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

# Overview

> Learn about how Relace Repos offers convenient source control for your AI application.

## Introduction

Relace Repos is a fully managed version control system specifically designed for AI applications, offering:

* Lightweight read/write operations enabling easy integration with your backend.
* Agentic search that maintains low latency on large codebases.
* Multi-tenancy that scales to millions of repositories across thousands of users.
* Permissive rate limits that allow interactions across many repos simultaneously.
* Full git compatibility, allowing easy interactions from the git CLI.

## Source Management for AI

Building a scalable AI code editing application usually requires:

* Durable storage of source code
* Versioning, so that unsuccessful or undesired changes can be easily reverted
* Low-latency reads/writes on the working version of the code
* Support for automated interactions with thousands of isolated repos
* Integration with GitHub, to allow human developers to easily collaborate on the same code base

Relace Repos offers a centralized platform that satisfies all of these requirements, seamlessly integrated with our state-of-the-art models and agents.

## Existing Alternatives

Industry standard version control services like GitHub are designed primarily for human developers, which leads to some pain points for AI applications.

#### Service limits

Humans have low frequency manual interactions through websites and the git CLI. This results in relatively low limits that are insufficient for large scale automated AI applications:

* A single account/organization may not exceed [100,000 repos](https://docs.github.com/en/repositories/creating-and-managing-repositories/repository-limits#organization-and-account-limits)
* REST API requests may not exceed [5,000 per hour](https://docs.github.com/en/rest/using-the-rest-api/rate-limits-for-the-rest-api?apiVersion=2022-11-28#primary-rate-limit-for-authenticated-users) (15,000 for a GitHub app owned by an enterprise organization)

Most text-to-app systems treat each user application as a single repo, which leads to the repo limit being reached very quickly. Systems with many concurrent users will also hit the rate limit relatively quickly. Assuming that a single AI edit requires at least 2 API calls (pulling and pushing), your capacity would be \~2 requests per second.

#### Integration complexity

Human developer workflows are less constrained and more complex than a typical AI application. This leads to a system that requires many distinct steps to make a simple change to the code base:

1. Clone/checkout the repository locally
2. Edit the files
3. Stage and commit the changes
4. Push to the remote

Since commits are made locally, you must setup a git library or the git CLI on your host. Given that most AI workflows are run in a serverless or sandboxed environment, this also means that full source retrieval must happen every time you spin up the environment. This contributes to high cold-start latency, and often necessitates some sort of file caching strategy.

A relatively simple workflow where an agent reads a code base and edits some files can quickly become a highly complex infrastructure problem.

## Built-in Agentic Search

Providing the right code context to your LLM is essential to produce the best results without sacrificing cost or latency. Every Relace Repo can be searched with our [Fast Agentic Search](/docs/fast-agentic-search/agent) agent: call the [search endpoint](/docs/repos/search) with a query in natural language, and the agent explores the repo and reports back the relevant files with line ranges and an explanation.

## Next Steps

For more details on how to set up Relace Repos as your agents source control system see our [onboarding guide](/docs/repos/onboarding).