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This PR was opened by the [Changesets release](https://github.com/changesets/action) GitHub action. When you're ready to do a release, you can merge this and the packages will be published to npm automatically. If you're not ready to do a release yet, that's fine, whenever you add more changesets to main, this PR will be updated. # Releases ## @langchain/langgraph-checkpoint-mongodb@1.4.0 ### Minor Changes - [#1928](https://github.com/langchain-ai/langgraphjs/pull/1928) [`3d7fcea`](https://github.com/langchain-ai/langgraphjs/commit/3d7fcea7d7ea7f1203d24be9df607d5a8e8717bc) Thanks [@Mihailoff](https://github.com/Mihailoff)! - Add TTL support for automatic checkpoint expiration - Add optional `ttl` parameter to MongoDBSaver (value in seconds) - Add `setup()` method to create TTL indexes on collections - Add `upserted_at` timestamp to documents when TTL is enabled - Each write refreshes TTL (expires after inactivity, not creation) ### Patch Changes - [#2556](https://github.com/langchain-ai/langgraphjs/pull/2556) [`bee3c91`](https://github.com/langchain-ai/langgraphjs/commit/bee3c91d0adc315ebde0622d8c4b1fff041c1bfd) Thanks [@mohamedkhaled4053](https://github.com/mohamedkhaled4053)! - Fix `MongoDBSaver.putWrites` throwing `MongoServerError: Invalid BulkOperation, Batch cannot be empty` when called with an empty `writes` array. This is reached by human-in-the-loop / `interrupt()` flows, where a task can complete producing zero channel writes and LangGraph calls `putWrites(config, [], taskId)`. `putWrites` now no-ops on empty writes, matching the behavior of the postgres and sqlite savers (which iterate and naturally skip empty batches). - [#2550](https://github.com/langchain-ai/langgraphjs/pull/2550) [`2b8cc2f`](https://github.com/langchain-ai/langgraphjs/commit/2b8cc2f3fd5c9d3c33b56e013292daf5d936428e) Thanks [@lazydiv](https://github.com/lazydiv)! - feat(checkpoint-mongodb): add setup() to create required indexes ## @langchain/langgraph-api@1.4.0 ### Minor Changes - [#2559](https://github.com/langchain-ai/langgraphjs/pull/2559) [`48cbdd2`](https://github.com/langchain-ai/langgraphjs/commit/48cbdd23fdf29277530f6aa05c397c9902e81206) Thanks [@christian-bromann](https://github.com/christian-bromann)! - feat(langgraph-cli): add `deploy` command for LangSmith Deployment Port the Python CLI's `langgraph deploy` workflow to `@langchain/langgraph-cli`, including local and remote build paths, deployment lifecycle subcommands (`list`, `revisions list`, `delete`, `logs`), and host-backend client utilities with tests. ### Patch Changes - [#2557](https://github.com/langchain-ai/langgraphjs/pull/2557) [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e) Thanks [@christian-bromann](https://github.com/christian-bromann)! - fix(langgraph-api): preserve non-empty response_metadata on protocol-v2 state messages The protocol-v2 state normalizer stripped `response_metadata` from messages, dropping data that HITL flows rely on — an interrupt's card is carried on `AIMessage.response_metadata` (e.g. `{ cards: ... }`). Non-empty `response_metadata` is now retained so the card reaches the client. - [#2557](https://github.com/langchain-ai/langgraphjs/pull/2557) [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e) Thanks [@christian-bromann](https://github.com/christian-bromann)! - fix(sdk): apply state update and goto alongside interrupt resume `respond(decision, { update, goto })` now maps to LangGraph's `Command(resume, update, goto)`, so a human-in-the-loop UI can commit a state update (e.g. push the interrupt card into state) in the **same superstep** as the resume — one checkpoint, no separate `updateState` write, no flicker. `@langchain/langgraph-api` forwards `update`/`goto` through `input.respond`, and `@langchain/core` message instances in `update` are serialized to dicts before transport, exactly like `submit()`. Bumps `@langchain/protocol` to `^0.0.18` for the `Goto` type. `respond`/`respondAll` also apply `update` **optimistically** (mirroring `submit()`): the pushed messages paint immediately, with stable ids minted so the resumed run's echo reconciles them in place. Without this the interrupt is cleared the instant `respond()` dispatches while the pushed card only reappears a server round-trip later — so the card would flicker in that gap. The optimistic state settles on the resumed run's terminal (pending → sent, or rolled back on a failure before any echo). User-initiated optimistic writes (`submit()` / `respond()` / `respondAll()`) now commit to the store **synchronously**, in the same tick as the triggering event, instead of being coalesced onto the next macrotask. This lets a framework render the pushed message in the **same commit** as any local UI state the caller flips alongside it (e.g. a HITL form swapping its inputs for the resolved card), so the card no longer blinks out for the one-macrotask window before the flush lands. High-frequency streaming writes keep their macrotask coalescing. - Updated dependencies \[[`48cbdd2`](https://github.com/langchain-ai/langgraphjs/commit/48cbdd23fdf29277530f6aa05c397c9902e81206)]: - @langchain/langgraph-ui@1.4.0 ## @langchain/langgraph-cli@1.4.0 ### Minor Changes - [#2559](https://github.com/langchain-ai/langgraphjs/pull/2559) [`48cbdd2`](https://github.com/langchain-ai/langgraphjs/commit/48cbdd23fdf29277530f6aa05c397c9902e81206) Thanks [@christian-bromann](https://github.com/christian-bromann)! - feat(langgraph-cli): add `deploy` command for LangSmith Deployment Port the Python CLI's `langgraph deploy` workflow to `@langchain/langgraph-cli`, including local and remote build paths, deployment lifecycle subcommands (`list`, `revisions list`, `delete`, `logs`), and host-backend client utilities with tests. ### Patch Changes - Updated dependencies \[[`48cbdd2`](https://github.com/langchain-ai/langgraphjs/commit/48cbdd23fdf29277530f6aa05c397c9902e81206), [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e), [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph-api@1.4.0 ## @langchain/langgraph-ui@1.4.0 ### Minor Changes - [#2559](https://github.com/langchain-ai/langgraphjs/pull/2559) [`48cbdd2`](https://github.com/langchain-ai/langgraphjs/commit/48cbdd23fdf29277530f6aa05c397c9902e81206) Thanks [@christian-bromann](https://github.com/christian-bromann)! - feat(langgraph-cli): add `deploy` command for LangSmith Deployment Port the Python CLI's `langgraph deploy` workflow to `@langchain/langgraph-cli`, including local and remote build paths, deployment lifecycle subcommands (`list`, `revisions list`, `delete`, `logs`), and host-backend client utilities with tests. ## @langchain/langgraph@1.4.5 ### Patch Changes - [#2557](https://github.com/langchain-ai/langgraphjs/pull/2557) [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e) Thanks [@christian-bromann](https://github.com/christian-bromann)! - fix(sdk): apply state update and goto alongside interrupt resume `respond(decision, { update, goto })` now maps to LangGraph's `Command(resume, update, goto)`, so a human-in-the-loop UI can commit a state update (e.g. push the interrupt card into state) in the **same superstep** as the resume — one checkpoint, no separate `updateState` write, no flicker. `@langchain/langgraph-api` forwards `update`/`goto` through `input.respond`, and `@langchain/core` message instances in `update` are serialized to dicts before transport, exactly like `submit()`. Bumps `@langchain/protocol` to `^0.0.18` for the `Goto` type. `respond`/`respondAll` also apply `update` **optimistically** (mirroring `submit()`): the pushed messages paint immediately, with stable ids minted so the resumed run's echo reconciles them in place. Without this the interrupt is cleared the instant `respond()` dispatches while the pushed card only reappears a server round-trip later — so the card would flicker in that gap. The optimistic state settles on the resumed run's terminal (pending → sent, or rolled back on a failure before any echo). User-initiated optimistic writes (`submit()` / `respond()` / `respondAll()`) now commit to the store **synchronously**, in the same tick as the triggering event, instead of being coalesced onto the next macrotask. This lets a framework render the pushed message in the **same commit** as any local UI state the caller flips alongside it (e.g. a HITL form swapping its inputs for the resolved card), so the card no longer blinks out for the one-macrotask window before the flush lands. High-frequency streaming writes keep their macrotask coalescing. - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph-sdk@1.9.24 ## @langchain/langgraph-sdk@1.9.24 ### Patch Changes - [#2557](https://github.com/langchain-ai/langgraphjs/pull/2557) [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e) Thanks [@christian-bromann](https://github.com/christian-bromann)! - fix(sdk): apply state update and goto alongside interrupt resume `respond(decision, { update, goto })` now maps to LangGraph's `Command(resume, update, goto)`, so a human-in-the-loop UI can commit a state update (e.g. push the interrupt card into state) in the **same superstep** as the resume — one checkpoint, no separate `updateState` write, no flicker. `@langchain/langgraph-api` forwards `update`/`goto` through `input.respond`, and `@langchain/core` message instances in `update` are serialized to dicts before transport, exactly like `submit()`. Bumps `@langchain/protocol` to `^0.0.18` for the `Goto` type. `respond`/`respondAll` also apply `update` **optimistically** (mirroring `submit()`): the pushed messages paint immediately, with stable ids minted so the resumed run's echo reconciles them in place. Without this the interrupt is cleared the instant `respond()` dispatches while the pushed card only reappears a server round-trip later — so the card would flicker in that gap. The optimistic state settles on the resumed run's terminal (pending → sent, or rolled back on a failure before any echo). User-initiated optimistic writes (`submit()` / `respond()` / `respondAll()`) now commit to the store **synchronously**, in the same tick as the triggering event, instead of being coalesced onto the next macrotask. This lets a framework render the pushed message in the **same commit** as any local UI state the caller flips alongside it (e.g. a HITL form swapping its inputs for the resolved card), so the card no longer blinks out for the one-macrotask window before the flush lands. High-frequency streaming writes keep their macrotask coalescing. ## @langchain/angular@1.0.25 ### Patch Changes - [#2557](https://github.com/langchain-ai/langgraphjs/pull/2557) [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e) Thanks [@christian-bromann](https://github.com/christian-bromann)! - fix(sdk): apply state update and goto alongside interrupt resume `respond(decision, { update, goto })` now maps to LangGraph's `Command(resume, update, goto)`, so a human-in-the-loop UI can commit a state update (e.g. push the interrupt card into state) in the **same superstep** as the resume — one checkpoint, no separate `updateState` write, no flicker. `@langchain/langgraph-api` forwards `update`/`goto` through `input.respond`, and `@langchain/core` message instances in `update` are serialized to dicts before transport, exactly like `submit()`. Bumps `@langchain/protocol` to `^0.0.18` for the `Goto` type. `respond`/`respondAll` also apply `update` **optimistically** (mirroring `submit()`): the pushed messages paint immediately, with stable ids minted so the resumed run's echo reconciles them in place. Without this the interrupt is cleared the instant `respond()` dispatches while the pushed card only reappears a server round-trip later — so the card would flicker in that gap. The optimistic state settles on the resumed run's terminal (pending → sent, or rolled back on a failure before any echo). User-initiated optimistic writes (`submit()` / `respond()` / `respondAll()`) now commit to the store **synchronously**, in the same tick as the triggering event, instead of being coalesced onto the next macrotask. This lets a framework render the pushed message in the **same commit** as any local UI state the caller flips alongside it (e.g. a HITL form swapping its inputs for the resolved card), so the card no longer blinks out for the one-macrotask window before the flush lands. High-frequency streaming writes keep their macrotask coalescing. - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph-sdk@1.9.24 ## @langchain/react@1.0.25 ### Patch Changes - [#2557](https://github.com/langchain-ai/langgraphjs/pull/2557) [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e) Thanks [@christian-bromann](https://github.com/christian-bromann)! - fix(sdk): apply state update and goto alongside interrupt resume `respond(decision, { update, goto })` now maps to LangGraph's `Command(resume, update, goto)`, so a human-in-the-loop UI can commit a state update (e.g. push the interrupt card into state) in the **same superstep** as the resume — one checkpoint, no separate `updateState` write, no flicker. `@langchain/langgraph-api` forwards `update`/`goto` through `input.respond`, and `@langchain/core` message instances in `update` are serialized to dicts before transport, exactly like `submit()`. Bumps `@langchain/protocol` to `^0.0.18` for the `Goto` type. `respond`/`respondAll` also apply `update` **optimistically** (mirroring `submit()`): the pushed messages paint immediately, with stable ids minted so the resumed run's echo reconciles them in place. Without this the interrupt is cleared the instant `respond()` dispatches while the pushed card only reappears a server round-trip later — so the card would flicker in that gap. The optimistic state settles on the resumed run's terminal (pending → sent, or rolled back on a failure before any echo). User-initiated optimistic writes (`submit()` / `respond()` / `respondAll()`) now commit to the store **synchronously**, in the same tick as the triggering event, instead of being coalesced onto the next macrotask. This lets a framework render the pushed message in the **same commit** as any local UI state the caller flips alongside it (e.g. a HITL form swapping its inputs for the resolved card), so the card no longer blinks out for the one-macrotask window before the flush lands. High-frequency streaming writes keep their macrotask coalescing. - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph-sdk@1.9.24 ## @langchain/svelte@1.0.25 ### Patch Changes - [#2557](https://github.com/langchain-ai/langgraphjs/pull/2557) [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e) Thanks [@christian-bromann](https://github.com/christian-bromann)! - fix(sdk): apply state update and goto alongside interrupt resume `respond(decision, { update, goto })` now maps to LangGraph's `Command(resume, update, goto)`, so a human-in-the-loop UI can commit a state update (e.g. push the interrupt card into state) in the **same superstep** as the resume — one checkpoint, no separate `updateState` write, no flicker. `@langchain/langgraph-api` forwards `update`/`goto` through `input.respond`, and `@langchain/core` message instances in `update` are serialized to dicts before transport, exactly like `submit()`. Bumps `@langchain/protocol` to `^0.0.18` for the `Goto` type. `respond`/`respondAll` also apply `update` **optimistically** (mirroring `submit()`): the pushed messages paint immediately, with stable ids minted so the resumed run's echo reconciles them in place. Without this the interrupt is cleared the instant `respond()` dispatches while the pushed card only reappears a server round-trip later — so the card would flicker in that gap. The optimistic state settles on the resumed run's terminal (pending → sent, or rolled back on a failure before any echo). User-initiated optimistic writes (`submit()` / `respond()` / `respondAll()`) now commit to the store **synchronously**, in the same tick as the triggering event, instead of being coalesced onto the next macrotask. This lets a framework render the pushed message in the **same commit** as any local UI state the caller flips alongside it (e.g. a HITL form swapping its inputs for the resolved card), so the card no longer blinks out for the one-macrotask window before the flush lands. High-frequency streaming writes keep their macrotask coalescing. - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph-sdk@1.9.24 ## @langchain/vue@1.0.25 ### Patch Changes - [#2557](https://github.com/langchain-ai/langgraphjs/pull/2557) [`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e) Thanks [@christian-bromann](https://github.com/christian-bromann)! - fix(sdk): apply state update and goto alongside interrupt resume `respond(decision, { update, goto })` now maps to LangGraph's `Command(resume, update, goto)`, so a human-in-the-loop UI can commit a state update (e.g. push the interrupt card into state) in the **same superstep** as the resume — one checkpoint, no separate `updateState` write, no flicker. `@langchain/langgraph-api` forwards `update`/`goto` through `input.respond`, and `@langchain/core` message instances in `update` are serialized to dicts before transport, exactly like `submit()`. Bumps `@langchain/protocol` to `^0.0.18` for the `Goto` type. `respond`/`respondAll` also apply `update` **optimistically** (mirroring `submit()`): the pushed messages paint immediately, with stable ids minted so the resumed run's echo reconciles them in place. Without this the interrupt is cleared the instant `respond()` dispatches while the pushed card only reappears a server round-trip later — so the card would flicker in that gap. The optimistic state settles on the resumed run's terminal (pending → sent, or rolled back on a failure before any echo). User-initiated optimistic writes (`submit()` / `respond()` / `respondAll()`) now commit to the store **synchronously**, in the same tick as the triggering event, instead of being coalesced onto the next macrotask. This lets a framework render the pushed message in the **same commit** as any local UI state the caller flips alongside it (e.g. a HITL form swapping its inputs for the resolved card), so the card no longer blinks out for the one-macrotask window before the flush lands. High-frequency streaming writes keep their macrotask coalescing. - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph-sdk@1.9.24 ## @example/ai-elements@0.1.41 ### Patch Changes - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/react@1.0.25 - @langchain/langgraph@1.4.5 ## @examples/assistant-ui-claude@0.1.41 ### Patch Changes - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/react@1.0.25 - @langchain/langgraph@1.4.5 ## @examples/ui-angular@0.0.51 ### Patch Changes - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph-sdk@1.9.24 - @langchain/angular@1.0.25 - @langchain/langgraph@1.4.5 ## @examples/ui-multimodal@0.0.27 ### Patch Changes - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/react@1.0.25 - @langchain/langgraph@1.4.5 ## @examples/ui-react@0.0.27 ### Patch Changes - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph-sdk@1.9.24 - @langchain/react@1.0.25 - @langchain/langgraph@1.4.5 ## langgraph@1.0.45 ### Patch Changes - Updated dependencies \[[`b1e856d`](https://github.com/langchain-ai/langgraphjs/commit/b1e856d987ac16148dc0872d1fecf70e659ef28e)]: - @langchain/langgraph@1.4.5 Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Low-level orchestration framework for building stateful agents.
LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
npm install @langchain/langgraph @langchain/core
Tip
If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
For an equivalent Python library, check out LangGraph and the Python docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph’s ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
- Deep Agents (JS) — Build agents that can plan, use subagents, and leverage file systems for complex tasks. A higher-level package built on top of LangGraph.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
Additional resources
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- LangChain Academy: Learn the basics of LangGraph in our free, structured course.
- Streaming Cookbook: Documentation and examples around LangGraphs's streaming capabilities.
- API Reference: Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- Built with LangGraph: Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
Acknowledgements
LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.