openlit/openlit

---

See what your AI agents are actually doing

AI applications are no longer just LLM calls.

A production agent can involve:

flowchart TD
    U([User]) --> A[AI Agent]
    A --> L[LLM calls]
    A --> T[Tool calls]
    A --> R[Retrieval]
    A --> M[Memory]
    A --> S[Sub-agents]
    A --> P[Prompts]
    A --> C[Code changes]
    L & T & R & M & S & P & C --> E{{Evaluation}}
    E --> O[["Cost / Quality / Errors"]]
style U fill:#F97316,stroke:#7C2D12,color:#fff
    style A fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff
    style E fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff
    style O fill:#F97316,stroke:#7C2D12,color:#fff

OpenLIT gives you visibility across the entire workflow.

Trace every LLM call, tool invocation, prompt, agent step, token, cost, error, and evaluation — using OpenTelemetry.

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⚡ Get started in 5 minutes

1. Start OpenLIT

git clone https://github.com/openlit/openlit.git
cd openlit
docker compose up -d

Open:

http://127.0.0.1:3000

2. Install the SDK

Python:

pip install openlit

TypeScript:

npm install openlit

3. Instrument your application

Python:

import openlit
openlit.init()

That's it.

OpenLIT automatically instruments supported LLM providers, frameworks, vector databases, and other AI infrastructure and exports OpenTelemetry traces and metrics.

4. Send telemetry

By default, configure the OTLP endpoint:

export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"

Or:

import openlit
openlit.init(
    otlp_endpoint="http://127.0.0.1:4318"
)

Open your dashboard and start exploring your AI application's traces, metrics, costs, and performance.

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🤖 Observe Claude Code, Cursor & Codex

AI coding agents are powerful — but understanding what they actually did can be difficult.

OpenLIT gives you an OpenTelemetry-native view of coding-agent sessions.

Install the CLI:

macOS / Linux

curl -fsSL https://raw.githubusercontent.com/openlit/openlit/main/cli/scripts/install.sh | sh

Windows

iwr -useb https://raw.githubusercontent.com/openlit/openlit/main/cli/scripts/install.ps1 | iex

Configure OpenLIT:

openlit configure --endpoint http://127.0.0.1:4318

Install coding-agent instrumentation:

openlit coding install --vendor=all

Or install individual integrations:

openlit coding install --vendor=cursor
openlit coding install --vendor=claude-code
openlit coding install --vendor=codex

Check your installation:

openlit doctor

Now OpenLIT can capture:

flowchart LR
    S([Coding Agent Session]) --> P[User prompt]
    S --> L[LLM calls]
    S --> T[Tool calls]
    T --> T1[File reads]
    T --> T2[File edits]
    T --> T3[Shell commands]
    T --> T4[Search]
    S --> SA[Sub-agent activity]
    S --> TU[Token usage]
    S --> CO[Cost]
    S --> CI[Code impact]
style S fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff
    style T fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff

Explore the resulting sessions in the Coding Agents dashboard.

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🔍 What OpenLIT gives you

Traces

Understand exactly what happened during an AI request.

All represented using OpenTelemetry.

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💰 AI cost observability

Track the cost of your AI applications across:

Support custom pricing for custom and fine-tuned models.

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🧪 AI evaluations

Automatically evaluate LLM and agent outputs using LLM-as-a-Judge evaluations.

Built-in evaluation types include:

Use evaluations to move from:

"The agent produced an answer."

to:

"The agent produced a good answer."

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🐛 Debug production AI

Find the requests that matter.

Investigate:

Go from:

Something went wrong.

to a fully traced root cause:

flowchart TD
    A[Agent] --> P[Prompt] --> L1[LLM] --> T[Tool call] --> R[Retrieval] --> L2[LLM] --> E([Error])
style E fill:#DC2626,stroke:#7F1D1D,color:#fff
    style A fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff

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📸 See OpenLIT in action

Traces — full agent conversation, spans & cost	Evaluations — hallucination, bias & toxicity checks	Connectors — plug in ClickHouse, Tempo, Loki, Prometheus & more
Prompt Hub — versioned, centrally managed prompts	Rule Engine — conditional rules on trace attributes	Dashboards — cost, latency & usage charts at a glance

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🧠 Prompt management

Use Prompt Hub to:

Example:

prompt = openlit.prompts.get(
    "customer-support"
)

Keep prompt management separate from application code while maintaining version control and observability.

---

⚙️ Rule Engine

Define runtime rules based on trace attributes.

Use rules to dynamically control:

Example:

IF
  environment = production
  AND
  model = expensive-model
THEN
  run cost evaluation
  + retrieve production prompt

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🔌 OpenTelemetry-native

OpenLIT is built around OpenTelemetry, rather than creating a proprietary telemetry format.

Your telemetry can flow through the OpenTelemetry ecosystem:

flowchart TD
    A["AI App / AI Agent"] -->|OpenTelemetry| C[OpenTelemetry Collector]
    C --> B[OpenLIT Backend]
    C --> O["Other OTel backends
(Datadog, Grafana, Honeycomb, ...)"]
    B --> D[OpenLIT Dashboard]
style A fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff
    style C fill:#111827,stroke:#F97316,stroke-width:2px,color:#fff
    style B fill:#F97316,stroke:#7C2D12,color:#fff
    style D fill:#F97316,stroke:#7C2D12,color:#fff

This means you can integrate OpenLIT into an existing OpenTelemetry architecture instead of replacing it.

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🧩 70+ integrations

OpenLIT auto-instruments a growing ecosystem of AI providers, frameworks, vector databases, and GPU infrastructure with a single line of code. Click any badge to view its integration guide.

LLM Providers

Vector & Data Stores

AI Frameworks & Agents