{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-langchain-ai--langgraph","slug":"langchain-ai--langgraph","name":"langgraph","type":"agent","url":"https://docs.langchain.com/oss/python/langgraph/","page_url":"https://unfragile.ai/langchain-ai--langgraph","categories":["ai-agents"],"tags":["agents","ai","ai-agents","chatgpt","deepagents","enterprise","framework","gemini","generative-ai","langchain","langgraph","llm","multiagent","open-source","openai","pydantic","python","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-langchain-ai--langgraph__cap_0","uri":"capability://planning.reasoning.declarative.graph.based.agent.orchestration.via.stategraph.api","name":"declarative graph-based agent orchestration via stategraph api","description":"Defines multi-step agent workflows as directed acyclic graphs (DAGs) using the StateGraph class, where nodes represent typed functions and edges represent control flow. Developers declare state schema as TypedDict, add nodes with callable handlers, and define conditional edges for branching logic. The framework compiles this declarative definition into an executable Pregel-based state machine that manages state transitions, channel updates, and execution ordering without requiring manual orchestration code.","intents":["I want to define a multi-step agent workflow with clear state transitions and conditional branching","I need to build an agent that can loop, branch, and handle different execution paths based on state","I want to compose complex agent logic without writing custom orchestration code"],"best_for":["Teams building production LLM agents with complex control flow","Developers migrating from imperative orchestration to declarative graph definitions","Enterprise applications requiring auditable, version-controlled agent workflows"],"limitations":["Graph must be acyclic or explicitly handle cycles via conditional edges; no implicit loop detection","State schema must be defined upfront as TypedDict; dynamic schema changes require graph recompilation","Conditional edge logic is evaluated synchronously; complex branching logic can add latency per step"],"requires":["Python 3.9+","LangChain core library (langchain-core)","Type hints and TypedDict for state schema definition"],"input_types":["TypedDict schema (state definition)","Callable functions (node handlers)","Conditional routing functions"],"output_types":["Compiled StateGraph object","Executable agent with invoke/stream methods"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_1","uri":"capability://code.generation.editing.functional.decorator.based.task.definition.with.task.and.entrypoint","name":"functional decorator-based task definition with @task and @entrypoint","description":"Allows developers to define agent tasks as decorated Python functions using @task and @entrypoint decorators, automatically converting them into graph nodes with type-aware input/output handling. The framework introspects function signatures to infer state channel bindings, parameter types, and return value merging strategies. This functional API provides a lighter-weight alternative to StateGraph for simple workflows while maintaining compatibility with the underlying Pregel execution engine.","intents":["I want to quickly define agent tasks without boilerplate graph construction code","I need to compose simple sequential or branching workflows using Python functions","I want type hints to automatically wire up state channels and parameter passing"],"best_for":["Rapid prototyping of simple agent workflows","Teams preferring functional programming patterns over graph APIs","Developers building task pipelines with clear input/output contracts"],"limitations":["Decorator-based approach requires explicit function signatures; implicit state binding can be opaque","Limited support for complex conditional branching compared to StateGraph edges","Function composition order matters; no automatic dependency resolution"],"requires":["Python 3.9+","Type annotations on all task functions","Understanding of decorator semantics and function signature introspection"],"input_types":["Python functions with type hints","Pydantic models or TypedDict for structured inputs"],"output_types":["Decorated task functions","Compiled graph from @entrypoint"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_10","uri":"capability://automation.workflow.distributed.execution.with.kafka.based.coordination","name":"distributed execution with kafka-based coordination","description":"Supports distributed agent execution across multiple workers using Kafka for coordination and state synchronization. The framework distributes graph nodes across workers, uses Kafka topics for inter-node communication, and maintains checkpoint consistency across the distributed system. Developers configure Kafka connection details and worker topology, and the framework handles all message routing and state marshaling automatically.","intents":["I need to scale agent execution across multiple machines","I want to distribute long-running agents to avoid blocking single workers","I need fault tolerance across distributed infrastructure"],"best_for":["High-scale systems running many concurrent agents","Long-running agents that need to be distributed across workers","Infrastructure with existing Kafka deployments"],"limitations":["Kafka coordination adds network latency; distributed execution is slower than local execution","Requires careful configuration of worker topology and topic partitioning","No built-in load balancing; uneven node distribution can cause worker imbalance","Kafka message ordering guarantees must be maintained; out-of-order messages can break execution semantics"],"requires":["Python 3.9+","Kafka cluster (3+ brokers recommended)","LangGraph distributed execution configuration","Worker processes with network connectivity to Kafka"],"input_types":["Kafka broker addresses","Worker topology configuration","Topic naming scheme"],"output_types":["Distributed execution across workers","Coordinated state updates via Kafka"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_11","uri":"capability://text.generation.language.assistants.api.with.thread.based.conversation.management","name":"assistants api with thread-based conversation management","description":"Provides a high-level Assistants API that manages conversation threads, runs, and state persistence automatically. Developers create an Assistant from a compiled graph, then invoke it with user messages; the framework manages thread creation, checkpoint storage, and message history. Each run executes the graph with the current thread state, and results are streamed back to the caller. The API abstracts away checkpoint and state management details, providing a simpler interface for conversational agents.","intents":["I want to build conversational agents without managing threads and checkpoints manually","I need to maintain conversation history across multiple user interactions","I want a simple API for deploying agents as chat interfaces"],"best_for":["Conversational AI applications and chatbots","Teams building user-facing agent interfaces","Rapid prototyping of interactive agents"],"limitations":["Assistants API abstracts away low-level control; customization requires dropping to StateGraph level","Thread management is opaque; no direct access to thread state or history","Message history is stored in checkpoints; querying conversation history requires checkpoint queries"],"requires":["Python 3.9+","Compiled StateGraph or RemoteGraph","Checkpoint storage for thread persistence","LangGraph server for remote Assistants API"],"input_types":["User message string","Thread ID (optional; auto-created if not provided)"],"output_types":["Assistant response string","Run metadata (run ID, status)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_12","uri":"capability://tool.use.integration.prebuilt.react.agent.with.tool.integration.and.toolnode","name":"prebuilt react agent with tool integration and toolnode","description":"Provides a factory function create_react_agent() that generates a fully configured ReAct (Reasoning + Acting) agent graph with built-in tool calling, result aggregation, and loop termination logic. The ToolNode component handles tool execution, error handling, and result formatting. Developers pass an LLM and list of tools, and the framework generates a complete agent graph with proper state management, tool invocation, and response formatting without requiring manual graph construction.","intents":["I want to quickly build a ReAct agent without writing graph code","I need a production-ready agent with tool calling and error handling","I want to focus on tools and prompts rather than orchestration logic"],"best_for":["Rapid prototyping of tool-using agents","Teams building agents with standard ReAct patterns","Developers prioritizing speed over customization"],"limitations":["ReAct pattern is opinionated; customization requires modifying generated graph","Tool execution is synchronous; no async tool support","Error handling is basic; complex error recovery requires custom node logic"],"requires":["Python 3.9+","LLM instance (OpenAI, Anthropic, Ollama, etc.)","Tool definitions as Pydantic models or function signatures","LangChain tool wrappers"],"input_types":["LLM instance","List of Tool objects","Optional system prompt"],"output_types":["Compiled StateGraph implementing ReAct pattern","Agent ready for invoke/stream"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_13","uri":"capability://automation.workflow.cli.and.docker.deployment.with.langgraph.json.configuration","name":"cli and docker deployment with langgraph.json configuration","description":"Provides a command-line interface (langgraph CLI) and Docker image generation for deploying agents as services. Developers define agent configuration in langgraph.json (graph path, environment variables, dependencies), and the CLI generates a Dockerfile, builds images, and deploys to local or cloud environments. The framework handles dependency management, environment setup, and service configuration automatically, enabling one-command deployment.","intents":["I want to deploy agents as Docker services without writing Dockerfiles","I need to configure agents via configuration files rather than code","I want to deploy to cloud platforms with minimal setup"],"best_for":["DevOps teams deploying agents to production","Organizations using Docker and Kubernetes","Teams wanting infrastructure-as-code for agents"],"limitations":["langgraph.json configuration is LangGraph-specific; no standard format","Docker images are Python-based; no support for other runtimes","CLI deployment is basic; complex infrastructure requires custom Kubernetes manifests"],"requires":["Python 3.9+","Docker installed and running","langgraph.json configuration file","LangGraph CLI (pip install langgraph-cli)"],"input_types":["langgraph.json configuration","Graph Python module path","Environment variables"],"output_types":["Generated Dockerfile","Docker image","Deployed