Merge branch 'main' into wfh/cli_from_module

This PR only allows this in the HTTP client. 

I can follow up with a PR to allow throughout the entire API.

The use case is to allow instantiating the client once (w/ a single connection pool), but allowing changing api keys and any other headers at run time

docs/_scripts/third_party_page/packages.yml

@@ -36,3 +36,6 @@ packages:
   - name: "langgraph-reflection"
     repo: "langchain-ai/langgraph-reflection"
     description: "LangGraph agent that runs a reflection step."
+  - name: "langmanus"
+    repo: "langmanus/langmanus"
+    description: "A community-driven AI automation framework that builds upon the incredible work of the open source community. Our goal is to combine language models with specialized tools for tasks like web search, crawling, and Python code execution, while giving back to the community that made this possible."

docs/docs/concepts/high_level.md

@@ -2,7 +2,7 @@
 
 ## LLM applications
 
-LLMs make it possible to embed intelligence into a new class of applications. There are many patterns for building applications that use LLMs. [Workflows](https://www.anthropic.com/research/building-effective-agents) have scaffolding of predefined code paths around LLM calls. LLMs can direct the control flow through these predefined code paths, which some consider to be an "[agentic system](https://www.anthropic.com/research/building-effective-agents)". In other cases, it's possible to remove this scaffolding, creating autonomous agents that can [plan](https://huyenchip.com/2025/01/07/agents.html), take actions via [tool calls](https://python.langchain.com/docs/concepts/tool_calling/), and directly respond [to the feedback from their own actions](https://research.google/blog/react-synergizing-reasoning-and-acting-in-language-models/) with further actions.
+LLMs make it possible to embed intelligence into a new class of applications. There are many patterns for building applications that use LLMs. Workflows have scaffolding of predefined code paths around LLM calls. LLMs can direct the control flow through these predefined code paths, which some consider to be an "agentic system". In other cases, it's possible to remove this scaffolding, creating autonomous agents that can [plan](https://huyenchip.com/2025/01/07/agents.html), take actions via [tool calls](https://python.langchain.com/docs/concepts/tool_calling/), and directly respond [to the feedback from their own actions](https://research.google/blog/react-synergizing-reasoning-and-acting-in-language-models/) with further actions.
 
 ![Agent Workflow](img/agent_workflow.png)
 

docs/docs/concepts/low_level.md

@@ -360,7 +360,7 @@ Use [conditional edges](#conditional-edges) to route between nodes conditionally
 If you are using [subgraphs](#subgraphs), you might want to navigate from a node within a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:
 
 ```python
-def my_node(state: State) -> Command[Literal["my_other_node"]]:
+def my_node(state: State) -> Command[Literal["other_subgraph"]]:
     return Command(
         update={"foo": "bar"},
         goto="other_subgraph",  # where `other_subgraph` is a node in the parent graph

docs/docs/concepts/memory.md

@@ -275,7 +275,7 @@ See this how-to [video](https://www.youtube.com/watch?v=37VaU7e7t5o) for example
 
 [Procedural memory](https://en.wikipedia.org/wiki/Procedural_memory), in both humans and AI agents, involves remembering the rules used to perform tasks. In humans, procedural memory is like the internalized knowledge of how to perform tasks, such as riding a bike via basic motor skills and balance. Episodic memory, on the other hand, involves recalling specific experiences, such as the first time you successfully rode a bike without training wheels or a memorable bike ride through a scenic route. For AI agents, procedural memory is a combination of model weights, agent code, and agent's prompt that collectively determine the agent's functionality. 
 
-In practice, it is fairly uncommon for agents to modify their model weights or rewrite their code. However, it is more common for agents to [modify their own prompts](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/prompt-generator). 
+In practice, it is fairly uncommon for agents to modify their model weights or rewrite their code. However, it is more common for agents to modify their own prompts. 
 
 One effective approach to refining an agent's instructions is through ["Reflection"](https://blog.langchain.dev/reflection-agents/) or meta-prompting. This involves prompting the agent with its current instructions (e.g., the system prompt) along with recent conversations or explicit user feedback. The agent then refines its own instructions based on this input. This method is particularly useful for tasks where instructions are challenging to specify upfront, as it allows the agent to learn and adapt from its interactions.
 

docs/docs/llms.txt

@@ -1,191 +1,204 @@
 # LangGraph
 
-## Quickstart
+## Tutorials
 
-These guides are designed to help you get started with LangGraph.
+[Learn the basics](https://langchain-ai.github.io/langgraph/tutorials/introduction/): LLM should read this page when needing to build a LangGraph chatbot or when learning about chat agents with memory, human-in-the-loop functionality, and state management. This page provides a comprehensive LangGraph quickstart tutorial covering building a support chatbot with web search capability, conversation memory, human review routing, custom state management, and time travel functionality to explore alternative conversation paths.
 
-- [LangGraph Quickstart](https://langchain-ai.github.io/langgraph/tutorials/introduction/): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
-- [Common Workflows](https://langchain-ai.github.io/langgraph/tutorials/workflows/): Overview of the most common workflows using LLMs implemented with LangGraph.
-- [LangGraph Server Quickstart](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
-- [Deploy with LangGraph Cloud Quickstart](https://langchain-ai.github.io/langgraph/cloud/quick_start/): Deploy a LangGraph app using LangGraph Cloud.
+[Local Deploy](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/): LLM should read this page when setting up a LangGraph app locally using `langgraph dev` and troubleshooting LangGraph server deployment. This page contains a quickstart guide for launching a LangGraph server locally, including installation steps, app creation from templates, environment setup, API testing with Python/JS SDKs, and links to deployment options and further documentation.
 
-## Concepts
+[Workflows and Agents](https://langchain-ai.github.io/langgraph/tutorials/workflows/): LLM should read this page when implementing agent systems, designing workflow architectures, or troubleshooting LLM orchestration strategies. The page covers patterns for LLM system design, comparing workflows (predefined paths) vs agents (dynamic control), with implementations of prompt chaining, parallelization, routing, orchestrator-worker, evaluator-optimizer, and agent patterns using both graph and functional APIs in LangGraph.
 
-These guides provide explanations of the key concepts behind the LangGraph framework.
+## Concepts 
 
-- [Why LangGraph?](https://langchain-ai.github.io/langgraph/concepts/high_level/): Motivation for LangGraph, a library for building agentic applications with LLMs.
-- [LangGraph Glossary](https://langchain-ai.github.io/langgraph/concepts/low_level/): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
-- [Common Agentic Patterns](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/): An agent uses an LLM to pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.
-- [Multi-Agent Systems](https://langchain-ai.github.io/langgraph/concepts/multi_agent/): Complex LLM applications can often be broken down into multiple agents, each responsible for a different part of the application. This guide explains common patterns for building multi-agent systems.
-- [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/breakpoints/): Breakpoints allow pausing the execution of a graph at specific points. Breakpoints allow stepping through graph execution for debugging purposes.
-- [Human-in-the-Loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Explains different ways of integrating human feedback into a LangGraph application.
-- [Time Travel](https://langchain-ai.github.io/langgraph/concepts/time-travel/): Time travel allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues.
-- [Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
-- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.
-- [Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
-- [Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/): `@entrypoint` and `@task` decorators that allow you to add LangGraph functionality to an existing codebase.
-- [Durable Execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): LangGraph's built-in [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) layer provides durable execution for workflows, ensuring that the state of each execution step is saved to a durable store. 
-- [Pregel](https://langchain-ai.github.io/langgraph/concepts/pregel/): Pregel is LangGraph's runtime, which is responsible for managing the execution of LangGraph applications.
-- [FAQ](https://langchain-ai.github.io/langgraph/concepts/faq/): Frequently asked questions about LangGraph.
+[Concepts](https://langchain-ai.github.io/langgraph/concepts/): LLM should read this page when needing to understand LangGraph's key concepts or when planning to deploy LangGraph applications. Comprehensive guide covering LangGraph fundamentals (graph primitives, agents, multi-agent systems, breakpoints, persistence), features (time travel, memory, streaming), and LangGraph Platform deployment options (self-hosted, cloud, enterprise).
 
-## How-tos
+[Agent architectures](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/): LLM should read this page when designing agent architectures, implementing control flows for LLM applications, or customizing agent behavior patterns. This page covers different LLM agent architectures including routers, tool calling agents (ReAct), structured outputs, memory systems, planning capabilities, and advanced customization options like human-in-the-loop, parallelization, subgraphs, and reflection mechanisms.
 
-Here you’ll find answers to “How do I...?” types of questions. 
+[Application Structure](https://langchain-ai.github.io/langgraph/concepts/application_structure/): LLM should read this page when needing to understand LangGraph application structure, preparing to deploy a LangGraph application, or troubleshooting configuration issues. This page details the structure of LangGraph applications, including required components (graphs, langgraph.json config file, dependency files, optional .env), file organization patterns for Python/JavaScript projects, configuration file format with all supported fields, and how to specify dependencies, graphs, and environment variables.
 
-These guides are **goal-oriented** and concrete. 
+[Assistants](https://langchain-ai.github.io/langgraph/concepts/assistants/): LLM should read this page when looking for information about LangGraph assistants, understanding assistant configuration in LangGraph Platform, or learning about versioning agent configurations. This page explains LangGraph assistants, which allow developers to modify agent configurations (prompts, models, etc.) without changing graph logic, supports versioning for tracking changes, and is available only in LangGraph Platform (not open source).
 
-They're meant to help you complete a specific task.
+[Authentication & Access Control](https://langchain-ai.github.io/langgraph/concepts/auth/): LLM should read this page when implementing authentication in LangGraph Platform, designing access control for LangGraph applications, or troubleshooting security issues in LangGraph deployments. This page explains LangGraph's authentication and authorization system, covering the difference between authentication and authorization, system architecture, implementing custom auth handlers, common access patterns, and supported resources/actions for access control.
 
-### Graph API Basics
+[Bring Your Own Cloud (BYOC)](https://langchain-ai.github.io/langgraph/concepts/bring_your_own_cloud/): LLM should read this page when learning about LangGraph Platform deployment options, understanding Bring Your Own Cloud architecture, or managing deployments in AWS. This page explains LangGraph's BYOC deployment model, detailing how it separates control plane (managed by LangChain) from data plane (in customer's AWS account), outlines AWS requirements, infrastructure setup via Terraform, required permissions, and explains the deployment workflow.
 
-- [How to update graph state from nodes](https://langchain-ai.github.io/langgraph/how-tos/state-reducers/)
-- [How to create a sequence of steps](https://langchain-ai.github.io/langgraph/how-tos/sequence/)
-- [How to create branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/branching/)
-- [How to create and control loops with recursion limits](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/)
-- [How to visualize your graph](https://langchain-ai.github.io/langgraph/how-tos/visualization/)
+[Deployment Options](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): LLM should read this page when needing information about LangGraph deployment options, comparing different deployment methods, or understanding LangGraph Platform plans. This page outlines four deployment options for LangGraph Platform: Self-Hosted Lite (available for all plans), Self-Hosted Enterprise (Enterprise plan only), Cloud SaaS (Plus and Enterprise plans), and Bring Your Own Cloud (Enterprise plan only, AWS-only).
 
-### Fine-grained Control
+[Double Texting](https://langchain-ai.github.io/langgraph/concepts/double_texting/): LLM should read this page when handling concurrent user interactions in LangGraph Platform, implementing double-texting safeguards, or designing stateful conversation systems. This page explains four approaches to handling "double texting" in LangGraph (when users send a second message before the first completes): Reject, Enqueue, Interrupt, and Rollback, noting these features are currently only available in LangGraph Platform.
 
-These guides demonstrate LangGraph features that grant fine-grained control over the execution of your graph.
+[Durable Execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): LLM should read this page when needing to understand durable execution in LangGraph, implementing workflow persistence, or troubleshooting workflow resumption. This page explains durable execution in LangGraph: how workflows save progress to resume later, requirements (checkpointers and thread IDs), determinism guidelines for consistent replay, using tasks to encapsulate non-deterministic operations, and approaches for pausing/resuming workflows.
