{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-opendevin","slug":"opendevin","name":"OpenDevin","type":"agent","url":"https://github.com/OpenDevin/OpenDevin","page_url":"https://unfragile.ai/opendevin","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-opendevin__cap_0","uri":"capability://planning.reasoning.autonomous.agent.task.execution","name":"autonomous-agent-task-execution","description":"Executes multi-step software development tasks autonomously by decomposing user intent into sub-tasks, making decisions about tool usage, and iterating toward completion. Uses an agentic loop pattern where the LLM observes environment state (file system, test results, error logs), reasons about next actions, and executes them through a unified action interface. Supports long-running workflows that span code generation, testing, debugging, and deployment without human intervention between steps.","intents":["I want an AI agent to implement a feature end-to-end without me writing code","I need to automate repetitive development tasks like refactoring, testing, and bug fixes","I want to delegate complex multi-file changes to an agent that understands my codebase context"],"best_for":["teams building internal tools and automating development workflows","developers prototyping AI-driven development pipelines","organizations seeking to reduce manual coding effort for well-defined tasks"],"limitations":["agent decision-making quality depends heavily on LLM capability and prompt engineering","no built-in mechanism to prevent infinite loops or runaway execution — requires external timeout/monitoring","struggles with ambiguous requirements or tasks requiring deep domain knowledge not in training data","context window limitations may prevent handling very large codebases or long task histories"],"requires":["Python 3.9+","LLM API access (OpenAI, Anthropic, or local Ollama instance)","Docker or containerized environment for sandboxed code execution","Node.js 18+ for frontend/UI components"],"input_types":["natural language task description","code files and repository structure","error messages and test output","environment configuration"],"output_types":["modified source code files","test results and execution logs","structured action traces","task completion status and artifacts"],"categories":["planning-reasoning","automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_1","uri":"capability://memory.knowledge.codebase.aware.context.management","name":"codebase-aware-context-management","description":"Maintains and retrieves relevant code context from the user's repository to inform agent decision-making, using file indexing, semantic search, and dependency analysis. The system tracks which files are relevant to a task, builds a dependency graph, and selectively includes code snippets in LLM prompts to stay within token budgets while preserving architectural understanding. Implements sliding-window context selection that prioritizes recently-modified files and files related to the current task.","intents":["I want the agent to understand my codebase structure without sending the entire repo to the LLM","I need the agent to make changes that respect my existing code patterns and architecture","I want to limit token usage while maintaining enough context for accurate code generation"],"best_for":["teams with large codebases (10k+ lines) where full context is infeasible","projects with strict API rate limits or token budgets","developers who want architectural consistency across agent-generated code"],"limitations":["dependency analysis is language-specific and may miss implicit dependencies in dynamic languages","semantic search quality depends on embedding model quality and may miss relevant files with different naming conventions","no built-in support for monorepos with complex cross-package dependencies","context window still limits the amount of code that can be included per LLM call"],"requires":["Python 3.9+","embedding model (local or API-based) for semantic search","language-specific parser for dependency analysis (tree-sitter or AST parser)","file system access to the target repository"],"input_types":["repository file structure","source code files","task description or query","dependency manifests (package.json, requirements.txt, etc.)"],"output_types":["ranked list of relevant files","code snippets with line numbers","dependency graph visualization","context budget utilization metrics"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_10","uri":"capability://safety.moderation.security.vulnerability.scanning.and.remediation","name":"security-vulnerability-scanning-and-remediation","description":"Scans generated code for security vulnerabilities using static analysis tools and generates fixes for identified issues. The agent integrates with security scanners (SAST tools, dependency checkers) to identify common vulnerabilities (SQL injection, XSS, insecure dependencies, etc.) and generates secure code that addresses them. Implements security-aware code generation that follows secure coding practices.","intents":["I want the agent to generate secure code without common vulnerabilities","I need the agent to identify and fix security issues in generated code","I want to enforce security standards and compliance requirements in generated code"],"best_for":["security-sensitive applications (financial, healthcare, government)","teams with strict security compliance requirements (HIPAA, PCI-DSS, SOC 2)","organizations building security-critical infrastructure"],"limitations":["static analysis tools have high false-positive rates and may flag safe code as vulnerable","security scanning adds latency to code generation (seconds to minutes per scan)","no built-in support for runtime security analysis or behavioral security checks","generated fixes may not address the root cause or may introduce new vulnerabilities"],"requires":["Python 3.9+","SAST tools and security scanners (Bandit, Semgrep, Snyk, etc.)","dependency vulnerability