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122 lines
6.0 KiB
Markdown
122 lines
6.0 KiB
Markdown
# LangGraph Checkpoint
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[](https://pypi.org/project/langgraph-checkpoint/#history)
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[](https://opensource.org/licenses/MIT)
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[](https://pypistats.org/packages/langgraph-checkpoint)
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[](https://x.com/langchain_oss)
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To help you ship LangGraph apps to production faster, check out [LangSmith](https://www.langchain.com/langsmith).
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[LangSmith](https://www.langchain.com/langsmith) is a unified developer platform for building, testing, and monitoring LLM applications.
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## Quick Install
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```bash
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uv add langgraph-checkpoint
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```
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## 🤔 What is this?
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This library defines the base interface for LangGraph checkpointers. Checkpointers provide a persistence layer for LangGraph: they save graph state at every superstep, enabling human-in-the-loop, memory between interactions, durable execution, and more.
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## 📖 Documentation
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For full documentation, see the [API reference](https://reference.langchain.com/python/langgraph.checkpoint). For conceptual guides on persistence and memory, see the [LangGraph Docs](https://docs.langchain.com/oss/python/langgraph/overview).
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## Key concepts
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### Checkpoint
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Checkpoint is a snapshot of the graph state at a given point in time. Checkpoint tuple refers to an object containing checkpoint and the associated config, metadata and pending writes.
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### Thread
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Threads enable the checkpointing of multiple different runs, making them essential for multi-tenant chat applications and other scenarios where maintaining separate states is necessary. A thread is a unique ID assigned to a series of checkpoints saved by a checkpointer. When using a checkpointer, you must specify a `thread_id` and optionally `checkpoint_id` when running the graph.
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- `thread_id` is simply the ID of a thread. This is always required.
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- `checkpoint_id` can optionally be passed. This identifier refers to a specific checkpoint within a thread. This can be used to kick off a run of a graph from some point halfway through a thread.
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You must pass these when invoking the graph as part of the configurable part of the config, e.g.
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```python
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{"configurable": {"thread_id": "1"}} # valid config
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{"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}} # also valid config
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```
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### Serde
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`langgraph-checkpoint` also defines protocol for serialization/deserialization (serde) and provides a default implementation (`langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer`) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.
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> [!IMPORTANT]
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> **Checkpoint deserialization security:** By default the serializer allows any Python type found in checkpoint data. New applications should set the environment variable `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list to `JsonPlusSerializer` to restrict deserialization to known-safe types.
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### Pending writes
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When a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes.
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## Interface
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Each checkpointer should conform to `langgraph.checkpoint.base.BaseCheckpointSaver` interface and must implement the following methods:
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- `.put` - Store a checkpoint with its configuration and metadata.
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- `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).
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- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).
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- `.list` - List checkpoints that match a given configuration and filter criteria.
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- `.delete_thread()` - Delete all checkpoints and writes associated with a thread.
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- `.get_next_version()` - Generate the next version ID for a channel.
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If the checkpointer will be used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), checkpointer must implement asynchronous versions of the above methods (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`). Similarly, the checkpointer must implement `.adelete_thread()` if asynchronous thread cleanup is desired. The base class provides a default implementation of `.get_next_version()` that generates an integer sequence starting from 1, but this method should be overridden for custom versioning schemes.
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## Usage
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```python
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from langgraph.checkpoint.memory import InMemorySaver
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write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
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read_config = {"configurable": {"thread_id": "1"}}
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checkpointer = InMemorySaver()
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checkpoint = {
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"v": 4,
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"ts": "2024-07-31T20:14:19.804150+00:00",
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"id": "1ef4f797-8335-6428-8001-8a1503f9b875",
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"channel_values": {
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"my_key": "meow",
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"node": "node"
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},
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"channel_versions": {
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"__start__": 2,
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"my_key": 3,
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"start:node": 3,
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"node": 3
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},
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"versions_seen": {
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"__input__": {},
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"__start__": {
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"__start__": 1
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},
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"node": {
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"start:node": 2
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}
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},
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}
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# store checkpoint
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checkpointer.put(write_config, checkpoint, {}, {})
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# load checkpoint
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checkpointer.get(read_config)
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# list checkpoints
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list(checkpointer.list(read_config))
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```
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## 📕 Releases & Versioning
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See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
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## 💁 Contributing
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As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
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For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).
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