> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Control Plane

> The open-source CI/CD platform for AI agents: ship, observe, and iterate with confidence.

Control Plane is an open-source CI/CD and observability platform built specifically for AI agents. It gives teams the deployment pipelines, tracing, evaluation, and dataset management that agent development demands, without the vendor lock-in of hosted platforms.

Think of Control Plane as the open-source alternative to LangSmith: self-hostable, GitOps-native, and designed to slot into the engineering workflows your team already uses.

## What Control Plane does

| Capability                     | Description                                                                       |
| ------------------------------ | --------------------------------------------------------------------------------- |
| **Pipeline CI/CD**             | Automated build, test, and deploy pipelines for agent projects                    |
| **Observability**              | Distributed tracing for every LLM call, tool invocation, and agent step           |
| **Evaluation**                 | Run eval suites on every commit; catches regressions before they reach production |
| **Dataset management**         | Version-control your prompts, test cases, and golden sets alongside your code     |
| **GitHub Actions integration** | First-class Actions workflows; triggers evals on PRs and blocks merges on failure |
| **Self-hostable**              | Run entirely on your infrastructure; no data leaves your environment              |

## Why Control Plane

Agent development breaks standard CI/CD. A unit test can pass while the agent produces worse answers. A deploy can succeed while latency doubles. Control Plane adds the agent-specific checks (LLM evaluation, trace comparison, cost budgets) as first-class pipeline steps.

```
git push → CI pipeline → eval suite → trace diff → deploy
```

Every PR gets an automated quality gate. Every deploy generates a trace baseline. Every rollback is one command.

## Key features

**GitOps-native**: Your `langship.yaml` lives in the repo. Pipelines, eval configs, and deployment targets are all version-controlled.

**Trace-first observability**: Every agent run produces an OpenTelemetry-compatible trace with LLM call details (model, tokens, latency, cost), tool calls, memory reads, and final outputs.

**Eval-in-CI**: Define evaluators (LLM-as-judge, exact match, semantic similarity, custom Python) and run them on every push. Block merges when quality drops.

**Dataset versioning**: Store prompt templates, test cases, and expected outputs in Control Plane's dataset store. Pin evals to specific dataset versions so results are reproducible.

**Multi-environment deployments**: Promote agent versions from dev → staging → production with environment-specific configs and automatic rollback on eval failure.

## Architecture overview

Control Plane has three components:

1. **Control Plane Server**: Receives traces, stores datasets, runs evals, manages deployments. Self-hosted via Docker or Kubernetes.
2. **Control Plane CLI**: `langship` command for local development, running evals, and triggering deploys.
3. **Control Plane SDK**: Lightweight instrumentation library (`pip install langship`) that adds trace collection to any LangChain, LlamaIndex, or raw OpenAI agent.

## Where to go next

* [Quickstart](./quickstart): running Control Plane Server and tracing your first agent in 10 minutes
* [How It Works](./how-it-works): pipeline architecture and core data model
* [Deployment](./deployment): Docker Compose, Kubernetes, and scaling
* [GitHub Actions](./github-actions): integrating Control Plane evals into your PR workflow