databases (NVD, GitHub Security Advisory, etc.)","security policy definitions and compliance requirements"],"input_types":["generated code","dependency manifests","security policies and compliance requirements","vulnerability databases and threat models"],"output_types":["vulnerability scan results","identified security issues with severity levels","remediated code with security fixes","security compliance reports"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_11","uri":"capability://automation.workflow.deployment.and.infrastructure.automation","name":"deployment-and-infrastructure-automation","description":"Automates deployment and infrastructure provisioning by generating deployment configurations, container images, and infrastructure-as-code. The agent can generate Dockerfiles, Kubernetes manifests, Terraform configurations, and CI/CD pipeline definitions based on application requirements. 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The agent analyzes the user's intent, identifies required steps, estimates effort and complexity, and creates a task plan that can be executed sequentially or in parallel. Implements backtracking and replanning when tasks fail or new information emerges.","intents":["I want the agent to break down a complex feature request into manageable tasks","I need the agent to understand task dependencies and execute them in the right order","I want visibility into the agent's plan before it starts executing"],"best_for":["complex projects with multi-step requirements","teams wanting to understand agent reasoning before execution","developers building custom agent workflows with specific task structures"],"limitations":["task decomposition quality depends on LLM reasoning capability and may miss dependencies","estimated effort and complexity are often inaccurate","no built-in support for parallel task execution or resource constraints","replanning overhead can be significant if many tasks fail"],"requires":["Python 3.9+","LLM with strong reasoning capabilities","task execution framework with dependency tracking"],"input_types":["high-level user request or feature description","project context and constraints","existing task definitions and templates"],"output_types":["task plan with steps and dependencies","effort and complexity estimates","task execution status and results","replanning decisions and rationale"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_13","uri":"capability://planning.reasoning.multi.agent.collaboration.and.delegation","name":"multi-agent-collaboration-and-delegation","description":"Enables multiple specialized agents to collaborate on complex tasks by delegating sub-tasks to appropriate agents and coordinating results. Implements agent-to-agent communication, result aggregation, and conflict resolution. Each agent can specialize in specific domains (frontend, backend, DevOps) and coordinate through a central orchestrator.","intents":["I want multiple specialized agents to work on different parts of a project simultaneously","I need agents to coordinate and share results across different domains","I want to leverage specialized agents for specific expertise areas"],"best_for":["large teams with specialized roles (frontend, backend, DevOps, QA)","complex projects spanning multiple domains and technologies","organizations wanting to scale agent capabilities through specialization"],"limitations":["agent coordination overhead increases latency and complexity","no built-in mechanism for resolving conflicts between agent decisions","specialization requires careful agent design and training","debugging multi-agent workflows is significantly more complex than single-agent"],"requires":["Python 3.9+","multi-agent orchestration framework","inter-agent communication protocol (message queues, APIs, etc.)","specialized agent implementations for each domain"],"input_types":["complex task spanning multiple domains","agent specialization definitions","coordination policies and constraints"],"output_types":["delegated sub-tasks and assignments","agent results and artifacts","coordination logs and decision traces","aggregated final results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_2","uri":"capability://tool.use.integration.unified.tool.action.interface","name":"unified-tool-action-interface","description":"Provides a standardized abstraction layer for executing diverse tools (file operations, shell commands, code execution, API calls) through a single action schema that the LLM can invoke. Each action type (read_file, write_file, bash, python_exec, etc.) is defined with input/output schemas, validation rules, and sandboxed execution contexts. The framework handles marshaling between LLM-generated action specifications and actual tool implementations, with built-in error handling and result formatting.","intents":["I want the agent to execute code and shell commands safely without direct system access","I need a consistent interface for the agent to interact with different tools and APIs","I want to audit and log all actions the agent takes for compliance and debugging"],"best_for":["teams building custom agent workflows with heterogeneous tool requirements","organizations needing audit trails and action logging for compliance","developers extending OpenDevin with domain-specific tools"],"limitations":["sandboxing overhead adds latency to each action execution (typically 100-500ms per action)","schema-based approach requires explicit tool registration — dynamic tool discovery not supported","error messages from failed actions may not provide sufficient context for agent recovery","no built-in rate limiting or quota management across tool invocations"],"requires":["Python 3.9+","Docker or container runtime for sandboxed execution","explicit tool definitions with JSON schema specifications"],"input_types":["action type identifier","action parameters (file paths, commands, code snippets)","execution context (working directory, environment variables)"],"output_types":["action execution status (success/failure)","stdout/stderr output","file contents or structured results","execution metadata (duration, resource usage)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_3","uri":"capability://planning.reasoning.interactive.agent.human.collaboration","name":"interactive-agent-human-collaboration","description":"Enables human-in-the-loop workflows where the agent can pause execution, request clarification or approval, and incorporate human feedback into ongoing tasks. Implements a message-passing protocol between agent and user interface where the agent can ask questions, present options, or request confirmation before executing risky actions. Maintains conversation history and allows humans to redirect agent behavior mid-execution without restarting the task.","intents":["I want to supervise the agent and approve major changes before they're committed","I need to provide clarification when the agent is uncertain about requirements","I want to interactively refine the agent's approach based on intermediate results"],"best_for":["teams requiring human oversight for production changes","projects where agent autonomy must be limited for safety or compliance reasons","developers iterating on complex features with evolving requirements"],"limitations":["human response time introduces latency — agent cannot maintain momentum during long waits","no built-in mechanism to handle multiple concurrent human-agent conversations","context loss if human feedback is delayed — agent may need to re-explain its reasoning","UI/UX complexity increases significantly to support rich interaction patterns"],"requires":["Python 3.9+ backend","WebSocket or similar real-time communication protocol","frontend UI (web or IDE extension) for human interaction","session management and state persistence"],"input_types":["human text messages and clarifications","approval/rejection decisions","parameter adjustments and constraints","task redirections or pivots"],"output_types":["agent questions and clarification requests","intermediate results and progress updates","action proposals awaiting approval","task completion summaries"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_4","uri":"capability://code.generation.editing.multi.language.code.generation.and.execution","name":"multi-language-code-generation-and-execution","description":"Generates and executes code across multiple programming languages (Python, JavaScript, TypeScript, Java, C++, etc.) with language-specific syntax validation and runtime error handling. Uses language-specific parsers (tree-sitter) to validate generated code before execution, and maintains separate execution environments for each language with appropriate interpreters/compilers. Handles language-specific idioms and best practices through language-aware prompting and code review patterns.","intents":["I want the agent to write code in my project's primary language without manual translation","I need the agent to generate polyglot code that spans multiple languages in a single task","I want syntax validation and type checking before code execution to catch errors early"],"best_for":["polyglot projects with multiple languages (backend + frontend + infrastructure)","teams building language-agnostic tools or SDKs","organizations with strict code quality requirements"],"limitations":["language support is not comprehensive — less common languages may have limited or no support","syntax validation catches structural errors but not semantic/logic errors","cross-language type checking and compatibility validation is not built-in","execution environment setup overhead increases with number of supported languages"],"requires":["Python 3.9+","language-specific interpreters/compilers (Python, Node.js, Java, etc.)","tree-sitter or equivalent parser for each supported language","Docker for isolated execution environments"],"input_types":["code generation requests in natural language","existing code snippets in various languages","language-specific requirements and constraints"],"output_types":["generated source code files","syntax validation results","execution output and error messages","code quality metrics"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_5","uri":"capability://code.generation.editing.test.driven.development.integration","name":"test-driven-development-integration","description":"Integrates test execution and result analysis into the agent's decision loop, allowing the agent to write tests, run them, analyze failures, and iterate on implementation. The agent can interpret test output, identify root causes of failures, and generate fixes that satisfy test requirements. Supports multiple testing frameworks and assertion styles, with built-in parsing of test results to extract actionable failure information.","intents":["I want the agent to write code that passes my existing test suite","I need the agent to generate tests for new features and verify implementation correctness","I want the agent to debug failing tests and fix the underlying code issues"],"best_for":["teams with comprehensive test coverage and TDD practices","projects where correctness is critical (financial, medical, security-sensitive)","developers wanting to automate test-driven feature development"],"limitations":["test framework support is limited to popular frameworks — custom or niche frameworks may not be recognized","flaky tests can cause agent confusion and infinite retry loops","test execution time adds significant latency to the agent loop (tests may take seconds to minutes)","agent may struggle with integration tests that require external services or complex setup"],"requires":["Python 3.9+","test framework installed and configured (pytest, Jest, JUnit, etc.)","test files and test data accessible to the agent","ability to parse test output in standard formats"],"input_types":["test files and test specifications","test execution results and failure messages","code coverage reports","implementation requirements"],"output_types":["generated test code","test execution results","code changes that satisfy tests","coverage metrics and reports"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_6","uri":"capability://automation.workflow.git.aware.version.control.integration","name":"git-aware-version-control-integration","description":"Integrates with Git to understand repository history, branch structure, and commit context, allowing the agent to make changes that respect version control practices. The agent can create branches, commit changes with meaningful messages, and understand the impact of changes relative to the main branch. Implements diff analysis to show what changed and why, and can revert changes if needed.","intents":["I want the agent to create feature branches and make commits with meaningful messages","I need to understand what changes the agent made and why through Git history","I want the agent to respect my branching strategy and merge practices"],"best_for":["teams using Git-based workflows and code review processes","organizations requiring audit trails and change tracking","developers wanting to integrate agent changes into existing CI/CD pipelines"],"limitations":["merge conflict resolution is not automated — agent cannot resolve conflicts in complex merges","commit message generation may be generic or unhelpful without careful prompting","no built-in support for non-Git version control systems (SVN, Mercurial, etc.)","rebasing and history rewriting are not supported"],"requires":["Git 2.0+","Git repository with proper configuration","agent access to Git credentials or SSH keys","Python 3.9+ with GitPython or similar library"],"input_types":["repository state and branch information","commit history and diffs","branch protection rules and merge policies"],"output_types":["new branches and commits","pull request descriptions","diff summaries and change analysis","merge status and conflict information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_7","uri":"capability://code.generation.editing.error.recovery.and.debugging.assistance","name":"error-recovery-and-debugging-assistance","description":"Automatically detects execution errors (syntax errors, runtime exceptions, test failures) and generates debugging strategies to resolve them. The agent analyzes error messages, stack traces, and logs to identify root causes, then generates fixes or debugging code to investigate further. Implements backtracking to revert failed changes and retry with different approaches.","intents":["I want the agent to fix errors it encounters without asking me for help","I need the agent to debug complex issues by analyzing error messages and logs","I want the agent to try alternative approaches if the first attempt fails"],"best_for":["teams automating development workflows where errors are expected and recoverable","projects with good error messages and logging that aid debugging","developers wanting to reduce manual debugging effort"],"limitations":["error recovery quality depends on error message clarity — cryptic errors may not be debuggable","agent may enter infinite retry loops if the error is not recoverable","no built-in support for debugging distributed systems or network-dependent code","stack traces may not provide sufficient context for root cause analysis in complex codebases"],"requires":["Python 3.9+","error logging and stack trace parsing","ability to execute debugging code and inspect state","version control for reverting failed changes"],"input_types":["error messages and stack traces","execution logs and debug output","code context around the error","previous attempts and their outcomes"],"output_types":["identified root cause","generated fixes or debugging code","retry attempts with different approaches","debugging reports and analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_8","uri":"capability://text.generation.language.documentation.generation.and.maintenance","name":"documentation-generation-and-maintenance","description":"Automatically generates and updates documentation (docstrings, README files, API documentation) based on code changes and task context. The agent analyzes generated code, extracts key functionality, and generates documentation that matches the project's documentation style and conventions. Integrates with documentation tools like Sphinx or MkDocs to keep documentation in sync with code.","intents":["I want the agent to generate documentation for code it creates","I need to keep documentation in sync with code changes automatically","I want the agent to follow my project's documentation conventions and style"],"best_for":["teams with comprehensive documentation requirements","open-source projects where documentation quality affects adoption","organizations with strict documentation standards"],"limitations":["generated documentation may lack nuance and context that human-written docs provide","documentation style inference from existing docs may not be accurate","no support for non-standard documentation formats or custom conventions","documentation generation adds latency to the task execution"],"requires":["Python 3.9+","documentation framework (Sphinx, MkDocs, etc.) if applicable","existing documentation samples to infer style and conventions","language-specific docstring parsers"],"input_types":["generated code and function signatures","existing documentation samples","documentation style guidelines","code comments and annotations"],"output_types":["generated docstrings and comments","README sections and API documentation","documentation files in standard formats (Markdown, RST, etc.)","documentation validation results"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-opendevin__cap_9","uri":"capability://code.generation.editing.performance.profiling.and.optimization","name":"performance-profiling-and-optimization","description":"Profiles generated code to identify performance bottlenecks and generates optimizations. 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