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Productive Devs Need Great Tools. So Does Your AI.

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

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from runloop_api_client import Runloop
from openai import OpenAI
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from runloop_api_client import Runloop
from anthropic import Anthropic

ai = Anthropic()
completion = ai.messages.create(
  model="claude-3-5-sonnet-latest",
  messages=[{"role": "user", "content": "Generate a python script to generate a maze!"}]
)

client = Runloop()
devbox = client.devboxes.create()
client.devboxes.write_file(
  id=devbox.id,
  contents=message.content,
  file_path="maze_generator.py"
)

diagnostics = client.devboxes.language_server.get_diagnostics(devbox.id,file="maze_generator.py")
client.devboxes.language_server.apply_autofixes(devbox.id, diagnostics=diagnostics)
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from runloop_api_client import Runloop
from openai import OpenAI

ai_client = OpenAI(
  base_url="https://api-inference.huggingface.co/v1/", api_key=os.environ.get("HF_API_KEY")
)
completion = ai_client.completions.create(
  model="meta-llama/CodeLlama-70b-hf",
  messages=[{"role": "user", "content": "Generate a python script to generate a maze!"}]
)

runloop_client = Runloop()
devbox = runloop_client.devboxes.create() runloop_client.devboxes.write_file(
  id=devbox.id,
  contents=completion.choices[0].message,
  file_path="maze_generator.py"
)

snapshot = runloop_client.devboxes.snapshot_disk(id=devbox.id)
runloop_client.devboxes.create(snapshot_id=snapshot.id)
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from runloop_api_client import Runloop
from openai import OpenAI

ai_client = OpenAI(
  base_url="https://api-inference.huggingface.co/v1/", api_key=os.environ.get("HF_API_KEY")
)
completion = ai_client.completions.create(
  model="meta-llama/CodeLlama-70b-hf",
  messages=[{"role": "user", "content": "Generate a python script to generate a maze!"}]
)

runloop_client = Runloop()
devbox = runloop_client.devboxes.create() runloop_client.devboxes.write_file(
  id=devbox.id,
  contents=completion.choices[0].message,
  file_path="maze_generator.py"
)

snapshot = runloop_client.devboxes.snapshot_disk(id=devbox.id)
runloop_client.devboxes.create(snapshot_id=snapshot.id)
Background radial gradient in teal and blue
Background radial gradient in teal and blue
// 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.

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// 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 coding solution faster.

Stop building infrastructure. Start building your AI engineering product.

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