SWE-Gym
Train software agents on real repositories, executable tasks and unit tests.
Train software agents on real repositories, executable tasks and unit tests.
Evaluate repository-level fixes for real GitHub issues.
Creates many repository tasks with failing tests, for training software agents.
Procedurally built repository environments with hybrid verifiers, for training software agents.
Hand-written Python programming problems, each checked by unit tests.
New problems collected over time from LeetCode, AtCoder and Codeforces, to avoid contamination.
Tasks written from your repository, each with a check, for evals and RL training.
Real bugs from your repository’s history, each with the tests from the real fix.
Complete realistic, multi-step tasks across self-hosted websites.
A common Gym-style interface for studying web agents across benchmarks.
Ground shopping instructions in product search, attributes and choices.
Practice focused web interactions in small, repeatable browser tasks.
Realistic web tasks that depend on images and other visual page content.
Over 2,000 tasks collected on 137 real websites, for generalist web agents.
Everyday knowledge-work tasks on the ServiceNow platform, run through BrowserGym.
Jobs in your web app, done through a browser and checked on the app’s data.
Evaluate agents that coordinate with users and act through tools.
Retail and airline tasks where an agent works with a simulated user, tools and policies.
Tool-use instructions over more than 16,000 real-world APIs.
Agents write code against the APIs of nine everyday apps used by simulated people.
Tasks that use your own APIs or tools, checked on what the calls changed.
Work across real desktop applications using computer interaction.
Over 150 tasks across Windows applications, on a real Windows machine, run in parallel.
Tasks across 20 real Android apps, with goals that change from run to run.
Solve practical tasks in a terminal with a reproducible task harness.
Interactive coding in Bash, SQL and Python, with execution feedback after every action.
Command-line jobs in your project: build, debug, migrate and configure.
Learn household tasks through aligned text and embodied environments.
A text world for running elementary-school science experiments.
Generates text-based games for training and testing reinforcement learning agents.
The game NetHack as a fast, hard, procedurally generated RL environment.
Spreadsheet tasks built from 912 real questions on online Excel forums.
Office workflows that need long plans and switching between several applications.
Questions about your own documents, with answers checked against them.
Enterprise text-to-SQL workflows on large databases and cloud data warehouses.
Text-to-SQL questions grounded in large, real-world databases.
Data science coding problems across NumPy, pandas and other Python libraries.
Questions about your data, answered by writing SQL against a copy of it.
40 professional capture-the-flag tasks from four competitions.
Capture-the-flag challenges from popular competitions, with an automated agent harness.
Tool-using agents face prompt-injection attacks; measures both task success and security.
Security weaknesses in your code to find and fix, checked by tests that try them.
75 Kaggle competitions as tasks for machine learning engineering agents.
Agents replicate 20 ICML 2024 papers from scratch, graded against author-checked rubrics.
Over 100 generators and verifiers for reasoning tasks, for RL with verifiable rewards.
Grade-school math word problems with checkable final answers.
Eight environments, from operating systems and databases to games, for testing LLMs as agents.
Real-world questions for general assistants that need browsing, tools and reasoning.
A simulated small software company where agents browse, write code and message coworkers.
Research entries are public projects, not hosted by BoltzLabs. Ready to run evals come with a sample task we checked, and On your repo evals are ones we prepare from your code; both are started from Prepare eval in the BoltzLabs desktop app. Coming soon lists kinds of eval that are not available yet. Hosted environments are in RL Pools; to compare agents on your own task, use Evals.