routing tests for your MCP server: run them on Claude and OpenAI models, see where each request lan