LangGraph vs Claude Agent SDK

Defines multi-step LLM workflows as directed acyclic graphs using the StateGraph class, where nodes are Python functions and edges define control flow. Implements a Bulk Synchronous Parallel (BSP) execution model inspired by Google's Pregel, enabling developers to declare complex agent architectures with branching, loops, and conditional routing without imperative orchestration code. State flows through typed channels with merge semantics (LastValue, Topic, BinaryOperatorAggregate), ensuring deterministic composition of multi-actor workflows.

Unique: Uses BSP (Bulk Synchronous Parallel) execution model from Pregel paper with typed state channels and merge semantics, enabling deterministic multi-actor synchronization without explicit locking or message passing primitives

vs alternatives: More explicit control flow than LangChain chains and more structured than imperative orchestration, but less flexible than fully dynamic execution engines like Temporal or Airflow

Provides a functional programming API using Python decorators (@task, @entrypoint) as an alternative to StateGraph for defining workflows. Tasks are decorated functions that automatically integrate into a graph, with dependency injection of runtime context and automatic state threading. This approach reduces boilerplate compared to explicit node/edge declaration while maintaining the same underlying Pregel execution semantics and persistence guarantees.

Unique: Decorator-based functional API that automatically constructs StateGraph under the hood, enabling implicit state threading and dependency injection while maintaining full Pregel execution semantics

vs alternatives: More concise than explicit StateGraph for simple workflows, but less transparent than imperative code for complex control flow

Implements a caching layer that stores node outputs based on input hash, enabling deterministic execution and cost reduction for expensive operations (LLM calls, API requests). Cache is keyed by node input and can be persisted across executions, allowing subsequent runs with identical inputs to skip execution and return cached results. This integrates with the checkpoint system to ensure cache consistency.

Unique: Input-hash-based caching integrated with Pregel execution, enabling deterministic node execution and cost reduction without explicit cache management code

vs alternatives: More transparent than manual caching, but less flexible than semantic caching based on embedding similarity

Provides native SDKs for Python and JavaScript/TypeScript enabling graph definition and execution in multiple languages. The SDKs share the same underlying execution semantics (Pregel, checkpointing, state management) while providing language-idiomatic APIs. JavaScript/TypeScript SDK uses HTTP client for remote execution against LangGraph Server, enabling browser-based and Node.js clients.

Unique: Native SDKs for Python and JavaScript/TypeScript with shared execution semantics (Pregel, checkpointing) and language-idiomatic APIs, enabling multi-language agent development

vs alternatives: More language-native than REST-only APIs, but less integrated than single-language frameworks

Enables deploying compiled graphs to LangGraph Server, a cloud-hosted execution environment accessible via HTTP/WebSocket APIs. Clients invoke graphs remotely, with support for streaming results, authentication (API keys, OAuth), and multi-tenant isolation. Server handles persistence, checkpointing, and execution scheduling, allowing agents to run independently of client lifecycle.

Unique: HTTP/WebSocket-based remote execution with streaming, authentication, and multi-tenant isolation, enabling browser-based and cross-language agent interaction

vs alternatives: More accessible than self-hosted deployment, but less flexible than local execution and subject to vendor lock-in

Provides a high-level Assistants API for managing long-running conversations with agents, using threads to maintain conversation history and state. Each thread is a persistent conversation context with its own checkpoint history, enabling multi-turn interactions without explicit state management. Threads support message history, file attachments, and tool execution, abstracting away graph-level details for end-user interactions.

Unique: Thread-based conversation API abstracting graph execution details, enabling multi-turn interactions with persistent history and checkpoint-based resumption

vs alternatives: Simpler than graph-level APIs for conversational use cases, but less flexible than direct graph control

Provides a BaseStore interface for persistent, cross-thread storage of long-term memory and knowledge bases. Unlike channels (which are per-execution state), the Store persists data across multiple graph executions and threads, enabling agents to build and access shared knowledge. Store supports key-value operations and is pluggable (in-memory, PostgreSQL, custom implementations).

Unique: Pluggable BaseStore interface for cross-thread persistent storage, enabling agents to build and access shared knowledge bases independent of execution checkpoints

vs alternatives: More flexible than in-memory state, but less queryable than full databases; requires custom key-value patterns

Provides a pre-built ReAct (Reasoning + Acting) agent pattern via create_react_agent factory, implementing the think-act-observe loop where agents reason about tasks, select and execute tools, and observe results. The factory creates a StateGraph with predefined nodes (agent reasoning, tool execution) and routing logic, reducing boilerplate for common agent patterns. Supports multiple LLM providers and tool schemas.

Unique: Factory function generating ReAct agent graphs with predefined think-act-observe loop, reducing boilerplate while maintaining full Pregel execution semantics

vs alternatives: More opinionated than custom StateGraph but more flexible than high-level agent frameworks

anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Overview Relevant source files CHANGELOG.md CLAUDE.md

Core Concepts | anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Core Concepts Relevant source files CHANG

Architecture Overview | anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examples Error Handling Patterns Stderr Callback and Agents Examples Development Guide Project Structure Testing Strategy Build and Release Process Code Quality Standards Claude AI Integration in CI Glossary Menu Architecture Overview Relevant source

anthropics/claude-agent-sdk-python | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki anthropics/claude-agent-sdk-python Index your code with Devin Edit Wiki Share Loading... Last indexed: 5 June 2026 ( f83c87 ) Overview Quick Start Installation and Setup Version Information and Changelog Core Concepts Architecture Overview Type System and Message Architecture ClaudeAgentOptions Configuration Reference Bundled CLI Version Management Basic Usage query() Function ClaudeSDKClient Message Types and Content Blocks Transport and Communication Subprocess CLI Transport Control Protocol Message Streaming and Buffering Extension Points Custom Tools (SDK MCP Servers) Permission System and Callbacks Lifecycle Hooks Plugins and External MCP Servers Advanced Features Session Management and Forking SessionStore: Transcript Persistence File Checkpointing and Rewinding Resource Limits and Cost Control Sandbox Settings Model Selection, Thinking, and Output Formats Skills System Distributed Tracing (OpenTelemetry) Examples and Usage Patterns Interactive Streaming Examples Tool Integration Examp