service"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_14","uri":"capability://memory.knowledge.store.api.for.cross.thread.persistent.memory.and.knowledge.bases","name":"store api for cross-thread persistent memory and knowledge bases","description":"Provides a BaseStore interface for persisting data across multiple execution threads, enabling agents to maintain long-term memory and shared knowledge bases. Unlike channels (which are thread-specific), the Store API provides a key-value interface for storing and retrieving data that persists across different conversation threads or agent runs. Developers implement custom stores (e.g., vector databases, SQL databases) or use prebuilt implementations, and access them via store.put() and store.get() methods.","intents":["I need agents to remember information across multiple conversations","I want to build shared knowledge bases that multiple agents can access","I need to store long-term context (user profiles, conversation history) separately from execution state"],"best_for":["Multi-turn conversational agents with user context","Multi-agent systems sharing knowledge","Systems requiring long-term memory and personalization"],"limitations":["Store access is asynchronous; node functions must await store operations","No built-in query language; custom stores must implement filtering logic","Store consistency is eventual; no transactional guarantees across store and checkpoint updates"],"requires":["Python 3.9+","BaseStore implementation (custom or prebuilt)","External storage system (database, vector store, etc.)","Async/await support in node functions"],"input_types":["Key-value pairs for storage","Query parameters for retrieval"],"output_types":["Stored data","Retrieved values from store"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_15","uri":"capability://automation.workflow.caching.system.for.deterministic.node.execution.and.memoization","name":"caching system for deterministic node execution and memoization","description":"Implements a caching layer that memoizes node execution results based on input state, avoiding redundant computation when the same state is encountered. The framework uses content-addressable caching where cache keys are derived from input state hashes, enabling automatic deduplication across different execution paths. Developers can configure cache backends (in-memory, Redis, custom) and cache invalidation policies per node.","intents":["I want to avoid redundant LLM calls when the same state is encountered","I need to speed up agent execution by caching expensive operations","I want to share cached results across different execution branches"],"best_for":["Agents with expensive operations (LLM calls, API requests)","Systems with repeated patterns in execution","Cost-sensitive applications where redundant computation is expensive"],"limitations":["Caching assumes deterministic node functions; non-deterministic functions produce incorrect results","Cache keys are based on input state; changes to state schema invalidate all cached results","Cache invalidation is manual; no automatic detection of stale cache entries"],"requires":["Python 3.9+","Deterministic node functions","Cache backend (in-memory, Redis, custom)","Cache configuration per node"],"input_types":["Node input state","Cache backend configuration"],"output_types":["Cached node results","Cache hit/miss metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_16","uri":"capability://data.processing.analysis.serialization.and.deserialization.with.custom.type.support","name":"serialization and deserialization with custom type support","description":"Handles serialization of state, checkpoints, and messages to JSON and other formats, with pluggable serializers for custom types. The framework provides default serializers for Pydantic models, dataclasses, and standard Python types, and allows developers to register custom serializers for domain-specific types. Serialization is used for checkpoint persistence, remote execution, and API responses, ensuring all state can be safely transmitted and stored.","intents":["I need to serialize agent state for storage and transmission","I want to support custom types in agent state without manual serialization code","I need to ensure all state is JSON-serializable for API responses"],"best_for":["Systems with complex state types (Pydantic models, dataclasses)","Remote execution and distributed agents","APIs that need to return agent state as JSON"],"limitations":["Custom serializers must be registered globally; no per-instance serialization configuration","Serialization adds overhead; large state objects can bottleneck on serialization time","Some types (e.g., functions, file handles) cannot be serialized; developer must handle manually"],"requires":["Python 3.9+","Serializable state types (Pydantic, dataclasses, primitives)","Custom serializers for non-standard types"],"input_types":["State objects","Custom type definitions"],"output_types":["JSON-serialized state","Deserialized state objects"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_2","uri":"capability://automation.workflow.pregel.based.bulk.synchronous.parallel.execution.engine","name":"pregel-based bulk synchronous parallel execution engine","description":"Implements a Bulk Synchronous Parallel (BSP) execution model where each superstep atomically updates all active nodes' state channels, then synchronizes before the next superstep. The Pregel engine manages node scheduling, channel merging, and state consistency across distributed or local execution