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AI Infrastructure for the Future of Software Engineering AI.

Foundational AI Infrastructure

Build in Secure & Scalable Development Environments
Micro VMs available on demand with robust connections like GitHub repositories and SSH secured data stores
Standardize Containers & Streamline Work with Blueprints
Construct SDEs to match every task or agent from configuration settings to program packages

Codifying Engineering Expertise

Language Server
Runloop's Language Server empowers AI agents with IDE-like capabilities. Our dedicated API enables efficient navigation and manipulation of any codebase
Code Understanding
Beyond syntax, Runloop gives AI agents contextual awareness of functions, dependencies, and logical relationships with a semantic index of your code

Public & Custom Benchmarks

Public Benchmarks Beyond SWE-Bench
Runloop provides automated benchmarking tools to evaluate AI agents on real-world coding tasks, ensuring measurable progress and increased reliability
Custom Defined Code Scenarios & Scoring Functions
Compound proprietary advantages by constructing custom benchmarks to refine agent's performance on your priorities

Self-Improving Code Agents

Supervised and Reinforcement Fine Tuning
Leverage the data produced by benchmarks to perform Supervised Fine-Tuning and Reinforcement Learning Fine-Tuning with Runloop's naive capabilities
AI Research in Production
Realize the benefits of the latest AI research without the delays and overhead of in-house solutions

The complete platform for building, testing, and scaling AI-powered software engineering products.

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// Features

The Building Blocks for AI-Powered Developer Tools

Everything you need to build reliable, production-ready AI development tools.

Want to learn more about Runloop?

Explore our developer docs to see what's possible.

Explore Docs

Want to learn more about Runloop?

Explore our developer docs to see what's possible.

Explore Docs
//USE CASES

Solutions for Every Phase of AI-Driven Software Engineering

Discover how Runloop empowers teams of every stage to build, test, and optimize AI solutions for software engineering.

AI Native Startup

AI-first startup is developing sophisticated coding assistants & can't afford to build and maintain extensive infrastructure while racing to market.

High-Performance Infrastructure

Deploy code execution environments instantly without managing containers or VMs. Provide SDEs instantly

Contextual Code Analysis

Utilize deep code understanding for relevant recommendations. Enable your AI to parse and comprehend complex multi-file projects

Custom Benchmarking

Measure your AI's performance against industry standards and your own KPIs. Track key metrics like solution accuracy, response time, and code quality.

Mid-Size Company Leveraging Expertise in Vertical AI Application

Looking to reduce operational expenses while refining its vertical AI application, an industrial enterprise can focus on what they do best

Eliminate Infrastructure Overhead

Rapidly prototype and test AI-assisted coding features without building infrastructure from scratch.

Scale Efficiently

Scale up or down depending on application without the hassle of orchestration

Continuously Improve

Measure the accuracy and relevance of the AI coding agent and iteratively improve to compound the power of vertical expertise

Fortune 500 Company Optimizing Internal Coding Agents

Major enterprise with internal coding agents finds virtuous cycle of performance refinement

SOC2 Compliant Environments

Test coding agents in secure, isolated DevBoxes that meet organization's compliance standards

Sophisticated Benchmarking

Measure AI performance against custom metrics tailored to company's specific code patterns and quality standards without exposing its codebase

Easy Enterprise Integration

Seamlessly connect with existing development tools, CI/CD pipelines, and security frameworks

// Programming Languages

Run AI-Generated Code in Production

Secure, scalable development environments ready in milliseconds.

Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
Python Environment

Complete Python development environment

Core Tools
> Python 3.x runtime
> pip, conda package managers
> venv environment management
Development Tools
> Pytest test framework
> black code formatter
> mypy type checking
• Enterprise security • Native debugging
 • Enterprise security • Native debugging
 • Enterprise security • Native debugging
Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
TypeScript Environment

Complete TypeScript development environment

Core Tools
> Node.js runtime
> npm, yarn package managers
> TypeScript compiler
Development Tools
> jest testing framework
> eslint linter
> prettier formatter
• Enterprise security • Native debugging
 • Enterprise security • Instant scaling • Native debugging • Full system access • 
Enterprise security • Instant scaling • Native debugging • Full system access 
Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
Java Environment

Complete Java development environment

Core Tools
> JDK environment
> maven, gradle build tools
> jar packaging support
Development Tools
> junit test framework
> checkstyle linter
> debugger integration
• Enterprise security • Native debugging
 • Enterprise security • Instant scaling • Native debugging • Full system access • 
Enterprise security • Instant scaling • Native debugging • Full system access • 
Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
C++ Environment

Complete C++ development environment

Core Tools
> gcc/clang compilers
> cmake build system
> package managers (conan/vcpkg)
Development Tools
> gtest/catch2 testing
> clang-format
> debugging tools
• Enterprise security • Native debugging
 • Enterprise security • Instant scaling • Native debugging • Full system access • 
Enterprise security • Instant scaling • Native debugging • Full system access • 
Boot: 300ms
Auto-scaling
Secure sandbox
Production ready
Go Environment

Complete Go development environment

Core Tools
> Go toolchain
> module support
> dependency management
Development Tools
> go test framework
> golangci-lint
> delve debugger
• Enterprise security • Native debugging
// Use Cases

The Platform for AI-Driven Software Engineering Tools

Explore the types of AI-powered developer tools you can build

AI Pair Programming Assistant

Your company is creating an AI that provides real-time coding suggestions and assistance.

High-Performance Infrastructure

Ensure your AI responds rapidly to user inputs.

Contextual Code Analysis

Utilize deep code understanding for relevant recommendations.

Suggestion Quality Metrics

Evaluate the helpfulness and accuracy of your AI-generated code snippets and advice.

Code editor displaying a JavaScript function checking for null and undefined values in user data. Below, a question asks why undefined !== null, with an AI bot explaining their distinct meanings.
Code snippet showing a calculation for lastLoginTime in TypeScript, with an AI-bot comment explaining an error related to daylight saving time inaccuracies and providing a suggested fix.

AI-Enhanced Code Review System

Your product streamlines code reviews using AI to identify issues and suggest improvements.

Parallel Processing Capabilities

Analyze multiple pull requests concurrently, enhancing scalability.

Customizable Evaluation Criteria

Adapt your AI's review standards to different coding guidelines.

Review Quality Assessments

Measure the accuracy and relevance of your AI-generated comments.

Intelligent Test Generation Platform

You're developing an AI solution that automatically generates comprehensive test coverage.

Language-Agnostic Environments

Deploy your AI across various programming languages.

Development Tool Integrations

Leverage IDE and language server connections for precise code analysis.

Test Coverage Evaluations

Quantify the comprehensiveness and effectiveness of your AI-generated tests.

Graph labeled 'Coverage Over Time,' showing test coverage increasing across six test runs, with an 89% completion rate highlighted at the top right. Below the graph, test statistics display 368 total tests, 322 passed, and 46 failed.

Scale your AI Infrastructure
solution faster.

Stop building infrastructure. Start building your AI engineering product.

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Explore Docs