{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-swe-agent","slug":"swe-agent","name":"SWE Agent","type":"agent","url":"https://github.com/princeton-nlp/SWE-agent","page_url":"https://unfragile.ai/swe-agent","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-swe-agent__cap_0","uri":"capability://tool.use.integration.agentic.code.repository.navigation.and.exploration","name":"agentic code repository navigation and exploration","description":"Enables autonomous agents to explore, understand, and navigate software repositories through a command-based interface that abstracts filesystem operations, git history inspection, and code search. The agent uses a specialized action space (bash-like commands: find, grep, cat, git log, etc.) that maps to safe, sandboxed operations rather than direct shell execution, allowing structured traversal of large codebases without exposing the underlying filesystem.","intents":["I want an AI agent to autonomously explore a GitHub repository and understand its structure without manual guidance","I need the agent to search for specific code patterns or functions across multiple files efficiently","I want to trace git history and understand how a particular file or feature evolved over time"],"best_for":["teams building autonomous code understanding systems","developers creating AI-powered code review or refactoring agents","researchers prototyping agentic software engineering workflows"],"limitations":["Command abstraction adds latency compared to direct filesystem access (~50-200ms per command)","Large repositories (>100k files) may require pagination or filtering to avoid context explosion","No built-in support for binary files or non-text content analysis"],"requires":["Python 3.9+","Git installed and accessible in the environment","Target repository cloned or accessible locally","Sufficient disk space for repository checkout"],"input_types":["repository path (string)","search queries (regex or literal strings)","file paths (relative or absolute)"],"output_types":["file contents (text)","directory listings (structured)","git metadata (commit hashes, authors, timestamps)","search results (line numbers, context)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_1","uri":"capability://code.generation.editing.autonomous.code.editing.with.multi.file.context.awareness","name":"autonomous code editing with multi-file context awareness","description":"Allows agents to generate and apply code changes across multiple files simultaneously while maintaining awareness of dependencies and cross-file references. The system uses a diff-based editing model where changes are represented as structured patches that can be validated, previewed, and applied atomically, with rollback capability if validation fails. The agent can understand how changes in one file affect imports, type definitions, and function signatures in dependent files.","intents":["I want the agent to fix a bug that spans multiple files and update all affected imports automatically","I need the agent to refactor a function and update all call sites across the codebase","I want to apply a consistent code style change across multiple files while respecting file-specific configurations"],"best_for":["autonomous bug-fixing workflows","large-scale refactoring tasks","teams using AI agents for code maintenance"],"limitations":["Requires accurate AST parsing for the target language; unsupported languages fall back to text-based editing with lower accuracy","Cross-file dependency resolution is heuristic-based and may miss indirect dependencies through dynamic imports","Atomic multi-file transactions are not guaranteed if the underlying version control system fails mid-operation"],"requires":["Python 3.9+","Target repository with write permissions","Language-specific parsers for accurate AST analysis (tree-sitter bindings for supported languages)","Git or version control system for change tracking"],"input_types":["code snippets (string)","file paths (string)","change descriptions (natural language or structured patches)"],"output_types":["unified diffs (text)","modified file contents (text)","validation reports (structured)","git commits (if version control integration enabled)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_10","uri":"capability://search.retrieval.web.search.and.information.retrieval.for.context.gathering","name":"web search and information retrieval for context gathering","description":"Enables agents to search the web and retrieve relevant information to inform decision-making and code generation. The system integrates with search APIs (Google Search, Bing, etc.) and can parse search results to extract relevant information. Supports both keyword-based and semantic search, with result ranking and deduplication. Can retrieve documentation, API references, and code examples from the web to provide context for code generation tasks.","intents":["I want the agent to search for documentation or examples when it encounters an unfamiliar library or API","I need the agent to find relevant Stack Overflow answers or GitHub issues related to a problem","I want the agent to retrieve the latest API documentation to ensure generated code is up-to-date"],"best_for":["agents working with unfamiliar libraries or APIs","teams implementing context-aware code generation","applications requiring up-to-date information from the web"],"limitations":["Web search adds latency (1-5 seconds per search) and depends on external service availability","Search result quality varies; irrelevant or outdated results may mislead the agent","Rate limiting on search APIs may limit the number of searches per session","Parsing search results is fragile and may fail for unexpected page structures"],"requires":["Python 3.9+","API key for search service (Google Search, Bing, etc.)","Network connectivity","HTML parsing library (BeautifulSoup, etc.)"],"input_types":["search query (string)","search filters (language, date range, domain, etc.)","result count (integer)"],"output_types":["search results (list of dicts with title, URL, snippet)","parsed content (text extracted from web pages)","relevance scores (float 0-1)","metadata (publication date, source domain, etc.)