# Laminar
[Laminar](https://laminar.sh) is an open-source observability platform purpose-built for AI agents.
- [x] Tracing. [Docs](https://laminar.sh/docs/tracing/introduction)
- [x] OpenTelemetry-native powerful tracing SDK - 1 line of code to automatically trace **Vercel AI SDK, Browser Use, Stagehand, LangChain, OpenAI, Anthropic, Gemini, and more**.
- [x] Signals. [Docs](https://laminar.sh/docs/signals/introduction)
- [x] Describe any behavior of your agent that you want to track in plain English (e.g. "agent is stuck in a loop")
- [x] Laminar reads every agent run and pings you in Slack when it happens.
- [x] Evals. [Docs](https://laminar.sh/docs/evaluations/introduction)
- [x] Unopinionated, extensible SDK and CLI for running evals locally or in CI/CD pipeline.
- [x] UI for visualizing evals and comparing results.
- [x] [MCP](https://laminar.sh/docs/platform/mcp) / [CLI](https://laminar.sh/docs/platform/cli) access for your coding agent
- [x] Query traces, spans, metrics, and events with SQL
- [x] Let your coding agent investigate and debug issues based on your traces
- [x] Dashboards. [Docs](https://laminar.sh/docs/custom-dashboards/overview)
- [x] Powerful dashboard builder for traces, metrics, and events with support of custom SQL queries.
- [x] Data annotation & Datasets. [Docs](https://laminar.sh/docs/datasets/introduction)
- [x] Custom data rendering UI for fast data annotation and dataset creation for evals.
- [x] Extremely high performance.
- [x] Written in Rust 🦀
- [x] 20x trace compression for efficient ingestion and storage. Read more about it [here](https://laminar.sh/blog/laminar-20x-agent-trace-compression).
- [x] Custom realtime engine for viewing traces as they happen.
- [x] Ultra-fast full-text search over span data.
- [x] gRPC exporter for tracing data.
## Documentation
Check out the full documentation here [laminar.sh/docs](https://laminar.sh/docs).
## Getting started
The fastest and easiest way to get started is with our managed platform -> [laminar.sh](https://laminar.sh)
### Self-hosting with Docker compose
Laminar is very easy to self-host locally. For a quick start, clone the repo and start the services with docker compose:
```sh
git clone https://github.com/lmnr-ai/lmnr
cd lmnr
docker compose up -d
```
This will spin up a lightweight but full-featured version of the stack. This is good for a quickstart
or for lightweight usage. You can access the UI at http://localhost:5667 in your browser.
You will also need to properly configure the SDK, with `baseUrl` and correct ports. See [guide on self-hosting](https://laminar.sh/docs/hosting-options#self-hosted-docker-compose).
For production environment, we recommend using our [managed platform](https://laminar.sh) or `docker compose -f docker-compose-full.yml up -d`.
### Configuring LLM provider (optional)
Frontend AI features (chat-with-trace, SQL-with-AI) and server-side AI workers require an LLM provider. Configure one in your `.env` file at the repo root.
Pick one of the following provider setups. `LLM_MODEL_SMALL|MEDIUM|LARGE` are optional — per-provider defaults apply when unset. `LLM_DEFAULT_HEADERS_JSON` is optional for any provider or gateway that requires static headers.
```sh
# Optional for any provider/gateway that requires static headers
# LLM_DEFAULT_HEADERS_JSON='{"X-Gateway-Tenant":"tenant"}'
# Option A: Gemini
LLM_PROVIDER=gemini
LLM_API_KEY=your_gemini_key
# Option B: OpenAI (or any OpenAI-compatible gateway such as LiteLLM, OpenRouter, vLLM)
LLM_PROVIDER=openai
# LLM_BASE_URL=http://localhost:4000 # optional, for OpenAI-compatible gateways
LLM_API_KEY=your_openai_key
# Option C: AWS Bedrock (Anthropic Claude). Uses AWS credentials instead of LLM_API_KEY.
LLM_PROVIDER=bedrock
AWS_ACCESS_KEY_ID=...
AWS_SECRET_ACCESS_KEY=...
AWS_REGION=us-east-1
```
### Custom Postgres schema (optional)
By default Laminar uses the `public` schema. To target a different schema (e.g.
when deploying alongside other services in a shared Postgres instance), set the
same value for both the frontend and the app-server:
```sh
POSTGRES_SCHEMA=laminar
# Set to false if the schema is pre-provisioned or the DB role lacks CREATE.
# POSTGRES_CREATE_SCHEMA=true
```
The schema is applied as the connection `search_path`, so all tables, foreign
keys, and migrations target it. When a non-public schema is set, the frontend
also tracks migrations inside that schema (`.__drizzle_migrations`)
rather than the shared `drizzle` schema. Note that running Laminar alongside
another Drizzle-managed service in the same database may still require manual
intervention, since Drizzle's migration journal is versioned per-schema.
## Anonymous usage telemetry
Self-hosted deployments collect anonymized usage telemetry. To opt out, set `LAMINAR_TELEMETRY_DISABLED=true` in your `.env`.
## Contributing
For running and building Laminar locally, or to learn more about docker compose files,
follow the guide in [Contributing](/CONTRIBUTING.md).
## TS quickstart
First, [create a project](https://laminar.sh/projects) and generate a project API key. Then,
```sh
npm add @lmnr-ai/lmnr
```
It will install Laminar TS SDK and all instrumentation packages (OpenAI, Anthropic, LangChain ...)
To start tracing LLM calls just add
```typescript
import { Laminar } from '@lmnr-ai/lmnr';
Laminar.initialize({ projectApiKey: process.env.LMNR_PROJECT_API_KEY });
```
To trace inputs / outputs of functions use `observe` wrapper.
```typescript
import { OpenAI } from 'openai';
import { observe } from '@lmnr-ai/lmnr';
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const poemWriter = observe({name: 'poemWriter'}, async (topic) => {
const response = await client.chat.completions.create({
model: "gpt-4o-mini",
messages: [{ role: "user", content: `write a poem about ${topic}` }],
});
return response.choices[0].message.content;
});
await poemWriter();
```
## Python quickstart
First, [create a project](https://laminar.sh/projects) and generate a project API key. Then,
```sh
pip install --upgrade 'lmnr[all]'
```
It will install Laminar Python SDK and all instrumentation packages. See list of all instruments [here](https://laminar.sh/docs/tracing/integrations/overview)
To start tracing LLM calls just add
```python
from lmnr import Laminar
Laminar.initialize(project_api_key="")
```
To trace inputs / outputs of functions use `@observe()` decorator.
```python
import os
from openai import OpenAI
from lmnr import observe, Laminar
Laminar.initialize(project_api_key="")
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
@observe() # annotate all functions you want to trace
def poem_writer(topic):
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "user", "content": f"write a poem about {topic}"},
],
)
poem = response.choices[0].message.content
return poem
if __name__ == "__main__":
print(poem_writer(topic="laminar flow"))
```
## Client libraries
To learn more about instrumenting your code, check out our client libraries:
