#Llm-observability
Showing 26 of 26 repositories tagged #llm-observability, ranked by stars
🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
Fastest enterprise AI gateway (50x faster than LiteLLM) with adaptive load balancer, cluster mode, guardrails, 1000+ models support & <100 µs overhead at 5k RPS.
🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓
Next-generation AI Agent Optimization Platform: Cozeloop addresses challenges in AI agent development by providing full-lifecycle management capabilities from development, debugging, and evaluation to monitoring.
AI observability platform for production LLM and agent systems.
The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.
Agent Skills as a Memory Layer
Laminar - open-source observability platform purpose-built for AI agents. YC S24.
Observal is a sandboxed artifactory and analytics platform for your AI development stack. Setup Observal, define the scope and share your Skills, MCPs and Agents.
The Continuous-Improvement Stack for Agents. Our environment data and evals power agent improvement and monitoring.
TraceRoot - open-source observability and self-healing layer for AI agents. YC S25
React components for visualizing traces from AI agents
Build, Improve Performance, and Productionize your AI Application
A powerful AI observability framework that provides comprehensive insights into agent interactions across platforms, enabling developers to monitor, analyze, and optimize AI-driven applications with minimal integration effort.
A comprehensive solution for monitoring your AI models in production
the agent experience layer · observability + memory for AI coding agents (Claude Code + Codex) · local-first, typed, yours
Continuum — the agent runtime by ShyftLabs. Build, orchestrate, ship.
Visualize Pydantic AI agent workflows from Logfire traces as an interactive HTML diagram — tools, nested sub-agents, tokens and exact cost.
🪢 Auto-generated Java Client for Langfuse API
htop for AI coding agents — monitor token usage, costs, and workflows across Claude Code, Cursor, Kiro, Codex, and Copilot
GPU Observability with workload attribution. One OTLP agent per node ties hardware metrics (NVIDIA, AMD, Intel Gaudi) to the K8s pod or Slurm job burning the GPU.
Local-first flight recorder for coding agents : replay every run as a live session map, score the context bill, and write the fix back into AGENTS.md — no API key, one npx command.
AI Firewall & LLM security toolkit - protect your AI applications from prompt injection, jailbreaks, PII leakage, and adversarial attacks
Production-ready RAG framework for Python — multi-tenant chatbots with streaming, tool calling, agent mode (LangGraph), vector search (FAISS), and persistent MongoDB memory. Built on LangChain.