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Observability

Observability, tracing, and evaluation tools for AI agents in production — Langfuse, LangSmith, OpenTelemetry integrations and more. Track token usage, call chains, latency, cost, and output quality to keep agents reliable.

111 projects

Netdata

80.2k · Go
Active A+

Netdata is an open-source real-time observability platform with second-resolution metrics, AI health checks and zero-config deployment, widely used for monitoring AI agent services.

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  • · Real-time metrics — per-second collection of system, app, container and Kubernetes metrics
  • · AI anomaly detection — learns baselines and alerts on outliers automatically
  • · Zero-config deploy — one command starts an agent with dashboards, no extra config

Kong

44.0k · Lua
Active A+

The cloud-native API and AI Gateway providing LLM request routing, rate limiting, load balancing and observability for AI agent applications.

observabilityapiagent +2
  • · API Traffic Management — Advanced routing, load balancing, health checks via RESTful Admin API or declarative config
  • · AI Gateway Capabilities — Universal LLM API routing across OpenAI, Anthropic, Gemini, Bedrock and more
  • · MCP Traffic Governance — MCP security, observability, and auto-generation from any RESTful API

PostHog

37.7k · Python
Active B

PostHog is a full-stack observability platform for AI agents and self-driving products, combining analytics, session replay, feature flags and error tracking.

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  • · AI observability — Trace, token usage and diagnostic dashboards purpose-built for AI agents
  • · Session replay — Capture complete agent execution event streams for replay and debugging
  • · Feature flags & experiments — A/B testing and progressive rollouts coordinated with agent behavior

Langfuse

33.2k · TypeScript
Active A

Open-source LLM engineering platform providing tracing, evaluations, prompt management, and dataset management with integrations for LangChain, OpenAI, Anthropic, and more.

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  • · LLM application observability — trace LLM calls, retrieval, embeddings, agent actions with user session debugging
  • · Prompt management — centrally manage, version control, and iterate on prompts with server/client-side caching
  • · Evaluation pipelines — supports LLM-as-a-Judge, code evaluators, user feedback collection, and custom pipelines

Prompt Optimizer

33.1k · TypeScript
Active A+

An AI prompt optimizer that helps users write better prompts and achieve improved AI results.

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  • · One-click Intelligent Optimization — Input simple prompts; AI auto-generates optimized versions with multi-round iteration
  • · Dual Mode Optimization — Supports both system prompt and user prompt optimization for different scenarios
  • · Multi-model Integration — Supports OpenAI, Gemini, DeepSeek, Grok, Zhipu AI, SiliconFlow, and more

MLflow

27.5k · Python
Active A

MLflow is the open-source AI engineering platform for debugging, evaluating, monitoring, and optimizing AI agents and LLM applications, with model and data access management.

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  • · LLM Observability — Capture complete traces of Agent and LLM applications via OpenTelemetry
  • · Systematic Evaluation — 50+ built-in metrics and LLM Judges for automatic output quality assessment
  • · Prompt Registry & Optimization — Version-controlled prompts with auto-optimization algorithms

12 Factor Agents

25.3k · TypeScript
Stale B

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

agentframeworkevaluation +2
  • · 12 core principles framework — systematic best practices from NL-to-tool-calls to stateless reducer pattern
  • · Own your context window (Factor 3) — build and manage LLM context yourself, not via framework black box
  • · Tools as structured outputs (Factor 4) — treat tool calls as structured outputs, simplify agent interaction

Promptfoo

24.3k · TypeScript
Active A

Test and evaluate LLM prompts, agents, and RAG pipelines. Built-in red teaming and security evaluation for reliable AI applications.

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  • · Automated LLM evaluations — Batch test prompts, models, and RAG pipeline output quality
  • · Red team security testing — Built-in vulnerability scanning and adversarial testing for LLM security
  • · Multi-model comparison — Side-by-side comparison of OpenAI, Anthropic, Azure, Bedrock, Ollama models

Opik

21.4k · Python
Active A+

Opik is an open-source LLM observability platform providing agent tracing, evaluation testing, and prompt experiment management to help developers monitor and optimize AI agent systems.

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  • · Comprehensive LLM observability with deep tracing of calls, conversations, and agent activity
  • · LLM-as-a-judge metrics for hallucination detection, moderation, and RAG assessment
  • · Production-ready monitoring dashboards handling 40M+ traces per day at scale

Agents Towards Production

21.3k · Jupyter Notebook
Active A+

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

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  • · 28 production-grade tutorials — Covering stateful workflows, vector memory, web search APIs and more
  • · End-to-end coverage — From Docker deployment, FastAPI endpoints to security guardrails and GPU scaling
  • · Multi-agent coordination — Teaching multi-Agent collaboration architecture design and implementation

OpenAI Evals

19.2k · Python
Stale B

OpenAI's framework for evaluating LLMs and LLM systems, providing an open-source registry of benchmarks and tools for systematic model assessment.

