{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-openhands--openhands","slug":"openhands--openhands","name":"OpenHands","type":"product","url":"https://openhands.dev","page_url":"https://unfragile.ai/openhands--openhands","categories":["app-builders"],"tags":["agent","artificial-intelligence","chatgpt","claude-ai","cli","developer-tools","gpt","llm","openai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-openhands--openhands__cap_0","uri":"capability://tool.use.integration.multi.model.llm.abstraction.layer.with.unified.interface","name":"multi-model llm abstraction layer with unified interface","description":"OpenHands implements a provider-agnostic LLM abstraction layer that normalizes API calls across OpenAI, Anthropic, Claude, GPT, and other models through a unified message formatting and serialization system. The layer handles model-specific quirks, token counting, cost tracking, and retry logic transparently, allowing agents to switch between providers without code changes. Built on LiteLLM integration with metrics collection and budget management per model.","intents":["I want to swap between Claude and GPT-4 without rewriting agent code","I need to track token usage and costs per model across conversations","I want automatic retry logic with exponential backoff for LLM failures","I need to configure different models for different task types"],"best_for":["teams building multi-model agent systems","developers optimizing for cost vs latency tradeoffs","enterprises requiring budget controls and provider flexibility"],"limitations":["Model-specific features (vision, function calling) require conditional logic despite abstraction","Token counting accuracy varies by model; estimates may differ from actual billing","Retry logic adds latency overhead; no built-in circuit breaker for cascading failures"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Python 3.9+","LiteLLM library configured in environment"],"input_types":["text prompts","structured messages with role/content","function schemas for tool calling"],"output_types":["text completions","structured JSON responses","function call specifications"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_1","uri":"capability://automation.workflow.sandboxed.code.execution.with.multi.runtime.support","name":"sandboxed code execution with multi-runtime support","description":"OpenHands provides isolated code execution environments through a pluggable Runtime Architecture that supports Docker, Kubernetes, and local process runtimes. The Sandbox Specification Service defines execution contexts with configurable resource limits, file system isolation, and network policies. Actions execute through an Action Execution Server that marshals code/commands into the sandbox, captures output, and enforces timeout constraints without exposing the host system.","intents":["I want to safely execute untrusted code generated by the agent without compromising my system","I need to run agents in Kubernetes clusters with resource quotas and network policies","I want to test code changes in isolated environments before merging to production","I need persistent file systems across multiple agent actions within a conversation"],"best_for":["teams running agents in production with security requirements","enterprises deploying OpenHands on Kubernetes infrastructure","developers building code generation agents that need execution verification"],"limitations":["Docker runtime adds 500ms-2s overhead per action due to container startup/teardown","Kubernetes runtime requires cluster setup and RBAC configuration; no built-in multi-tenancy isolation","File system isolation prevents agents from accessing host resources; requires explicit mount configuration","Network policies must be pre-configured; no dynamic network rule generation per action"],"requires":["Docker daemon running (for Docker runtime)","Kubernetes cluster with API access (for K8s runtime)","Python 3.9+","Runtime plugins installed for chosen execution environment"],"input_types":["shell commands","Python code snippets","file paths and content","environment variables"],"output_types":["stdout/stderr text","exit codes","file system changes","execution logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_10","uri":"capability://text.generation.language.interactive.web.ui.with.real.time.conversation.management","name":"interactive web ui with real-time conversation management","description":"OpenHands provides a Frontend Application built with React that enables interactive agent conversations through a web browser. The UI implements real-time message streaming via WebSocket, conversation history browsing, and settings management. State Management handles client-side state for conversations, messages, and UI state; Internationalization supports multiple languages. The UI integrates with the backend through REST API (V1) or WebSocket (V0) for seamless real-time updates.","intents":["I want to interact with agents through a web interface without CLI","I need to see real-time updates as agents execute actions","I want to browse and resume previous conversations","I need to configure agent settings and LLM models through the UI"],"best_for":["non-technical users interacting with agents","teams preferring web UI over CLI","enterprises requiring browser-based access"],"limitations":["Web UI adds network latency for real-time updates; high-frequency actions may appear delayed","Browser storage is limited; conversation history is stored on server, not client","UI responsiveness depends on browser performance; large conversations (1000+ messages) may lag","Internationalization is incomplete; some UI strings are hardcoded in English"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Backend API server running (V0 or V1)","JavaScript enabled"],"input_types":["user text input","file uploads","UI interactions (clicks, selections)"],"output_types":["rendered UI","conversation messages","real-time action updates","configuration changes"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_11","uri":"capability://automation.workflow.local.development.environment.with.hot.reload.and.debugging","name":"local development environment with hot-reload and debugging","description":"OpenHands provides a Development Environment Setup with Docker Compose configuration for local development, enabling developers to run the full stack (backend, frontend, database, sandbox) locally. The Local Development Workflow supports hot-reload for code changes without restarting services. Testing Strategy includes unit tests, integration tests, and end-to-end tests; Code Quality and Linting enforce standards through automated checks.","intents":["I want to set up a local development environment quickly without manual configuration","I need to test agent behavior locally before deploying to production","I want to debug agent execution and see detailed logs","I need to contribute to OpenHands development with proper testing"],"best_for":["developers contributing to OpenHands","teams building custom agents locally","engineers debugging agent behavior"],"limitations":["Docker Compose setup requires 8GB+ RAM; resource-constrained machines may struggle","Hot-reload works for Python code but requires manual restart for dependency changes","Testing suite is comprehensive but slow; full test run takes 10+ minutes","Debugging requires understanding of multi-service architecture; logs are scattered across services"],"requires":["Docker and Docker Compose","Python 3.9+","Node.js 18+ (for frontend development)","Git"],"input_types":["source code","configuration files","test cases"],"output_types":["running services","test results","debug logs","code quality reports"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_12","uri":"capability://tool.use.integration.api.driven.agent.orchestration.with.rest.and.websocket.protocols","name":"api-driven agent orchestration with rest and websocket protocols","description":"OpenHands exposes agent functionality through a comprehensive REST API (V1 Conversation Endpoints, Settings Endpoints, Secrets Endpoints, Git Endpoints) and WebSocket protocol (V0 WebSocket Protocol) for real-time communication. The API enables programmatic agent creation, message sending, action execution, and conversation management. REST API follows standard HTTP conventions with JSON payloads; WebSocket protocol uses event-based messaging for streaming updates.","intents":["I want to integrate OpenHands agents into my application via API","I need to programmatically create conversations and send messages","I want to stream real-time agent updates to my frontend","I need to manage agent settings and secrets through API calls"],"best_for":["developers building applications that use OpenHands agents","teams integrating agents into existing platforms","builders creating custom agent UIs"],"limitations":["API rate limiting is not enforced; high-frequency requests may overload the server","WebSocket connections are stateful; client disconnections may lose in-flight updates","REST API requires polling for updates; no built-in server-sent events (SSE) alternative","API versioning is not explicit; breaking changes may occur between versions"],"requires":["OpenHands backend server running","HTTP client library (requests, fetch, etc.)","WebSocket client library (for real-time updates)"],"input_types":["JSON payloads","conversation IDs","message content","action specifications"],"output_types":["JSON responses","event streams","conversation metadata","action results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_2","uri":"capability://planning.reasoning.agent.driven.task.decomposition.and.execution.planning","name":"agent-driven task decomposition and execution planning","description":"OpenHands implements a planning-reasoning system where agents decompose user requests into discrete actions (code execution, file operations, tool calls) through an Agent Controller that manages conversation state and action sequencing. The system uses chain-of-thought reasoning to decide which actions to take next, with support for both synchronous step-by-step execution and asynchronous parallel action batching. Conversation Lifecycle management tracks state across multiple agent iterations, enabling multi-turn problem solving.","intents":["I want the agent to break down 'build a REST API' into smaller steps like scaffolding, writing handlers, and testing","I need the agent to reason about dependencies between tasks and execute them in the right order","I want to see the agent's reasoning process and intermediate decisions in real-time","I need the agent to recover from failures and retry with different approaches"],"best_for":["developers building autonomous coding agents","teams using agents for multi-step development workflows","builders who need transparency into agent decision-making"],"limitations":["Planning overhead adds 1-3 LLM calls per task; no built-in optimization for simple single-action requests","Agent reasoning quality depends heavily on model capability; weaker models produce suboptimal decompositions","No built-in backtracking; if an agent chooses a wrong path, it must be corrected via user intervention","Conversation state grows linearly with iterations; no automatic summarization or context pruning"],"requires":["LLM provider configured with reasoning-capable model (GPT-4, Claude 3+)","Python 3.9+","Sandbox runtime for action execution"],"input_types":["natural language task descriptions","code context and file paths","previous action results and errors"],"output_types":["action sequences","reasoning traces","execution results","conversation state"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_3","uri":"capability://tool.use.integration.skill.and.tool.discovery.with.dynamic.mcp.integration","name":"skill and tool discovery with dynamic mcp integration","description":"OpenHands implements a Skill Loading System that dynamically discovers and registers tools available to agents through Model Context Protocol (MCP) integration. Skills are loaded at conversation start, exposing capabilities like Git operations, file manipulation, and custom tools through a unified function-calling interface. The Microagent Discovery System allows agents to find and compose smaller specialized agents as tools, enabling hierarchical task decomposition.","intents":["I want agents to automatically discover available Git operations without hardcoding tool definitions","I need to add custom tools to agents without modifying core agent code","I want agents to compose smaller specialized agents to solve complex problems","I need to manage tool permissions and access control per agent or user"],"best_for":["teams building extensible agent systems","enterprises with custom tool ecosystems","developers building agent-of-agents architectures"],"limitations":["Skill discovery adds latency at conversation start; no lazy loading for rarely-used tools","MCP integration requires explicit server implementation; no auto-generation from existing APIs","Tool composition (microagents) lacks built-in dependency resolution; circular dependencies must be prevented manually","No built-in versioning for skills; breaking changes require conversation restart"],"requires":["MCP servers running and discoverable","Python 3.9+","Tool definitions in MCP-compatible format"],"input_types":["MCP server configurations","tool/skill schemas","microagent definitions"],"output_types":["available tools list","function call results","microagent outputs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_4","uri":"capability://memory.knowledge.conversation.based.state.management.with.event.streaming","name":"conversation-based state management with event streaming","description":"OpenHands manages agent state through a Conversation Service that tracks all actions, messages, and results across multiple agent iterations. The system uses an event-driven architecture where each action generates events (action_start, action_end, error) that are streamed to clients in real-time via WebSocket (V0) or REST polling (V1). Conversation metadata is persisted to SQL storage, enabling conversation history retrieval, resumption, and analysis.","intents":["I want to see real-time updates as the agent executes actions","I need to persist conversation history and resume interrupted tasks","I want to analyze agent behavior across multiple conversations","I need to replay conversations for debugging or auditing"],"best_for":["teams building interactive agent UIs","enterprises requiring audit trails and conversation history","developers debugging agent behavior"],"limitations":["Event streaming adds network overhead; high-frequency actions (100+/sec) may saturate WebSocket connections","SQL storage requires external database; no built-in persistence for local-only deployments","Conversation state grows unbounded; no automatic archival or cleanup of old conversations","Event replay for debugging requires full event log; partial logs cannot reconstruct state accurately"],"requires":["SQL database (PostgreSQL, MySQL, etc.) for conversation storage","WebSocket support in client (V0) or REST client (V1)","Python 3.9+"],"input_types":["user messages","agent actions","action results","errors"],"output_types":["event stream","conversation metadata","action history","execution logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_5","uri":"capability://tool.use.integration.git.repository.integration.with.provider.agnostic.vcs.operations","name":"git repository integration with provider-agnostic vcs operations","description":"OpenHands provides Git Repository Setup and Git Provider Integration that abstracts version control operations across GitHub, GitLab, and other providers. Git operations are exposed through both MCP tools and dedicated REST API endpoints, enabling agents to clone repositories, create branches, commit changes, and open pull requests. Provider Token Management handles authentication securely through a secrets management system.","intents":["I want agents to automatically clone and modify repositories without manual git commands","I need agents to create feature branches and pull requests as part of their workflow","I want to support multiple Git providers (GitHub, GitLab) with the same agent code","I need secure credential management for Git operations across conversations"],"best_for":["teams using agents for code contribution workflows","enterprises with multi-provider Git infrastructure","developers building CI/CD automation with agents"],"limitations":["Git operations are synchronous; large repository clones (>1GB) block agent execution","Provider token management requires secure storage; no built-in encryption at rest","Merge conflict resolution is not automated; agents cannot resolve conflicts without user intervention","Rate limiting from Git providers is not handled; agents may hit API quotas on large operations"],"requires":["Git credentials (GitHub token, GitLab token, etc.)","Python 3.9+","Git binary installed in sandbox runtime"],"input_types":["repository URLs","branch names","commit messages","pull request descriptions"],"output_types":["cloned repository state","commit hashes","pull request URLs","merge status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_6","uri":"capability://tool.use.integration.multi.tenant.configuration.and.enterprise.user.management","name":"multi-tenant configuration and enterprise user management","description":"OpenHands implements a Settings Architecture with multi-tenant support for enterprise deployments, including User and Organization Management, Authentication Flow, and BYOR (Bring Your Own Reason) API Keys. The system separates configuration sources (environment variables, config files, API) with clear precedence rules. Secrets Management stores sensitive data securely, and LiteLLM Integration with Budget Management enables per-user or per-organization cost controls and billing.","intents":["I want to deploy OpenHands for multiple teams with isolated configurations and budgets","I need to manage API keys securely without exposing them in configuration files","I want to track costs per user or organization and enforce spending limits","I need to support SSO authentication for enterprise users"],"best_for":["enterprises deploying OpenHands as a managed service","SaaS platforms offering OpenHands as a feature","teams requiring multi-tenant isolation and cost controls"],"limitations":["Multi-tenant isolation is logical, not cryptographic; requires careful RBAC configuration to prevent data leakage","Budget enforcement is soft-limit only; agents can exceed budgets before being throttled","SSO integration requires custom implementation; no built-in SAML/OIDC provider","Configuration precedence rules are complex; misconfiguration can lead to unexpected behavior"],"requires":["SQL database for user and organization storage","LiteLLM configured for budget tracking","Python 3.9+","Secrets management backend (environment variables, vault, etc.)"],"input_types":["user credentials","organization configurations","API keys","budget limits"],"output_types":["user tokens","configuration objects","billing reports","access control decisions"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_7","uri":"capability://data.processing.analysis.real.time.agent.monitoring.and.metrics.collection","name":"real-time agent monitoring and metrics collection","description":"OpenHands collects comprehensive metrics throughout the agent lifecycle including LLM token usage, action execution times, error rates, and cost tracking. The Metrics and Cost Tracking system integrates with LiteLLM to capture per-model costs; the Event Callback system persists all events to SQL for analysis. Real-time monitoring is exposed through WebSocket streams (V0) or REST endpoints (V1), enabling dashboards and alerting systems.","intents":["I want to monitor agent performance and identify bottlenecks in execution","I need to track LLM costs per conversation and identify expensive operations","I want to set up alerts when agents exceed error thresholds or latency targets","I need to analyze agent behavior patterns across thousands of conversations"],"best_for":["teams operating agents in production","enterprises requiring cost visibility and optimization","developers debugging agent performance issues"],"limitations":["Metrics collection adds 5-10% overhead per action due to event serialization and storage","Cost tracking relies on LLM provider billing; estimates may differ from actual charges","Real-time monitoring requires WebSocket connections; high-frequency updates can saturate networks","Historical analysis requires querying SQL database; no built-in time-series database for efficient aggregation"],"requires":["SQL database for event storage","LiteLLM configured for cost tracking","Python 3.9+","Monitoring/alerting system (optional, for downstream integration)"],"input_types":["agent actions","LLM calls","execution results","errors"],"output_types":["metrics objects","cost reports","performance traces","event logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_8","uri":"capability://automation.workflow.containerized.deployment.with.docker.and.kubernetes.support","name":"containerized deployment with docker and kubernetes support","description":"OpenHands provides Application Docker Image Building and Runtime Image Building for containerized deployments, with native Kubernetes support through the Kubernetes runtime implementation. The system includes CI/CD Pipeline integration for automated builds and Deployment Options for various environments (local, cloud, on-premise). Docker Compose configuration enables local development; Kubernetes manifests support production deployments with auto-scaling and resource management.","intents":["I want to deploy OpenHands as a containerized service in production","I need to run OpenHands in a Kubernetes cluster with auto-scaling","I want to set up CI/CD pipelines for automated builds and deployments","I need to isolate agent execution in separate containers with resource limits"],"best_for":["teams deploying OpenHands in cloud environments","enterprises with Kubernetes infrastructure","developers building containerized agent services"],"limitations":["Docker image size is large (~2GB) due to dependencies; slow to pull in bandwidth-constrained environments","Kubernetes deployment requires cluster setup and RBAC configuration; no managed service option","CI/CD pipeline integration requires custom scripting; no built-in GitHub Actions or GitLab CI templates","Resource limits must be tuned per workload; no automatic resource optimization"],"requires":["Docker daemon or Kubernetes cluster","Python 3.9+","Docker Compose (for local development)","Kubernetes 1.20+ (for K8s deployments)"],"input_types":["Dockerfile configurations","docker-compose.yml","Kubernetes manifests","CI/CD pipeline definitions"],"output_types":["container images","deployment manifests","build artifacts","deployment logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-openhands--openhands__cap_9","uri":"capability://tool.use.integration.extensible.runtime.architecture.with.custom.plugin.support","name":"extensible runtime architecture with custom plugin support","description":"OpenHands implements a Runtime Builder Extensibility system that allows custom runtime implementations beyond Docker and Kubernetes. The Runtime Plugins architecture enables teams to add support for custom execution environments (e.g., cloud functions, HPC clusters). Storage Backend Extension allows pluggable storage implementations for conversation history and events, decoupling the system from specific databases.","intents":["I want to run agents in a custom execution environment (e.g., AWS Lambda, HPC cluster)","I need to store conversation history in a proprietary database instead of SQL","I want to add custom monitoring or logging to the agent execution pipeline","I need to integrate OpenHands with existing infrastructure without modifying core code"],"best_for":["enterprises with custom infrastructure requirements","teams building specialized agent platforms","developers extending OpenHands for niche use cases"],"limitations":["Plugin development requires deep understanding of OpenHands architecture; no plugin SDK documentation","Runtime plugins must implement full execution interface; partial implementations are not supported","Storage backend plugins must handle consistency guarantees; incorrect implementations can cause data loss","Plugin compatibility is not guaranteed across OpenHands versions; upgrades may break custom plugins"],"requires":["Python 3.9+","Understanding of OpenHands runtime architecture","Custom runtime or storage implementation"],"input_types":["runtime plugin code","storage backend implementation","configuration for custom backends"],"output_types":["execution results","stored conversation data","plugin integration status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Python 3.9+","LiteLLM library configured in environment","Docker daemon running (for Docker runtime)","Kubernetes cluster with API access (for K8s runtime)","Runtime plugins installed for chosen execution environment","Modern web browser (Chrome, Firefox, Safari, Edge)","Backend API server running (V0 or V1)","JavaScript enabled","Docker and Docker Compose"],"failure_modes":["Model-specific features (vision, function calling) require conditional logic despite abstraction","Token counting accuracy varies by model; estimates may differ from actual billing","Retry logic adds latency overhead; no built-in circuit breaker for cascading failures","Docker runtime adds 500ms-2s overhead per action due to container startup/teardown","Kubernetes runtime requires cluster setup and RBAC configuration; no built-in multi-tenancy isolation","File system isolation prevents agents from accessing host resources; requires explicit mount configuration","Network policies must be pre-configured; no dynamic network rule generation per action","Web UI adds network latency for real-time updates; high-frequency actions may appear delayed","Browser storage is limited; conversation history is stored on server, not client","UI responsiveness depends on browser performance; large conversations (1000+ messages) may lag","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.44880173608106,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:19.180Z","last_commit":"2026-05-02T18:13:44Z"},"community":{"stars":72547,"forks":9174,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openhands--openhands","compare_url":"https://unfragile.ai/compare?artifact=openhands--openhands"}},"signature":"k7iiVd12fG5B7P4wCd7Wjec3LFzqmGjJESIRkmgI3Be/HNkmeSIA1SO+gQqzt+zmo9two2DMHMAOiGZN7a1xBg==","signedAt":"2026-06-20T07:42:08.378Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openhands--openhands","artifact":"https://unfragile.ai/openhands--openhands","verify":"https://unfragile.ai/api/v1/verify?slug=openhands--openhands","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"}}