 
-- [How to create map-reduce branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/)
-- [How to update state and jump to nodes in graphs and subgraphs](https://langchain-ai.github.io/langgraph/how-tos/command/)
-- [How to add runtime configuration to your graph](https://langchain-ai.github.io/langgraph/how-tos/configuration/)
-- [How to add node retries](https://langchain-ai.github.io/langgraph/how-tos/node-retries/)
-- [How to return state before hitting recursion limit](https://langchain-ai.github.io/langgraph/how-tos/return-when-recursion-limit-hits/)
+[FAQ](https://langchain-ai.github.io/langgraph/concepts/faq/): LLM should read this page when needing to understand differences between LangGraph and LangChain, exploring deployment options for LangGraph Platform, or determining compatibility with various LLMs. FAQ covering LangGraph basics, comparisons with other frameworks, deployment options (free self-hosted, Cloud SaaS, BYOC, Enterprise), compatibility with different LLMs including OSS models, and feature differences between open-source LangGraph and proprietary LangGraph Platform.
 
-### Persistence
- 
-Persistence makes it easy to persist state across graph runs (per-thread persistence) and across threads (cross-thread persistence). 
+[Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/): LLM should read this page when implementing workflows with persistent state, adding human-in-the-loop features, or converting existing code to use LangGraph. The page documents LangGraph's Functional API, which allows adding persistence, memory, and human-in-the-loop capabilities with minimal code changes using @entrypoint and @task decorators, handling serialization requirements, state management, and common patterns for parallel execution and error handling.
 
-These how-to guides show how to add persistence to your graph.
+[Why LangGraph?](https://langchain-ai.github.io/langgraph/concepts/high_level/): LLM should read this page when understanding LangGraph's core capabilities, exploring LLM application infrastructure, or evaluating agent/workflow persistence options. LangGraph provides infrastructure for LLM applications with three key benefits: persistence for memory and human-in-the-loop capabilities, streaming of workflow events and LLM outputs, and tools for debugging and deployment via LangGraph Platform.
 
-- [How to add thread-level persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
-- [How to add thread-level persistence to a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-persistence/)
-- [How to add cross-thread persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence/)
-- [How to use Postgres checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/)
-- [How to use MongoDB checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_mongodb/)
-- [How to create a custom checkpointer using Redis](https://langchain-ai.github.io/langgraph/how-tos/persistence_redis/)
+[Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): LLM should read this page when implementing human-in-the-loop workflows in LangGraph, designing approval systems with LLMs, or creating interactive multi-turn conversation agents. This page explains human-in-the-loop patterns in LangGraph using the interrupt function, showing how to pause graph execution for human review/input and resume with Command. Includes design patterns for approval workflows, state editing, tool call reviews, and multi-turn conversations, with code examples and warnings about execution flow and common pitfalls.
 
-See the below guides for how-to add persistence to your workflow using the [Functional API](https://langchain-ai.github.io/langgraph/concepts/functional_api/):
+[LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/): LLM should read this page when looking for information about LangGraph CLI installation or when needing to deploy a LangGraph API server locally. The page covers LangGraph CLI installation methods (Homebrew, pip), key commands (build, dev, up, dockerfile), and features like hot reloading, debugger support, and database management for running LangGraph servers.
 
-- [How to add thread-level persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/persistence-functional/)
-- [How to add cross-thread persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence-functional/)
+[Cloud SaaS](https://langchain-ai.github.io/langgraph/concepts/langgraph_cloud/): LLM should read this page when learning about LangGraph's Cloud SaaS offering, understanding deployment options for LangGraph Servers, or planning autoscaling infrastructure for LangGraph applications. This page describes LangGraph Cloud SaaS, a managed deployment service for LangGraph Servers with details on deployment types (Development/Production), revisions, persistence, autoscaling capabilities (up to 10 containers), LangSmith integration, IP whitelisting, and automatic deletion policies after 28 days of non-use.
 
-### Memory
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/): LLM should read this page when seeking information about LangGraph Platform's components or evaluating production deployment options for agentic applications. The page details the LangGraph Platform, a commercial solution for deploying agentic applications, including its components (Server, Studio, CLI, SDK, Remote Graph) and key benefits like streaming support, background runs, long run handling, burstiness management, and human-in-the-loop capabilities.
 
-LangGraph makes it easy to manage conversation memory in your graph. These how-to guides show how to implement different strategies for that.
+[LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server/): LLM should read this page when developing applications with LangGraph Server, deploying agent-based applications, or integrating persistent state management in agent workflows. LangGraph Server provides an API for creating and managing agent applications with key features like streaming endpoints, background runs, task queues, persistence, webhooks, cron jobs, and monitoring capabilities through a structured system of assistants, threads, runs, and stores.
 
-- [How to manage conversation history](https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/)
-- [How to delete messages](https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages/)
-- [How to add summary conversation memory](https://langchain-ai.github.io/langgraph/how-tos/memory/add-summary-conversation-history/)
-- [How to add long-term memory (cross-thread)](https://langchain-ai.github.io/langgraph/how-tos/memory/cross-thread-persistence/)
-- [How to use semantic search for long-term memory](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/)
+[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/): LLM should read this page when looking for information about LangGraph Studio features, needing to troubleshoot LangGraph Studio issues, or learning how to connect a LangGraph application to the Studio. LangGraph Studio is a specialized agent IDE for visualizing, interacting with, and debugging LLM applications, offering features such as graph visualization, state editing, assistant management, and integration with LangSmith, with instructions for connecting via deployed applications or local development servers, plus troubleshooting FAQs.
 
-### Human-in-the-loop
+[LangGraph Glossary](https://langchain-ai.github.io/langgraph/concepts/low_level/): LLM should read this page when needing to understand LangGraph terminology, implementing agent workflows as graphs, or developing modular multi-step AI systems. The page covers core LangGraph concepts including StateGraph, nodes, edges, state management, messaging, persistence, configuration, human-in-the-loop features, subgraphs, and visualization capabilities.
 
-Human-in-the-loop functionality allows you to involve humans in the decision-making process of your graph.
+[Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): LLM should read this page when implementing memory systems for AI agents, managing conversation context across sessions, or designing systems that require both short-term and long-term information retention. This page explains memory systems in LangGraph, covering short-term (thread-scoped) memory for managing conversation history and long-term memory across threads, with techniques for handling long conversations, summarizing past interactions, and organizing persistent memories in namespaces.
 
-These how-to guides show how to implement human-in-the-loop workflows in your graph.
+[Multi-agent Systems](https://langchain-ai.github.io/langgraph/concepts/multi_agent/): LLM should read this page when implementing multi-agent systems, troubleshooting complex agent architectures, or designing agent communication patterns. Multi-agent systems organize LLMs into modular architectures (network, supervisor, hierarchical, custom) with different communication patterns, using Command objects for handoffs between agents, and supporting various state management approaches.
 
-- [How to wait for user input](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/): A basic example that shows how to implement a human-in-the-loop workflow in your graph using the `interrupt` function.
-- [How to review tool calls](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/review-tool-calls/): Incorporate human-in-the-loop for reviewing/editing/accepting tool call requests before they executed using the `interrupt` function.
-- [How to add static breakpoints](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): Use for debugging purposes. For human-in-the-loop workflows, we recommend the [`interrupt` function](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.interrupt) instead.
-- [How to edit graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/): Edit graph state using `graph.update_state` method. Use this if implementing a **human-in-the-loop** workflow via **static breakpoints**.
+[Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/): LLM should read this page when needing to understand LangGraph persistence mechanisms, implementing stateful workflows, or managing conversation history across interactions. This page covers LangGraph's persistence features including checkpointers, threads, state snapshots, replay functionality, forking state, cross-thread memory via InMemoryStore, and semantic search capabilities for stored memories.
 
-See the below guides for how-to implement human-in-the-loop workflows with the Functional API.
+[LangGraph Platform Plans](https://langchain-ai.github.io/langgraph/concepts/plans/): LLM should read this page when determining LangGraph Platform pricing tiers, comparing deployment options, or researching features available across different plans. This page outlines LangGraph Platform plans (Developer, Plus, Enterprise), detailing deployment options, usage limitations, feature availability, and pricing structure for agentic application deployment.
 
-- [How to wait for user input (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/wait-user-input-functional/)
-- [How to review tool calls (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/review-tool-calls-functional/)
+[LangGraph Platform Architecture](https://langchain-ai.github.io/langgraph/concepts/platform_architecture/): LLM should read this page when needing to understand LangGraph Platform's technical architecture or troubleshooting deployment issues. The page details how LangGraph Platform uses Postgres for persistent storage of user/run data and Redis for worker communication (run cancellation, output streaming) and ephemeral metadata storage (retry attempts).
 
-### Time Travel
+[LangGraph's Runtime (Pregel)](https://langchain-ai.github.io/langgraph/concepts/pregel/): LLM should read this page when learning about LangGraph's runtime, implementing applications with Pregel directly, or understanding how LangGraph executes graph applications. Explains LangGraph's Pregel runtime which manages graph application execution through a three-phase process (Plan, Execution, Update), describes different channel types (LastValue, Topic, Context, BinaryOperatorAggregate), provides direct implementation examples, and contrasts the StateGraph API with the Functional API.
 
-[Time travel](https://langchain-ai.github.io/langgraph/concepts/time-travel/) allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues. These how-to guides show how to use time travel in your graph.
+[LangGraph Platform: Scalability & Resilience](https://langchain-ai.github.io/langgraph/concepts/scalability_and_resilience/): LLM should read this page when needing to understand LangGraph Platform's scaling capabilities, designing high-availability LangGraph deployments, or troubleshooting resilience issues. This page details LangGraph Platform's horizontal scaling features including stateless server instances, queue worker scaling, resilience mechanisms for handling crashes, and database failover strategies in Postgres and Redis.
 
-- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/time-travel/)
+[LangGraph SDK](https://langchain-ai.github.io/langgraph/concepts/sdk/): LLM should read this page when looking for installation instructions for LangGraph SDK, needing to choose between sync and async Python clients, or requiring SDK API references. The page covers LangGraph SDK installation for Python and JS, provides API reference links, explains the difference between synchronous and asynchronous Python clients, and includes code examples for both client types.
 
-### Streaming
+[Self-Hosted](https://langchain-ai.github.io/langgraph/concepts/self_hosted/): LLM should read this page when looking for LangGraph deployment options, understanding self-hosted versions, or seeking requirements for self-hosting LangGraph. This page details two self-hosted deployment options for LangGraph Platform: Self-Hosted Lite (limited to 1M nodes/year) and Self-Hosted Enterprise (full version requiring license). Includes requirements, deployment process using Redis/Postgres, Docker, and optional Kubernetes deployment via Helm chart.
 
-[Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
+[Streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/): LLM should read this page when implementing streaming features in LangGraph applications, understanding different streaming modes, or building responsive LLM applications. This page explains streaming in LangGraph, covering the main types (workflow progress, LLM tokens, custom updates) and streaming modes (values, updates, custom, messages, debug, events), with details on how to use multiple modes simultaneously and differences between LangGraph library and Platform implementations.
 
-- [How to stream](https://langchain-ai.github.io/langgraph/how-tos/streaming/)
-- [How to stream LLM tokens](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens/)
-- [How to stream LLM tokens from specific nodes](https://langchain-ai.github.io/langgraph/how-tos/streaming-specific-nodes/)
-- [How to stream data from within a tool](https://langchain-ai.github.io/langgraph/how-tos/streaming-events-from-within-tools/)
-- [How to stream from subgraphs](https://langchain-ai.github.io/langgraph/how-tos/streaming-subgraphs/)
-- [How to disable streaming for models that don't support it](https://langchain-ai.github.io/langgraph/how-tos/disable-streaming/)
+[Template Applications](https://langchain-ai.github.io/langgraph/concepts/template_applications/): LLM should read this page when looking for LangGraph template applications, setting up a new LangGraph project, or finding reference implementations for agentic workflows. This page presents LangGraph template applications with installation requirements, available templates (including ReAct Agent, Memory Agent, Retrieval Agent, etc.), instructions for creating new apps using the CLI, deployment options, and links to further learning resources.
 
-### Tool calling
+[Time Travel ⏱️](https://langchain-ai.github.io/langgraph/concepts/time-travel/): LLM should read this page when debugging LLM-based agent behavior, analyzing decision-making paths, or exploring alternative execution branches in LangGraph. This page explains LangGraph's Time Travel debugging features: Replaying (reproducing past actions up to specific checkpoints) and Forking (creating alternative execution paths from specific points), with code examples for retrieving checkpoints, configuring replay, and creating forked states.
 
-[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of [chat model](https://python.langchain.com/docs/concepts/chat_models/) API.
+## How Tos
 
-It accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
+[How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): LLM should read this page when looking for specific implementation techniques in LangGraph or when trying to deploy LangGraph applications to production environments. This page contains an extensive collection of how-to guides for LangGraph, covering graph fundamentals, persistence, memory management, human-in-the-loop features, tool calling, multi-agent systems, streaming, and deployment options through LangGraph Platform.
 
-These how-to guides show common patterns for tool calling with LangGraph:
+[How to implement handoffs between agents](https://langchain-ai.github.io/langgraph/how-tos/agent-handoffs/): LLM should read this page when implementing multi-agent systems that require agent coordination, when building systems with specialized agents that need to work together, or when needing to implement handoffs between agents. This page explains how to implement handoffs between agents in LangGraph using Command objects, both directly from agent nodes and through specialized handoff tools, with code examples for creating multi-agent systems.
 
-- [How to call tools using ToolNode](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/)
-- [How to handle tool calling errors](https://langchain-ai.github.io/langgraph/how-tos/tool-calling-errors/)
-- [How to pass runtime values to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/)
-- [How to pass config to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-config-to-tools/)
-- [How to update graph state from tools](https://langchain-ai.github.io/langgraph/how-tos/update-state-from-tools/)
-- [How to handle large numbers of tools](https://langchain-ai.github.io/langgraph/how-tos/many-tools/)
+[How to run a graph asynchronously](https://langchain-ai.github.io/langgraph/how-tos/async/): LLM should read this page when needing to implement asynchronous graph execution in LangGraph or when optimizing IO-bound LLM applications. This page explains how to convert synchronous graphs to asynchronous in LangGraph, including updating node definitions with async/await, using StateGraph with TypedDict, implementing conditional edges, and streaming results.
 
-### Subgraphs
+[How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](https://langchain-ai.github.io/langgraph/how-tos/autogen-integration/): LLM should read this page when integrating LangGraph with other agent frameworks, building multi-agent systems, or adding persistence features to agents. The page demonstrates how to combine LangGraph with AutoGen by calling AutoGen agents inside LangGraph nodes, showing code examples for setting up the integration with memory and conversation persistence.
 
-Subgraphs allow you to reuse an existing graph from another graph. 
+[How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks](https://langchain-ai.github.io/langgraph/how-tos/autogen-integration-functional/): LLM should read this page when integrating LangGraph with other agent frameworks, building multi-agent systems with different frameworks, or adding LangGraph features to existing agent systems. This page demonstrates how to integrate LangGraph's functional API with AutoGen, including code examples for creating a workflow that calls AutoGen agents, leveraging LangGraph's memory and persistence features.
 
-These how-to guides show how to use subgraphs:
+[How to create branches for parallel node execution](https://langchain-ai.github.io/langgraph/how-tos/branching/): LLM should read this page when needing to implement parallel node execution in LangGraph, optimizing graph performance, or handling conditional branching in workflows. This page explains how to create branches for parallel execution in LangGraph using fan-out/fan-in mechanisms, reducer functions for state accumulation, handling exceptions during parallel execution, and implementing conditional branching logic between nodes.
 
-- [How to use subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraph/)
-- [How to view and update state in subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraphs-manage-state/)
-- [How to transform inputs and outputs of a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-transform-state/)
+[How to combine control flow and state updates with Command](https://langchain-ai.github.io/langgraph/how-tos/command): LLM should read this page when learning how to combine control flow with state updates in LangGraph, understanding Command objects, or navigating between parent graphs and subgraphs. This page explains how to use Command objects to simultaneously update state and control flow between nodes, demonstrates using Command.PARENT to navigate from subgraphs to parent graphs, and includes examples of implementing reducers for state updates across graph hierarchies.
 
-### Multi-agent
+[How to add runtime configuration to your graph](https://langchain-ai.github.io/langgraph/how-tos/configuration/): LLM should read this page when implementing runtime configuration for LangGraph, adding model selection options to agents, or enabling dynamic system messages. This page demonstrates how to configure LangGraph at runtime, including selecting different LLMs dynamically and adding custom configuration options like system messages through the configurable dictionary.
 
-Multi-agent systems are useful to break down complex LLM applications into multiple agents, each responsible for a different part of the application. 
+[How to use the pre-built ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/): LLM should read this page when implementing a ReAct agent, needing pre-built agent solutions, or learning how to integrate tools with LLM agents. This page covers how to use the pre-built ReAct agent in LangGraph, including setup instructions, creating a weather checking tool, implementing the agent architecture, and examples of running the agent with and without tool calls.
 
-These how-to guides show how to implement multi-agent systems in LangGraph:
+[How to add human-in-the-loop processes to the prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent-hitl/): LLM should read this page when implementing human-in-the-loop processes for ReAct agents, debugging tool calls, or learning about interrupts in LangGraph. This guide demonstrates how to add human-in-the-loop functionality to prebuilt ReAct agents using interrupt_before=["tools"], working with MemorySaver checkpoints, and showing how to approve or edit tool calls before they execute.
 
-- [How to implement handoffs between agents](https://langchain-ai.github.io/langgraph/how-tos/agent-handoffs/)
-- [How to build a multi-agent network](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network/)
-- [How to add multi-turn conversation in a multi-agent application](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-multi-turn-convo/)
+[How to add thread-level memory to a ReAct Agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent-memory/): LLM should read this page when adding memory to ReAct agents, implementing thread-level persistence in LangGraph, or building stateful conversational agents. This guide demonstrates how to add memory to a ReAct agent using LangGraph's checkpointer interface, with code examples showing MemorySaver implementation, thread_id configuration, and persistent chat context across multiple interactions.
 
-### State Management
+[How to return structured output from the prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent-structured-output/): LLM should read this page when implementing structured output with ReAct agents, customizing agent response formats, or working with LangGraph agents. This page explains how to return structured output from prebuilt ReAct agents by providing a response_format parameter with a Pydantic schema, including examples with weather data and options for customizing the prompt.
 
-- [How to use Pydantic model as graph state](https://langchain-ai.github.io/langgraph/how-tos/state-model/)
-- [How to define input/output schema for your graph](https://langchain-ai.github.io/langgraph/how-tos/input_output_schema/)
-- [How to pass private state between nodes inside the graph](https://langchain-ai.github.io/langgraph/how-tos/pass_private_state/)
+[How to add a custom system prompt to the prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent-system-prompt/): LLM should read this page when learning to customize ReAct agents, needing to add system prompts to agents, or working with LangGraph's prebuilt agents. This tutorial demonstrates how to add a custom system prompt to a prebuilt ReAct agent, with code examples showing model setup, tool creation, and using the prompt parameter in the create_react_agent function.
 
-### Other
+[How to add cross-thread persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence): LLM should read this page when needing to implement persistence across multiple threads in LangGraph, when storing user data between conversations, or when implementing shared memory in graph-based LLM applications. This page demonstrates how to use LangGraph's Store API to persist data across threads, including creating an InMemoryStore with embedding search capabilities, passing stores to graph nodes, and accessing user-specific memories in different conversation threads.
 
-- [How to run graph asynchronously](https://langchain-ai.github.io/langgraph/how-tos/async/)
-- [How to force tool-calling agent to structure output](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/)
-- [How to pass custom LangSmith run ID for graph runs](https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/)
-- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](https://langchain-ai.github.io/langgraph/how-tos/autogen-integration/)
+[How to add cross-thread persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence-functional): LLM should read this page when needing to implement cross-thread persistence in LangGraph functional API, storing user data across different conversation threads, or creating shared memory between workflows. This page explains how to add cross-thread persistence to LangGraph using the Store interface, including defining a store, configuring the entrypoint decorator, and implementing a workflow that can store and retrieve user information across different conversation threads.
 
-## Use cases 
+[How to do a Self-hosted deployment of LangGraph](https://langchain-ai.github.io/langgraph/how-tos/deploy-self-hosted/): LLM should read this page when implementing a self-hosted deployment of LangGraph, configuring required environment variables, or building Docker images for LangGraph applications. This page explains how to deploy LangGraph applications using Docker, covering environment requirements (Redis, Postgres), how to build Docker images with the LangGraph CLI, configuration using environment variables, and deployment options using Docker or Docker Compose.
 
-Explore practical implementations tailored for specific scenarios:
+[How to disable streaming for models that don't support it](https://langchain-ai.github.io/langgraph/how-tos/disable-streaming/): LLM should read this page when handling models that don't support streaming, implementing LangGraph with non-streaming models, or troubleshooting streaming errors with OpenAI's O1 models. This page explains how to use the disable_streaming=True parameter with ChatOpenAI to make non-streaming models work with LangGraph's astream_events API, with code examples showing the error case and proper implementation.
 
-### Chatbots
+[How to edit graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/): LLM should read this page when needing to implement human intervention in LangGraph workflows, wanting to edit graph state during execution, or implementing breakpoints in agent systems. This page explains how to edit graph state in LangGraph using breakpoints, including implementing human-in-the-loop interactions, setting up interruptions before specific nodes, and updating state during agent execution.
 
-- [Customer Support](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/): Build a multi-functional support bot for flights, hotels, and car rentals.
-- [Prompt Generation from User Requirements](https://langchain-ai.github.io/langgraph/tutorials/chatbots/information-gather-prompting/): Build an information gathering chatbot.
-- [Code Assistant](https://langchain-ai.github.io/langgraph/tutorials/code_assistant/langgraph_code_assistant/): Build a code analysis and generation assistant.
+[How to Review Tool Calls](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/review-tool-calls/): LLM should read this page when implementing human review of tool calls, creating interactive agent workflows, or building approval systems for AI actions. This page explains how to implement human-in-the-loop review for tool calls in LangGraph, including approving tool calls, modifying tool calls manually, and providing natural language feedback to agents with complete code examples and explanations.
 
-### RAG
+[How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/): LLM should read this page when needing to access or modify past states in LangGraph, when debugging agent execution, or when implementing user interventions in agent workflows. This page demonstrates how to view and update past graph states in LangGraph using get_state and update_state methods, with examples of replaying execution from checkpoints and branching workflows.
 
-- [Agentic RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/): Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
-- [Adaptive RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag/): Adaptive RAG is a strategy for RAG that unites (1) query analysis with (2) active / self-corrective RAG. Implementation of: https://arxiv.org/abs/2403.14403
-    - For a version that uses a local LLM: [Adaptive RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/)
-- [Corrective RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag/): Uses an LLM to grade the quality of the retrieved information from the given source, and if the quality is low, it will try to retrieve the information from another source. Implementation of: https://arxiv.org/pdf/2401.15884.pdf 
-    - For a version that uses a local LLM: [Corrective RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag_local/)
-- [Self-RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag/): Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. Implementation of https://arxiv.org/abs/2310.11511.
-    - For a version that uses a local LLM: [Self-RAG using local LLMs](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag_local/) 
-- [SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/): Build a SQL agent that can answer questions about a SQL database.
+[How to wait for user input using interrupt](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/wait-user-input/): LLM should read this page when implementing wait-for-user functions in LangGraph, implementing human-in-the-loop interactions, or learning how to use the interrupt() function. This page explains how to pause graph execution to collect user input using LangGraph's interrupt() function, with examples of simple feedback collection and more complex agent interactions that ask clarifying questions.
 
-### Multi-Agent Systems
+[How to define input/output schema for your graph](https://langchain-ai.github.io/langgraph/how-tos/input_output_schema/): LLM should read this page when needing to define separate input/output schemas for LangGraph, implementing schema-based data filtering, or understanding schema definitions in StateGraph. This page explains how to define distinct input and output schemas for a StateGraph, showing how input schema validates the provided data structure while output schema filters internal data to return only relevant information, with code examples demonstrating implementation.
+
+[How to handle large numbers of tools](https://langchain-ai.github.io/langgraph/how-tos/many-tools/): LLM should read this page when handling large tool collections, implementing dynamic tool selection, or creating retrieval-based tool management in LangGraph. This page demonstrates how to manage large numbers of tools by using vector search to dynamically select relevant tools based on user queries, implementing tool selection nodes in LangGraph, and handling tool selection errors with retry mechanisms.
+
+[How to create map-reduce branches for parallel execution](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/): LLM should read this page when learning to implement parallel execution in LangGraph, creating map-reduce operations, or handling dynamic task decomposition. This guide explains how to use LangGraph's Send API to create map-reduce workflows, breaking tasks into parallel sub-tasks and recombining results, with examples showing joke generation across multiple subjects.
+
+[How to add summary of the conversation history](https://langchain-ai.github.io/langgraph/how-tos/memory/add-summary-conversation-history/): LLM should read this page when implementing conversation summarization, managing context windows, or building chatbots with memory management. This page demonstrates how to add summary functionality to conversation history using LangGraph, including checking conversation length, creating summaries, and removing old messages while maintaining context.
+
+[How to delete messages](https://langchain-ai.github.io/langgraph/how-tos/memory/delete-messages): LLM should read this page when attempting to manage message history in LangGraph, needing to delete specific messages from conversational state, or implementing memory management in LLM applications. This page explains how to delete messages from a LangGraph application using RemoveMessage modifiers, covering both manual deletion with message IDs and programmatic deletion within graph logic to maintain conversation history limits.
+
+[How to manage conversation history](https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/): LLM should read this page when managing conversation history in LangGraph, preventing context window issues, or implementing custom message filtering. This page explains how to manage conversation history in LangGraph to prevent context window overflow by implementing message filtering functions that control which messages are sent to the LLM.
+
+[How to add semantic search to your agent's memory](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/): LLM should read this page when implementing semantic search in agent memory, enabling memory-aware AI assistants, or configuring advanced memory retrieval systems. This page demonstrates how to add semantic search to LangGraph agent memory stores, covering basic setup with embeddings, storing memories, searching by semantic similarity, integrating memory in agents and ReAct agents, and advanced usage like multi-vector indexing and selective memory indexing.
+
+[How to add multi-turn conversation in a multi-agent application](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-multi-turn-convo/): LLM should read this page when implementing multi-turn conversations between agents, creating interactive agent systems with human input, or learning about langgraph interrupts and agent handoffs. This page demonstrates how to build a multi-agent system with multi-turn conversations, including human-in-the-loop interactions, agent handoffs, and state management using LangGraph, Command objects, and interrupts.
+
+[How to add multi-turn conversation in a multi-agent application (functional API)](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-multi-turn-convo-functional/): LLM should read this page when building multi-turn conversational agents, implementing agent-to-agent handoffs, or using interrupts to collect user input in LangGraph. This guide demonstrates how to create a multi-agent system with multi-turn conversations using LangGraph's functional API, featuring agent handoffs, interrupt mechanics for user input, and a complete example of travel and hotel advisor agents that can transfer control between each other.
+
+[How to build a multi-agent network](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network/): LLM should read this page when implementing multi-agent networks, setting up agent communication via handoffs, or building travel assistance agents. This page explains how to create a fully-connected multi-agent network with LangGraph where agents can communicate with each other via handoffs, including custom agent implementation and using prebuilt ReAct agents with tools.
+
+[How to build a multi-agent network (functional API)](https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network-functional/): LLM should read this page when building multi-agent systems, implementing agent handoffs between specialists, or creating fully-connected agent networks. This guide demonstrates how to create a multi-agent network using LangGraph's functional API, with tasks for individual agents and entrypoint functions to manage agent handoffs based on tool calls.
+
+[How to add node retry policies](https://langchain-ai.github.io/langgraph/how-tos/node-retries/): LLM should read this page when implementing error handling in LangGraph nodes, configuring API retry mechanisms, or troubleshooting node failures in graph workflows. Shows how to add custom retry policies to LangGraph nodes, including specifying which exceptions to retry on, setting max attempts, intervals, backoff factors, and implementing different retry behaviors for different node types.
+
+[How to pass config to tools](https://langchain-ai.github.io/langgraph/how-tos/pass-config-to-tools/): LLM should read this page when implementing secure tool configuration in LangChain, passing user-specific parameters to tools, or configuring tools with runtime values. This page explains how to pass configuration to LangChain tools using RunnableConfig, allowing application-controlled values (like user IDs) to be securely passed to tools without LLM control, with examples of implementing tools that access user-specific data.
+
+[How to pass private state between nodes](https://langchain-ai.github.io/langgraph/how-tos/pass_private_state/): LLM should read this page when implementing data sharing between specific nodes in LangGraph, handling private state in graph workflows, or designing multi-node sequential processes with selective data visibility. This page demonstrates how to pass private data between specific nodes in a LangGraph without making it part of the main schema, using typed dictionaries to define both public and private states, and showing a three-node example where private data flows only between the first two nodes.
+
+[How to add thread-level persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/): LLM should read this page when implementing persistence in LangGraph, needing to preserve context across user interactions, or learning about thread-level state management. This page explains how to add thread-level persistence to LangGraph applications using MemorySaver, including code examples for creating stateful conversations where context is maintained across multiple interactions.
+
+[How to add thread-level persistence (functional API)](https://langchain-ai.github.io/langgraph/how-tos/persistence-functional/): LLM should read this page when implementing thread-level persistence in LangGraph, creating conversational agents with memory, or using functional API with state management. This page explains how to add thread-level persistence to LangGraph functional API workflows using checkpointers, including code examples for creating a simple chatbot with memory across conversation turns.
+
+[How to use MongoDB checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_mongodb/): LLM should read this page when implementing persistence in LangGraph agents, setting up MongoDB for state checkpointing, or working with MongoDB connections in LangGraph applications. This page explains how to use the MongoDB checkpointer for LangGraph persistence, covering connection methods (direct, client-based, async), basic setup requirements, and practical examples of saving and retrieving agent state between interactions.
+
+[How to use Postgres checkpointer for persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/): LLM should read this page when setting up persistence for LangGraph agents, implementing PostgreSQL as a checkpoint storage backend, or working with either synchronous or asynchronous database connections. This page details how to use PostgreSQL for persisting LangGraph agent state, covering setup and configuration of PostgresSaver and AsyncPostgresSaver with different connection methods (pool, direct connection, connection string).
+
+[How to create a custom checkpointer using Redis](https://langchain-ai.github.io/langgraph/how-tos/persistence_redis/): LLM should read this page when implementing persistence in LangGraph applications, creating custom checkpoint mechanisms for agents, or working with Redis as a storage backend. This page demonstrates how to create custom checkpointers for LangGraph agents using Redis, including implementations for both synchronous and asynchronous interfaces that save and retrieve agent state.
+
+[How to create a ReAct agent from scratch](https://langchain-ai.github.io/langgraph/how-tos/react-agent-from-scratch/): LLM should read this page when needing to create a custom ReAct agent, wanting more control than prebuilt agents, or implementing ReAct from scratch with LangGraph. This guide shows how to build a custom ReAct agent using LangGraph, covering state definition, model/tool setup, node/edge configuration, graph creation, and testing the implementation with a weather query example.
+
+[How to create a ReAct agent from scratch (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/react-agent-from-scratch-functional): LLM should read this page when creating a ReAct agent using LangGraph's Functional API, implementing tool-calling workflows, or building conversational agents with thread persistence. This page explains how to build a ReAct agent from scratch using LangGraph's Functional API, including model and tool setup, defining tasks for model/tool calling, creating an entrypoint for orchestration, and adding thread-level persistence for conversational experiences.
+
+[How to force tool-calling agent to structure output](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output): LLM should read this page when needing to force tool-calling agents to produce structured output, implementing consistent output formats for downstream software, or choosing between single-LLM vs two-LLM structured output approaches. The page explains two methods for implementing structured output with tool-calling agents: binding output as a tool (single LLM approach) and using two LLMs with structured output conversion, with code examples for both approaches using LangGraph.
+
+[How to create and control loops](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/): LLM should read this page when building loops in computational graphs, needing to implement termination conditions, or handling recursion limits in LangGraph. The page explains how to create graphs with loops using conditional edges for termination, set recursion limits, handle GraphRecursionError, and implement complex loops with branches.
+
+[How to review tool calls (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/review-tool-calls-functional/): LLM should read this page when implementing human review of tool calls, creating ReAct agents with Functional API, or adding human-in-the-loop workflows. This page demonstrates how to review tool calls before execution in a ReAct agent using LangGraph's Functional API, including accepting, revising, or generating custom tool messages with the interrupt function.
+
+[How to pass custom run ID or set tags and metadata for graph runs in LangSmith](https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/): LLM should read this page when needing to customize trace information in LangSmith for LangGraph runs or when debugging graph runs with custom identifiers. The page explains how to pass custom run_id, set tags, add metadata, and customize run names for LangGraph traces in LangSmith using RunnableConfig, with examples showing implementation with a ReAct agent.
+
+[How to create a sequence of steps](https://langchain-ai.github.io/langgraph/how-tos/sequence/): LLM should read this page when implementing sequential workflows in LangGraph, creating multi-step processes in applications, or learning about state management in graph-based systems. This page explains how to create sequences in LangGraph, covering methods for building sequential graphs using .add_node/.add_edge or the shorthand .add_sequence, defining state with TypedDict, creating nodes as functions that update state, and compiling/invoking graphs with examples.
+
+[How to use Pydantic model as graph state](https://langchain-ai.github.io/langgraph/how-tos/state-model): LLM should read this page when implementing Pydantic models for state validation in LangGraph, handling complex state schema definitions, or troubleshooting validation errors in graph nodes. This guide explains how to use Pydantic BaseModel as a state schema in LangGraph for runtime validation, covering basic implementation, limitations, validation behavior across multiple nodes, serialization patterns, type coercion, and working with message models.
+
+[How to update graph state from nodes](https://langchain-ai.github.io/langgraph/how-tos/state-reducers/): LLM should read this page when needing to update state in LangGraph, designing graphs with nodes that modify state, or implementing reducers for state management. This page explains how to define state schemas in LangGraph using TypedDict, how nodes can update state, and how to use reducers to control state updates, with specific examples using message handling.
+
+[How to stream](https://langchain-ai.github.io/langgraph/how-tos/streaming/): LLM should read this page when needing to implement streaming in LangGraph applications, understanding different streaming modes, or troubleshooting LLM response delivery. This page explains how to stream LLM outputs using LangGraph, covering different streaming modes (values, updates, custom, messages, debug), with code examples for each mode and how to combine multiple streaming modes.
+
+[How to stream data from within a tool](https://langchain-ai.github.io/langgraph/how-tos/streaming-events-from-within-tools/): LLM should read this page when implementing streaming functionality in tools, integrating LLM outputs with custom data streams, or developing LangGraph applications with real-time feedback. This page explains how to stream data from within tools using LangGraph, covering custom data streaming with stream_mode="custom", LLM token streaming with stream_mode="messages", and implementation approaches both with and without LangChain.
+
+[How to stream LLM tokens from specific nodes](https://langchain-ai.github.io/langgraph/how-tos/streaming-specific-nodes/): LLM should read this page when needing to filter token streaming from specific nodes in LangGraph, implementing selective streaming in multi-node workflows, or controlling which node outputs are displayed. Guide explains how to stream LLM tokens from specific nodes using stream_mode="messages" and filtering by the langgraph_node metadata field, with complete code examples for implementing this in StateGraph applications.
+
+[How to stream from subgraphs](https://langchain-ai.github.io/langgraph/how-tos/streaming-subgraphs/): LLM should read this page when needing to stream outputs from subgraphs in LangGraph, implementing nested graph streaming, or debugging hierarchical graph execution. This page explains how to stream outputs from subgraphs in LangGraph by using the subgraphs=True parameter in the parent graph's stream() method, with a complete code example showing the difference between regular streaming and subgraph streaming.
+
+[How to stream LLM tokens from your graph](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens): LLM should read this page when needing to stream LLM tokens from a LangGraph application, implementing custom token streaming, or filtering streamed outputs. This page explains how to stream individual LLM tokens from LangGraph nodes using graph.stream() with different stream_mode options, including examples with and without LangChain, async implementations, and how to filter streamed tokens using metadata.
+
+[How to use subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraph/): LLM should read this page when building complex systems with subgraphs, implementing multi-agent systems, or needing to share state between parent graphs and subgraphs. The page explains two methods for using subgraphs: adding compiled subgraphs when schemas share keys, and invoking subgraphs via node functions when schemas differ, with code examples for both approaches.
+
+[How to add thread-level persistence to a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-persistence/): LLM should read this page when implementing persistence in nested LangGraph architectures, adding thread-level storage to subgraphs, or debugging state propagation in LangGraph applications. This guide demonstrates how to add thread-level persistence to subgraphs by passing a checkpointer only to the parent graph during compilation, accessing persisted states from both parent and child graphs, and retrieving subgraph state using the proper configuration parameters.
+
+[How to transform inputs and outputs of a subgraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph-transform-state/): LLM should read this page when needing to work with nested subgraphs, transforming state between parent and child graphs, or integrating independent state components in LangGraph. This page demonstrates how to transform inputs and outputs between parent graphs and subgraphs with different state structures, showing implementation of three nested graphs (parent, child, grandchild) with separate state dictionaries and transformation functions.
+
+[How to view and update state in subgraphs](https://langchain-ai.github.io/langgraph/how-tos/subgraphs-manage-state/): LLM should read this page when working with state management in nested subgraphs, implementing human-in-the-loop patterns, or debugging complex graph flows. This guide covers viewing and updating state in LangGraph subgraphs, including how to resume execution from breakpoints, modify subgraph state, act as specific nodes, and work with multi-level nested subgraphs.
+
+[How to call tools using ToolNode](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/): LLM should read this page when learning how to implement tool calling with LangGraph, when working with the ToolNode component, or when building ReAct agents. This page covers using LangGraph's ToolNode for tool calling, including setup, manual invocation, working with chat models, building a ReAct agent, handling single and parallel tool calls, and error handling.
+
+[How to handle tool calling errors](https://langchain-ai.github.io/langgraph/how-tos/tool-calling-errors/): LLM should read this page when handling tool call errors, implementing error handling for LLM-tool interactions, or creating fallback strategies for failed tool calls. This page covers strategies for handling tool calling errors in LangGraph, including using the prebuilt ToolNode with built-in error handling, implementing custom error handling patterns, and fallback mechanisms with model upgrades when tools fail.
+
+[How to update graph state from tools](https://langchain-ai.github.io/langgraph/how-tos/update-state-from-tools/): LLM should read this page when needing to update graph state from tools in LangGraph, implementing personalized responses based on tool updates, or using Command objects to modify state. This page details how to update graph state from tools using Command objects, creating personalized agents with state tracking, and implementing dynamic prompt construction based on updated state values.
+
+[How to interact with the deployment using RemoteGraph](https://langchain-ai.github.io/langgraph/how-tos/use-remote-graph/): LLM should read this page when needing to interact with LangGraph Platform deployments remotely, when implementing RemoteGraph interfaces, or when using deployed graphs as subgraphs. This page explains how to use RemoteGraph to interact with LangGraph Platform deployments, covering initialization methods (URL-based or client-based), synchronous/asynchronous invocation, thread-level persistence, and using RemoteGraph as a subgraph in larger applications.
+
+[How to visualize your graph](https://langchain-ai.github.io/langgraph/how-tos/visualization): LLM should read this page when needing to visualize LangGraph graphs, looking for graph visualization methods, or working with graph visualization in Python. Comprehensive guide for visualizing graphs in LangGraph with multiple methods: Mermaid syntax, Mermaid.ink API for PNG rendering, Pyppeteer-based visualization, and Graphviz, with customization options for colors, styles, and layout.
+
+[How to wait for user input (Functional API)](https://langchain-ai.github.io/langgraph/how-tos/wait-user-input-functional/): LLM should read this page when implementing human-in-the-loop workflows, integrating user input into agent systems, or adding interruption capabilities to LangGraph applications. The page explains how to use the `interrupt()` function in LangGraph's Functional API to pause execution for human input, with examples for both simple workflows and ReAct agents, including code implementations with checkpointing.
 
-- [Network](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/): Enable two or more agents to collaborate on a task
-- [Supervisor](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/): Use an LLM to orchestrate and delegate to individual agents
-- [Hierarchical Teams](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/): Orchestrate nested teams of agents to solve problems

docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb

@@ -125,7 +125,7 @@
     "\n",
     "### Code solution\n",
     "\n",
-    "First, we will try OpenAI and [Claude3](https://docs.anthropic.com/en/docs/about-claude/models) with function calling.\n",
+    "First, we will try OpenAI and [Claude3](https://python.langchain.com/docs/integrations/providers/anthropic/) with function calling.\n",
     "\n",
     "We will create a `code_gen_chain` w/ either OpenAI or Claude and test them here."
    ]

docs/docs/tutorials/storm/storm.ipynb

@@ -153,7 +153,7 @@
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
-    "from pydantic import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field, field_validator\n",
     "\n",
     "direct_gen_outline_prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
@@ -336,6 +336,10 @@
     "        description=\"Description of the editor's focus, concerns, and motives.\",\n",
     "    )\n",
     "\n",
+    "    @field_validator(\"name\", mode=\"before\")\n",
+    "    def sanitize_name(cls, value: str) -> str:\n",
+    "        return value.replace(\" \", \"\").replace(\".\", \"\")\n",
+    "\n",
     "    @property\n",
     "    def persona(self) -> str:\n",
     "        return f\"Name: {self.name}\\nRole: {self.role}\\nAffiliation: {self.affiliation}\\nDescription: {self.description}\\n\"\n",
@@ -362,9 +366,9 @@
     "    ]\n",
     ")\n",
     "\n",
-    "gen_perspectives_chain = gen_perspectives_prompt | ChatOpenAI(\n",
-    "    model=\"gpt-3.5-turbo\"\n",
-    ").with_structured_output(Perspectives)"
+    "gen_perspectives_chain = gen_perspectives_prompt | fast_llm.with_structured_output(\n",
+    "    Perspectives, method=\"function_calling\"\n",
+    ")"
    ]
   },
   {
@@ -451,7 +455,7 @@
     }
    ],
    "source": [
-    "perspectives.dict()"
+    "perspectives.model_dump()"
    ]
   },
   {
@@ -559,7 +563,7 @@
     "    converted = []\n",
     "    for message in state[\"messages\"]:\n",
     "        if isinstance(message, AIMessage) and message.name != name:\n",
-    "            message = HumanMessage(**message.dict(exclude={\"type\"}))\n",
+    "            message = HumanMessage(**message.model_dump(exclude={\"type\"}))\n",
     "        converted.append(message)\n",
     "    return {\"messages\": converted}\n",
     "\n",
@@ -637,9 +641,9 @@
     "        MessagesPlaceholder(variable_name=\"messages\", optional=True),\n",
     "    ]\n",
     ")\n",
-    "gen_queries_chain = gen_queries_prompt | ChatOpenAI(\n",
-    "    model=\"gpt-3.5-turbo\"\n",
-    ").with_structured_output(Queries, include_raw=True)"
+    "gen_queries_chain = gen_queries_prompt | fast_llm.with_structured_output(\n",
+    "    Queries, include_raw=True, method=\"function_calling\"\n",
+    ")"
    ]
   },
   {
@@ -1695,6 +1699,13 @@
     "# We will down-header the sections to create less confusion in this notebook\n",
     "Markdown(article.replace(\"\\n#\", \"\\n##\"))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/docs/tutorials/workflows/index.md

@@ -1,6 +1,6 @@
 # Workflows and Agents
 
-This guide reviews common patterns for agentic systems. In describing these systems, it can be useful to make a distinction between "workflows" and "agents". One way to think about this difference is nicely explained [here](https://www.anthropic.com/research/building-effective-agents) by Anthropic:
+This guide reviews common patterns for agentic systems. In describing these systems, it can be useful to make a distinction between "workflows" and "agents". One way to think about this difference is nicely explained in [Anthropic's](https://python.langchain.com/docs/integrations/providers/anthropic/) `Building Effective Agents` blog post:
 
 > Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
 > Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
@@ -9,7 +9,7 @@ Here is a simple way to visualize these differences:
 
 ![Agent Workflow](../../concepts/img/agent_workflow.png)
 
-When building agents and workflows, LangGraph [offers a number of benefits](https://langchain-ai.github.io/langgraph/concepts/high_level/) including persistence, streaming, and support for debugging as well as deployment.
+When building agents and workflows, LangGraph offers a number of benefits including persistence, streaming, and support for debugging as well as deployment.
 
 ## Set up
 
@@ -41,7 +41,7 @@ llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
 
 ## Building Blocks: The Augmented LLM 
 
-LLM have [augmentations](https://www.anthropic.com/research/building-effective-agents) that support building workflows and agents. These include [structured outputs](https://python.langchain.com/docs/concepts/structured_outputs/) and [tool calling](https://python.langchain.com/docs/concepts/tool_calling/), as shown in this image from the Anthropic [blog](https://www.anthropic.com/research/building-effective-agents):
+LLM have augmentations that support building workflows and agents. These include [structured outputs](https://python.langchain.com/docs/concepts/structured_outputs/) and [tool calling](https://python.langchain.com/docs/concepts/tool_calling/), as shown in this image from the Anthropic blog on `Building Effective Agents`:
 
 ![augmented_llm.png](./img/augmented_llm.png)
 
@@ -81,7 +81,7 @@ msg.tool_calls
 
 In prompt chaining, each LLM call processes the output of the previous one. 
 
-As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): 
+As noted in the Anthropic blog on `Building Effective Agents`: 
 
 > Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one. You can add programmatic checks (see "gate” in the diagram below) on any intermediate steps to ensure that the process is still on track.
 
@@ -392,7 +392,7 @@ With parallelization, LLMs work simultaneously on a task:
 
 ## Routing
 
-Routing classifies an input and directs it to a followup task. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): 
+Routing classifies an input and directs it to a followup task. As noted in the Anthropic blog on `Building Effective Agents`: 
 
 > Routing classifies an input and directs it to a specialized followup task. This workflow allows for separation of concerns, and building more specialized prompts. Without this workflow, optimizing for one kind of input can hurt performance on other inputs.
 
@@ -603,7 +603,7 @@ Routing classifies an input and directs it to a followup task. As noted in the [
 
 ## Orchestrator-Worker
 
-With orchestrator-worker, an orchestrator breaks down a task and delegates each sub-task to workers. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents): 
+With orchestrator-worker, an orchestrator breaks down a task and delegates each sub-task to workers. As noted in the Anthropic blog on `Building Effective Agents`: 
 
 > In the orchestrator-workers workflow, a central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results.
 
@@ -948,7 +948,7 @@ In the evaluator-optimizer workflow, one LLM call generates a response while ano
 
     **Examples**
 
-    [Here](https://github.com/langchain-ai/research-rabbit) is an assistant that uses evaluator-optimizer to improve a report. See our video [here](https://www.youtube.com/watch?v=XGuTzHoqlj8).
+    [Here](https://github.com/langchain-ai/local-deep-researcher) is an assistant that uses evaluator-optimizer to improve a report. See our video [here](https://www.youtube.com/watch?v=XGuTzHoqlj8).
 
     [Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is a RAG workflow that grades answers for hallucinations or errors. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI).
 
@@ -1012,7 +1012,7 @@ In the evaluator-optimizer workflow, one LLM call generates a response while ano
 
 ## Agent
 
-Agents are typically implemented as an LLM performing actions (via tool-calling) based on environmental feedback in a loop. As noted in the [Anthropic blog](https://www.anthropic.com/research/building-effective-agents):
+Agents are typically implemented as an LLM performing actions (via tool-calling) based on environmental feedback in a loop. As noted in the Anthropic blog on `Building Effective Agents`:
 
 > Agents can handle sophisticated tasks, but their implementation is often straightforward. They are typically just LLMs using tools based on environmental feedback in a loop. It is therefore crucial to design toolsets and their documentation clearly and thoughtfully.
 

docs/pyproject.toml

@@ -10,6 +10,7 @@ readme = "README.md"
 python = "^3.10"
 aiohappyeyeballs = "2.4.3"
 hub = "^3.0.1"
+xxhash = "^3.5.0"
 
 [tool.poetry.group.docs.dependencies]
 langgraph = { path = "../libs/langgraph/", develop = true }

libs/checkpoint-postgres/README.md

@@ -25,7 +25,7 @@ with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
     # call .setup() the first time you're using the checkpointer
     checkpointer.setup()
     checkpoint = {
-        "v": 1,
+        "v": 2,
         "ts": "2024-07-31T20:14:19.804150+00:00",
         "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
         "channel_values": {
@@ -67,7 +67,7 @@ from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
 
 async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
     checkpoint = {
-        "v": 1,
+        "v": 2,
         "ts": "2024-07-31T20:14:19.804150+00:00",
         "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
         "channel_values": {

libs/checkpoint-postgres/langgraph/store/postgres/aio.py

@@ -78,7 +78,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
             await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)  # don't index
 
             # Search by similarity
-            results = await store.asearch(("docs",), "programming guides", limit=2)
+            results = await store.asearch(("docs",), query="programming guides", limit=2)
         ```
 
         Using connection pooling for better performance:

libs/checkpoint-sqlite/README.md

@@ -12,7 +12,7 @@ read_config = {"configurable": {"thread_id": "1"}}
 
 with SqliteSaver.from_conn_string(":memory:") as checkpointer:
     checkpoint = {
-        "v": 1,
+        "v": 2,
         "ts": "2024-07-31T20:14:19.804150+00:00",
         "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
         "channel_values": {
@@ -54,7 +54,7 @@ from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
 
 async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer:
     checkpoint = {
-        "v": 1,
+        "v": 2,
         "ts": "2024-07-31T20:14:19.804150+00:00",
         "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
         "channel_values": {

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py

@@ -70,15 +70,18 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         >>> from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
         >>> from langgraph.graph import StateGraph
         >>>
-        >>> builder = StateGraph(int)
-        >>> builder.add_node("add_one", lambda x: x + 1)
-        >>> builder.set_entry_point("add_one")
-        >>> builder.set_finish_point("add_one")
-        >>> async with AsyncSqliteSaver.from_conn_string("checkpoints.db") as memory:
-        >>>     graph = builder.compile(checkpointer=memory)
-        >>>     coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
-        >>>     print(asyncio.run(coro))
-        Output: 2
+        >>> async def main():
+        >>>     builder = StateGraph(int)
+        >>>     builder.add_node("add_one", lambda x: x + 1)
+        >>>     builder.set_entry_point("add_one")
+        >>>     builder.set_finish_point("add_one")
+        >>>     async with AsyncSqliteSaver.from_conn_string("checkpoints.db") as memory:
+        >>>         graph = builder.compile(checkpointer=memory)
+        >>>         coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
+        >>>         print(await asyncio.gather(coro))
+        >>>
+        >>> asyncio.run(main())
+        Output: [2]
         ```
         Raw usage:
 
@@ -90,12 +93,12 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         >>> async def main():
         >>>     async with aiosqlite.connect("checkpoints.db") as conn:
         ...         saver = AsyncSqliteSaver(conn)
-        ...         config = {"configurable": {"thread_id": "1"}}
-        ...         checkpoint = {"ts": "2023-05-03T10:00:00Z", "data": {"key": "value"}}
+        ...         config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+        ...         checkpoint = {"ts": "2023-05-03T10:00:00Z", "data": {"key": "value"}, "id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}
         ...         saved_config = await saver.aput(config, checkpoint, {}, {})
         ...         print(saved_config)
         >>> asyncio.run(main())
-        {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}
+        {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '0c62ca34-ac19-445d-bbb0-5b4984975b2a'}}
         ```
     """
 

libs/checkpoint/README.md

@@ -51,7 +51,7 @@ read_config = {"configurable": {"thread_id": "1"}}
 
 checkpointer = MemorySaver()
 checkpoint = {
-    "v": 1,
+    "v": 2,
     "ts": "2024-07-31T20:14:19.804150+00:00",
     "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
     "channel_values": {

libs/checkpoint/langgraph/checkpoint/base/__init__.py

@@ -30,6 +30,7 @@ from langgraph.checkpoint.serde.types import (
 
 V = TypeVar("V", int, float, str)
 PendingWrite = Tuple[str, str, Any]
+LATEST_VERSION = 2
 
 
 # Marked as total=False to allow for future expansion.
@@ -101,7 +102,7 @@ class Checkpoint(TypedDict):
 
 def empty_checkpoint() -> Checkpoint:
     return Checkpoint(
-        v=1,
+        v=LATEST_VERSION,
         id=str(uuid6(clock_seq=-2)),
         ts=datetime.now(timezone.utc).isoformat(),
         channel_values={},
@@ -144,7 +145,7 @@ def create_checkpoint(
             except EmptyChannelError:
                 pass
     return Checkpoint(
-        v=1,
+        v=LATEST_VERSION,
         ts=ts,
         id=id or str(uuid6(clock_seq=step)),
         channel_values=values,

libs/langgraph/bench/__main__.py

@@ -30,6 +30,26 @@ async def arun(graph: Pregel, input: dict):
     )
 
 
+async def arun_first_event_latency(graph: Pregel, input: dict) -> None:
+    """Latency for the first event.
+
+    Run the graph until the first event is processed and then stop.
+    """
+    stream = graph.astream(
+        input,
+        {
+            "configurable": {"thread_id": str(uuid4())},
+            "recursion_limit": 1000000000,
+        },
+    )
+
+    try:
+        async for _ in stream:
+            break
+    finally:
+        await stream.aclose()
+
+
 def run(graph: Pregel, input: dict):
     len(
         [
@@ -45,6 +65,26 @@ def run(graph: Pregel, input: dict):
     )
 
 
+def run_first_event_latency(graph: Pregel, input: dict) -> None:
+    """Latency for the first event.
+
+    Run the graph until the first event is processed and then stop.
+    """
+    stream = graph.stream(
+        input,
+        {
+            "configurable": {"thread_id": str(uuid4())},
+            "recursion_limit": 1000000000,
+        },
+    )
+
+    try:
+        for _ in stream:
+            break
+    finally:
+        stream.close()
+
+
 def compile_graph(graph: StateGraph) -> None:
     """Compile the graph."""
     graph.compile()
@@ -342,6 +382,21 @@ for name, agraph, graph, input in benchmarks:
     if graph is not None:
         r.bench_func(name + "_sync", run, graph, input)
 
+
+# First event latency
+for name, agraph, graph, input in benchmarks:
+    r.bench_async_func(
+        name + "_first_event_latency",
+        arun_first_event_latency,
+        agraph,
+        input,
+        loop_factory=new_event_loop,
+    )
+    if graph is not None:
+        r.bench_func(
+            name + "_first_event_latency_sync", run_first_event_latency, graph, input
+        )
+
 # Graph compilation times
 compilation_benchmarks = (
     (

libs/langgraph/bench/sequential.py

@@ -4,7 +4,7 @@ from langgraph.graph import MessagesState, StateGraph
 from langgraph.utils.runnable import RunnableCallable
 
 
-def create_sequential(number_nodes) -> StateGraph:
+def create_sequential(number_nodes: int) -> StateGraph:
     """Create a sequential no-op graph consisting of a few hundred nodes."""
     builder = StateGraph(MessagesState)
 

libs/langgraph/langgraph/__main__.py

@@ -0,0 +1,30 @@
+try:
+    import sys
+
+    from langgraph_cli.cli import cli
+
+except ImportError:
+    # Provide more detailed error message with installation instructions
+    error_message = (
+        "\nError: langgraph_cli package not found.\n\n"
+        "This could be due to one of the following reasons:\n"
+        "1. You haven't installed the CLI package\n"
+        "2. Your virtual environment doesn't have the package installed\n"
+        "3. There's a path issue with your Python environment\n\n"
+        "To fix this, try one of the following solutions:\n"
+        "- Install the CLI package: pip install langgraph-cli\n"
+        "- If you're using a virtual environment, activate it first\n"
+        "- Use the standalone CLI directly by running: langgraph\n\n"
+        "For more help, visit: https://github.com/langchain-ai/langgraph/tree/main/libs/cli"
+    )
+    raise ImportError(error_message)
+
+try:
+    cli()
+except Exception as e:
+    # Catch any exceptions that might occur when running the CLI
+    print(f"\nError occurred while running langgraph CLI: {str(e)}")
+    print(
+        "If this problem persists, please report it at: https://github.com/langchain-ai/langgraph/issues"
+    )
+    sys.exit(1)

libs/langgraph/langgraph/pregel/algo.py

@@ -23,6 +23,7 @@ from typing import (
 from langchain_core.callbacks import Callbacks
 from langchain_core.callbacks.manager import AsyncParentRunManager, ParentRunManager
 from langchain_core.runnables.config import RunnableConfig
+from xxhash import xxh3_64_hexdigest
 
 from langgraph.channels.base import BaseChannel
 from langgraph.checkpoint.base import (
@@ -506,6 +507,7 @@ def prepare_single_task(
     uniquely identifies a PUSH or PULL task within the graph."""
     configurable = config.get(CONF, {})
     parent_ns = configurable.get(CONFIG_KEY_CHECKPOINT_NS, "")
+    task_id_func = _xxhash_str if checkpoint["v"] > 1 else _uuid5_str
 
     if task_path[0] == PUSH and isinstance(task_path[-1], Call):
         # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
@@ -518,7 +520,7 @@ def prepare_single_task(
         # create task id
         triggers: Sequence[str] = PUSH_TRIGGER
         checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
-        task_id = _uuid5_str(
+        task_id = task_id_func(
             checkpoint_id_bytes,
             checkpoint_ns,
             str(step),
@@ -614,7 +616,7 @@ def prepare_single_task(
             checkpoint_ns = (
                 f"{parent_ns}{NS_SEP}{packet.node}" if parent_ns else packet.node
             )
-            task_id = _uuid5_str(
+            task_id = task_id_func(
                 checkpoint_id_bytes,
                 checkpoint_ns,
                 str(step),
@@ -738,7 +740,7 @@ def prepare_single_task(
 
             # create task id
             checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
-            task_id = _uuid5_str(
+            task_id = task_id_func(
                 checkpoint_id_bytes,
                 checkpoint_ns,
                 str(step),
@@ -948,7 +950,7 @@ def _proc_input(
 
 
 def _uuid5_str(namespace: bytes, *parts: str) -> str:
-    """Generate a UUID from the SHA-1 hash of a namespace UUID and a name."""
+    """Generate a UUID from the SHA-1 hash of a namespace and str parts."""
 
     sha = sha1(namespace, usedforsecurity=False)
     sha.update(b"".join(p.encode() for p in parts))
@@ -956,6 +958,12 @@ def _uuid5_str(namespace: bytes, *parts: str) -> str:
     return f"{hex[:8]}-{hex[8:12]}-{hex[12:16]}-{hex[16:20]}-{hex[20:32]}"
 
 
+def _xxhash_str(namespace: bytes, *parts: str) -> str:
+    """Generate a UUID from the XXH3 hash of a namespace and str parts."""
+    hex = xxh3_64_hexdigest(namespace + b"".join(p.encode() for p in parts))
+    return f"{hex[:8]}-{hex[8:12]}-{hex[12:16]}-{hex[16:20]}-{hex[20:32]}"
+
+
 def task_path_str(tup: Union[str, int, tuple]) -> str:
     """Generate a string representation of the task path."""
     return (

libs/langgraph/poetry.lock

@@ -3527,6 +3527,139 @@ files = [
     {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
 ]
 
+[[package]]
+name = "xxhash"
+version = "3.5.0"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
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+]
+
 [[package]]
 name = "zipp"
 version = "3.20.2"
@@ -3551,4 +3684,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9.0,<4.0"
-content-hash = "b8641a0b2d92bee0363602e69f99b23366b2035b7e17ff017708194e6fbd0ac5"
+content-hash = "b03760d1062e13e4df0b4052a194bedb8abb3baf80da0c036b39d2ebe26b0b5c"

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.3.18"
+version = "0.3.19"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"
@@ -13,6 +13,7 @@ langchain-core = ">=0.1,<0.4"
 langgraph-checkpoint = "^2.0.10"
 langgraph-sdk = "^0.1.42"
 langgraph-prebuilt = ">=0.1.1,<0.2"
+xxhash = "^3.5.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.3.2"

libs/langgraph/tests/test_pregel.py

@@ -1236,7 +1236,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 1,
+            "v": 2,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],
@@ -1296,7 +1296,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 1,
+            "v": 2,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],
@@ -1349,7 +1349,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 1,
+            "v": 2,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],

libs/langgraph/tests/test_pregel_async.py

@@ -2070,7 +2070,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 1,
+                "v": 2,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],
@@ -2132,7 +2132,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 1,
+                "v": 2,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],
@@ -2187,7 +2187,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 1,
+                "v": 2,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],

libs/prebuilt/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
 
 [[package]]
 name = "aiosqlite"
@@ -448,6 +448,7 @@ langchain-core = ">=0.1,<0.4"
 langgraph-checkpoint = "^2.0.10"
 langgraph-prebuilt = ">=0.1.1,<0.2"
 langgraph-sdk = "^0.1.42"
+xxhash = "^3.5.0"
 
 [package.source]
 type = "directory"
@@ -1358,6 +1359,139 @@ files = [
 [package.extras]
 watchmedo = ["PyYAML (>=3.10)"]
 
+[[package]]
+name = "xxhash"
+version = "3.5.0"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+groups = ["dev"]
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libs/scheduler-kafka/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
 
 [[package]]
 name = "aiokafka"
@@ -6,6 +6,7 @@ version = "0.11.0"
 description = "Kafka integration with asyncio"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
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@@ -58,6 +59,7 @@ version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
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 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
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@@ -91,6 +94,7 @@ version = "4.0.3"
 description = "Timeout context manager for asyncio programs"
 optional = false
 python-versions = ">=3.7"
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 files = [
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@@ -102,17 +106,100 @@ version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
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 files = [
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+[package.dependencies]
+pycparser = "*"
+
 [[package]]
 name = "charset-normalizer"
 version = "3.3.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
+groups = ["main", "dev"]
 files = [
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@@ -212,6 +299,7 @@ version = "2.3.0"
 description = "Codespell"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
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@@ -229,6 +317,8 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["dev"]
+markers = "sys_platform == \"win32\""
 files = [
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@@ -240,6 +330,7 @@ version = "2.7.post1"
 description = "A python package implementing the crc32c algorithm in hardware and software"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
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@@ -356,6 +447,8 @@ version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version < \"3.11\""
 files = [
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@@ -370,6 +463,7 @@ version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
@@ -381,6 +475,7 @@ version = "1.0.5"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
     {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
@@ -402,6 +497,7 @@ version = "0.27.2"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0"},
     {file = "httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2"},
@@ -427,6 +523,7 @@ version = "3.8"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"},
     {file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"},
@@ -438,6 +535,7 @@ version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -449,6 +547,7 @@ version = "1.33"
 description = "Apply JSON-Patches (RFC 6902)"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+groups = ["main", "dev"]
 files = [
     {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
     {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
@@ -463,6 +562,7 @@ version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
@@ -474,6 +574,7 @@ version = "2.2.2"
 description = "Pure Python client for Apache Kafka"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "kafka-python-ng-2.2.2.tar.gz", hash = "sha256:87ad3a766e2c0bec71d9b99bdd9e9c5cda62d96cfda61a8ca16510484d6ad7d4"},
     {file = "kafka_python_ng-2.2.2-py2.py3-none-any.whl", hash = "sha256:3fab1a03133fade1b6fd5367ff726d980e59031c4aaca9bf02c516840a4f8406"},
@@ -488,39 +589,44 @@ zstd = ["zstandard"]
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0"
+version = "0.3.47"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
+groups = ["main", "dev"]
 files = [
-    {file = "langchain_core-0.3.0-py3-none-any.whl", hash = "sha256:bee6dae2366d037ef0c5b87401fed14b5497cad26f97724e8c9ca7bc9239e847"},
-    {file = "langchain_core-0.3.0.tar.gz", hash = "sha256:1249149ea3ba24c9c761011483c14091573a5eb1a773aa0db9c8ad155dd4a69d"},
+    {file = "langchain_core-0.3.47-py3-none-any.whl", hash = "sha256:ef7c78202e6fd9df79fe8128c828d08a95d3878cf4aec282580baf358a3f2ec5"},
+    {file = "langchain_core-0.3.47.tar.gz", hash = "sha256:975a3daabf8e8d583749b3c553cc412d0527d935dfe820317431b475fa0c585a"},
 ]
 
 [package.dependencies]
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.1.117,<0.2.0"
+langsmith = ">=0.1.125,<0.4"
 packaging = ">=23.2,<25"
 pydantic = [
     {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 PyYAML = ">=5.3"
-tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10.0.0"
 typing-extensions = ">=4.7"
 
 [[package]]
 name = "langgraph"
-version = "0.2.20"
+version = "0.3.18"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = ">=3.9.0,<4.0"
+groups = ["main", "dev"]
 files = []
 develop = true
 
 [package.dependencies]
-langchain-core = ">=0.2.39,<0.4"
-langgraph-checkpoint = "^1.0.2"
+langchain-core = ">=0.1,<0.4"
+langgraph-checkpoint = "^2.0.10"
+langgraph-prebuilt = ">=0.1.1,<0.2"
+langgraph-sdk = "^0.1.42"
+xxhash = "^3.5.0"
 
 [package.source]
 type = "directory"
@@ -528,10 +634,11 @@ url = "../langgraph"
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "1.0.9"
+version = "2.0.21"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
+groups = ["main", "dev"]
 files = []
 develop = true
 
@@ -545,42 +652,85 @@ url = "../checkpoint"
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "1.0.6"
+version = "2.0.19"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
+groups = ["dev"]
 files = []
 develop = true
 
 [package.dependencies]
-langgraph-checkpoint = "^1.0.8"
+langgraph-checkpoint = "^2.0.21"
 orjson = ">=3.10.1"
-psycopg = "^3.0.0"
-psycopg-pool = "^3.0.0"
+psycopg = "^3.2.0"
+psycopg-pool = "^3.2.0"
 
 [package.source]
 type = "directory"
 url = "../checkpoint-postgres"
 
+[[package]]
+name = "langgraph-prebuilt"
+version = "0.1.3"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+groups = ["main", "dev"]
+files = [
+    {file = "langgraph_prebuilt-0.1.3-py3-none-any.whl", hash = "sha256:4bb8a6b9c9c7f8eee9c6b151e8b379fad02f60679fed9554cfeb4382c4b9f858"},
+    {file = "langgraph_prebuilt-0.1.3.tar.gz", hash = "sha256:9a9be529cd4419b9ec16b4bc80f6b260d5a680b0705f1a58eb09e78962dc2d71"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.43,<0.3.0 || >0.3.0,<0.3.1 || >0.3.1,<0.3.2 || >0.3.2,<0.3.3 || >0.3.3,<0.3.4 || >0.3.4,<0.3.5 || >0.3.5,<0.3.6 || >0.3.6,<0.3.7 || >0.3.7,<0.3.8 || >0.3.8,<0.3.9 || >0.3.9,<0.3.10 || >0.3.10,<0.3.11 || >0.3.11,<0.3.12 || >0.3.12,<0.3.13 || >0.3.13,<0.3.14 || >0.3.14,<0.3.15 || >0.3.15,<0.3.16 || >0.3.16,<0.3.17 || >0.3.17,<0.3.18 || >0.3.18,<0.3.19 || >0.3.19,<0.3.20 || >0.3.20,<0.3.21 || >0.3.21,<0.3.22 || >0.3.22,<0.4.0"
+langgraph-checkpoint = ">=2.0.10,<3.0.0"
+
+[[package]]
+name = "langgraph-sdk"
+version = "0.1.58"
+description = "SDK for interacting with LangGraph API"
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+groups = ["main", "dev"]
+files = [
+    {file = "langgraph_sdk-0.1.58-py3-none-any.whl", hash = "sha256:65f88cf5582da0c316714dc475126fa03c5f74d72bc0b9221dd42649de8e23d4"},
+    {file = "langgraph_sdk-0.1.58.tar.gz", hash = "sha256:ef8b0e4c08af8c7efd3919497879c87a3627806b51e4ba5e8b06e0717e3d44cd"},
+]
+
+[package.dependencies]
+httpx = ">=0.25.2"
+orjson = ">=3.10.1"
+
 [[package]]
 name = "langsmith"
-version = "0.1.120"
+version = "0.3.18"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
-python-versions = "<4.0,>=3.8.1"
+python-versions = "<4.0,>=3.9"
+groups = ["main", "dev"]
 files = [
-    {file = "langsmith-0.1.120-py3-none-any.whl", hash = "sha256:54d2785e301646c0988e0a69ebe4d976488c87b41928b358cb153b6ddd8db62b"},
-    {file = "langsmith-0.1.120.tar.gz", hash = "sha256:25499ca187b41bd89d784b272b97a8d76f60e0e21bdf20336e8a2aa6a9b23ac9"},
+    {file = "langsmith-0.3.18-py3-none-any.whl", hash = "sha256:7ad65ec26084312a039885ef625ae72a69ad089818b64bacf7ce6daff672353a"},
+    {file = "langsmith-0.3.18.tar.gz", hash = "sha256:18ff2d8f2e77b375485e4fb3d0dbf7b30fabbd438c7347c3534470e9b7d187b8"},
 ]
 
 [package.dependencies]
 httpx = ">=0.23.0,<1"
-orjson = ">=3.9.14,<4.0.0"
+orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
+packaging = ">=23.2"
 pydantic = [
     {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 requests = ">=2,<3"
+requests-toolbelt = ">=1.0.0,<2.0.0"
+zstandard = ">=0.23.0,<0.24.0"
+
+[package.extras]
+langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
+openai-agents = ["openai-agents (>=0.0.3,<0.0.4)"]
+otel = ["opentelemetry-api (>=1.30.0,<2.0.0)", "opentelemetry-exporter-otlp-proto-http (>=1.30.0,<2.0.0)", "opentelemetry-sdk (>=1.30.0,<2.0.0)"]
+pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
 
 [[package]]
 name = "msgpack"
@@ -588,6 +738,7 @@ version = "1.1.0"
 description = "MessagePack serializer"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7ad442d527a7e358a469faf43fda45aaf4ac3249c8310a82f0ccff9164e5dccd"},
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74bed8f63f8f14d75eec75cf3d04ad581da6b914001b474a5d3cd3372c8cc27d"},
@@ -661,6 +812,7 @@ version = "1.11.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "mypy-1.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a3824187c99b893f90c845bab405a585d1ced4ff55421fdf5c84cb7710995229"},
     {file = "mypy-1.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:96f8dbc2c85046c81bcddc246232d500ad729cb720da4e20fce3b542cab91287"},
@@ -708,6 +860,7 @@ version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
 files = [
     {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
@@ -719,6 +872,7 @@ version = "3.10.7"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "orjson-3.10.7-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:74f4544f5a6405b90da8ea724d15ac9c36da4d72a738c64685003337401f5c12"},
     {file = "orjson-3.10.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:34a566f22c28222b08875b18b0dfbf8a947e69df21a9ed5c51a6bf91cfb944ac"},
@@ -785,6 +939,7 @@ version = "24.1"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"},
     {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
@@ -796,6 +951,7 @@ version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
     {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
@@ -811,6 +967,7 @@ version = "3.2.1"
 description = "PostgreSQL database adapter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "psycopg-3.2.1-py3-none-any.whl", hash = "sha256:ece385fb413a37db332f97c49208b36cf030ff02b199d7635ed2fbd378724175"},
     {file = "psycopg-3.2.1.tar.gz", hash = "sha256:dc8da6dc8729dacacda3cc2f17d2c9397a70a66cf0d2b69c91065d60d5f00cb7"},
@@ -834,6 +991,7 @@ version = "3.2.2"
 description = "Connection Pool for Psycopg"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "psycopg_pool-3.2.2-py3-none-any.whl", hash = "sha256:273081d0fbfaced4f35e69200c89cb8fbddfe277c38cc86c235b90a2ec2c8153"},
     {file = "psycopg_pool-3.2.2.tar.gz", hash = "sha256:9e22c370045f6d7f2666a5ad1b0caf345f9f1912195b0b25d0d3bcc4f3a7389c"},
@@ -842,12 +1000,26 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.4"
 
+[[package]]
+name = "pycparser"
+version = "2.22"
+description = "C parser in Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "platform_python_implementation == \"PyPy\""
+files = [
+    {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"},
+    {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
+]
+
 [[package]]
 name = "pydantic"
 version = "2.9.0"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "pydantic-2.9.0-py3-none-any.whl", hash = "sha256:f66a7073abd93214a20c5f7b32d56843137a7a2e70d02111f3be287035c45370"},
     {file = "pydantic-2.9.0.tar.gz", hash = "sha256:c7a8a9fdf7d100afa49647eae340e2d23efa382466a8d177efcd1381e9be5598"},
@@ -871,6 +1043,7 @@ version = "2.23.2"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "pydantic_core-2.23.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:7d0324a35ab436c9d768753cbc3c47a865a2cbc0757066cb864747baa61f6ece"},
     {file = "pydantic_core-2.23.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:276ae78153a94b664e700ac362587c73b84399bd1145e135287513442e7dfbc7"},
@@ -972,6 +1145,7 @@ version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
     {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
@@ -994,6 +1168,7 @@ version = "3.14.0"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pytest-mock-3.14.0.tar.gz", hash = "sha256:2719255a1efeceadbc056d6bf3df3d1c5015530fb40cf347c0f9afac88410bd0"},
     {file = "pytest_mock-3.14.0-py3-none-any.whl", hash = "sha256:0b72c38033392a5f4621342fe11e9219ac11ec9d375f8e2a0c164539e0d70f6f"},
@@ -1011,6 +1186,7 @@ version = "0.4.2"
 description = "Automatically rerun your tests on file modifications"
 optional = false
 python-versions = "<4.0.0,>=3.7.0"
+groups = ["dev"]
 files = [
     {file = "pytest_watcher-0.4.2-py3-none-any.whl", hash = "sha256:a43949ba67dd8d7e1fd0de5eea44a999081f0aec9f93b4e744264b4c6a3d9bbe"},
     {file = "pytest_watcher-0.4.2.tar.gz", hash = "sha256:7b292f025ca19617cd7567c228c6187b5087f2da9e4d2cf6e144e5764a0471b0"},
@@ -1026,6 +1202,7 @@ version = "6.0.2"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"},
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf"},
@@ -1088,6 +1265,7 @@ version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
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 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "requests-toolbelt"
+version = "1.0.0"
+description = "A utility belt for advanced users of python-requests"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main", "dev"]
+files = [
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+]
+
+[package.dependencies]
+requests = ">=2.0.1,<3.0.0"
+
 [[package]]
 name = "ruff"
 version = "0.6.2"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
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@@ -1136,6 +1330,7 @@ version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
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@@ -1147,6 +1342,7 @@ version = "8.5.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
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@@ -1162,6 +1358,8 @@ version = "2.0.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version < \"3.11\""
 files = [
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@@ -1173,6 +1371,7 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
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@@ -1184,6 +1383,7 @@ version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
+groups = ["main", "dev"]
 files = [
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@@ -1195,6 +1395,7 @@ version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
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@@ -1212,6 +1413,7 @@ version = "4.0.1"
 description = "Filesystem events monitoring"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
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@@ -1250,7 +1452,253 @@ files = [
 [package.extras]
 watchmedo = ["PyYAML (>=3.10)"]
 
+[[package]]
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+version = "3.5.0"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+groups = ["main", "dev"]
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+]
+
+[package.dependencies]
+cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
+
+[package.extras]
+cffi = ["cffi (>=1.11)"]
+
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = "^3.9.0,<4.0"
-content-hash = "4fd0a2d16956a5e92ef42cbd23a1649cc5cefcc2da1d58d0065fa355668dfaa9"
+content-hash = "1e37504d248cca9356d305105acbcf7acfac714f33d034e7b635bb388dc5e0d3"

libs/scheduler-kafka/pyproject.toml

@@ -13,7 +13,7 @@ python = "^3.9.0,<4.0"
 orjson = "^3.10.7"
 crc32c = "^2.7.post1"
 aiokafka = "^0.11.0"
-langgraph = "^0.2.19"
+langgraph = ">=0.2.19,<0.4"
 
 [tool.poetry.group.dev.dependencies]
 ruff = "^0.6.2"

libs/sdk-py/langgraph_sdk/client.py

@@ -208,9 +208,15 @@ class HttpClient:
     def __init__(self, client: httpx.AsyncClient) -> None:
         self.client = client
 
-    async def get(self, path: str, *, params: Optional[QueryParamTypes] = None) -> Any:
+    async def get(
+        self,
+        path: str,
+        *,
+        params: Optional[QueryParamTypes] = None,
+        headers: Optional[dict[str, str]] = None,
+    ) -> Any:
         """Send a GET request."""
-        r = await self.client.get(path, params=params)
+        r = await self.client.get(path, params=params, headers=headers)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -222,13 +228,22 @@ class HttpClient:
             raise e
         return await adecode_json(r)
 
-    async def post(self, path: str, *, json: Optional[dict]) -> Any:
+    async def post(
+        self,
+        path: str,
+        *,
+        json: Optional[dict],
+        headers: Optional[dict[str, str]] = None,
+    ) -> Any:
         """Send a POST request."""
         if json is not None:
-            headers, content = await aencode_json(json)
+            request_headers, content = await aencode_json(json)
         else:
-            headers, content = {}, b""
-        r = await self.client.post(path, headers=headers, content=content)
+            request_headers, content = {}, b""
+        # Merge headers, with runtime headers taking precedence
+        if headers:
+            request_headers.update(headers)
+        r = await self.client.post(path, headers=request_headers, content=content)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -240,10 +255,14 @@ class HttpClient:
             raise e
         return await adecode_json(r)
 
-    async def put(self, path: str, *, json: dict) -> Any:
+    async def put(
+        self, path: str, *, json: dict, headers: Optional[dict[str, str]] = None
+    ) -> Any:
         """Send a PUT request."""
-        headers, content = await aencode_json(json)
-        r = await self.client.put(path, headers=headers, content=content)
+        request_headers, content = await aencode_json(json)
+        if headers:
+            request_headers.update(headers)
+        r = await self.client.put(path, headers=request_headers, content=content)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -255,10 +274,14 @@ class HttpClient:
             raise e
         return await adecode_json(r)
 
-    async def patch(self, path: str, *, json: dict) -> Any:
+    async def patch(
+        self, path: str, *, json: dict, headers: Optional[dict[str, str]] = None
+    ) -> Any:
         """Send a PATCH request."""
-        headers, content = await aencode_json(json)
-        r = await self.client.patch(path, headers=headers, content=content)
+        request_headers, content = await aencode_json(json)
+        if headers:
+            request_headers.update(headers)
+        r = await self.client.patch(path, headers=request_headers, content=content)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -270,9 +293,15 @@ class HttpClient:
             raise e
         return await adecode_json(r)
 
-    async def delete(self, path: str, *, json: Optional[Any] = None) -> None:
+    async def delete(
+        self,
+        path: str,
+        *,
+        json: Optional[Any] = None,
+        headers: Optional[dict[str, str]] = None,
+    ) -> None:
         """Send a DELETE request."""
-        r = await self.client.request("DELETE", path, json=json)
+        r = await self.client.request("DELETE", path, json=json, headers=headers)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -290,14 +319,18 @@ class HttpClient:
         *,
         json: Optional[dict] = None,
         params: Optional[QueryParamTypes] = None,
+        headers: Optional[dict[str, str]] = None,
     ) -> AsyncIterator[StreamPart]:
         """Stream results using SSE."""
-        headers, content = await aencode_json(json)
-        headers["Accept"] = "text/event-stream"
-        headers["Cache-Control"] = "no-store"
+        request_headers, content = await aencode_json(json)
+        request_headers["Accept"] = "text/event-stream"
+        request_headers["Cache-Control"] = "no-store"
+        # Add runtime headers with precedence
+        if headers:
+            request_headers.update(headers)
 
         async with self.client.stream(
-            method, path, headers=headers, content=content, params=params
+            method, path, headers=request_headers, content=content, params=params
         ) as res:
             # check status
             try:
@@ -2447,9 +2480,15 @@ class SyncHttpClient:
     def __init__(self, client: httpx.Client) -> None:
         self.client = client
 
-    def get(self, path: str, *, params: Optional[QueryParamTypes] = None) -> Any:
+    def get(
+        self,
+        path: str,
+        *,
+        params: Optional[QueryParamTypes] = None,
+        headers: Optional[dict[str, str]] = None,
+    ) -> Any:
         """Send a GET request."""
-        r = self.client.get(path, params=params)
+        r = self.client.get(path, params=params, headers=headers)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2461,13 +2500,21 @@ class SyncHttpClient:
             raise e
         return decode_json(r)
 
-    def post(self, path: str, *, json: Optional[dict]) -> Any:
+    def post(
+        self,
+        path: str,
+        *,
+        json: Optional[dict],
+        headers: Optional[dict[str, str]] = None,
+    ) -> Any:
         """Send a POST request."""
         if json is not None:
-            headers, content = encode_json(json)
+            request_headers, content = encode_json(json)
         else:
-            headers, content = {}, b""
-        r = self.client.post(path, headers=headers, content=content)
+            request_headers, content = {}, b""
+        if headers:
+            request_headers.update(headers)
+        r = self.client.post(path, headers=request_headers, content=content)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2494,10 +2541,14 @@ class SyncHttpClient:
             raise e
         return decode_json(r)
 
-    def patch(self, path: str, *, json: dict) -> Any:
+    def patch(
+        self, path: str, *, json: dict, headers: Optional[dict[str, str]] = None
+    ) -> Any:
         """Send a PATCH request."""
-        headers, content = encode_json(json)
-        r = self.client.patch(path, headers=headers, content=content)
+        request_headers, content = encode_json(json)
+        if headers:
+            request_headers.update(headers)
+        r = self.client.patch(path, headers=request_headers, content=content)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2509,9 +2560,15 @@ class SyncHttpClient:
             raise e
         return decode_json(r)
 
-    def delete(self, path: str, *, json: Optional[Any] = None) -> None:
+    def delete(
+        self,
+        path: str,
+        *,
+        json: Optional[Any] = None,
+        headers: Optional[dict[str, str]] = None,
+    ) -> None:
         """Send a DELETE request."""
-        r = self.client.request("DELETE", path, json=json)
+        r = self.client.request("DELETE", path, json=json, headers=headers)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2529,11 +2586,16 @@ class SyncHttpClient:
         *,
         json: Optional[dict] = None,
         params: Optional[QueryParamTypes] = None,
+        headers: Optional[dict[str, str]] = None,
     ) -> Iterator[StreamPart]:
         """Stream the results of a request using SSE."""
-        headers, content = encode_json(json)
+        request_headers, content = encode_json(json)
+        request_headers["Accept"] = "text/event-stream"
+        request_headers["Cache-Control"] = "no-store"
+        if headers:
+            request_headers.update(headers)
         with self.client.stream(
-            method, path, headers=headers, content=content, params=params
+            method, path, headers=request_headers, content=content, params=params
         ) as res:
             # check status
             try: