Elior Nataf LackritzandGitHub
9d92f33cca
A fork from a checkpoint with pending writes to a DeltaChannel snapshots
the channel on the fork's first checkpoint, so the fork doesn't replay
the other branch's writes. If the channel had never been written there,
the snapshot also gives it its first version, and `_mark_bumps_seen`
only marked the bump seen for nodes that already have a `versions_seen`
entry. A node subscribed to the channel that never ran has none, so the
bump started it, and it read the empty value before the node that writes
the channel had run:
```python
graph.invoke("go", config) # writer writes [1] to d, reader reads [1]
fork = graph.update_state(first_checkpoint, {"a": "go"}, as_node="__input__")
graph.get_state(fork).next # ("writer", "reader"): resuming runs reader on [] first
```
A replay from that checkpoint saves the same kind of fork from the loop.
A channel bumped from no version was never written, so
`create_checkpoint` now also marks the bump seen for the nodes the
channel triggers, from the graph's `trigger_to_nodes`, at every call
that can bump. Only raw `Pregel` graphs hit this: `StateGraph` nodes
trigger on their `branch:to:*` channels, not on state keys. An
`as_node="__copy__"` fork still starts such a node, with a plain list
channel as well, so that case isn't DeltaChannel-specific and isn't
changed here.
Raised in review of langchain-ai/langgraphjs#2974. JS twin:
langchain-ai/langgraphjs#2983
## Tests
`test_delta_channel_seal_subscribers.py`: an update as input (sync and
async) and a replay from the checkpoint before a DeltaChannel's first
write leave only the writer next, and the reader then reads the written
value once. All three fail on main. The `langgraph` suite passes
(without Redis), and `lint_package` and `lint_tests` are clean.
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.
pip install -U langgraph
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 JS/TS library, check out LangGraph.js and the JS docs.
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Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
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To improve your LLM application development, pair LangGraph with:
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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.