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  • · Open-source registry of evals for testing different dimensions of LLM performance
  • · Custom eval creation using basic and model-graded templates without writing code
  • · Private evals support for evaluating LLM patterns in your workflow without exposing data

DeepEval

17.6k · Python
Active A

DeepEval is an open-source evaluation framework for LLM applications. It provides rich evaluation metrics and tools, supporting unit testing and integration testing to help developers build reliable LLM applications.

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  • · Pytest-compatible LLM evaluation framework with ready-to-use metrics for agents, RAG, and chatbots
  • · Agentic metrics including Task Completion, Tool Correctness, Step Efficiency, and Plan Adherence
  • · RAG metrics covering Answer Relevancy, Faithfulness, Contextual Recall/Precision/Relevancy, and RAGAS

RagaAI Catalyst

16.1k · Python
Stale B

RagaAI Catalyst is an observability, monitoring, and evaluation framework for Agent AI, supporting agent/LLM/tool tracing, multi-agent debugging, and self-hosted dashboard analytics.

observabilitytracingevaluation +2
  • · Comprehensive LLM project management: project management, dataset management, evaluation, tracing, and prompt management in one platform
  • · Agentic Tracing module: tracks agent/LLM/tool call full chains, monitors token usage and execution patterns
  • · Built-in evaluation metrics (Faithfulness, Hallucination, etc.) with custom thresholds and batch evaluation support

Ragas

15.3k · Python
Stale B

Ragas is a framework for evaluating RAG (Retrieval Augmented Generation) systems. It provides various evaluation metrics including faithfulness, answer relevance, context precision, helping developers optimize RAG application performance.

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  • · Objective evaluation metrics using both LLM-based and traditional approaches for precise LLM app assessment
  • · Automatic test data generation covering a wide range of scenarios for RAG systems
  • · Seamless integrations with LangChain, major observability tools, and popular LLM frameworks

OpenMetadata

14.9k · TypeScript
Active A

OpenMetadata is a unified metadata platform for data and AI, providing data asset discovery, lineage, governance, and agent context retrieval capabilities.

observabilitymetadatadata-governance +2
  • · Unified metadata model — One-stop management of tables, columns, metrics, dashboards, pipelines, and ML models
  • · Automatic lineage — End-to-end data lineage across databases, BI tools, and orchestration platforms
  • · Data quality SLA — Built-in data quality testing framework with rules, SLA alerts, and failure tracking

LM Evaluation Harness

13.7k · Python
Active A

A framework for few-shot evaluation of language models by EleutherAI, providing standardized evaluation pipelines supporting hundreds of benchmark tasks and widely adopted as a core LLM evaluation tool in the community.

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  • · Over 60 standard academic benchmarks with hundreds of subtasks
  • · Support for transformers, vLLM, GPT-NeoX, and multiple inference backends
  • · Commercial API model evaluation for OpenAI, Anthropic, and more

TensorZero

11.7k · Rust
Normal B

TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and A/B testing, designed for production agents.

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  • · Unified LLM gateway - one API for Anthropic, OpenAI, Bedrock, Gemini, vLLM, and 20+ providers
  • · Sub-1ms p99 overhead at 10k+ QPS - Rust core built for production-grade throughput
  • · Inference and feedback storage - own your data in your own database

Crucix

11.4k · JavaScript
Normal B

Crucix is a personal intelligence agent that watches the world from multiple data sources and pings you when something changes, helping you stay on top of information in real time.

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  • · Aggregates 27 OSINT sources: satellite fire data, flight tracking, radiation monitoring, and more
  • · Jarvis-style 3D WebGL dashboard with globe, flat map, and animated flight corridors
  • · LLM integration for two-way intelligence assistant with Telegram and Discord alerts

Weights & Biases

11.2k · Python
Active A

Weights & Biases is an experiment tracking, visualization, and collaboration platform for ML and LLM applications, covering agent training evaluation, hyperparameter management, and model registry workflows.

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  • · Experiment tracking — Automatically log hyperparameters, metrics, system resources, and code versions with side-by-side comparison
  • · W&B Models — Provides model artifacts registry, versioning, and promotion to production
  • · W&B Weave — LLM and agent tracing tool with prompt evaluation, conversation replay, and quality scoring

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