Observability, Evaluation & Testing

LLM evaluation and observability platform that identifies hallucinations, data errors, and prompt issues in production. Provides confidence scoring and uncertainty quantification alongside traditional metrics. Enterprise-focused with SOC 2 compliance.

Open-source observability and analytics platform for LLM apps. Tracks conversations, tool calls, and agent runs with a clean dashboard. Includes user session management, feedback collection, and cost attribution.

Open standard for capturing LLM observability data as OpenTelemetry spans. Defines semantic conventions for LLM calls, RAG retrievals, and agent runs. Instrumentation libraries for LangChain, LlamaIndex, and raw OpenAI available.

Observability platform from the Pydantic team with first-class support for tracing Python AI applications. Instruments Pydantic AI agents automatically. Clean UI for viewing structured traces with type-validated span attributes.

Comet's LLMOps platform for tracking, comparing, and debugging LLM prompts and responses. Provides a prompt management UI with version history and A/B testing. Deep integration with the Comet ML experiment tracking platform.

LLM observability and evaluation platform with thread-based conversation tracking and dataset management. Tight integration with Chainlit for building and monitoring chatbot applications. Provides a prompt playground with automatic evaluation scoring.

OpenTelemetry-native LLM observability platform that auto-instruments popular frameworks with a single line of code. Provides tracing without vendor lock-in, exporting to any OpenTelemetry-compatible backend. Free open-source SDK with optional cloud dashboard.

Open-source LLM observability and evaluation platform with an OpenTelemetry-native SDK. Auto-instruments 20+ LLM providers and vector databases. Ships with a self-hosted UI and Grafana dashboard templates for production monitoring.

W&B's toolkit for tracking LLM application calls, inputs, and outputs alongside traditional ML experiments. Integrates deeply with the W&B ecosystem for comparing model versions and prompt iterations. Ideal for teams already using W&B for ML experiments.

Open-source alternative to Datadog with native OpenTelemetry support. Provides distributed tracing, metrics, and logs in a single platform. LLM-specific dashboards make it easy to monitor inference latency and token consumption in production.

Application performance monitoring with native LLM span support in the Elastic Stack. Traces LLM calls alongside traditional service spans for unified observability. Integrates with the full Elastic ecosystem including alerting and anomaly detection.

Open-source ML and LLM monitoring platform. Generates visual reports and data quality checks for ML pipelines. LLM module tracks text quality, semantic similarity, and toxicity metrics over time. Integrates with existing data pipelines for continuous monitoring of agent outputs.

Open-source LLM observability proxy — route all your LLM calls through Helicone with one line of code to get logging, caching, rate limiting, and cost tracking. No SDK required, works with any OpenAI-compatible API. Manages prompt versioning and A/B testing.

Enterprise observability for LLM applications built into the Datadog platform. Distributed tracing for agent calls, cost dashboards, latency percentiles, and error tracking. Integrates with existing Datadog infrastructure monitoring for end-to-end visibility from user request to LLM response.

ML observability platform with dedicated support for LLM and agent monitoring.

Automated evaluation and red-teaming platform for enterprise LLM deployments. Tests models against custom criteria reflecting business policies, compliance requirements, and brand guidelines. Continuous monitoring in production with alerts on policy violations or quality degradations.

AI observability and evaluation platform for collaborative prompt engineering and agent debugging. Trace-first design with session-level analysis. Supports dataset curation from production traffic, custom evaluators, and A/B testing of agent configurations with statistical significance.

Observability, testing, and evaluation platform by LangChain. Traces every LLM call, chain step, and tool invocation with millisecond timestamps. Side-by-side prompt comparison, dataset management, and A/B testing. The most widely used observability tool for LangChain applications.

Arize's open-source tool for evaluating agent loops, tracing LLM chains, and analyzing embedding quality. Provides interactive visualizations of execution traces, built-in LLM-as-judge evaluators, and a local-first architecture that doesn't require sending data to the cloud.

Open-source LLM observability and analytics platform. SDKs for Python, TypeScript, and all major frameworks. Provides traces, evaluations, prompt management, and metrics in one tool. Self-hostable Docker/Kubernetes deployment or managed cloud. Growing fast as the open-source alternative to LangSmith.

Enterprise ML observability platform with deep support for LLM and agent monitoring. Tracks model drift, embedding drift, retrieval quality, and generation performance at scale — providing the alerting and dashboarding infrastructure needed to maintain agent quality in production.

Open-source AI observability tool for tracing and evaluating LLM apps and agents. Records traces, spans, and evaluations to identify hallucinations, latency spikes, and failures. Integrates with OpenTelemetry. Runs locally as a lightweight Python package.

End-to-end AI product quality platform for prompt engineering, tracing, and evaluation. Logs every experiment, compares versions side-by-side, and tracks regressions over time. Dataset management with human and LLM-as-judge scoring. Used by teams shipping production AI products.

LLM tracing and evaluation framework from W&B. Automatic tracing of LLM calls with decorator-based instrumentation. Built-in evaluation runners and leaderboards. Integrates naturally with W&B's existing experiment tracking for teams that already use W&B for ML training.

OpenTelemetry-based specification and SDK for instrumenting LLM and agent applications. Provides a vendor-neutral standard for capturing spans across model calls, tool invocations, and retrieval steps — enabling observability data to flow into any OTLP-compatible backend.

OpenTelemetry-based instrumentation for LLM applications from Traceloop. Auto-instruments LangChain, LlamaIndex, OpenAI, Anthropic, and others. Sends traces to any OpenTelemetry backend (Datadog, Honeycomb, Jaeger). Standard-based approach avoiding vendor lock-in.

Open-source LLM evaluation and tracing platform from Comet. Log traces, create datasets, run evaluations, and track production metrics in one tool. CI/CD integration for automated regression testing. Self-hostable with a clean web dashboard.

AI Gateway with built-in observability, caching, and reliability features. Route requests across LLM providers with automatic fallbacks and load balancing. Full request logging, cost tracking, and semantic caching. Drop-in replacement for OpenAI SDK with multi-provider support.

EleutherAI's unified evaluation framework for language models across 60+ benchmarks. The standard tool for evaluating open-weights models on academic and industry benchmarks. Powers the Hugging Face Open LLM Leaderboard.

Enterprise LLM evaluation platform with automated red-teaming and continuous monitoring. Tests models against a comprehensive set of safety and accuracy scenarios before deployment. Provides compliance reporting for regulated industries deploying AI agents.

Stanford CRFM's Holistic Evaluation of Language Models — one of the most comprehensive LLM benchmarks covering 42 scenarios and 59 metrics. Evaluates accuracy, calibration, robustness, fairness, and efficiency simultaneously. The gold standard for thorough model evaluation.

The updated v2 release of the most widely used open-source LLM evaluation framework, now supporting chat templates, multi-turn conversations, and 300+ tasks. Used to generate all Open LLM Leaderboard scores. Essential for custom model evaluation.

Open-source evaluation library from Relari AI focused on consistent, reproducible LLM pipeline metrics. Implements deterministic and probabilistic metrics for retrieval, generation, and end-to-end pipeline quality. Designed to plug into CI/CD workflows.

Open-source AI observability and evaluation library for LLM and computer vision. Provides a local UI for inspecting traces, running evals, and exploring embedding clusters. Works offline and ships with a comprehensive set of LLM evaluators.

Prompt management and evaluation platform for building reliable AI features. Provides a prompt playground, version control, and A/B testing for prompts. Evaluation suite includes human feedback collection and automated LLM-as-judge scoring.

Cloud platform for running, tracking, and regression-testing LLM evaluation pipelines. Works with DeepEval for running evals and provides a dashboard for tracking quality over time. Helps teams catch prompt regressions before they reach production.

Metrics and test sets for evaluating RAG pipeline quality automatically.

Evaluation and tracking library for LLM applications using feedback functions as metrics. Measure groundedness, answer relevance, context relevance, and custom metrics. Integrates with LangChain and LlamaIndex. Dashboard UI for visualizing evaluation trends over time.

Open-source evaluation and monitoring tool for LLM applications with 20+ pre-built checks. Evaluates response quality, factual accuracy, context relevance, and code quality. Provides a hosted dashboard for tracking metrics over time and detecting performance regressions.

Evaluation library from Arize for measuring RAG performance, tool call accuracy, and agent reasoning quality. LLM-as-judge evaluators with pre-built templates for common metrics. Integrates with Phoenix tracing to evaluate traces automatically.

EleutherAI's framework for evaluating language models on hundreds of benchmarks. The standard tool used by model creators to report benchmark scores. Supports 200+ tasks including MMLU, HellaSwag, HumanEval, and custom task definitions for domain-specific evaluation.

Built-in red-teaming module in Promptfoo that automatically generates adversarial prompts including jailbreaks, prompt injections, and policy violations to stress-test your LLM outputs.

MLOps platform for experiment tracking, model production management, model registry, and complete data lineage. Free for individuals and academics with unlimited experiments.

Developer-first MLOps platform for experiment tracking, dataset versioning, and model management. Free tier for personal projects with 100 GB storage.

The definitive benchmark for evaluating AI coding agents on real GitHub issues. Contains 2,294 tasks from 12 popular Python repositories. Measures ability to generate patches that pass existing test suites. Every major coding agent reports SWE-bench scores.

Open-source LLM evaluation framework with 14+ built-in metrics including hallucination detection, bias, toxicity, and RAG-specific metrics. Pytest-compatible — run LLM evaluations in CI pipelines. Benchmarks models against common datasets. Generates synthetic test datasets from documents.

Multi-dimensional benchmark evaluating LLM agents across 8 distinct environments: OS, DB, Knowledge Graph, Digital Card Game, Lateral Thinking Puzzle, HouseHolding, Web Shopping, and Web Browsing. Tests generalizable agent capabilities beyond narrow coding tasks.

Quality and security testing platform for machine learning and LLM systems. Automatically scans models and agents for hallucinations, biases, prompt injections, and PII leakage — generating detailed vulnerability reports and suggesting mitigations before production deployment.

General AI Assistants benchmark by Meta, HuggingFace, and AutoGPT. Tests real-world assistant capabilities requiring multi-step reasoning, web browsing, document reading, and code execution. Considered harder and more realistic than most existing benchmarks.

Tool-Agent-User benchmark evaluating agents in realistic customer service scenarios. Tests multi-turn conversations where agents must use tools, follow policies, and satisfy user requests. More realistic than task-completion benchmarks for deployment-focused evaluation.

CLI and CI/CD tool for testing, evaluating, and red-teaming LLM applications. Define test cases in YAML, run evaluations across multiple providers, and catch regressions automatically. Supports LLM-as-judge scoring, adversarial testing, and custom metrics.

Framework for evaluating LLMs and LLM-powered systems by OpenAI. Includes a large registry of existing evals and a framework for building custom ones. The standard approach for systematic model evaluation in the OpenAI ecosystem.

Open-source evaluation framework from the UK AI Safety Institute. Structured evaluation tasks with solvers, scorers, and sandbox execution environments. Designed for rigorous safety evaluations of frontier models and agents. Extensible plugin system for custom evaluation scenarios.