Local-first agent debugger with replay, failure memory, smart highlights, and drift detection.
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Updated
Oct 7, 2026 - Python
Local-first agent debugger with replay, failure memory, smart highlights, and drift detection.
Find where AI apps break first — and block regressions before they ship.
MCP server for rr reverse debugging — enables AI assistants to record, replay, and inspect program executions via GDB/MI
Production-ready full-stack AI debugging platform with FastAPI, React, semantic search, and authenticated analysis history.
Claude Code skill + CLI that lets an AI agent drive Rider's MCP debugger to debug a running Unity Editor at runtime, no session restart needed.
一个新手用AI写的小玩具级别代码编辑器,不喜勿喷
Debug and analyze RAG pipelines with trace capture, grounding checks, diffing, reports, and CLI tooling.
QA Continuity AI — a private, on-premise platform that turns scattered project knowledge (rules, flows, bugs, tests, CI) into test plans, Playwright automation, and searchable QA memory. 100% local LLMs via Ollama + HuggingFace, zero API cost.
AI-guided low-fidelity audits for physical simulation results
🔬 Comprehensive Multi-Model Analysis of AI-Driven Debugging Prompts | 13 AI Models Evaluated | Cross-Tier Evaluation Capability Discovered | MIT Licensed Academic Research | v0.1.0
基于 MCP 协议的 AI 调试追踪平台,提供会话管理、链路追踪、错误分析与 Dashboard 可视化
Toolkit for systematically probing, classifying, and debugging failure modes in LLM and RAG systems — reasoning errors, hallucination, and API-level behaviour.
Package failures into redacted evidence bundles and compare what changed across local, CI, and OpenTelemetry incidents.
A machine learning debugging tool that analyzes prediction failures, detects overfitting, imbalance and data leakage, and provides improvement suggestions.
Turn error logs and code context into a practical debugging plan and regression test suite.
Decision-level observability for LLM pipelines, making system behavior explainable even when no outputs exist.
accel-sim is a high-performance, CPU-based estimation tool designed to predict the latency and memory footprint of Transformer-based workloads on hardware accelerators (GPUs, TPUs).
AI-powered debugging assistant — upload a codebase + error logs, get root cause analysis and fix suggestions
Context-aware CLI tool for terminal error capture, AST parsing, and AI-powered automated code remediation.
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