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_11","uri":"capability://tool.use.integration.git.integration.for.version.control.and.change.tracking","name":"git integration for version control and change tracking","description":"Integrates with git repositories to track changes, manage commits, and handle version control operations. The system can create branches, commit changes with descriptive messages, create pull requests, and manage merge conflicts. Supports analyzing git history to understand code evolution and identify relevant commits. Can validate changes against git hooks and pre-commit checks before committing.","intents":["I want the agent to create a feature branch, make changes, and submit a pull request automatically","I need the agent to analyze git history to understand how a particular feature was implemented","I want the agent to commit changes with meaningful commit messages that explain the modifications"],"best_for":["teams implementing autonomous code contribution workflows","developers automating pull request creation and management","applications requiring git-based change tracking"],"limitations":["Git operations add latency (100-500ms per operation depending on repository size)","Merge conflict resolution is limited to simple cases; complex conflicts require manual intervention","Commit message generation is heuristic-based and may not capture all relevant details","Pull request creation requires authentication and may be rate-limited by the hosting platform"],"requires":["Python 3.9+","Git installed and configured","Repository with write permissions","GitHub/GitLab API token (for pull request creation)"],"input_types":["repository path (string)","branch name (string)","commit message (string)","file changes (diffs or file contents)"],"output_types":["commit hash (string)","branch name (string)","pull request URL (string)","git status (structured: modified files, staged changes, etc.)","merge conflict information (if applicable)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_12","uri":"capability://data.processing.analysis.evaluation.and.benchmarking.of.agent.performance","name":"evaluation and benchmarking of agent performance","description":"Measures agent performance on software engineering tasks using standardized benchmarks and custom evaluation metrics. The system can run agents on test cases, compare results against expected outputs, and generate performance reports. Supports multiple evaluation dimensions including correctness, efficiency, code quality, and test coverage. Can track performance over time to identify improvements or regressions.","intents":["I want to measure how well the agent performs on a set of bug-fixing tasks","I need to compare agent performance across different LLM models or configurations","I want to identify which types of tasks the agent struggles with and focus improvements there"],"best_for":["teams developing and improving SWE agents","researchers benchmarking agent capabilities","organizations evaluating agent suitability for their workflows"],"limitations":["Evaluation is task-dependent; benchmarks may not generalize to real-world scenarios","Correctness evaluation requires ground truth (expected outputs), which may be expensive to obtain","Performance metrics are multidimensional and may conflict (e.g., speed vs code quality)","Benchmarking can be computationally expensive (hours to days for large test suites)"],"requires":["Python 3.9+","Test cases with expected outputs","Evaluation metrics (custom or standard)","Sufficient computational resources for running benchmarks"],"input_types":["test cases (list of dicts with task description, expected output, etc.)","evaluation metrics (list of metric names or custom functions)","agent configuration (dict)"],"output_types":["performance report (structured with metrics and statistics)","per-task results (list with task ID, result, metrics)","comparison reports (agent vs agent, model vs model)","trend analysis (performance over time)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_2","uri":"capability://code.generation.editing.test.generation.and.validation.for.code.changes","name":"test generation and validation for code changes","description":"Automatically generates unit tests for code changes and validates that modifications don't break existing functionality. The system analyzes the modified code to infer test cases, generates test code in the appropriate framework (pytest, unittest, jest, etc.), and executes tests in an isolated environment to verify correctness. It uses coverage analysis to identify untested code paths and can suggest additional test cases.","intents":["I want the agent to generate tests for the code it just wrote to ensure it works correctly","I need to verify that a refactoring didn't break any existing functionality","I want the agent to identify code paths that lack test coverage after making changes"],"best_for":["autonomous code generation workflows requiring quality assurance","teams implementing continuous integration with AI-assisted testing","developers validating agent-generated code before merging"],"limitations":["Test generation