# OpenLIT

OpenTelemetry-native open-source AI engineering platform for LLM observability, evaluations, guardrails, prompts, and GPU monitoring.

## Agent Decision Summary
- Risk level: low
- Source confidence: high
- Recommended workflows: Browser automation, Evaluation and observability, Reusable skill workflow
- Permission surface: low explicit permission surface in metadata
- Agent JSON: https://www.openagent.bot/tools/openlit.agent.json

## Summary
OpenLIT is an open-source AI engineering platform for observability, evaluations, guardrails, prompt management, vault workflows, playgrounds, and GPU monitoring. It integrates with many LLM providers, vector databases, and agent frameworks.


## Guide
OpenLIT is an open-source AI engineering platform for observability, evaluations, guardrails, and prompt workflows.

### What it is
It is a tool layer around LLM and agent applications, not an agent framework.

### Why it matters
Teams need to see what agents are doing in production and catch regressions before users do.

### How it works
Start by instrumenting one agent workflow, then add evaluation and guardrail checks around the highest-risk steps.


### FAQ
- Is OpenLIT open source?
  - Yes. The GitHub repository is listed under the Apache-2.0 license.
- How does OpenLIT fit with MLflow or Langfuse?
  - OpenLIT is especially interesting for teams that want OpenTelemetry-native observability and operational monitoring around LLM and agent systems.
## What It Does
It is a tool layer around LLM and agent applications, not an agent framework.

## How To Evaluate
Start by instrumenting one agent workflow, then add evaluation and guardrail checks around the highest-risk steps.

## Why It Matters
Production agents need traces, metrics, evaluations, guardrails, and operational visibility. OpenLIT brings those layers into an OpenTelemetry-native toolchain.


## Best For
- Teams operating production LLM and agent applications
- Developers who want OpenTelemetry-native AI observability
- Builders comparing evaluation and guardrail platforms

## Not For
- Solo prototypes that only need a small prompt test file
- Teams looking for a low-level agent framework

## What It Actually Does
- OpenTelemetry-native observability: OpenLIT focuses on AI observability through OpenTelemetry-native tracing and monitoring.
  - Why it matters: Teams can connect agent behavior to existing observability systems instead of creating isolated AI dashboards.
- Evaluation and guardrails: The platform includes evaluations and guardrail workflows.
  - Why it matters: Operational visibility is stronger when paired with repeatable quality and safety checks.
- Broad integration surface: OpenLIT describes integrations across LLM providers, vector databases, agent frameworks, and GPUs.
  - Why it matters: Agent stacks are heterogeneous, so observability tools need broad coverage.

## Typical Use Cases
- Agent tracing: Trace model calls, tools, latency, and failures across production agent workflows.
- Evaluation monitoring: Connect evaluations and guardrails to ongoing LLM application operations.
- AI platform operations: Monitor provider usage, GPU behavior, and prompt workflows in one engineering platform.

## How It Compares
- When to choose OpenLIT: Compare it with nearby tools by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.

## Fit Matrix
- Browser automation: strong. OpenLIT has multiple signals for browser automation, including matching tags, capabilities, category, or positioning. Required check: Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Evaluation and observability: strong. OpenLIT has multiple signals for evaluation and observability, including matching tags, capabilities, category, or positioning. Required check: Add one repeatable test case and confirm results can run again in review or CI.
- Reusable skill workflow: strong. OpenLIT has multiple signals for reusable skill workflow, including matching tags, capabilities, category, or positioning. Required check: Run one skill end to end and check whether it produces evidence or structured output.
- Coding agent workflow: partial. OpenLIT has at least one signal for coding agent workflow, but should be checked against a real task before adoption. Required check: Run a small repository change and inspect the diff, tests, and rollback path.
- Local or private AI stack: partial. OpenLIT has at least one signal for local or private ai stack, but should be checked against a real task before adoption. Required check: Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
- Connector or protocol layer: weak. OpenLIT is not primarily positioned for connector or protocol layer in the current metadata. Required check: Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.

## Evidence
- verified: OpenLIT is listed as open source. Source: License metadata: Apache-2.0
- verified: OpenLIT has a recorded GitHub repository: openlit/openlit. Source: Resource facts and GitHub source link.
- inferred: OpenLIT supports these recorded deployment modes: self hosted, cloud. Source: OpenAgent decision signal metadata.
- inferred: OpenLIT is tagged with automation, workflow capabilities. Source: OpenAgent capability taxonomy.

## Missing Checks
- Dedicated docs link is missing.
- Repository freshness has not been recorded.

## Next Actions
- Inspect repository: https://github.com/openlit/openlit
- Open Homepage: https://docs.openlit.io

## Facts
- Category: tools
- Resource type: tool
- Open source: yes
- License: Apache-2.0
- Last verified: 2026-06-10
- GitHub repo: openlit/openlit
- GitHub stars: 2516

## Capabilities
- automation
- workflow

## Structured Use Case Tags
- self-hosted-ai

## Getting Started
- Open the GitHub repository: https://github.com/openlit/openlit
- Read the documentation: https://docs.openlit.io

## Links
- GitHub: https://github.com/openlit/openlit
- Homepage: https://docs.openlit.io

## Structured Outputs
- JSON: https://www.openagent.bot/tools/openlit.json
- Markdown: https://www.openagent.bot/tools/openlit.md
- Agent JSON: https://www.openagent.bot/tools/openlit.agent.json
- Canonical: https://www.openagent.bot/tools/openlit