| Name | langgraph-checkpoint JSON |
| Version |
2.1.2
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| Summary | Library with base interfaces for LangGraph checkpoint savers. |
| upload_time | 2025-10-07 17:45:17 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
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# LangGraph Checkpoint
This library defines the base interface for LangGraph checkpointers. Checkpointers provide a persistence layer for LangGraph. They allow you to interact with and manage the graph's state. When you use a graph with a checkpointer, the checkpointer saves a _checkpoint_ of the graph state at every superstep, enabling several powerful capabilities like human-in-the-loop, "memory" between interactions and more.
## Key concepts
### Checkpoint
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.
### Thread
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.
- `thread_id` is simply the ID of a thread. This is always required.
- `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.
You must pass these when invoking the graph as part of the configurable part of the config, e.g.
```python
{"configurable": {"thread_id": "1"}} # valid config
{"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}} # also valid config
```
### Serde
`langgraph_checkpoint` also defines protocol for serialization/deserialization (serde) and provides an default implementation (`langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer`) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.
### Pending writes
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.
## Interface
Each checkpointer should conform to `langgraph.checkpoint.base.BaseCheckpointSaver` interface and must implement the following methods:
- `.put` - Store a checkpoint with its configuration and metadata.
- `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).
- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).
- `.list` - List checkpoints that match a given configuration and filter criteria.
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`).
## Usage
```python
from langgraph.checkpoint.memory import InMemorySaver
write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
read_config = {"configurable": {"thread_id": "1"}}
checkpointer = InMemorySaver()
checkpoint = {
"v": 4,
"ts": "2024-07-31T20:14:19.804150+00:00",
"id": "1ef4f797-8335-6428-8001-8a1503f9b875",
"channel_values": {
"my_key": "meow",
"node": "node"
},
"channel_versions": {
"__start__": 2,
"my_key": 3,
"start:node": 3,
"node": 3
},
"versions_seen": {
"__input__": {},
"__start__": {
"__start__": 1
},
"node": {
"start:node": 2
}
},
}
# store checkpoint
checkpointer.put(write_config, checkpoint, {}, {})
# load checkpoint
checkpointer.get(read_config)
# list checkpoints
list(checkpointer.list(read_config))
```
Raw data
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"description": "# LangGraph Checkpoint\n\nThis library defines the base interface for LangGraph checkpointers. Checkpointers provide a persistence layer for LangGraph. They allow you to interact with and manage the graph's state. When you use a graph with a checkpointer, the checkpointer saves a _checkpoint_ of the graph state at every superstep, enabling several powerful capabilities like human-in-the-loop, \"memory\" between interactions and more.\n\n## Key concepts\n\n### Checkpoint\n\nCheckpoint 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.\n\n### Thread\n\nThreads 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.\n\n- `thread_id` is simply the ID of a thread. This is always required.\n- `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.\n\nYou must pass these when invoking the graph as part of the configurable part of the config, e.g.\n\n```python\n{\"configurable\": {\"thread_id\": \"1\"}} # valid config\n{\"configurable\": {\"thread_id\": \"1\", \"checkpoint_id\": \"0c62ca34-ac19-445d-bbb0-5b4984975b2a\"}} # also valid config\n```\n\n### Serde\n\n`langgraph_checkpoint` also defines protocol for serialization/deserialization (serde) and provides an default implementation (`langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer`) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.\n\n### Pending writes\n\nWhen 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.\n\n## Interface\n\nEach checkpointer should conform to `langgraph.checkpoint.base.BaseCheckpointSaver` interface and must implement the following methods:\n\n- `.put` - Store a checkpoint with its configuration and metadata.\n- `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).\n- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).\n- `.list` - List checkpoints that match a given configuration and filter criteria.\n\nIf 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`).\n\n## Usage\n\n```python\nfrom langgraph.checkpoint.memory import InMemorySaver\n\nwrite_config = {\"configurable\": {\"thread_id\": \"1\", \"checkpoint_ns\": \"\"}}\nread_config = {\"configurable\": {\"thread_id\": \"1\"}}\n\ncheckpointer = InMemorySaver()\ncheckpoint = {\n \"v\": 4,\n \"ts\": \"2024-07-31T20:14:19.804150+00:00\",\n \"id\": \"1ef4f797-8335-6428-8001-8a1503f9b875\",\n \"channel_values\": {\n \"my_key\": \"meow\",\n \"node\": \"node\"\n },\n \"channel_versions\": {\n \"__start__\": 2,\n \"my_key\": 3,\n \"start:node\": 3,\n \"node\": 3\n },\n \"versions_seen\": {\n \"__input__\": {},\n \"__start__\": {\n \"__start__\": 1\n },\n \"node\": {\n \"start:node\": 2\n }\n },\n}\n\n# store checkpoint\ncheckpointer.put(write_config, checkpoint, {}, {})\n\n# load checkpoint\ncheckpointer.get(read_config)\n\n# list checkpoints\nlist(checkpointer.list(read_config))\n```\n",
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