langgraph-checkpoint-firestore

A LangGraph checkpointer for Google Firestore with two things a plain checkpointer does not have: bounded message history and a long-term memory hook, both inside the save path, with no change to your graph.

• Bounded history. Every thread's message list is pruned before each checkpoint is written, by message count or token budget, whole messages only, tool-call pairs kept intact. The sawtooth above is the checkpoint size over time. Without a reducer it is a straight line up.
• Long-term memory. The turns that leave the window are handed to the reducer's on_prune hook, once each, together with the memory_namespace your app put in the run config. Wire that hook to any store or extraction engine; langgraph-memory is the ready-made one.
• One line of config.

from agentstate_reducer import MessageReducer, ReducerConfig, Background
from langgraph_checkpoint_firestore import FirestoreSaver

reducer = MessageReducer(config=ReducerConfig(max_messages=20))            # add on_prune=[Background(engine.on_prune)] for memory
saver = FirestoreSaver("my-gcp-project", "checkpoints", reducer=reducer)
graph = builder.compile(checkpointer=saver)

graph.invoke(input, config={"configurable": {"thread_id": thread_id, "memory_namespace": ("memories", user_id)}})

Details: Built-in Message Pruning below, and the full story with every framework and store at https://skamalj.github.io/agentstate-reducer/.

Installation

pip install "langgraph-checkpoint-firestore[reducer]"

pip install langgraph-checkpoint-firestore     # checkpointer only; history is unbounded

Without reducer=, the saver logs one INFO line per process saying so, with the link above. Set AGENTSTATE_QUIET=1 to silence it.

Requires Python 3.10+. Application Default Credentials, per-thread subcollections, sync and async, subgraphs. Passes all eight capabilities of langgraph-checkpoint-conformance (base plus copy_thread, delete_for_runs, prune), which is what LangSmith Deployment probes at startup; see LangSmith Deployment.

Firestore Setup

The saver uses Google Cloud Application Default Credentials. Set up auth with one of:

# Local development — authenticate with your Google account
gcloud auth application-default login

# Service account (CI / production)
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"

Your Firestore instance must be in Native mode (not Datastore mode). Collections are created automatically on first write — no manual setup required.

Quick Start

from langgraph.graph import StateGraph, MessagesState, START
from langchain_openai import ChatOpenAI
from langgraph_checkpoint_firestore import FirestoreSaver

model = ChatOpenAI(model="gpt-4o-mini")

def call_model(state: MessagesState):
    return {"messages": model.invoke(state["messages"])}

builder = StateGraph(MessagesState)
builder.add_node("call_model", call_model)
builder.add_edge(START, "call_model")

with FirestoreSaver.from_conn_info(project_id="my-gcp-project", checkpoints_collection="checkpoints") as checkpointer:
    graph = builder.compile(checkpointer=checkpointer)

    config = {"configurable": {"thread_id": "user-123"}}

    # First run — state is saved to Firestore
    graph.invoke({"messages": [{"role": "user", "content": "Hi, I'm Kamal"}]}, config)

    # Second run — picks up where it left off
    graph.invoke({"messages": [{"role": "user", "content": "What's my name?"}]}, config)

API Reference

FirestoreSaver(project_id, checkpoints_collection, reducer=None, messages_key="messages")

You can also use the context manager factory:

with FirestoreSaver.from_conn_info(
    project_id="my-gcp-project",
    checkpoints_collection="checkpoints",
    reducer=reducer,
    messages_key="messages"
) as saver:
    graph = builder.compile(checkpointer=saver)

Sync methods

Async methods

All sync methods have async counterparts: aput, aput_writes, aget_tuple, alist.

LangSmith Deployment

LangSmith Deployment (Agent Server) accepts a custom checkpointer through langgraph.json and checks its capabilities at startup. This saver implements the full set, so thread forking (copy_thread), the rollback multitask strategy (delete_for_runs) and history pruning (prune) are all available.

{
  "dependencies": ["."],
  "graphs": {"agent": "./src/agent/graph.py:graph"},
  "checkpointer": {"path": "./src/agent/checkpointer.py:generate_checkpointer"}
}

# src/agent/checkpointer.py
from contextlib import asynccontextmanager
from agentstate_reducer import MessageReducer, ReducerConfig
from langgraph_checkpoint_firestore import FirestoreSaver

@asynccontextmanager
async def generate_checkpointer():
    reducer = MessageReducer(config=ReducerConfig(max_messages=20))
    yield FirestoreSaver("my-gcp-project", "checkpoints", reducer=reducer)

Housekeeping methods, also usable outside the platform:

saver.prune([thread_id], strategy="keep_latest")   # keep only the newest checkpoint per namespace
saver.delete_for_runs([run_id])                    # remove everything a run wrote
saver.copy_thread(thread_id, new_thread_id)        # fork a conversation

prune is not DeltaChannel-aware; do not use keep_latest on threads whose graph uses DeltaChannel. delete_for_runs finds checkpoints by a run_id attribute written since this version; older checkpoints are not matched.

Built-in Message Pruning

Long-running agents accumulate message history with every turn. Left unchecked this inflates checkpoint size, increases Firestore storage costs, and eventually blows past LLM context limits.

This checkpointer solves that at the persistence layer: pass a MessageReducer and it automatically prunes the message list inside put() before the checkpoint is serialised and written to Firestore. Your graph code, state definition, and node logic stay untouched.

This is an alternative to — or complement of — the LangGraph Annotated[list, reducer_fn] pattern. Use the checkpoint-layer approach when:

• You don't own the graph or state definition (e.g. using a pre-built LangGraph agent)
• You want pruning to happen unconditionally at every save, regardless of which node triggered it
• You want to keep all in-memory state intact and only prune what gets persisted

Install with reducer support

pip install "langgraph-checkpoint-firestore[reducer]"

Usage

from agentstate_reducer import MessageReducer
from langgraph_checkpoint_firestore import FirestoreSaver

reducer = MessageReducer(min_messages=10, max_messages=20)

with FirestoreSaver.from_conn_info(
    project_id="my-gcp-project",
    checkpoints_collection="checkpoints",
    reducer=reducer,        # prune before each checkpoint save
    messages_key="messages" # state channel holding the message list (default)
) as checkpointer:
    graph = builder.compile(checkpointer=checkpointer)

When len(messages) > max_messages, the oldest human/ai messages are removed until min_messages remain. The following are never pruned:

• Index 0 (typically the system prompt) — controlled by preserve_first=True
• system and function messages
• tool messages — unless their parent ai message is pruned (cascade behaviour, configurable)

See agentstate-reducer on PyPI for full configuration: preserve_first, cascade_tool_messages, summarize_fn, and role alias support (user/assistant/agent).

Usage — long-term memory via on_prune (agentstate-reducer >= 0.4.0)

Messages pruned from the checkpoint are exactly the ones leaving the model's view. The saver forwards a memory namespace to the reducer, and any on_prune hook receives (pruned_messages, namespace) — so pruned turns can flow straight into a LangGraph BaseStore (or LangMem, or any memory engine) with no extra node and no package coupling:

from uuid import uuid4
from agentstate_reducer import MessageReducer, ReducerConfig, Background
from langgraph_checkpoint_firestore import FirestoreSaver

store = ...  # any langgraph BaseStore

def remember(pruned, namespace):
    for m in pruned:
        store.put(tuple(namespace), key=str(uuid4()), value={"role": m.type, "content": m.content})

reducer = MessageReducer(config=ReducerConfig(max_messages=20, on_prune=[remember]))
# on_prune=[Background(remember)] runs a slow hook (e.g. LLM extraction) off the request path
saver = FirestoreSaver("my-project", "checkpoints", reducer=reducer)
graph = builder.compile(checkpointer=saver, store=store)

graph.invoke(input, config={"configurable": {
    "thread_id": uuid4().hex,                    # short-term scope (this checkpoint)
    "memory_namespace": ("memories", user_id),   # long-term scope (the store)
}})

The saver reads memory_namespace (or whatever ReducerConfig.namespace_key names) from config["configurable"] on every put() and passes it through untouched. If the app never sets it, the namespace falls back to ("memories", thread_id). Each pruned message reaches the hooks once, even though LangGraph writes several checkpoints per turn.

Data Model

Checkpoints are stored in a hierarchical Firestore structure:

{checkpoints_collection}/
  {thread_id}_{checkpoint_ns}/          ← partition document
    checkpoints/
      {checkpoint_id}                   ← checkpoint document
        writes/
          {task_id}_{idx}               ← pending write documents

This structure enables efficient per-thread checkpoint queries and keeps checkpoint data co-located with its pending writes.

License

MIT