is heuristic-based and may miss edge cases or complex business logic","Requires test framework and dependencies to be installed in the execution environment","Coverage analysis overhead can add 1-5 seconds per test run depending on codebase size","Cannot generate tests for code with external dependencies or side effects without mocking setup"],"requires":["Python 3.9+","Test framework installed (pytest, unittest, jest, etc.)","Code coverage tools (coverage.py for Python, nyc for JavaScript)","Isolated execution environment (Docker, virtual environment, or sandboxed process)"],"input_types":["modified code (string or file path)","existing test suite (optional, for context)","code coverage baseline (optional)"],"output_types":["generated test code (string)","test execution results (structured: pass/fail, duration, error messages)","coverage reports (structured: line coverage %, branch coverage %)","suggested test cases (natural language descriptions)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_3","uri":"capability://automation.workflow.agent.action.tracing.and.execution.logging","name":"agent action tracing and execution logging","description":"Provides detailed logging and tracing of all agent actions, including command execution, code changes, test results, and decision points. Each action is recorded with timestamps, inputs, outputs, and success/failure status, enabling full auditability and debugging of agent behavior. The system supports multiple log levels and can export traces in structured formats (JSON, JSONL) for analysis and replay.","intents":["I want to understand exactly what actions the agent took to solve a problem and why it made certain decisions","I need to debug why the agent failed on a particular task by reviewing its execution trace","I want to analyze agent behavior patterns across multiple runs to improve its performance"],"best_for":["developers debugging agent behavior","teams implementing agent monitoring and observability","researchers analyzing agentic software engineering patterns"],"limitations":["Detailed logging adds overhead (~5-10% performance impact depending on log level)","Large traces (>10k actions) can consume significant disk space (100MB+)","Real-time log streaming may introduce latency in agent decision-making if not buffered properly"],"requires":["Python 3.9+","Logging infrastructure (file system or remote logging service)","Sufficient disk space for trace storage"],"input_types":["agent execution context (internal)","action metadata (internal)","log level configuration (string: DEBUG, INFO, WARNING, ERROR)"],"output_types":["structured logs (JSON, JSONL)","human-readable logs (text)","execution traces (structured with timeline)","summary reports (statistics on actions, success rates, timing)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_4","uri":"capability://tool.use.integration.language.model.integration.with.provider.abstraction","name":"language model integration with provider abstraction","description":"Abstracts interactions with multiple LLM providers (OpenAI, Anthropic, local models via Ollama, etc.) through a unified interface, allowing agents to switch providers without code changes. The system handles API authentication, rate limiting, token counting, and response parsing for each provider, with fallback mechanisms if a provider is unavailable. Supports both chat-based and completion-based APIs with consistent message formatting.","intents":["I want to use different LLM providers (OpenAI, Claude, local models) interchangeably in my agent","I need to implement fallback logic if my primary LLM provider is rate-limited or unavailable","I want to track token usage and costs across different LLM providers"],"best_for":["teams building multi-provider LLM applications","developers wanting flexibility to switch LLM backends","cost-conscious teams wanting to optimize LLM provider selection"],"limitations":["Provider abstraction adds ~50-100ms latency per request due to wrapper overhead","Not all providers support identical feature sets (e.g., function calling, vision); fallback behavior may degrade gracefully","Token counting is approximate for some providers and may not match actual billing","Rate limiting is handled at the wrapper level; provider-specific rate limits still apply"],"requires":["Python 3.9+","API keys for desired LLM providers (OpenAI, Anthropic, etc.)","Network connectivity for cloud-based providers","Ollama installed and running (for local model support)"],"input_types":["messages (list of dicts with role and content)","model name (string identifier)","provider configuration (dict with API keys, endpoints)"],"output_types":["model response (string)","token usage metadata (input tokens, output tokens, total)","cost estimates (float)","provider-specific metadata (finish reason, logprobs, etc.)