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AI governance and observability platform with 100+ automated tests and real-time guardrails to evaluate and monitor ML/LLM systems.
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
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- ✦100+ automated AI tests
- ✦Offline evaluation and CI/CD for AI
- ✦Real-time observability and tracing
- ✦Guardrails against PII leaks, injection, hallucination
- ✦Data-quality and drift monitoring
- ✦Compliance/governance alignment
- ✦Git, SDK, CLI and REST API integration
- ✦Unified proxy and protocol conversion across 100+ LLMs
- ✦Model-level fallback and routing
- ✦Semantic and exact-match AI caching
- ✦Token tracking and quota controls
- ✦Content-safety and data-protection filtering
- ✦MCP service hosting and plugin marketplace
- ✦Unified access to 100+ LLMs in OpenAI format
- ✦Cost/spend tracking per key, user and team
- ✦Budgets and rate limiting
- ✦Automatic provider fallbacks and retries
- ✦Virtual keys and team management
- ✦Logging and observability integrations
- ✦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
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- →Evaluate models before production
- →Monitor live AI systems for issues
- →Prevent unsafe or non-compliant outputs
- →Catch data drift and quality problems
- →Centralizing access to multiple LLM providers
- →Building and governing AI agent/MCP services
- →Controlling token spend across teams
- →Adding caching and safety to LLM calls
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy