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Enterprise-Grade Sandbox Environments for AI Coding Agents

Build, Benchmark, and Deploy AI Coding Agents in the Cloud

Foundational AI Infrastructure

Build in Secure & Scalable Development Environments
Scalable 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

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

Get Started for Free

Start for free and receive $25 in credits to accelerate your AI software engineering

// AI INFRASTRUCTURE

AI Infrastructure with Performance, Scalability, Reliability

Turbo-charged infrastructure to accelerate AI Agent deployment

Scale-icon
Performance At Scale

Run 10k+ parallel sandboxes.
Sub 500ms sandbox boot times.
All with leading reliability guarantees.

Compute-icon
Elastic Compute

Automatically scale up/down sandbox CPU or Memory based on your Agent needs in realtime.
Pay only for what you use.

Sandboxes-icon
Long-Lived Code Sandboxes

Devboxes live as long as you need them.
Let your AI agent work with you from the first line to the last line of code on a project.

Security-icon
Enterprise Grade Security

Fully Isolated MicroVMs with Network Controls so your AI SWE can develop without risks.
Fully SOC2 Certified.

Observability-icon
Built-in Observability

Full logs for everything run in Devboxes, all easily accessible via a web Dashboard.
Optionally capture network traffic and agent trajectories with a configurable network proxy.

Cloud-icon
Our Cloud Or Yours

Use Runloop's fully hosted cloud solution or run in your own AWS/GCP account.

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.

Explore Docs
// PROGRAMMATIC CONTROL

File, Git, LSP, and Execute API.


async def create_repo_connect_and_await_build():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    """
    Create a repo-connect and for the repository https://github.com/psf/requests
    """
    repo_connection = client.repositories.create(owner="psf", name="requests")

    print(f"Repository connection created with ID: {repo_connection.id}")
    return repo_connection

async def snapshot_workflow():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    devbox = client.devboxes.create_and_await_running(name="snapshot-test-devbox")

    # Add a file to the devbox
    client.devboxes.write_file_contents(
        id=devbox.id,
        file_path="/home/user/test_file.txt",
        contents="This is a test file created for snapshot testing.\nTimestamp: "
        + str(time.time()),
    )

    # Create first snapshot
    snapshot1 = client.devboxes.snapshot_disk(
        id=devbox.id, name="snapshot-1-with-test-file"
    )

    # Add another file
    client.devboxes.write_file_contents(
        id=devbox.id,
        file_path="/home/user/test_file_2.txt",
        contents="This is the second test file.\nTimestamp: " + str(time.time()),
    )

    # Create second snapshot
    snapshot2 = client.devboxes.snapshot_disk(
        id=devbox.id, name="snapshot-2-with-two-files"
    )

    # List snapshots
    snapshots = client.devboxes.list_disk_snapshots(devbox_id=devbox.id)
    for snapshot in snapshots.snapshots:
        print(f"  - {snapshot.name} (ID: {snapshot.id})")

    # Create new devbox from first snapshot
    new_devbox = client.devboxes.create_and_await_running(
        name="devbox-from-snapshot-1", snapshot_id=snapshot1.id
    )

    # Clean up original devbox
    client.devboxes.shutdown(id=devbox.id)

    return {
        "original_devbox": devbox,
        "snapshots": [snapshot1, snapshot2],
        "new_devbox": new_devbox,
    }

async def trajectory_executor():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    devbox = client.devboxes.create_and_await_running(name="trajectory-executor-devbox")

    async def execute_with_snapshot(command, snapshot_name):
        """Execute a command and create a snapshot before execution"""
        print(f"Creating snapshot '{snapshot_name}' before executing: {command}")

        # Create snapshot before command execution
        snapshot = client.devboxes.snapshot_disk(id=devbox.id, name=snapshot_name)
        print(f"Snapshot created with ID: {snapshot.id}")

        # Execute the command
        print(f"Executing command: {command}")
        result = client.devboxes.execute_sync(id=devbox.id, command=command)

        print(f"Command executed. Exit code: {result.exit_status}")
        print(f"Output: {result.stdout}")
        if result.stderr:
            print(f"Error: {result.stderr}")

        return {"snapshot": snapshot, "command_result": result}

    # Execute a series of commands with snapshots
    commands = [
        "echo 'Step 1: Initial setup' > /home/user/trajectory_log.txt",
        "echo 'Step 2: Installing package' >> /home/user/trajectory_log.txt && apt-get update",
        "echo 'Step 3: Creating directory' >> /home/user/trajectory_log.txt && mkdir -p /home/user/test_dir",
        "echo 'Step 4: Final step' >> /home/user/trajectory_log.txt && ls -la /home/user/",
    ]

    results = []
    for i, command in enumerate(commands):
        print(f"\n--- Trajectory Step {i} ---")
        result = await execute_with_snapshot(command, f"before-step-{i}")
        results.append(result)
        time.sleep(2)  # Small delay between commands

    # Show final trajectory log
    print("\n--- Final Trajectory Log ---")
    log_content = client.devboxes.read_file_contents(
        id=devbox.id, file_path="/home/user/trajectory_log.txt"
    )
    print(log_content)

    return {"devbox": devbox, "trajectory_results": results}

async def start_benchmark_with_link():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    # Start princeton-nlp/SWE-bench_Verified in a single line
    benchmark_run = client.benchmarks.start_run(
        benchmark_id="bmd_2zmp3Mu3LhWu7yDVIfq3m",
        run_name="SWE-bench_Verified_run",
    )
    # Choose your agent and complete eval cases with our harness @ https://github.com/runloopai/public_benchmarks_example

    print(f"Benchmark run started with ID: {benchmark_run.id}")
    print(f"Benchmark ID: {benchmark_run.benchmark_id}")
    print(f"State: {benchmark_run.state}")

    return benchmark_run

async def multi_language_demo():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    devbox = client.devboxes.create_and_await_running(
        name="multi-language-boot-demo",
    )

    # Test Node.js/npm functionality
    client.devboxes.execute_sync(
        id=devbox.id, command="npm --version && node --version"
    )

    # Create a simple Node.js project and run npm install
    setup_commands = [
        "mkdir -p /home/user/nodejs-demo && cd /home/user/nodejs-demo",
        "npm init -y",
        "npm install express",
        "echo 'console.log(\"Node.js environment ready!\");' > index.js",
    ]

    for command in setup_commands:
        result = client.devboxes.execute_sync(id=devbox.id, command=command)

    # Test Python/venv functionality
    client.devboxes.execute_sync(
        id=devbox.id, command="python3 --version && python3 -m venv --help"
    )

    # Create a Python virtual environment and install packages
    python_commands = [
        "mkdir -p /home/user/python-demo && cd /home/user/python-demo",
        "python3 -m venv myenv",
        "source myenv/bin/activate && pip install requests flask",
        "source myenv/bin/activate && python3 -c \"import requests, flask; print('Python environment ready!')\"",
    ]

    for command in python_commands:
        result = client.devboxes.execute_sync(id=devbox.id, command=command)
        print(f"Command: {command} - Exit code: {result.exit_status}")
        if result.stdout:
            print(f"Output: {result.stdout}")
    return devbox
// DEVBOXES

A Complete Infra Toolbox for AI SWEs

Everything you need to prototype your Agent for one use case and scale to thousands

Beyond Generating-icon
Beyond Generating Code - Create Full AI Engineers
Devboxes allow you to let agents do more than just evaluate code. Let agents work on features for you without risk with full shell and build toolchains.
Simplify Deployments by deploying agents directly into the isolated environment for full control.
Out-of-the-box-icon
Browser + Computer Use out of the box
Use Runloop's Browser and Computer use templates to let your Agent gather context using any developer tools or automate real world workflows.
Tunnels-icon
Tunnels
Share local development servers or processes from the Devbox with others by opening secure web tunnels to expose local ports.
Snapshot-icon
Snapshot, Fork, and Explore
Snapshot the full state of a Devbox at any point to save progress and resume later.
Create new forked Devboxes from the snapshot and allow your agent to explore different ideas in parallel.
Docker-icon
Support Any Developer Environment
Use Runloop Blueprints to create custom environment images to meet any development requirements:
- Full Dockerfile compatibility
- Docker in Docker support
- Any architecture (x86, arm etc) and any OS
Terminal-icon
Connect From Your IDE and Terminal
Connect to any Devbox directly from your favorite IDE or Terminal using SSH.
Enable developers to take the lead with human-in-the-loop flows when an Agent needs low level guidance.
// BENCHMARKS

Benchmarking At Scale

Test Your Agents against existing Frontier Benchmarks like SWE bench in minutes.
Or create the next frontier benchmark yourself and share in one click.

Frontier Benchmarks-icon
Easily Run Against Existing Frontier Benchmarks
Run against popular benchmarks like SWE-Bench, SWE-Smith, Web Voyager and more in just a few lines of code with our standard interface.
Parallel-icon
Run Large Benchmarks Fully in Parallel
Run thousands of test cases in parallel to improve your iteration loop speed. Quickly identify agent performance issues and improve.
Custom Benchmark-icon
Custom Benchmarks
Create your own benchmarks to test frontier capabilities of models. Runloop supports all environments you might need from standalone coding cases to browser use to computer use.
// TOOLS

Developer Tools for Agentic Workflows


async def create_repo_connect_and_await_build():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    """
    Create a repo-connect and for the repository https://github.com/psf/requests
    """
    repo_connection = client.repositories.create(owner="psf", name="requests")

    print(f"Repository connection created with ID: {repo_connection.id}")
    return repo_connection

async def snapshot_workflow():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    devbox = client.devboxes.create_and_await_running(name="snapshot-test-devbox")

    # Add a file to the devbox
    client.devboxes.write_file_contents(
        id=devbox.id,
        file_path="/home/user/test_file.txt",
        contents="This is a test file created for snapshot testing.\nTimestamp: "
        + str(time.time()),
    )

    # Create first snapshot
    snapshot1 = client.devboxes.snapshot_disk(
        id=devbox.id, name="snapshot-1-with-test-file"
    )

    # Add another file
    client.devboxes.write_file_contents(
        id=devbox.id,
        file_path="/home/user/test_file_2.txt",
        contents="This is the second test file.\nTimestamp: " + str(time.time()),
    )

    # Create second snapshot
    snapshot2 = client.devboxes.snapshot_disk(
        id=devbox.id, name="snapshot-2-with-two-files"
    )

    # List snapshots
    snapshots = client.devboxes.list_disk_snapshots(devbox_id=devbox.id)
    for snapshot in snapshots.snapshots:
        print(f"  - {snapshot.name} (ID: {snapshot.id})")

    # Create new devbox from first snapshot
    new_devbox = client.devboxes.create_and_await_running(
        name="devbox-from-snapshot-1", snapshot_id=snapshot1.id
    )

    # Clean up original devbox
    client.devboxes.shutdown(id=devbox.id)

    return {
        "original_devbox": devbox,
        "snapshots": [snapshot1, snapshot2],
        "new_devbox": new_devbox,
    }

async def trajectory_executor():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    devbox = client.devboxes.create_and_await_running(name="trajectory-executor-devbox")

    async def execute_with_snapshot(command, snapshot_name):
        """Execute a command and create a snapshot before execution"""
        print(f"Creating snapshot '{snapshot_name}' before executing: {command}")

        # Create snapshot before command execution
        snapshot = client.devboxes.snapshot_disk(id=devbox.id, name=snapshot_name)
        print(f"Snapshot created with ID: {snapshot.id}")

        # Execute the command
        print(f"Executing command: {command}")
        result = client.devboxes.execute_sync(id=devbox.id, command=command)

        print(f"Command executed. Exit code: {result.exit_status}")
        print(f"Output: {result.stdout}")
        if result.stderr:
            print(f"Error: {result.stderr}")

        return {"snapshot": snapshot, "command_result": result}

    # Execute a series of commands with snapshots
    commands = [
        "echo 'Step 1: Initial setup' > /home/user/trajectory_log.txt",
        "echo 'Step 2: Installing package' >> /home/user/trajectory_log.txt && apt-get update",
        "echo 'Step 3: Creating directory' >> /home/user/trajectory_log.txt && mkdir -p /home/user/test_dir",
        "echo 'Step 4: Final step' >> /home/user/trajectory_log.txt && ls -la /home/user/",
    ]

    results = []
    for i, command in enumerate(commands):
        print(f"\n--- Trajectory Step {i} ---")
        result = await execute_with_snapshot(command, f"before-step-{i}")
        results.append(result)
        time.sleep(2)  # Small delay between commands

    # Show final trajectory log
    print("\n--- Final Trajectory Log ---")
    log_content = client.devboxes.read_file_contents(
        id=devbox.id, file_path="/home/user/trajectory_log.txt"
    )
    print(log_content)

    return {"devbox": devbox, "trajectory_results": results}

async def start_benchmark_with_link():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    # Start princeton-nlp/SWE-bench_Verified in a single line
    benchmark_run = client.benchmarks.start_run(
        benchmark_id="bmd_2zmp3Mu3LhWu7yDVIfq3m",
        run_name="SWE-bench_Verified_run",
    )
    # Choose your agent and complete eval cases with our harness @ https://github.com/runloopai/public_benchmarks_example

    print(f"Benchmark run started with ID: {benchmark_run.id}")
    print(f"Benchmark ID: {benchmark_run.benchmark_id}")
    print(f"State: {benchmark_run.state}")

    return benchmark_run

async def multi_language_demo():
    client = Runloop(bearer_token=os.environ.get("RUNLOOP_API_KEY"))

    devbox = client.devboxes.create_and_await_running(
        name="multi-language-boot-demo",
    )

    # Test Node.js/npm functionality
    client.devboxes.execute_sync(
        id=devbox.id, command="npm --version && node --version"
    )

    # Create a simple Node.js project and run npm install
    setup_commands = [
        "mkdir -p /home/user/nodejs-demo && cd /home/user/nodejs-demo",
        "npm init -y",
        "npm install express",
        "echo 'console.log(\"Node.js environment ready!\");' > index.js",
    ]

    for command in setup_commands:
        result = client.devboxes.execute_sync(id=devbox.id, command=command)

    # Test Python/venv functionality
    client.devboxes.execute_sync(
        id=devbox.id, command="python3 --version && python3 -m venv --help"
    )

    # Create a Python virtual environment and install packages
    python_commands = [
        "mkdir -p /home/user/python-demo && cd /home/user/python-demo",
        "python3 -m venv myenv",
        "source myenv/bin/activate && pip install requests flask",
        "source myenv/bin/activate && python3 -c \"import requests, flask; print('Python environment ready!')\"",
    ]

    for command in python_commands:
        result = client.devboxes.execute_sync(id=devbox.id, command=command)
        print(f"Command: {command} - Exit code: {result.exit_status}")
        if result.stdout:
            print(f"Output: {result.stdout}")
    return devbox

client = Runloop(bearer_token="ak_71BhFKHw0inHn3I6cCVOz")

# Create a Repo Connection to https://github.com/psf/requests
repo_connection = client.repositories.create(owner="psf", name="requests")

client = Runloop(bearer_token="ak_71BhFKHw0inHn3I6cCVOz")

devbox = client.devboxes.create_and_await_running(name="snapshot-test-devbox")

# Add a file to the devbox
client.devboxes.write_file_contents(
    id=devbox.id,
    file_path="/home/user/test_file.txt",
    contents=f"Hello World! \nTimestamp: {str(time.time())}"
)

# Take snapshot
snapshot = client.devboxes.snapshot_disk(
    id=devbox.id, name="snapshot-1-with-test-file"
)

async def trajectory_executor():
    client = Runloop(bearer_token="ak_71BhFKHw0inHn3I6cCVOz")

    devbox = client.devboxes.create_and_await_running(name="trajectory-executor-devbox")

    async def execute_with_snapshot(command, snapshot_name):
        """Execute a command and create a snapshot before execution"""
        snapshot = client.devboxes.snapshot_disk(id=devbox.id, name=snapshot_name)

        # Execute the command
        result = client.devboxes.execute_sync(id=devbox.id, command=command)

        return {"snapshot": snapshot, "command_result": result}

    # Execute a series of commands with snapshots
    commands = [
        "echo 'Step 1: Initial setup' > /home/user/trajectory_log.txt",
        "echo 'Step 2: Installing package' >> /home/user/trajectory_log.txt && apt-get update",
        "echo 'Step 3: Creating directory' >> /home/user/trajectory_log.txt && mkdir -p /home/user/test_dir",
        "echo 'Step 4: Final step' >> /home/user/trajectory_log.txt && ls -la /home/user/",
    ]

    results = []
    for i, command in enumerate(commands):
        result = await execute_with_snapshot(command, f"before-step-{i}")
        results.append(result)
        time.sleep(2)  # Small delay between commands

    return {"devbox": devbox, "trajectory_results": results}

# Choose your agent and complete eval cases with our harness 
# @ https://github.com/runloopai/public_benchmarks_example

client = Runloop(bearer_token="ak_71BhFKHw0inHn3I6cCVOz")

# Start princeton-nlp/SWE-bench_Verified in a single line
benchmark_run = client.benchmarks.start_run(
    benchmark_id="bmd_2zmp3Mu3LhWu7yDVIfq3m",
    run_name="SWE-bench_Verified_run",
)



client = Runloop(bearer_token="ak_71BhFKHw0inHn3I6cCVOz")

devbox = client.devboxes.create_and_await_running(
    name="multi-language-boot-demo",
)

# Test Node.js/npm functionality
client.devboxes.execute_sync(
    id=devbox.id, command="npm --version && node --version"
)

# Create a simple Node.js project and run npm install
setup_commands = [
    "mkdir -p /home/user/nodejs-demo && cd /home/user/nodejs-demo",
    "npm init -y",
    "npm install express",
    "echo 'console.log(\"Node.js environment ready!\");' > index.js",
]

for command in setup_commands:
    result = client.devboxes.execute_sync(id=devbox.id, command=command)

# Test Python/venv functionality
client.devboxes.execute_sync(
    id=devbox.id, command="python3 --version && python3 -m venv --help"
)

# Create a Python virtual environment and install packages
python_commands = [
    "mkdir -p /home/user/python-demo && cd /home/user/python-demo",
    "python3 -m venv myenv",
    "source myenv/bin/activate && pip install requests flask",
    "source myenv/bin/activate && python3 -c \"import requests, flask; print('Python environment ready!')\"",
]

for command in python_commands:
    client.devboxes.execute_sync(id=devbox.id, command=command)
    
// DEVBOXES

A Complete Infra Toolbox for AI SWEs

Everything you need to prototype your Agent for one use case and scale to thousands

Beyond Generating-icon
Beyond Generating Code - Create Full AI Engineers
Devboxes enable agents do more than just evaluate code. Let agents work on features for you with full shell and build toolchains.
Simplify Deployments by deploying agents directly into the isolated environment for full control.
Out-of-the-box-icon
Browser + Computer Use out of the box
Use Runloop's Browser and Computer templates to enable your Agent to gather context using any developer tools or automate real world workflows.
Tunnels-icon
Tunnels
Share local development servers or processes from the Devbox with others by opening secure web tunnels to expose local ports.
Snapshot-icon
Snapshot, Fork, and Explore
Snapshot the full state of a Devbox at any point to save progress and resume later.
Create new forked Devboxes from the snapshot and allow your agent to explore different ideas in parallel.
Docker-icon
Support Any Developer Environment
Use Runloop Blueprints to create custom environment images to meet any development requirements:
- Full Dockerfile compatibility
- Docker in Docker support
- Any architecture (x86, arm etc) and any OS
Terminal-icon
Connect From Your IDE and Terminal
Connect to any Devbox directly from your IDE or Terminal using SSH.
Enable developers to take the lead with human-in-the-loop flows when an Agent needs low level guidance.
// BENCHMARKS

Benchmarking At Scale

Test Your Agents against existing Frontier Benchmarks like SWE bench in minutes.
Or create the next frontier benchmark yourself and share in one click.

Frontier Benchmarks-icon
Easily Run Agents on Existing Frontier Benchmarks
Run against popular benchmarks like SWE-Bench, SWE-Smith, Web Voyager and more in just a few lines of code with our standard interface.
Parallel-icon
Run Large Benchmarks Fully in Parallel
Run thousands of test cases in parallel to improve your iteration loop speed. Quickly identify agent performance issues and improve.
Custom Benchmark-icon
Custom Benchmarks
Create your own benchmarks to test custom capabilities of models. Runloop supports all environments you might need from standalone coding cases to browser use to computer use.
// FAQs

Everything You Need to Know

We’re a talented team of former Google and Stripe engineers dedicated to solving the complex challenges of productionizing AI for software engineering at scale.

How does Runloop support agentic AI workflows?
chevron-faq
Why do AI coding agents need new infrastructure?
chevron-faq
What types of AI use cases benefit from Runloop's infrastructure?
chevron-faq
How does Runloop ensure safe and secure code execution for AI agents?
chevron-faq
Why are AI coding agent benchmarks essential?
chevron-faq
How does Runloop pricing work?
chevron-faq
How easy is it to integrate Runloop with existing AI development pipelines?
chevron-faq
What makes Runloop's AI code execution infrastructure enterprise-grade?
chevron-faq
Is Runloop suitable for both individual developers and enterprises?
chevron-faq
// 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.