{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-langgraph","slug":"pypi-langgraph","name":"langgraph","type":"framework","url":"https://pypi.org/project/langgraph/","page_url":"https://unfragile.ai/pypi-langgraph","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-langgraph__cap_0","uri":"capability://planning.reasoning.declarative.state.graph.definition.with.stategraph.api","name":"declarative state graph definition with stategraph api","description":"Enables developers to define multi-step agentic workflows as directed acyclic graphs using a declarative API where nodes are functions and edges define control flow. StateGraph uses TypedDict schemas to enforce typed state contracts across nodes, with automatic channel management for state mutations. The framework validates graph topology at definition time and compiles it into an executable Pregel engine that enforces deterministic execution ordering.","intents":["Define a multi-step agent workflow with explicit state transitions","Ensure type safety across agent state mutations","Validate graph structure before runtime execution","Compose complex agentic behaviors from reusable node functions"],"best_for":["Teams building production LLM agents with strict state contracts","Developers migrating from imperative agent code to declarative workflows","Organizations requiring graph-level type safety and validation"],"limitations":["StateGraph requires explicit node and edge definitions — no implicit control flow inference","Cyclic graphs must be explicitly handled via conditional edges; infinite loops are not auto-detected","TypedDict schema changes require recompilation of all dependent graphs"],"requires":["Python 3.9+","typing.TypedDict for state schema definition","LangChain base classes (BaseLanguageModel, BaseTool)"],"input_types":["Python functions (node implementations)","TypedDict schemas (state definitions)","Callable predicates (edge conditions)"],"output_types":["Compiled StateGraph object","Executable Pregel engine","Validated DAG topology"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_1","uri":"capability://automation.workflow.bulk.synchronous.parallel.bsp.execution.engine.with.pregel.model","name":"bulk synchronous parallel (bsp) execution engine with pregel model","description":"Implements a Pregel-inspired BSP execution model where all nodes execute in synchronized supersteps, with state mutations collected and applied atomically between steps. The Pregel engine manages message passing between nodes through typed channels, enforces deterministic ordering, and supports both synchronous and asynchronous node execution. Each superstep reads current channel state, executes eligible nodes in parallel, collects mutations, and applies them atomically before advancing to the next superstep.","intents":["Execute multi-actor agent workflows with guaranteed deterministic ordering","Coordinate parallel node execution without explicit synchronization","Ensure atomic state transitions across distributed agent steps","Support both synchronous and asynchronous node implementations"],"best_for":["Teams building distributed multi-agent systems requiring deterministic replay","Applications needing guaranteed consistency across agent state mutations","Workflows with complex inter-node dependencies and parallel execution"],"limitations":["BSP model adds synchronization overhead — all nodes must wait for slowest node in superstep","Asynchronous nodes are wrapped in sync boundaries, limiting true async parallelism","Message passing through channels incurs serialization overhead for complex state objects","No built-in load balancing across nodes — execution is sequential per superstep"],"requires":["Python 3.9+","Pregel execution engine (built into langgraph.graph.StateGraph)","Channel definitions with merge semantics (LastValue, Topic, BinaryOperatorAggregate)"],"input_types":["Node functions with consistent input/output signatures","Channel state objects (TypedDict instances)","Control flow predicates for conditional edges"],"output_types":["Execution trace with step-by-step state snapshots","Final state after all supersteps complete","Intermediate checkpoints for persistence"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_10","uri":"capability://code.generation.editing.functional.api.with.task.and.entrypoint.decorators","name":"functional api with @task and @entrypoint decorators","description":"Provides a functional programming interface for defining agents using @task and @entrypoint decorators, enabling developers to compose workflows without explicit StateGraph definitions. Tasks are decorated functions that become nodes in an implicit graph, with @entrypoint marking the workflow entry point. The framework automatically infers state schema from function signatures and manages state threading, reducing boilerplate compared to declarative StateGraph definitions.","intents":["Define agent workflows using functional composition instead of explicit graph definitions","Reduce boilerplate for simple agents with linear or tree-like execution","Enable rapid prototyping of agent workflows","Compose workflows from existing functions without graph refactoring"],"best_for":["Developers preferring functional programming patterns","Rapid prototyping of simple agent workflows","Teams migrating from function-based agent code"],"limitations":["Functional API infers state schema from function signatures — complex state requires explicit TypedDict definitions","Limited control flow compared to declarative StateGraph — no explicit conditional edges or loops","Debugging functional workflows is harder due to implicit graph construction","State threading is automatic but opaque — difficult to reason about state flow"],"requires":["Python 3.9+","Decorator syntax support (@task, @entrypoint)","Function signature type hints for state inference"],"input_types":["Decorated functions with type hints","Task dependencies (implicit from function signatures)","Entrypoint configuration"],"output_types":["Implicit StateGraph","Compiled workflow","Execution results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_11","uri":"capability://tool.use.integration.remote.graph.execution.via.http.with.streaming.support","name":"remote graph execution via http with streaming support","description":"Enables executing graphs deployed on a LangGraph server from Python or JavaScript clients via HTTP, with streaming support for real-time output. RemoteGraph wraps a deployed graph and provides the same interface as local StateGraph, transparently handling serialization, network communication, and streaming. The framework supports both request-response and streaming execution modes, with automatic retry and error handling for network failures.","intents":["Execute agents deployed on remote servers from client applications","Stream agent outputs in real-time to client applications","Decouple agent execution from client code","Scale agent execution across multiple server instances"],"best_for":["Applications with client-server architecture","Teams deploying agents on centralized servers","Workflows requiring real-time streaming of agent outputs"],"limitations":["Network latency adds overhead per superstep — typically 50-200ms depending on server distance","Streaming requires persistent HTTP connections — not suitable for serverless environments","Remote execution loses local debugging capabilities — errors are harder to diagnose","Serialization of complex state objects requires custom serializers"],"requires":["Python 3.9+ or Node.js 18+","LangGraph server running and accessible","API authentication credentials (if required)"],"input_types":["Remote graph URL","Input state (TypedDict-compatible dict)","Execution configuration (streaming mode, timeout)"],"output_types":["Streamed execution events","Final state after execution","Execution metadata (duration, node execution times)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_12","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 for deploying graphs as HTTP services and a configuration system (langgraph.json) for specifying deployment parameters. The CLI generates Docker images, manages local development servers, and handles multi-service orchestration. Configuration includes graph definitions, environment variables, dependencies, and deployment targets, enabling one-command deployment of agent services.","intents":["Deploy agent graphs as HTTP services without manual Docker configuration","Manage multiple agent services in a single deployment","Configure environment variables and dependencies declaratively","Run local development servers for testing before deployment"],"best_for":["Teams deploying agents to cloud platforms","Organizations requiring standardized deployment processes","Developers building multi-service agent systems"],"limitations":["langgraph.json configuration is LangGraph-specific — not compatible with other frameworks","Docker image generation requires Docker to be installed and running","Multi-service orchestration is limited to Kubernetes-style deployments","No built-in support for serverless platforms (AWS Lambda, Google Cloud Functions)"],"requires":["Python 3.9+","Docker (for image generation)","langgraph CLI installed","langgraph.json configuration file"],"input_types":["langgraph.json configuration","Graph definitions (Python modules)","Environment variables","Dependency specifications"],"output_types":["Docker images","HTTP service endpoints","Deployment manifests","Local development server"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_13","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 API for managing multi-turn conversations through threads, where each thread maintains independent execution state and checkpoint history. The Assistants API abstracts away graph execution details, exposing a simple interface for creating threads, sending messages, and retrieving responses. Threads are persisted in the checkpoint store, enabling long-lived conversations that survive process restarts.","intents":["Build conversational agents with multi-turn memory","Manage multiple concurrent conversations without explicit state threading","Persist conversation history for long-term interactions","Simplify agent integration for non-technical users"],"best_for":["Conversational AI applications","Teams building chatbots or virtual assistants","Applications requiring multi-turn conversation management"],"limitations":["Assistants API abstracts graph execution — limited control over execution flow","Thread state is opaque — difficult to inspect or modify intermediate states","No built-in support for multi-agent conversations — each thread is single-agent","Conversation history is not automatically pruned — long conversations can consume large storage"],"requires":["Python 3.9+","LangGraph server with Assistants API support","Thread storage backend (SQLite, PostgreSQL, or custom)"],"input_types":["Thread creation parameters (metadata, initial state)","User messages (text)","Thread identifiers"],"output_types":["Thread objects with metadata","Assistant responses (text)","Conversation history","Thread state snapshots"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_14","uri":"capability://automation.workflow.cron.job.scheduling.for.periodic.agent.execution","name":"cron job scheduling for periodic agent execution","description":"Enables scheduling agent graphs to execute on a recurring basis using cron expressions, with execution results persisted as runs in the checkpoint store. Cron jobs are defined declaratively in langgraph.json or via the Assistants API, with configurable schedules, input parameters, and error handling. The framework manages job scheduling and execution, with built-in support for timezone handling and missed execution recovery.","intents":["Schedule agents to run periodically (e.g., hourly, daily)","Implement background tasks that execute on a schedule","Monitor and manage scheduled agent executions","Handle missed executions and retries"],"best_for":["Applications requiring periodic agent execution","Background task systems built on agents","Teams needing scheduled monitoring or data collection"],"limitations":["Cron scheduling is limited to time-based triggers — no event-based scheduling","Missed executions are not automatically recovered — manual intervention required","Timezone handling is complex for distributed systems — UTC is recommended","No built-in support for job dependencies or conditional scheduling"],"requires":["Python 3.9+","LangGraph server with cron job support","Cron expression syntax knowledge"],"input_types":["Cron expressions (e.g., '0 * * * *')","Job input parameters","Error handling configuration"],"output_types":["Scheduled job metadata","Execution runs with results","Job execution history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_15","uri":"capability://planning.reasoning.prebuilt.react.agent.with.tool.use.loop","name":"prebuilt react agent with tool-use loop","description":"Provides a factory function (create_react_agent) that generates a complete ReAct agent graph with built-in tool-use loop, reasoning, and action execution. The prebuilt agent handles tool selection, execution, and result integration without requiring manual graph definition. It supports both LLM-based tool selection and explicit tool routing, with configurable system prompts and tool definitions.","intents":["Quickly create tool-using agents without manual graph definition","Implement ReAct pattern (reasoning + acting) with minimal code","Integrate custom tools into agents","Extend prebuilt agents with custom nodes or edges"],"best_for":["Developers building tool-using agents quickly","Teams implementing ReAct pattern","Rapid prototyping of agentic systems"],"limitations":["Prebuilt agent is opinionated — limited customization of reasoning or tool selection logic","Tool definitions must follow LangChain BaseTool interface — custom tools require adaptation","No built-in support for multi-step tool reasoning — each tool call is independent","Error handling in tool execution is limited — custom error handlers require graph modification"],"requires":["Python 3.9+","LangChain BaseTool implementations","LLM with function calling support (OpenAI, Anthropic, etc.)"],"input_types":["LLM instance","Tool list (BaseTool objects)","System prompt (optional)","Input state (dict with 'messages' key)"],"output_types":["StateGraph object (ReAct agent)","Final state with tool execution results","Reasoning and action traces"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_16","uri":"capability://data.processing.analysis.serialization.and.deserialization.with.custom.type.support","name":"serialization and deserialization with custom type support","description":"Provides serialization infrastructure for persisting complex state objects (LLM messages, tool calls, custom types) to checkpoint storage and remote execution. The framework supports JSON serialization with custom type handlers, enabling developers to define serializers for non-standard types. Serialization is transparent during checkpoint persistence and remote execution, with automatic fallback to JSON for standard types.","intents":["Persist complex state objects (LLM messages, tool calls) to checkpoints","Support custom types in agent state without manual serialization","Enable remote execution of agents with complex state","Ensure reproducibility of agent execution across serialization boundaries"],"best_for":["Agents with complex state objects (LLM messages, tool calls)","Teams using custom types in agent state","Applications requiring remote execution with complex state"],"limitations":["Custom serializers must be deterministic — non-deterministic serialization breaks reproducibility","Serialization overhead adds latency per checkpoint — complex objects can cause slowdowns","Custom serializers must be available on both client and server — distribution is manual","No built-in support for versioning serialized objects — schema changes require migration"],"requires":["Python 3.9+","Custom serializer implementations (optional)","JSON-compatible base types for standard serialization"],"input_types":["State objects (TypedDict instances with custom types)","Custom serializer functions","Serialization configuration"],"output_types":["Serialized JSON representations","Deserialized state objects","Serialization metadata (type information)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_2","uri":"capability://memory.knowledge.typed.channel.based.state.management.with.merge.semantics","name":"typed channel-based state management with merge semantics","description":"Manages agent state through a channel system where each channel is a typed container with configurable merge semantics (LastValue, Topic, BinaryOperatorAggregate). Channels enforce type contracts via TypedDict schemas and handle state mutations across nodes by collecting updates and applying them atomically. The framework supports both simple value channels (LastValue) and aggregation channels (Topic for lists, BinaryOperatorAggregate for custom reductions), enabling flexible state composition patterns.","intents":["Define typed state containers with automatic merge conflict resolution","Accumulate multi-step agent outputs (e.g., reasoning traces, tool calls)","Enforce type safety on state mutations across nodes","Support complex state aggregation patterns (lists, custom reductions)"],"best_for":["Agents requiring structured state with type enforcement","Workflows accumulating multi-step outputs (reasoning traces, tool results)","Teams needing automatic conflict resolution for concurrent state updates"],"limitations":["Channel merge semantics are fixed at graph definition time — cannot change merge behavior at runtime","BinaryOperatorAggregate requires commutative operations; non-commutative reductions may produce inconsistent results","Large state objects in Topic channels can cause memory bloat — no built-in size limits or eviction","Custom merge functions must be serializable for persistence and distributed execution"],"requires":["Python 3.9+","typing.TypedDict for channel type definitions","Merge function implementations (LastValue, Topic, or custom BinaryOperatorAggregate)"],"input_types":["TypedDict schema definitions","Node output values matching channel types","Merge function callables (for BinaryOperatorAggregate)"],"output_types":["Typed channel objects","Merged state snapshots","Channel value history (for Topic channels)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_3","uri":"capability://automation.workflow.durable.execution.with.checkpoint.based.persistence","name":"durable execution with checkpoint-based persistence","description":"Persists agent execution state to external storage (SQLite, PostgreSQL, or custom BaseCheckpointSaver implementations) after each superstep, enabling resumption from exact execution point after failures. Checkpoints capture channel values, execution metadata, and version information, allowing agents to recover without re-executing completed steps. The framework supports multiple checkpoint backends through a pluggable interface, with built-in implementations for SQLite and PostgreSQL.","intents":["Resume agent execution after crashes without losing progress","Persist long-running agent workflows across process restarts","Implement fault-tolerant multi-step agent systems","Support custom persistence backends (e.g., cloud storage, distributed databases)"],"best_for":["Production agents requiring fault tolerance and recovery","Long-running workflows spanning hours or days","Teams needing custom persistence backends for compliance or performance"],"limitations":["Checkpoint overhead adds latency per superstep — typically 50-200ms depending on backend","Custom checkpoint implementations must handle concurrent writes and version conflicts","Serialization of complex state objects (e.g., LLM message objects) requires custom serializers","No built-in checkpoint compression — storage grows linearly with execution length"],"requires":["Python 3.9+","External storage backend (SQLite, PostgreSQL, or custom BaseCheckpointSaver)","Serializable state objects (JSON-compatible or custom serializers)"],"input_types":["Channel state objects after each superstep","Execution metadata (node names, timestamps, versions)","Custom serialization functions for non-standard types"],"output_types":["Persisted checkpoint records","Checkpoint metadata (version, timestamp, node)","Recovered state for resumption"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_4","uri":"capability://planning.reasoning.human.in.the.loop.interrupts.with.state.inspection.and.modification","name":"human-in-the-loop interrupts with state inspection and modification","description":"Enables pausing agent execution at any superstep to inspect and modify state before resumption, supporting human oversight and intervention workflows. The interrupt system uses breakpoint predicates to pause execution when conditions are met, allowing external systems to read current state via get_state() and modify it via update_state() before resuming. Interrupts are persisted as part of checkpoint metadata, enabling recovery of interrupted workflows across process restarts.","intents":["Pause agent execution for human review before critical decisions","Allow humans to modify agent state (e.g., correct reasoning, inject information)","Implement approval workflows for high-stakes agent actions","Debug agent behavior by inspecting state at specific execution points"],"best_for":["Applications requiring human oversight (e.g., financial decisions, content moderation)","Workflows with approval gates or manual intervention steps","Teams debugging complex agent behaviors in production"],"limitations":["Interrupt predicates are evaluated after each superstep — cannot interrupt mid-node execution","State modifications must maintain type contracts; invalid modifications are rejected at resumption","No built-in timeout for human intervention — interrupted workflows can block indefinitely","Interrupt state is not automatically cleaned up; manual management required for long-lived applications"],"requires":["Python 3.9+","Breakpoint predicate functions (callables returning bool)","External system for state inspection and modification (e.g., API, UI)"],"input_types":["Breakpoint predicates (functions evaluating state)","State modification payloads (TypedDict-compatible dicts)","Resumption signals (continue execution flag)"],"output_types":["Paused execution state snapshots","Interrupt metadata (node, timestamp, reason)","Modified state after human intervention"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_5","uri":"capability://planning.reasoning.conditional.edge.routing.with.dynamic.control.flow","name":"conditional edge routing with dynamic control flow","description":"Routes execution to different nodes based on runtime predicates evaluated on current state, enabling dynamic control flow without explicit branching logic. Conditional edges accept a predicate function that reads current state and returns the next node name, supporting both deterministic routing (based on state values) and probabilistic routing (based on LLM outputs). The framework validates that all predicate return values correspond to valid nodes, catching routing errors at graph definition time.","intents":["Route agent execution based on reasoning outputs (e.g., tool selection)","Implement dynamic control flow without explicit if/else chains","Support probabilistic routing for exploration or A/B testing","Create conditional loops (e.g., retry on failure, iterate until convergence)"],"best_for":["Agents with dynamic tool selection or reasoning-based routing","Workflows requiring conditional loops or retries","Teams building adaptive agents that adjust behavior based on state"],"limitations":["Predicate functions are evaluated synchronously — cannot use async predicates","Routing errors (predicate returns invalid node name) are caught at runtime, not definition time","Complex routing logic in predicates can become difficult to debug and maintain","No built-in support for weighted probabilistic routing — requires manual implementation"],"requires":["Python 3.9+","Predicate functions with signature (state: TypedDict) -> str","Valid node names for all possible predicate return values"],"input_types":["Current state (TypedDict instance)","Predicate functions (callables returning node names)","Routing metadata (edge names, conditions)"],"output_types":["Next node name (string)","Routing decision trace","Execution path through graph"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_6","uri":"capability://automation.workflow.graph.composition.and.nested.subgraph.execution","name":"graph composition and nested subgraph execution","description":"Enables composing complex workflows from reusable subgraphs, where a node can invoke another StateGraph as a nested execution unit. Nested graphs receive a subset of parent state, execute independently with their own checkpoint boundaries, and return results that are merged back into parent state. The framework handles state mapping between parent and child graphs, enabling modular workflow composition without explicit state threading.","intents":["Decompose complex agents into reusable subgraph components","Create hierarchical agent architectures with independent execution units","Reuse common workflow patterns (e.g., tool-use loops) across multiple agents","Isolate subgraph failures without affecting parent execution"],"best_for":["Teams building large-scale agent systems with multiple reusable components","Organizations requiring modular workflow composition","Applications with hierarchical agent architectures"],"limitations":["State mapping between parent and child graphs requires explicit configuration — no implicit state sharing","Nested graph failures propagate to parent; no built-in isolation or fallback mechanisms","Checkpoint overhead increases with nesting depth — each level adds persistence latency","Debugging nested execution requires tracing across multiple graph boundaries"],"requires":["Python 3.9+","Parent and child StateGraph definitions","State mapping configuration (input/output channel mappings)"],"input_types":["Parent state (TypedDict instance)","Child graph definition (StateGraph object)","State mapping configuration (dict of channel mappings)"],"output_types":["Child execution results","Merged parent state after child completion","Nested execution traces"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_7","uri":"capability://automation.workflow.error.handling.and.retry.policies.with.exponential.backoff","name":"error handling and retry policies with exponential backoff","description":"Provides configurable error handling and retry mechanisms at the node level, supporting exponential backoff, max retry limits, and custom error handlers. Nodes can define retry policies that automatically re-execute on failure with configurable backoff strategies, and custom error handlers can transform exceptions into state mutations or routing decisions. Retry state is persisted in checkpoints, enabling recovery of partially-retried nodes across process restarts.","intents":["Automatically retry transient failures (e.g., API rate limits, network timeouts)","Implement exponential backoff for resilient external service calls","Transform errors into state mutations or alternative routing paths","Track retry history for debugging and monitoring"],"best_for":["Agents making external API calls prone to transient failures","Workflows requiring resilience to temporary service unavailability","Teams needing detailed error tracking and retry history"],"limitations":["Retry policies are defined per-node — no global retry configuration","Exponential backoff is applied sequentially, blocking other nodes from executing","Custom error handlers must be deterministic and serializable for persistence","No built-in circuit breaker pattern — repeated failures continue retrying indefinitely"],"requires":["Python 3.9+","Retry policy configuration (max_retries, backoff_factor)","Error handler functions (optional, for custom error transformation)"],"input_types":["Node function exceptions","Retry policy configuration (dict with max_retries, backoff_factor)","Error handler functions (callables transforming exceptions)"],"output_types":["Retry attempt metadata (attempt number, backoff delay)","Error handler results (state mutations or routing decisions)","Final exception if all retries exhausted"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_8","uri":"capability://planning.reasoning.time.travel.and.state.forking.for.debugging.and.exploration","name":"time travel and state forking for debugging and exploration","description":"Enables rewinding execution to any previous checkpoint and forking from that point to explore alternative execution paths without affecting the original execution history. The framework stores complete checkpoint history, allowing developers to query state at any superstep and create new execution branches from historical states. Forked executions are isolated from the original, enabling safe exploration and debugging without side effects.","intents":["Debug agent behavior by replaying execution from any checkpoint","Explore alternative agent decisions without affecting original execution","Implement rollback mechanisms for failed workflows","Analyze agent decision points and their consequences"],"best_for":["Teams debugging complex agent behaviors in production","Applications requiring rollback capabilities","Research and analysis of agent decision-making"],"limitations":["Time travel requires complete checkpoint history — storage grows linearly with execution length","Forked executions are isolated but not automatically garbage collected — manual cleanup required","External side effects (API calls, database writes) are not rolled back — only state is forked","Checkpoint history is not automatically pruned — long-running agents can accumulate large histories"],"requires":["Python 3.9+","Checkpoint storage backend with history support","Sufficient storage for complete execution history"],"input_types":["Checkpoint identifiers (thread_id, checkpoint_id)","Superstep numbers or timestamps","Forking configuration (new execution context)"],"output_types":["Historical state snapshots","Forked execution contexts","Alternative execution traces"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langgraph__cap_9","uri":"capability://memory.knowledge.store.system.for.cross.thread.persistent.memory","name":"store system for cross-thread persistent memory","description":"Provides a key-value store abstraction (BaseStore interface) for persisting data across multiple execution threads and agent instances, complementing the per-thread channel state. The store system supports custom implementations (e.g., Redis, DynamoDB) and enables agents to access shared knowledge bases, conversation history, and long-term memory without explicit state threading. Store operations are integrated into the execution model, with reads/writes captured in checkpoint metadata for reproducibility.","intents":["Share knowledge and context across multiple agent instances","Implement long-term memory for multi-turn conversations","Store and retrieve shared resources (e.g., conversation history, user profiles)","Enable agents to learn from previous interactions"],"best_for":["Multi-agent systems requiring shared knowledge bases","Conversational agents with long-term memory requirements","Applications needing cross-thread state sharing"],"limitations":["Store operations are not transactional — concurrent writes can cause conflicts","Custom store implementations must handle distributed consistency","Store reads/writes add latency per superstep — no built-in caching","No built-in garbage collection for store entries — manual cleanup required"],"requires":["Python 3.9+","BaseStore implementation (custom or built-in)","Key-value storage backend (Redis, DynamoDB, or custom)"],"input_types":["Store keys (strings)","Store values (JSON-serializable objects)","Store operation metadata (namespace, TTL)"],"output_types":["Retrieved store values","Store operation results (success/failure)","Store metadata (timestamps, versions)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","typing.TypedDict for state schema definition","LangChain base classes (BaseLanguageModel, BaseTool)","Pregel execution engine (built into langgraph.graph.StateGraph)","Channel definitions with merge semantics (LastValue, Topic, BinaryOperatorAggregate)","Decorator syntax support (@task, @entrypoint)","Function signature type hints for state inference","Python 3.9+ or Node.js 18+","LangGraph server running and accessible","API authentication credentials (if required)"],"failure_modes":["StateGraph requires explicit node and edge definitions — no implicit control flow inference","Cyclic graphs must be explicitly handled via conditional edges; infinite loops are not auto-detected","TypedDict schema changes require recompilation of all dependent graphs","BSP model adds synchronization overhead — all nodes must wait for slowest node in superstep","Asynchronous nodes are wrapped in sync boundaries, limiting true async parallelism","Message passing through channels incurs serialization overhead for complex state objects","No built-in load balancing across nodes — execution is sequential per superstep","Functional API infers state schema from function signatures — complex state requires explicit TypedDict definitions","Limited control flow compared to declarative StateGraph — no explicit conditional edges or loops","Debugging functional workflows is harder due to implicit graph construction","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:11.786Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-langgraph","compare_url":"https://unfragile.ai/compare?artifact=pypi-langgraph"}},"signature":"LCMPWcicvBKtoX6uRzfXzCsKwJse7vhs/7nIqpD7y8v6xPCL/cwmAQo+o2iSrBtpucrE1/pG7yAh47v25RyKDw==","signedAt":"2026-06-22T14:33:42.703Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-langgraph","artifact":"https://unfragile.ai/pypi-langgraph","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}