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_5","uri":"capability://tool.use.integration.function.calling.and.tool.use.with.schema.validation","name":"function calling and tool use with schema validation","description":"Enables agents to invoke external tools and APIs through a structured function-calling interface with JSON schema validation. The system defines tool schemas (input parameters, output types, descriptions) and validates agent-generated function calls against these schemas before execution. Supports both synchronous and asynchronous tool execution with error handling and retry logic. Integrates with LLM provider function-calling APIs (OpenAI, Anthropic) when available, falling back to prompt-based function calling for providers without native support.","intents":["I want the agent to call external APIs or tools with validated parameters to accomplish tasks","I need the agent to handle tool execution errors gracefully and retry if appropriate","I want to define a set of tools the agent can use and ensure it calls them correctly"],"best_for":["teams building tool-using agents","developers integrating agents with external APIs and services","applications requiring structured agent-tool interactions"],"limitations":["Schema validation adds ~20-50ms overhead per function call","Prompt-based function calling (for providers without native support) is less reliable than native function calling APIs","Async tool execution requires proper event loop management and can introduce complexity","Tool execution errors may not be recoverable; retry logic is heuristic-based"],"requires":["Python 3.9+","Tool definitions with JSON schemas","Callable functions or API endpoints for each tool","LLM provider with function-calling support (optional; falls back to prompt-based)"],"input_types":["tool schemas (JSON schema format)","tool implementations (callable functions or API endpoints)","function call requests (from LLM, as JSON or structured objects)"],"output_types":["function call results (any JSON-serializable type)","execution status (success/failure)","error messages (string)","execution metadata (duration, retry count)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_6","uri":"capability://memory.knowledge.agent.state.management.and.context.persistence","name":"agent state management and context persistence","description":"Manages agent state across multiple steps, including conversation history, working memory, and task context. The system maintains a structured state object that tracks the agent's progress, decisions, and intermediate results. Supports serialization and deserialization of state for persistence across sessions, enabling agents to resume interrupted tasks. Implements memory management strategies (e.g., summarization, pruning) to keep context within LLM token limits while preserving critical information.","intents":["I want the agent to remember its progress and context across multiple steps without losing information","I need to save the agent's state and resume a task later without starting from scratch","I want the agent to manage its context window efficiently so it doesn't exceed LLM token limits"],"best_for":["long-running agent tasks requiring state persistence","teams implementing agent checkpointing and recovery","applications with complex multi-step workflows"],"limitations":["State serialization/deserialization adds ~100-500ms overhead depending on state size","Memory management strategies (summarization, pruning) may lose important context if not tuned carefully","No built-in distributed state management; requires external storage for multi-agent scenarios","State size grows with task complexity; large states (>1MB) may cause performance issues"],"requires":["Python 3.9+","Storage backend for state persistence (file system, database, or cloud storage)","Serialization format (JSON, pickle, or custom)"],"input_types":["state snapshots (dict or custom objects)","context updates (incremental changes)","memory management policies (configuration)"],"output_types":["serialized state (JSON, pickle, or custom format)","state summaries (natural language or structured)","context statistics (token count, memory usage)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_7","uri":"capability://automation.workflow.error.handling.and.recovery.with.agent.retry.logic","name":"error handling and recovery with agent retry logic","description":"Implements intelligent error handling and recovery mechanisms that allow agents to detect failures, analyze root causes, and retry with different strategies. The system categorizes errors (transient vs permanent, recoverable vs fatal) and applies appropriate recovery tactics (retry with backoff, alternative approach, escalation). Supports custom error handlers for domain-specific failures and integrates with logging to capture error context for debugging.","intents":["I want the agent to automatically retry failed operations with exponential backoff instead of giving up immediately","I need the agent to detect when it's stuck in a loop and try a different approach","I want detailed error information so I can debug why the agent failed on a particular task"],"best_for":["robust agent systems requiring high reliability","teams implementing production-grade autonomous workflows","applications with external dependencies prone to transient failures"],"limitations":["Retry logic adds latency (exponential backoff can delay recovery by 10-60 seconds)","Error categorization is heuristic-based and may misclassify errors","Recovery strategies are limited to predefined tactics; novel failure modes may not be handled gracefully","Excessive retries can consume significant resources and API quota"],"requires":["Python 3.9+","Error classification rules (built-in or custom)","Retry policies (backoff strategy, max retries)"],"input_types":["exceptions (Python Exception objects)","error context (dict with operation details, state, etc.)","retry policies (configuration)"],"output_types":["recovery decision (retry, escalate, fail)","error reports (structured with root cause analysis)","retry metadata (attempt count, backoff duration, next retry time)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_8","uri":"capability://planning.reasoning.task.decomposition.and.planning.with.subgoal.generation","name":"task decomposition and planning with subgoal generation","description":"Breaks down complex tasks into smaller, manageable subtasks and generates execution plans that guide agent behavior. The system uses LLM