What is Runloop?

Runloop is the batteries included platform designed for building and optimizing AI-driven software engineering agents.

With the Runloop platform, you get:

• Fast, isolated, snapshottable virtual machines for executing agents & agent tools (Devboxes).
• Team-shareable templates for launching new devboxes with custom configuration (Blueprints).
• Zero-configuration code repository integration (Code Mounts) and a fully-featured, ready-to-use language server (Code Understanding APIs).
• Turnkey benchmarking and evaluation services for fine-tuning your agent’s behavior (Code Scenarios).

Whether you are trying to build an AI agent that can respond to pull requests, or an AI agent that can generate new UI components, Runloop makes it possible to get from zero to POC in just a few lines of code.

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Why Runloop?

Our mission at Runloop is to keep you focused on the things differentiate your AI agent. Leave the building blocks to us and spend time on what actually matters.

As you agent evolves, your needs will evolve too. Runloop is designed for builders at all stages:

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Use cases

Our customers are already leveraging Runloop to build AI agents that can:

• Respond to Pull Requests and enhance the code review process
• Enable users to chat with and navigate their codebase
• Generate new test cases for existing codebases
• Act as pair programmers
• Generate new UI components for their frontend

Have a use case that we didn’t cover? Send us an email at support@runloop.ai to learn more about how Runloop can help you build AI agents.

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Core Components of Runloop

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Devboxes

Devboxes are isolated, cloud-based development environments that can be controlled by AI agents via the Runloop API. You can give agents access to a devbox to let agents run and test code in a safe, isolated environment.

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Code Understanding APIs

A critical part of making AI SWE agents work reliably is giving them the right context to solve the problem. In many cases, this means extracting context from the existing codebase such as function signatures, finding tests that cover a specific segment of code, or understanding which files are often edited together. For example, one common heuristic that helps AI agents navigate codebases is the Repository Map used by Aider. Writing your own Repository Map style heuristic can be difficult as it requires static analysis of the codebase. Other heuristics can be even harder to create and rely on gathering information from the runtime dataflow of the codebase. The Code Understanding APIs aim to make it possible to create these types of heuristics in just a few lines of code.

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Code Scenarios

Tuning the behavior of your AI agent is a critical part of making it work reliably. However, going from POC to production is where most AI agents fail. Code Scenarios is a set of Benchmarking and Eval Tools that help you understand and improve your AI agent’s behavior in a methodical way. For example, with Code Scenarios:

• You can run your agent against well known benchmarks such as SWE-bench or create your own custom benchmarks
• You can record live production agent traces and monitor the Agent performance or use the traces to create new benchmarks
• You can create custom Reward Models based on production traces and benchmark data to fine tune your agent’s behavior