{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-langchain-ai--deepagents","slug":"langchain-ai--deepagents","name":"deepagents","type":"agent","url":"https://docs.langchain.com/deepagents","page_url":"https://unfragile.ai/langchain-ai--deepagents","categories":["ai-agents"],"tags":["ai","deepagents","langchain","langgraph"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-langchain-ai--deepagents__cap_0","uri":"capability://planning.reasoning.single.function.agent.instantiation.with.batteries.included.defaults","name":"single-function agent instantiation with batteries-included defaults","description":"Provides create_deep_agent() factory function that returns a fully-configured LangGraph compiled graph with planning, tool calling, and context management pre-wired. 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Middleware stack processes each tool invocation through registered handlers in sequence, with support for early termination and result eviction. Built on LangGraph's node-level hooks, enabling fine-grained control over tool execution without modifying core agent logic.","intents":["I need to sandbox or validate tool calls before they execute against production systems","I want to log all tool invocations and their results for audit trails without modifying agent code","I need to inject custom pre/post-processing logic (e.g., rate limiting, result filtering) into tool execution"],"best_for":["enterprises requiring audit trails and governance over agent tool usage","teams building agents that interact with sensitive systems (databases, APIs, filesystems)","developers implementing custom tool execution policies (rate limiting, cost tracking, approval workflows)"],"limitations":["Middleware stack adds latency per tool call; no built-in performance optimization for high-throughput scenarios","Middleware order matters; circular dependencies or conflicting handlers can cause unexpected behavior","No built-in middleware library; developers must implement custom handlers for common patterns (logging, validation)"],"requires":["Python 3.9+","LangGraph 0.1.0+","Understanding of LangGraph node execution model and state schema"],"input_types":["tool_call (dict with tool name, arguments, and call ID)","agent_state (full execution state including message history and context)"],"output_types":["tool_result (modified or original result dict)","execution_metadata (logging, timing, validation status)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_10","uri":"capability://automation.workflow.deployment.and.client.server.mode.with.remote.agent.execution","name":"deployment and client-server mode with remote agent execution","description":"Supports deploying agents as remote services via the 'deepagents deploy' command, exposing agents over HTTP/gRPC for client-server execution. 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Supports multiple sandbox backends with a unified interface; agents can switch providers at runtime. Enables safe execution of untrusted code or resource-intensive operations without impacting the agent's process.","intents":["I need to execute code generated by my agent in a sandboxed environment for safety","I want to run resource-intensive operations (data processing, ML inference) in a separate environment","I need to support multiple sandbox providers for flexibility and redundancy"],"best_for":["agents that generate and execute code (e.g., code generation, data analysis)","applications requiring isolation between agent logic and tool execution","organizations with security policies requiring sandboxed execution"],"limitations":["Sandbox integration adds latency; remote execution is slower than local execution","Sandbox providers have different capabilities and limitations; not all tools work with all providers","State sharing between agent and sandbox is limited; complex workflows may require explicit data passing"],"requires":["Python 3.9+","Account and credentials for at least one sandbox provider (Daytona, RunLoop, Modal, QuickJS)","Network connectivity to sandbox provider"],"input_types":["sandbox_config (dict with provider type and credentials)","code_to_execute (string with Python or JavaScript code)","execution_context (dict with environment variables and dependencies)"],"output_types":["execution_result (dict with stdout, stderr, and return value)","execution_metadata (dict with duration, resource usage, and provider info)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_12","uri":"capability://memory.knowledge.context.injection.and.local.file.awareness.for.cli.agents","name":"context injection and local file awareness for cli agents","description":"CLI agents can automatically discover and inject local files and directory context into the agent's system prompt, enabling agents to be aware of the current working directory and available files. Supports glob patterns for selective file inclusion and automatic content summarization for large files. Enables agents to understand the local environment without explicit file listing commands.","intents":["I want my agent to understand the structure of my project without me having to describe it","I need my agent to be aware of files in my current directory and use them as context","I want to inject specific files (e.g., README, config files) into the agent's context automatically"],"best_for":["CLI-based agents that interact with local projects (code generation, documentation, analysis)","developers who want agents to understand their project structure automatically","automation scenarios where agents need to be context-aware without explicit setup"],"limitations":["File discovery is based on filesystem traversal; large directories with many files may be slow","Content summarization for large files is lossy; important details may be omitted","No built-in support for binary files or non-text content; only text files are injected"],"requires":["Python 3.9+","Read permissions on files and directories to be injected","Optional: glob patterns for selective file inclusion"],"input_types":["root_directory (string path to project root)","include_patterns (list of glob patterns for files to include)","exclude_patterns (list of glob patterns for files to exclude)","max_file_size (int, maximum file size to include in context)"],"output_types":["context_string (string with file structure and content to inject into system prompt)","file_metadata (dict with file paths, sizes, and inclusion status)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_13","uri":"capability://planning.reasoning.evaluation.framework.with.harbor.integration.for.agent.benchmarking","name":"evaluation framework with harbor integration for agent benchmarking","description":"Integrates with the Harbor evaluation framework to benchmark agent performance on standardized tasks and datasets. Supports defining evaluation tasks, running agents against them, and collecting metrics (success rate, latency, cost, tool usage). Enables comparing different agent configurations, models, and strategies on the same benchmarks.","intents":["I want to benchmark my agent's performance on standard tasks to track improvements","I need to compare different models or configurations to choose the best one","I want to measure agent quality metrics (success rate, latency, cost) systematically"],"best_for":["teams optimizing agent performance and cost","researchers evaluating different agent architectures","organizations tracking agent quality over time"],"limitations":["Harbor integration is limited to predefined evaluation tasks; custom benchmarks require framework extension","Evaluation results are sensitive to task definition and metrics; different benchmarks may show different conclusions","No built-in statistical significance testing; results may be noisy or inconclusive"],"requires":["Python 3.9+","Harbor evaluation framework installed","Evaluation dataset or task definitions"],"input_types":["agent_config (dict with model, tools, memory settings)","evaluation_task (dict with task description, inputs, and expected outputs)","metrics (list of metric names to collect)"],"output_types":["evaluation_results (dict with metrics and metadata)","comparison_report (dict comparing multiple agent configurations)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_14","uri":"capability://tool.use.integration.agent.client.protocol.acp.support.for.standardized.agent.communication","name":"agent client protocol (acp) support for standardized agent communication","description":"Implements support for the Agent Client Protocol (ACP), a standardized protocol for client-agent communication. 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Handles protocol translation and ensures compatibility with ACP specifications.","intents":["I want my agent to work with ACP-compliant clients and tools","I need to integrate my agent into a larger system that uses ACP","I want to ensure my agent can be used by different clients without custom integration"],"best_for":["organizations adopting ACP as a standard for agent communication","teams building multi-agent systems with standardized protocols","developers integrating agents into larger platforms or ecosystems"],"limitations":["ACP is a new standard; not all tools and frameworks support it yet","ACP compliance adds overhead; agents must conform to protocol specifications","Protocol translation may lose provider-specific features or optimizations"],"requires":["Python 3.9+","ACP specification and reference implementation","ACP-compliant client or framework"],"input_types":["acp_request (dict conforming to ACP specification)","agent_config (dict with model, tools, memory settings)"],"output_types":["acp_response (dict conforming to ACP specification)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_2","uri":"capability://planning.reasoning.hierarchical.sub.agent.delegation.with.task.decomposition","name":"hierarchical sub-agent delegation with task decomposition","description":"Enables parent agents to spawn child agents (sub-agents) for specific subtasks, with automatic task decomposition and result aggregation. Sub-agents inherit parent's tools, memory, and configuration but execute in isolated contexts, allowing parallel or sequential delegation. Implemented via LangGraph's subgraph pattern, where each sub-agent is a compiled graph invoked as a node in the parent's execution flow.","intents":["I need to break down complex tasks into subtasks and assign each to a specialized sub-agent","I want to parallelize independent subtasks while maintaining a parent agent's oversight and aggregation","I need to create agent hierarchies where child agents report results back to a parent coordinator"],"best_for":["teams building multi-step workflows that benefit from task specialization (e.g., research + analysis + writing)","applications requiring parallel task execution with centralized coordination","organizations implementing agent-based orchestration for complex business processes"],"limitations":["Sub-agent context is isolated; sharing state between siblings requires explicit parent-mediated communication","No built-in load balancing or resource allocation for parallel sub-agents; all execute in same process","Debugging multi-level agent hierarchies is complex; error propagation and retry logic must be manually implemented"],"requires":["Python 3.9+","LangGraph 0.1.0+","Understanding of LangGraph subgraph composition and state schema inheritance"],"input_types":["task_description (string describing subtask for delegation)","parent_state (full parent agent state to pass to sub-agent)","sub_agent_config (optional overrides for sub-agent tools, memory, or model)"],"output_types":["sub_agent_result (structured output from child agent execution)","aggregated_result (parent's combined view of all sub-agent outputs)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_3","uri":"capability://memory.knowledge.persistent.memory.system.with.auto.summarization.and.context.window.management","name":"persistent memory system with auto-summarization and context window management","description":"Provides configurable memory backend (in-memory, file-based, or custom) that persists agent state across invocations and automatically summarizes long conversation histories to fit within LLM context windows. Uses token counting to estimate message sizes and evicts or compresses older messages when approaching limits. Supports both short-term (current conversation) and long-term (summarized history) memory with configurable retention policies.","intents":["I need my agent to remember previous conversations and context across multiple invocations","I want automatic summarization of long conversations so the agent doesn't lose context but stays within token limits","I need to configure memory retention policies (e.g., keep last 10 messages, summarize older ones)"],"best_for":["multi-turn conversational agents that need persistent context","long-running agents processing large amounts of historical data","applications requiring audit trails of agent decisions and memory state"],"limitations":["Auto-summarization may lose nuanced details; no configurable summarization strategy (uses fixed LLM-based approach)","Token counting is approximate; actual token usage may exceed estimates, causing context overflow","File-based memory backend has no built-in encryption; sensitive data is stored in plaintext unless externally encrypted"],"requires":["Python 3.9+","LangChain 0.1.0+","Token counter for target LLM (e.g., tiktoken for OpenAI models)","Optional: external storage backend (database, file system) for persistence"],"input_types":["memory_config (dict with backend type, max_tokens, retention_policy)","conversation_messages (list of message dicts with role and content)","summarization_prompt (optional custom prompt for auto-summarization)"],"output_types":["memory_state (dict with current messages, summaries, and metadata)","context_window_status (dict with token usage and available capacity)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_4","uri":"capability://tool.use.integration.filesystem.operations.with.sandboxed.path.validation.and.built.in.tools","name":"filesystem operations with sandboxed path validation and built-in tools","description":"Provides a suite of built-in tools for safe filesystem operations (read, write, list, delete) with automatic path validation to prevent directory traversal attacks. All filesystem operations are sandboxed to a configurable root directory; paths are validated against a whitelist/blacklist before execution. Implements secure file handling with proper error messages that don't leak sensitive path information.","intents":["I need my agent to safely read and write files without risking directory traversal or access to sensitive system paths","I want built-in filesystem tools that are pre-configured with security best practices","I need to restrict agent file access to specific directories while allowing flexible operations within those bounds"],"best_for":["agents that need to interact with local files (code generation, document processing, data analysis)","applications requiring sandboxed execution with filesystem access","teams building agents that must comply with security policies around file access"],"limitations":["Sandboxing is enforced at the Python level; determined attackers could potentially bypass via symlinks or race conditions","No built-in support for remote filesystems (S3, GCS, etc.); only local paths are supported","File size limits are not enforced; large file operations could consume excessive memory or disk space"],"requires":["Python 3.9+","Read/write permissions on configured root directory","Optional: custom path validation rules via configuration"],"input_types":["operation (string: 'read', 'write', 'list', 'delete')","path (relative path within sandboxed root)","content (string, for write operations)","options (dict with encoding, permissions, etc.)"],"output_types":["file_content (string for read operations)","file_list (list of dicts with name, type, size for list operations)","operation_result (dict with status and metadata)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_5","uri":"capability://tool.use.integration.multi.backend.llm.provider.abstraction.with.dynamic.model.switching","name":"multi-backend llm provider abstraction with dynamic model switching","description":"Abstracts LLM provider differences (Anthropic, OpenAI, Google, Ollama, etc.) through LangChain's model registry, allowing agents to switch providers or models at runtime without code changes. Handles provider-specific tool calling conventions, token limits, and system prompt formats transparently. Configuration is provider-agnostic; same agent code works with any supported LLM.","intents":["I want to switch between LLM providers (e.g., OpenAI to Anthropic) without rewriting my agent","I need to evaluate different models on the same task without duplicating agent logic","I want to fall back to a secondary provider if the primary one is unavailable or rate-limited"],"best_for":["teams evaluating multiple LLM providers for cost, latency, or capability reasons","applications requiring provider redundancy or failover","organizations with multi-cloud or hybrid LLM strategies"],"limitations":["Provider-specific features (e.g., vision capabilities, extended context windows) are not abstracted; agents must handle provider differences manually","Tool calling conventions differ subtly between providers; some edge cases may require provider-specific handling","Rate limits and quota management are not abstracted; developers must implement their own rate limiting logic"],"requires":["Python 3.9+","LangChain 0.1.0+","API keys for at least one supported LLM provider","Optional: environment variables or config file for provider credentials"],"input_types":["model_name (string identifier like 'gpt-4', 'claude-3-opus', 'gemini-pro')","provider_config (dict with API key, base URL, custom parameters)"],"output_types":["model_instance (LangChain BaseChatModel compatible with agent's tool calling interface)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_6","uri":"capability://automation.workflow.streaming.execution.with.real.time.token.and.event.emission","name":"streaming execution with real-time token and event emission","description":"Leverages LangGraph's native streaming support to emit agent execution events (tool calls, LLM responses, state updates) in real-time as the graph executes. Supports both token-level streaming (for LLM output) and event-level streaming (for tool calls and state changes). Enables building responsive UIs that show agent progress without waiting for full execution completion.","intents":["I want to show users real-time progress as my agent thinks and executes tools","I need to stream LLM tokens to the frontend for a ChatGPT-like experience","I want to emit custom events (tool calls, decisions) for monitoring and debugging"],"best_for":["web applications and chat interfaces requiring real-time feedback","monitoring and observability systems tracking agent execution","interactive applications where users need to see agent reasoning as it happens"],"limitations":["Streaming adds complexity to error handling; partial results may be emitted before failures occur","Token-level streaming is only available for LLMs that support it; some providers or models don't stream tokens","Event ordering is not guaranteed in distributed scenarios; clients must handle out-of-order events"],"requires":["Python 3.9+","LangGraph 0.1.0+","WebSocket or Server-Sent Events (SSE) infrastructure for client communication","LLM provider that supports streaming (most modern providers do)"],"input_types":["stream_mode (string: 'values', 'updates', 'debug', or custom)","config (dict with streaming options and event filters)"],"output_types":["event_stream (async iterator of dicts with event type, data, and metadata)","token_stream (async iterator of strings for LLM output)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_7","uri":"capability://safety.moderation.human.in.the.loop.approval.workflow.with.tool.call.interception","name":"human-in-the-loop approval workflow with tool call interception","description":"Implements a middleware-based approval system that pauses agent execution when certain tools are invoked, requiring human approval before proceeding. Integrates with the middleware pipeline to intercept tool calls, emit approval requests, and resume execution based on human decision. Supports custom approval policies (e.g., approve all read operations, require approval for write operations).","intents":["I need to require human approval before my agent executes dangerous operations (deleting files, modifying databases)","I want to implement a review workflow where humans can inspect and approve agent decisions before they're executed","I need to create audit trails showing which operations were approved and by whom"],"best_for":["high-stakes applications (financial transactions, healthcare decisions, infrastructure changes)","compliance-heavy industries requiring audit trails and human oversight","teams building agents that interact with production systems"],"limitations":["Approval workflow adds latency; agents must wait for human response, blocking execution","No built-in timeout mechanism; if humans don't respond, agents hang indefinitely","Approval state is not persisted; if the system crashes during approval, the request is lost"],"requires":["Python 3.9+","LangGraph 0.1.0+","Human-in-the-loop infrastructure (UI, notification system, approval backend)","Mechanism for humans to respond to approval requests (API, webhook, message queue)"],"input_types":["tool_call (dict with tool name, arguments, and call ID)","approval_policy (dict defining which tools require approval)","human_response (dict with approval decision and optional comments)"],"output_types":["approval_request (dict with tool details and request ID for human review)","approval_decision (dict with approved/rejected status and metadata)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_8","uri":"capability://tool.use.integration.skills.system.with.composable.tool.libraries.and.auto.documentation","name":"skills system with composable tool libraries and auto-documentation","description":"Provides a skills registry where custom tools can be bundled into reusable skill packages with automatic schema generation and documentation. Skills are composable; agents can load multiple skill packages and inherit their tools. Tool schemas are automatically generated from Python function signatures and docstrings, reducing boilerplate. Supports skill versioning and dependency management.","intents":["I want to create reusable tool libraries that multiple agents can share without duplicating code","I need automatic documentation and schema generation for my custom tools","I want to version and manage dependencies between different skill packages"],"best_for":["organizations building multiple agents that share common tool sets","teams creating domain-specific tool libraries (e.g., database skills, API skills)","developers who want to reduce boilerplate in tool definition and schema generation"],"limitations":["Auto-schema generation from docstrings is fragile; complex types or edge cases may not be captured correctly","No built-in skill marketplace or registry; organizations must manage skill distribution manually","Skill dependencies are not automatically resolved; circular dependencies or version conflicts must be handled manually"],"requires":["Python 3.9+","Pydantic for tool schema generation","Well-documented Python functions with type hints and docstrings"],"input_types":["skill_definition (Python class or module with tool functions)","skill_config (dict with name, version, dependencies)","tool_function (Python function with type hints and docstring)"],"output_types":["skill_package (dict with tools, schemas, and metadata)","tool_schema (Pydantic model or JSON schema for tool invocation)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--deepagents__cap_9","uri":"capability://automation.workflow.cli.application.with.interactive.mode.and.session.management","name":"cli application with interactive mode and session management","description":"Provides a command-line interface (deepagents CLI) for running agents interactively with chat-like input/output, autocomplete, and session persistence. Interactive mode maintains conversation history, supports multi-turn interactions, and provides commands for configuration, model switching, and session management. Non-interactive mode allows running agents from scripts or CI/CD pipelines with input/output redirection.","intents":["I want to interact with my agent from the command line like a chat interface","I need to save and resume agent sessions across multiple invocations","I want to switch models or configurations mid-conversation without restarting"],"best_for":["developers testing agents locally before deploying to production","non-technical users who want to interact with agents via CLI","automation scenarios where agents are invoked from scripts or CI/CD pipelines"],"limitations":["Interactive mode is terminal-based; no GUI or web interface","Session persistence is file-based; no built-in encryption or access control","Autocomplete is basic; no context-aware suggestions based on agent capabilities"],"requires":["Python 3.9+","deepagents CLI package installed (pip install deepagents-cli)","Terminal with ANSI color support (for interactive mode)"],"input_types":["user_input (string from terminal or stdin)","cli_command (string like '/model gpt-4' or '/save session_name')","config_file (YAML or JSON with agent configuration)"],"output_types":["agent_response (string printed to terminal)","session_file (JSON with conversation history and metadata)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LangChain 0.1.0+","LangGraph 0.1.0+","API key for at least one supported LLM provider (Anthropic, OpenAI, Google, etc.)","Understanding of LangGraph node execution model and state schema","Docker or container runtime for deployment","Optional: Kubernetes for orchestration","HTTP/gRPC infrastructure for client communication","Account and credentials for at least one sandbox provider (Daytona, RunLoop, Modal, QuickJS)","Network connectivity to sandbox provider"],"failure_modes":["Opinionated defaults may not suit highly specialized agent architectures requiring custom state schemas","System prompts are fixed unless explicitly overridden; no dynamic prompt generation based on task context","Single-agent instantiation pattern; multi-agent coordination requires manual graph composition","Middleware stack adds latency per tool call; no built-in performance optimization for high-throughput scenarios","Middleware order matters; circular dependencies or conflicting handlers can cause unexpected behavior","No built-in middleware library; developers must implement custom handlers for common patterns (logging, validation)","Deployment adds network latency; remote agents are slower than in-process execution","No built-in authentication or authorization; security must be implemented at the transport layer","Streaming responses over HTTP require WebSocket or Server-Sent Events; not all clients support these protocols","Sandbox integration adds latency; remote execution is slower than local execution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7540364780361125,"quality":0.5,"ecosystem":0.52,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-05-03T04:16:10Z"},"community":{"stars":22155,"forks":3112,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langchain-ai--deepagents","compare_url":"https://unfragile.ai/compare?artifact=langchain-ai--deepagents"}},"signature":"XxTLhH9Xo7XVP4LRZae68hDq6kUWfkTPlntPUabl+iZ9lZ2TyiOlgGjox4gxG1E/K/B3VIbLKiFL+N5yUxnaDg==","signedAt":"2026-06-20T21:20:23.156Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langchain-ai--deepagents","artifact":"https://unfragile.ai/langchain-ai--deepagents","verify":"https://unfragile.ai/api/v1/verify?slug=langchain-ai--deepagents","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"}}