reasoning to analyze task requirements, identify dependencies between subtasks, and create a structured plan. Supports both linear and branching task graphs, with conditional logic for handling different outcomes. The agent can dynamically adjust the plan based on intermediate results and detected obstacles.","intents":["I want the agent to break down a complex bug fix into smaller steps and execute them in the right order","I need the agent to identify dependencies between tasks and parallelize independent work","I want the agent to adapt its plan when it encounters unexpected obstacles or failures"],"best_for":["complex multi-step software engineering tasks","teams implementing hierarchical task planning for agents","applications requiring adaptive task execution"],"limitations":["Task decomposition is heuristic-based and may not identify optimal subtask boundaries","Plan generation adds latency (1-5 seconds for complex tasks) due to LLM reasoning","Dynamic plan adjustment can lead to thrashing if the agent keeps changing strategy","Dependency analysis is approximate and may miss implicit dependencies"],"requires":["Python 3.9+","LLM with reasoning capabilities (GPT-4, Claude, etc.)","Task description (natural language or structured format)"],"input_types":["task description (string)","task context (dict with relevant information)","planning constraints (time limit, resource constraints, etc.)"],"output_types":["task plan (structured: list of subtasks with dependencies)","execution order (topologically sorted or with branching)","estimated effort (time, complexity)","risk assessment (potential obstacles, mitigation strategies)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swe-agent__cap_9","uri":"capability://code.generation.editing.code.understanding.and.semantic.analysis","name":"code understanding and semantic analysis","description":"Analyzes code to extract semantic information including function signatures, type definitions, dependencies, and control flow. The system uses language-specific parsers (tree-sitter, AST libraries) to build abstract syntax trees and extract structured information about code. Supports cross-file analysis to understand how code is used and what dependencies exist. Can identify code smells, potential bugs, and architectural issues through pattern matching and heuristic analysis.","intents":["I want the agent to understand the structure and semantics of code before making changes","I need the agent to identify all places where a function is called so it can update them if needed","I want the agent to detect potential bugs or code quality issues in the codebase"],"best_for":["code analysis and refactoring workflows","teams implementing intelligent code review agents","applications requiring deep code understanding"],"limitations":["Language-specific parsers required for each supported language; unsupported languages fall back to text-based analysis with lower accuracy","Cross-file analysis is computationally expensive for large codebases (>100k files)","Type inference is incomplete for dynamically-typed languages without type hints","Pattern matching for code smells is heuristic-based and may have false positives/negatives"],"requires":["Python 3.9+","Language-specific parsers (tree-sitter bindings, AST libraries)","Source code in supported languages"],"input_types":["source code (string or file path)","language identifier (string)","analysis scope (single file, directory, or full codebase)"],"output_types":["AST representation (structured)","function/class definitions (list with metadata)","dependencies (import graph, call graph)","code quality issues (list with locations and severity)","type information (inferred or explicit)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Git installed and accessible in the environment","Target repository cloned or accessible locally","Sufficient disk space for repository checkout","Target repository with write permissions","Language-specific parsers for accurate AST analysis (tree-sitter bindings for supported languages)","Git or version control system for change tracking","API key for search service (Google Search, Bing, etc.)","Network connectivity","HTML parsing library (BeautifulSoup, etc.)"],"failure_modes":["Command abstraction adds latency compared to direct filesystem access (~50-200ms per command)","Large repositories (>100k files) may require pagination or filtering to avoid context explosion","No built-in support for binary files or non-text content analysis","Requires accurate AST parsing for the target language; unsupported languages fall back to text-based editing with lower accuracy","Cross-file dependency resolution is heuristic-based and may miss indirect dependencies through dynamic imports","Atomic multi-file transactions are not guaranteed if the underlying version control system fails mid-operation","Web search adds latency (1-5 seconds per search) and depends on external service availability","Search result quality varies; irrelevant or outdated results may mislead the agent","Rate limiting on search APIs may limit the number of searches per session","Parsing search results is fragile and may fail for unexpected page structures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=swe-agent","compare_url":"https://unfragile.ai/compare?artifact=swe-agent"}},"signature":"QXD5zwNQmAiUMsRcDQSYUqA/0gBdNOzqatfehPlNEc/JlstniKIiB1RWt1fOq9ZyO6pFRE1EAzYFCx1gU5yzBw==","signedAt":"2026-06-21T01:36:28.385Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/swe-agent","artifact":"https://unfragile.ai/swe-agent","verify":"https://unfragile.ai/api/v1/verify?slug=swe-agent","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}