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AI Frameworks · openlit

openlit

OpenLIT is an open-source observability platform for AI/LLM applications, providing OpenTelemetry-native monitoring, GPU tracking, prompt management, and evaluation tools. It supports 50+ LLM providers and integrates with vector databases and agent frameworks, enabling developers to monitor and optimize AI applications from development to production.

Source: GitHub — github.com/openlit/openlit
2.6k
GitHub stars
319
Forks
TypeScript
Primary language
Apache-2.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositoryopenlit/openlit
Owneropenlit
Primary languageTypeScript
LicenseApache-2.0 — OSI-approved
Stars2.6k
Forks319
Open issues50
Latest releasecontroller-0.9.0 (2026-06-10)
Last updated2026-07-07
Sourcehttps://github.com/openlit/openlit

What openlit is

TypeScript-based platform that instruments AI workloads via vendor-neutral SDKs (Python, TypeScript, Go) sending traces and metrics to OpenTelemetry Collector → ClickHouse backend. Provides rule engine, 11 built-in evaluation types, cost tracking, fleet management via OpAMP, and centralized secret/prompt vault.

Quickstart

Get the openlit source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/openlit/openlit.gitcd openlit# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-provider LLM observability

Monitor and trace requests across 50+ LLM providers, vector DBs, and agent frameworks from a single dashboard, with standardized OpenTelemetry conventions.

AI application cost and performance optimization

Track per-request costs for custom/fine-tuned models, identify bottlenecks via distributed tracing, and optimize token usage and latency across GPU and LLM layers.

Prompt and configuration management at scale

Centralize prompt versioning, rule-based context retrieval, and API key/secret management for teams running multiple LLM applications.

Implementation considerations

  • Requires ClickHouse setup and OpenTelemetry Collector deployment; Docker Compose quick-start available but Kubernetes production setup demands ops discipline.
  • SDKs are vendor-neutral but initialization and configuration of OTLP endpoint must be correct; console output fallback helps during development.
  • Rule engine and evaluation features require understanding of OpenTelemetry semantic conventions and trace attribute matching; documentation clarity critical here.
  • Cost tracking for custom models requires manual pricing file uploads; ensure pricing maintenance process is clear before relying on cost dashboards.
  • Fleet Hub OpAMP management adds operational capability but introduces TLS/certificate management and agent lifecycle considerations.

When to avoid it — and what to weigh

  • Proprietary observability lock-in preferred — If your team requires deep integration with vendor-specific observability platforms (Datadog, New Relic) without running your own ClickHouse backend, alternatives may be simpler.
  • Minimal operational overhead required — Self-hosting via Docker or Kubernetes requires managing ClickHouse, collectors, and the OpenLIT service; managed SaaS may be preferable if DevOps capacity is limited.
  • Evaluation-only use case — If you only need LLM evaluation and prompt management without full observability, lighter frameworks may be more cost-effective and easier to maintain.
  • Simple single-model deployments — For small teams running a single LLM with basic logging, OpenLIT's feature breadth may introduce unnecessary complexity.

License & commercial use

Apache-2.0 (Apache License 2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability disclaimer.

Apache-2.0 permits commercial use and modification. No license restrictions on building commercial products atop OpenLIT. However, integration of third-party dependencies or deployment in regulated environments (fintech, healthcare) requires review of transitive dependencies and data residency/compliance requirements.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityClear
Deployment complexityModerate
DEV.co fitGood
Assessment confidenceHigh
Security considerations

Project claims secure API key/secrets management via centralized vault, but no security audit or CVE history provided. Self-hosting means you own the security posture of ClickHouse, collector, and UI; ensure TLS/mTLS for OTLP and production secrets management. OpAMP Fleet Hub supports TLS but verification of certificate validation/rotation practices required. Trace data may contain sensitive model inputs; ensure ClickHouse access controls and data retention policies align with compliance requirements.

Alternatives to consider

Langfuse

Focused LLM-specific observability and evaluation; simpler deployment model if you don't need GPU monitoring or rule engine complexity.

Arize or WhyLabs

Managed SaaS observability for ML/AI; reduces ops overhead if you want vendor-hosted ClickHouse equivalent and curated evaluation templates.

Datadog / New Relic APM + custom instrumentation

Existing enterprise observability platforms with LLM instrumentation support; better fit if you're already standardized on one vendor and want unified dashboards.

Software development agency

Build on openlit with DEV.co software developers

Clone the OpenLIT repository, deploy with Docker Compose, install the SDK, and start collecting traces in minutes. Review docs.openlit.io for Kubernetes deployment and advanced configurations.

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openlit FAQ

Do I have to self-host OpenLIT?
Yes, based on the provided data. OpenLIT is designed for self-hosting via Docker or Kubernetes. No managed SaaS offering is mentioned.
Which LLM providers are supported?
The README states 50+ LLM providers are supported. Specific list not provided in the data; review docs.openlit.io or SDK source for full compatibility matrix.
Can I use OpenLIT with my existing observability stack?
Yes. OpenLIT is OpenTelemetry-native, so traces and metrics can be exported to any OTLP-compatible backend (Grafana, Jaeger, etc.) via the Collector. However, the OpenLIT UI requires ClickHouse.
What is the learning curve for the rule engine?
Unknown. README mentions AND/OR conditional logic, but complexity and documentation depth for real-world rule authoring is not detailed in the provided data.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If openlit is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to instrument your AI applications?

Clone the OpenLIT repository, deploy with Docker Compose, install the SDK, and start collecting traces in minutes. Review docs.openlit.io for Kubernetes deployment and advanced configurations.