contexts. Each superstep produces a deterministic checkpoint containing all channel values and execution metadata, enabling exact-state resumption after failures or interrupts.","intents":["I need deterministic, resumable agent execution that can survive failures and restart from exact state","I want to understand exactly what state exists at each execution step for debugging and auditing","I need to support both local and distributed execution with identical semantics"],"best_for":["Production systems requiring fault tolerance and exact-state recovery","Teams building long-running agents that must survive infrastructure failures","Enterprises needing audit trails and deterministic replay capabilities"],"limitations":["BSP model adds synchronization overhead; each superstep requires all active nodes to complete before proceeding","Checkpoint creation at every step incurs I/O cost; high-frequency updates can bottleneck on checkpoint storage","Distributed execution requires external coordination layer (e.g., Kafka); no built-in distributed consensus"],"requires":["Python 3.9+","BaseCheckpointSaver implementation for persistence","Deterministic node functions (no side effects that bypass state channels)"],"input_types":["StateGraph or compiled graph definition","Initial state dict matching schema"],"output_types":["Checkpoint objects with channel values and versions","Final state dict after all supersteps complete"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_3","uri":"capability://data.processing.analysis.typed.state.management.with.channel.based.merging.semantics","name":"typed state management with channel-based merging semantics","description":"Manages agent state through a channel system where each state field is a typed container with configurable merge semantics (LastValue, Topic, BinaryOperatorAggregate). Channels define how concurrent node updates are combined when multiple nodes write to the same state field in a superstep. The framework validates all state updates against the TypedDict schema and applies merge functions atomically, ensuring type safety and predictable state evolution.","intents":["I need to safely merge concurrent updates from multiple agent nodes without race conditions","I want to accumulate values (e.g., tool results, messages) across multiple steps","I need type-safe state management with validation and clear merge semantics"],"best_for":["Multi-node agent workflows where concurrent updates must be merged deterministically","Teams building agents that accumulate results (e.g., ReAct agents collecting tool outputs)","Systems requiring strong type safety and state validation"],"limitations":["Channel merge semantics must be declared upfront; changing merge behavior requires graph recompilation","Custom merge functions (BinaryOperatorAggregate) must be deterministic and serializable","No built-in conflict resolution for incompatible concurrent updates; developer must handle via merge logic"],"requires":["Python 3.9+","TypedDict schema with explicit channel type annotations","Understanding of merge semantics (LastValue overwrites, Topic appends, BinaryOperatorAggregate reduces)"],"input_types":["TypedDict state schema","Channel type definitions (LastValue, Topic, BinaryOperatorAggregate)","Merge function implementations"],"output_types":["Merged state dict with all channels updated","Type-validated state snapshots"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_4","uri":"capability://automation.workflow.human.in.the.loop.execution.with.interrupt.and.state.modification","name":"human-in-the-loop execution with interrupt and state modification","description":"Allows agents to pause execution at any superstep via the interrupt system, enabling external inspection and modification of state before resumption. Developers call interrupt() to halt execution, then use update_state() to modify any state field, and resume execution from the exact interruption point. The framework maintains full execution history and checkpoint state, allowing humans to inspect intermediate results, correct agent decisions, or inject new information before continuing.","intents":["I need to pause agent execution for human review or approval before proceeding","I want to correct agent state or inject human feedback without restarting from scratch","I need to build interactive agents that can ask for human input mid-execution"],"best_for":["Enterprise workflows requiring human approval gates","Interactive agents that need user feedback during execution","Systems where agent decisions must be auditable and correctable"],"limitations":["Interrupt points must be explicitly declared in node logic; no automatic detection of decision points","State modifications during interrupt are not validated against merge semantics; developer must ensure consistency","Long-running interrupts can block execution; no timeout or automatic resumption mechanism"],"requires":["Python 3.9+","Explicit interrupt() calls in node functions","Checkpoint storage for maintaining state across interrupt/resume cycles","External system to detect interrupts and call update_state()"],"input_types":["Interrupt signal with optional metadata","State modifications as dict updates"],"output_types":["Paused execution state with full checkpoint","Resumed execution from interruption point"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_5","uri":"capability://memory.knowledge.checkpointing.and.persistence.with.basecheckpointsaver.interface","name":"checkpointing and persistence with basecheckpointsaver interface","description":"Persists agent execution state at each superstep via the BaseCheckpointSaver interface, storing Checkpoint objects containing channel values, execution metadata, and version information. Developers implement custom checkpoint savers (e.g., SQLite, PostgreSQL, in-memory) by extending BaseCheckpointSaver with put() and get() methods. The framework automatically invokes checkpoint savers after each superstep, enabling exact-state recovery after failures, interrupts, or explicit resumption from past checkpoints.","intents":["I need to persist agent state so it survives process crashes or infrastructure failures","I want to resume agent execution from a specific past checkpoint","I need to store execution history for auditing and debugging"],"best_for":["Production agents requiring fault tolerance and recovery","Systems with long-running workflows that may be interrupted","Enterprises needing execution audit trails and replay capabilities"],"limitations":["Checkpoint I/O happens synchronously after each superstep; high-frequency updates can bottleneck on storage latency","Checkpoint serialization must handle all state types; custom objects require custom serializers","No built-in garbage collection; old checkpoints accumulate unless explicitly pruned"],"requires":["Python 3.9+","BaseCheckpointSaver implementation (custom or from prebuilt: SQLite, PostgreSQL)","Serializable state types (Pydantic models, dicts, primitives)","External storage system (database, file system, cloud storage)"],"input_types":["Checkpoint objects with channel values and metadata","Thread IDs and checkpoint IDs for retrieval"],"output_types":["Persisted checkpoint data","Retrieved checkpoint state for resumption"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_6","uri":"capability://planning.reasoning.nested.graph.composition.and.subgraph.execution","name":"nested graph composition and subgraph execution","description":"Allows developers to compose complex agents by nesting StateGraph instances as nodes within parent graphs, enabling hierarchical agent architectures. Subgraphs execute as atomic units within parent supersteps, with their internal state isolated from parent state via explicit input/output mapping. The framework handles state marshaling between parent and subgraph contexts, allowing developers to build modular agent components that can be reused across multiple parent graphs.","intents":["I want to build modular agent components that can be reused across multiple workflows","I need to compose complex agents from simpler sub-agents with clear boundaries","I want to isolate subgraph state from parent state to prevent unintended coupling"],"best_for":["Teams building multi-level agent hierarchies","Systems requiring reusable agent components","Complex workflows with clear functional decomposition"],"limitations":["State marshaling between parent and subgraph adds complexity; input/output mapping must be explicit","Subgraph execution is atomic within parent superstep; no fine-grained interleaving of parent/subgraph steps","Debugging nested graphs requires tracing across multiple execution contexts"],"requires":["Python 3.9+","Multiple StateGraph instances with compatible state schemas","Explicit input/output mapping functions"],"input_types":["Parent StateGraph","Subgraph StateGraph instances","Input/output mapping functions"],"output_types":["Composed graph with nested execution","Marshaled state between parent and subgraph contexts"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_7","uri":"capability://automation.workflow.error.handling.and.retry.policies.with.configurable.backoff","name":"error handling and retry policies with configurable backoff","description":"Provides built-in error handling and retry mechanisms for node execution failures, allowing developers to define retry policies (max attempts, backoff strategy) at the node or graph level. When a node fails, the framework can automatically retry with exponential backoff, linear backoff, or custom retry logic before propagating the error. Failed checkpoints are preserved, enabling inspection of failure context and manual recovery via state modification.","intents":["I need to handle transient failures (e.g., API rate limits, network timeouts) with automatic retries","I want to define different retry strategies for different nodes","I need to inspect failure state and manually recover from persistent errors"],"best_for":["Agents calling external APIs or services prone to transient failures","Production systems requiring resilience to infrastructure issues","Workflows where some failures are recoverable with retries"],"limitations":["Retry logic is synchronous; no async backoff between retries","Custom retry policies must be deterministic and serializable","No built-in circuit breaker or fallback mechanisms; developer must implement via conditional edges"],"requires":["Python 3.9+","Explicit retry policy configuration per node","Exception handling logic in node functions"],"input_types":["Retry policy definitions (max_retries, backoff_strategy)","Exception types to retry on"],"output_types":["Successful execution after retries","Failed checkpoint with error context"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_8","uri":"capability://memory.knowledge.time.travel.and.state.forking.for.execution.replay.and.branching","name":"time travel and state forking for execution replay and branching","description":"Enables developers to retrieve historical checkpoints and fork execution from any past state, creating alternative execution branches without restarting from the beginning. The framework maintains a complete execution history indexed by checkpoint ID and timestamp, allowing queries like 'get state at step 5' or 'fork from checkpoint X and execute with different parameters'. Forked executions are independent and can be persisted separately, enabling experimentation and what-if analysis.","intents":["I want to replay agent execution from a past checkpoint to test different decisions","I need to fork execution and explore alternative paths without restarting","I want to analyze what-if scenarios by branching from historical states"],"best_for":["Debugging and testing agent behavior","Experimentation with alternative agent strategies","Systems requiring execution replay for auditing or analysis"],"limitations":["Forked executions require separate checkpoint storage; no automatic deduplication of shared history","Time travel queries depend on checkpoint storage performance; large histories can be slow to query","Forked executions are independent; no automatic merging or conflict resolution if branches reconverge"],"requires":["Python 3.9+","Checkpoint storage with query capabilities (e.g., indexed by timestamp)","Deterministic node functions for reproducible replay"],"input_types":["Checkpoint ID or timestamp for retrieval","Fork parameters (alternative inputs or state modifications)"],"output_types":["Retrieved historical state","Forked execution branch with independent checkpoint history"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langchain-ai--langgraph__cap_9","uri":"capability://tool.use.integration.remote.graph.execution.with.http.client.and.streaming","name":"remote graph execution with http client and streaming","description":"Allows agents to be deployed as remote services and invoked via HTTP, with the Python SDK providing a RemoteGraph client that streams results back to the caller. The framework handles serialization of state, inputs, and outputs across the network, and supports streaming responses for long-running agents. Remote execution maintains full checkpoint semantics, enabling interrupt/resume and state modification on remote agents.","intents":["I want to deploy agents as microservices and invoke them from other applications","I need to stream agent execution results back to clients in real-time","I want to maintain checkpoint and interrupt capabilities for remote agents"],"best_for":["Distributed systems with agents deployed as services","Multi-tenant platforms where agents are shared across users","Real-time applications requiring streaming agent results"],"limitations":["Network latency adds overhead to each superstep; remote execution is slower than local execution","Serialization of state and results adds I/O cost; large state objects can bottleneck on network bandwidth","Remote interrupt/resume requires maintaining session state on the server; no built-in session management"],"requires":["Python 3.9+","LangGraph server deployment (Docker, cloud platform)","RemoteGraph client SDK","Network connectivity between client and server"],"input_types":["Agent input dict","Configuration (streaming, checkpoint retrieval)"],"output_types":["Streamed state updates","Final agent output"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LangChain core library (langchain-core)","Type hints and TypedDict for state schema definition","Type annotations on all task functions","Understanding of decorator semantics and function signature introspection","Kafka cluster (3+ brokers recommended)","LangGraph distributed execution configuration","Worker processes with network connectivity to Kafka","Compiled StateGraph or RemoteGraph","Checkpoint storage for thread persistence"],"failure_modes":["Graph must be acyclic or explicitly handle cycles via conditional edges; no implicit loop detection","State schema must be defined upfront as TypedDict; dynamic schema changes require graph recompilation","Conditional edge logic is evaluated synchronously; complex branching logic can add latency per step","Decorator-based approach requires explicit function signatures; implicit state binding can be opaque","Limited support for complex conditional branching compared to StateGraph edges","Function composition order matters; no automatic dependency resolution","Kafka coordination adds network latency; distributed execution is slower than local execution","Requires careful configuration of worker topology and topic partitioning","No built-in load balancing; uneven node distribution can cause worker imbalance","Kafka message ordering guarantees must be maintained; out-of-order messages can break execution semantics","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7953477303048868,"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":0.28,"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":"2026-05-03T13:57:06.483Z","last_commit":"2026-05-03T01:05:11Z"},"community":{"stars":31096,"forks":5301,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langchain-ai--langgraph","compare_url":"https://unfragile.ai/compare?artifact=langchain-ai--langgraph"}},"signature":"ZpEnMJQwZki30ClBRkW7elCxNfzpOBsHZSdqnW+QBb1gMjonlNkF07S94rC1CuHvOxSEOdmUgJxA4OvDLykCBA==","signedAt":"2026-06-22T21:16:23.840Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langchain-ai--langgraph","artifact":"https://unfragile.ai/langchain-ai--langgraph","verify":"https://unfragile.ai/api/v1/verify?slug=langchain-ai--langgraph","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"}}