SWE Agent

Enables an LLM agent to autonomously navigate and understand code repositories through a specialized command interface that provides file browsing, search, and contextual code inspection. The agent uses a curated set of bash-like commands (find, grep, cat, etc.) that are sandboxed and optimized for LLM token efficiency, allowing the agent to build a mental model of the codebase structure without requiring full repository context upfront.

Orchestrates a multi-step agentic workflow that takes a GitHub issue or bug description, decomposes it into sub-tasks, explores the codebase to locate relevant code, generates fixes, and creates pull requests with explanations. The workflow uses chain-of-thought reasoning to plan exploration steps, iteratively refines understanding based on findings, and validates fixes against test suites before submission.

Allows customization of agent behavior through configuration files and prompt templates. Developers can specify which tools the agent can use, what constraints apply (e.g., 'only modify files in src/'), how the agent should reason about problems, and what validation steps to perform. This enables tuning agent behavior for specific projects or domains without modifying the core agent code.

Provides evaluation frameworks to measure agent performance on standard benchmarks (e.g., SWE-bench) and custom metrics. The agent's success is measured by whether it resolves issues, passes tests, and generates valid code. Evaluation includes metrics like resolution rate, code quality, and efficiency (number of steps, tokens used). This enables systematic comparison of agent performance across different configurations and LLM models.

Generates code fixes by running tests, analyzing failures, and iteratively refining implementations until tests pass. The agent executes the test suite, parses error messages and stack traces, identifies the failing assertion or behavior, and uses that feedback to guide code modifications. This creates a tight feedback loop where test results directly inform the next generation step.

Generates code changes that span multiple files while maintaining consistency across the codebase. The agent understands dependencies between files, tracks how changes in one file affect others, and generates coordinated edits that preserve type safety, import statements, and API contracts. It uses the codebase exploration capability to map dependencies before generating changes.

Integrates with git to track changes made by the agent, generate meaningful commit messages, and create pull requests with proper attribution and descriptions. The agent understands git history, can reference related commits, and generates PR descriptions that explain the rationale for changes. It uses git diff to validate changes before committing.

Analyzes code in multiple programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, etc.) using language-agnostic patterns and tree-sitter AST parsing. The agent can identify functions, classes, imports, and dependencies across language boundaries, enabling it to work on polyglot repositories. It uses syntax-aware parsing rather than regex to ensure accurate code understanding.

Analyzes error messages, stack traces, and test failures to identify root causes and guide code fixes. The agent parses structured error output (including line numbers, error types, and messages), maps errors back to source code, and uses this information to generate targeted fixes. It can handle multiple error types (syntax errors, runtime exceptions, assertion failures, type errors) and iterate based on error feedback.

Implements chain-of-thought reasoning to decompose complex tasks into discrete steps, plan exploration and code generation actions, and reason about the consequences of changes. The agent uses explicit planning steps (e.g., 'First, I'll explore the repository structure. Then, I'll search for the bug. Finally, I'll generate a fix.') to guide its actions and can backtrack if a plan doesn't work. This enables more systematic and transparent problem-solving.

Generates concise summaries of repository structure, key files, dependencies, and architectural patterns without loading the entire codebase into context. The agent explores the repository, identifies important files (entry points, main modules, configuration), and creates a summary that can be used to guide subsequent exploration and code generation. This reduces the context overhead of understanding large repositories.

Provides a debugging interface where developers can inspect the agent's reasoning, view exploration steps, and intervene in the workflow. Developers can see what commands the agent executed, what results were returned, and what decisions were made at each step. This enables manual course-correction if the agent goes off track and helps developers understand why the agent made certain choices.