Ross ROSS = Recommend OSS · open-source software intelligence for agents

openlit/openlit

Open source platform for AI Engineering: OpenTelemetry-native LLM Observability, GPU Monitoring, Guardrails, Evaluations, Prompt Management, Vault, Playground. 🚀💻 Integrates with 50+ LLM Providers, VectorDBs, Agent Frameworks and GPUs. observed · 2026-08-28

github.com/openlit/openlit · homepage · TypeScript · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

89/100

  • Activity 99
  • Release rhythm 87
  • Longevity 68
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 0.0
  • age_days: 953
  • days_rel: 8
  • days_push: 7
  • n_releases_24m: 237

Full methodology

Adoption not part of the score

2718 stars · 363 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

OpenLIT is an open-source AI engineering platform providing OpenTelemetry-native LLM observability, evaluations, guardrails, prompt management, vault, and playground, with SDKs for Python, TypeScript, and Go. It is fully self-hostable via Docker or Kubernetes using a lightweight three-component stack (OpenLIT, ClickHouse, OpenTelemetry Collector) and integrates with 50+ LLM providers, vector databases, agent frameworks, and GPUs.

Use cases

  • trace llm calls in production
  • monitor gpu utilization for ai workloads
  • track llm token costs and latency
  • version and deploy prompts without code changes
  • evaluate llm outputs automatically
  • add guardrails to ai agents
  • self-host llm observability stack
  • instrument langchain or crewai agents with opentelemetry

When to choose

  • you need vendor-neutral OpenTelemetry-native LLM tracing and metrics
  • you want a fully self-hostable observability platform for GenAI apps
  • you need combined LLM, vector DB, and GPU monitoring in one dashboard
  • you want prompt versioning, evaluations, and guardrails alongside observability

When to avoid

  • you only need simple application logging without LLM-specific telemetry
  • you prefer a fully managed SaaS with no self-hosting components
  • your stack does not use LLMs or generative AI

Facets

service · maturity active

monitoring tracing llm-inference agent-framework prompt-engineering sdk self-hosted large-language-models monitoring developer-tools self-hosted python go llm-observability opentelemetry llmops gpu-monitoring guardrails evaluations prompt-management cost-tracking clickhouse genai ai-agents devops docker kubernetes nodejs web-server

4 sources

Member repositories

RepositoryRoleHealth v2
openlit/openlitmain89

For agents

markdown · JSON · MCP: product_card(name="openlit/openlit")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem