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Sydney RunkleandClaude Opus 4.7
ffacba950a
Four fixes from an independent review of the reconstruction pipeline, plus a structural cleanup: 1. Ancestor walk excludes the target checkpoint itself (matches pregel: writes stored under checkpoint_id=T are pending for the NEXT step and applied separately via apply_writes). Memory saver previously included them, diverging from Postgres and causing pending writes to be folded into the reconstructed snapshot — visible via get_state during interrupts and time-travel into a non-leaf checkpoint. 2. Pre-delta blob terminator. When the walk hits an ancestor whose blob for the channel is a real value (not DELTA_SENTINEL), bind that blob as DeltaChannelWrites.seed and stop. Without this, threads migrated from pre-delta storage would replay ancestor writes to the root forever AND lose any value that lived only in the old blob (e.g. from update_state). Per-ancestor, the blob is checked BEFORE its writes — a pre-delta blob subsumes writes at the same checkpoint, so including them would double-count. 3. Base-fallback get_channel_writes follows parent_checkpoint_id instead of list(before=...). The previous form returned every tuple with id<target, including sibling branches on forked threads. 4. seed replaces the Overwrite-wrapping hack for pre-delta values. DeltaChannelWrites(writes, seed=SEED_UNSET) makes the saver's reconstruction terminator semantically explicit; drops the lazy _make_overwrite import dance. User-emitted Overwrite still reset the chain via _apply_write as before. Postgres: recursive CTE enumerates on-path ancestors and joins once against checkpoint_writes and once against checkpoint_blobs for every delta channel in the get_tuple — one roundtrip instead of the previous 3 queries × N channels. Tests added: - Pre-delta blob seeding (seed binding, no double-counting of ancestor writes at the terminator, pending-at-target excluded). - Root checkpoint returns empty writes. - Seed-based from_checkpoint replay (three scenarios: with writes, seed-only, seed=None distinct from SEED_UNSET). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
Get started
Install LangGraph:
pip install -U langgraph
Create a simple workflow:
from langgraph.graph import START, StateGraph
from typing_extensions import TypedDict
class State(TypedDict):
text: str
def node_a(state: State) -> dict:
return {"text": state["text"] + "a"}
def node_b(state: State) -> dict:
return {"text": state["text"] + "b"}
graph = StateGraph(State)
graph.add_node("node_a", node_a)
graph.add_node("node_b", node_b)
graph.add_edge(START, "node_a")
graph.add_edge("node_a", "node_b")
print(graph.compile().invoke({"text": ""}))
# {'text': 'ab'}
Get started with the LangGraph Quickstart.
To quickly build agents with LangChain's create_agent (built on LangGraph), see the LangChain Agents documentation.
Core benefits
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
- 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.
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:
- 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.
- LangSmith Deployment — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
- LangChain – Provides integrations and composable components to streamline LLM application development.
Note
Looking for the JS version of LangGraph? See the JS repo and the JS docs.
Additional resources
- Guides: Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- Reference: Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- Examples: Guided examples on getting started with LangGraph.
- 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.
- Case studies: Hear how industry leaders use LangGraph to ship AI applications at scale.
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.