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AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.
Open-source, OpenTelemetry-native platform for LLM observability, tracing, evaluation and prompt management.
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
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- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦Request logging and LLM observability
- ✦AI gateway with routing and automatic fallbacks
- ✦Caching and rate limiting
- ✦Session, user and custom-property analytics
- ✦Prompts, playground and datasets for testing
- ✦Integrations with OpenAI, Anthropic, Azure and more
- ✦OpenTelemetry-native distributed tracing across 100+ LLMs and frameworks
- ✦Online evaluation via LLM-as-a-judge or code
- ✦Offline experiments and regression detection
- ✦Annotation queues for expert review
- ✦Alerts and drift detection
- ✦Prompt management, CLI and docs MCP server
- ✦OpenTelemetry-native distributed tracing
- ✦Token usage and cost tracking
- ✦LLM evaluations (online/offline)
- ✦Prompt management and versioning
- ✦GPU and vector-DB monitoring
- ✦60+ LLM/framework integrations
- ✦Self-hostable via Docker; export to Grafana/Datadog
- ✦Agent trace capture and conversation intelligence
- ✦Semantic and exact-text search across all traces
- ✦Automatic issue discovery with Slack/email/webhook alerts
- ✦OpenTelemetry-compatible SDK with no lock-in
- ✦Automated evals and golden dataset generation
- ✦Failure-mode clustering and MCP server integration
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
- →Debugging multi-agent systems
- →Monitoring live agent quality at scale
- →Catching regressions before release
- →Human review of edge cases
- →Aligning automated evaluators with domain experts
- →Trace and debug LLM applications
- →Monitor AI cost and performance
- →Evaluate prompts and models
- →Add observability without code changes
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues