langgraph_checkpoint_cosmosdb 0.2.7

LangGraph checkpoint saver for Azure CosmosDB with built-in message history pruning via agentstate-reducer

langgraph-checkpoint-cosmosdb

Azure CosmosDB checkpoint saver for LangGraph. Persists agent state between runs so your graphs can resume from any prior checkpoint.

What makes this checkpointer different: it has message history pruning built in. Pass a MessageReducer and the checkpointer automatically caps your message list before writing to CosmosDB — no extra code in your graph, no state annotation changes required. This is the only LangGraph CosmosDB checkpointer with this capability.

Features

• Full checkpoint persistence — save, retrieve, and list LangGraph checkpoints in CosmosDB
• Built-in message pruning — optional MessageReducer prunes message history at the persistence layer, keeping checkpoints lean without changing your graph code
• Sync and async API — put/get_tuple/list and their aput/aget_tuple/alist async counterparts
• Subgraph support — correctly checkpoints parent and subgraph state independently
• Flexible authentication — key-based or Azure RBAC (Managed Identity, az login, service principal)
• Auto-creates database and container — when using key-based auth

Installation

pip install langgraph-checkpoint-cosmosdb

With optional message pruning support:

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

Requires Python 3.10+

Database and Container Setup

Auth mode	Database	Container	Partition key
Key-based (COSMOSDB_KEY set)	Created automatically if absent	Created automatically if absent	/partition_key (set by saver)
RBAC / Managed Identity (no key)	Must pre-exist	Must pre-exist	/partition_key (must be pre-configured)

Key-based is the easiest way to get started — just point the saver at an existing CosmosDB account and it will provision everything.

RBAC is recommended for production. Because the saver only calls get_database_client / get_container_client (no write permissions needed at setup time), the database and container must already be provisioned before the saver is initialised. Create them via the Azure portal, Terraform, Bicep, or the Azure CLI:

az cosmosdb sql database create --account-name <account> --name <db>
az cosmosdb sql container create \
  --account-name <account> --database-name <db> --name <container> \
  --partition-key-path "/partition_key"

Important: The partition key path must be /partition_key regardless of how the container is created.

Authentication

Key-based (development / admin access)

export COSMOSDB_ENDPOINT="https://<account>.documents.azure.com:443/"
export COSMOSDB_KEY="<your-key>"

Azure RBAC / Managed Identity (production)

Set only the endpoint — no key. The saver uses DefaultAzureCredential, which resolves in this order: environment service principal → managed identity → az login.

export COSMOSDB_ENDPOINT="https://<account>.documents.azure.com:443/"
# COSMOSDB_KEY not set → DefaultAzureCredential is used

For user-assigned managed identity:

export AZURE_CLIENT_ID="<managed-identity-client-id>"

For service principal:

export AZURE_TENANT_ID="<tenant-id>"
export AZURE_CLIENT_ID="<client-id>"
export AZURE_CLIENT_SECRET="<client-secret>"

Quick Start

from langgraph.graph import StateGraph, MessagesState, START
from langchain_openai import ChatOpenAI
from langgraph_checkpoint_cosmosdb import CosmosDBSaver

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")

checkpointer = CosmosDBSaver(database_name="mydb", container_name="checkpoints")
graph = builder.compile(checkpointer=checkpointer)

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

# First run — state is saved to CosmosDB
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

CosmosDBSaver(database_name, container_name, reducer=None, messages_key="messages")

Parameter	Type	Default	Description
database_name	str	required	CosmosDB database name
container_name	str	required	CosmosDB container name
reducer	MessageReducer	None	Optional pruner — see Message Pruning
messages_key	str	"messages"	State channel name that holds the message list

Sync methods

Method	Description
put(config, checkpoint, metadata, new_versions)	Save a checkpoint
put_writes(config, writes, task_id)	Save pending writes for a checkpoint
get_tuple(config)	Retrieve the latest (or a specific) checkpoint
list(config, *, before, limit)	Iterate checkpoints for a thread

Async methods

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

# Async usage
checkpoint = await saver.aget_tuple(config)
await saver.adelete(thread_id="user-123", checkpoint_namespace="", checkpoint_id="<id>")

list usage

# List all checkpoints for a thread (newest first)
for cp in saver.list(config={"configurable": {"thread_id": "user-123"}}):
    print(cp.checkpoint["id"], cp.metadata)

# Limit results
for cp in saver.list(config, limit=5):
    print(cp)

Limitation: list only supports filtering by thread_id. The filter parameter (filtering by metadata) is not yet implemented.

Subgraph Support

Works transparently with LangGraph subgraphs — parent and subgraph checkpoints are stored under the same container using namespaced partition keys:

from langgraph.graph import StateGraph, START
from langgraph_checkpoint_cosmosdb import CosmosDBSaver
from typing import TypedDict

class SubState(TypedDict):
    foo: str
    bar: str

class State(TypedDict):
    foo: str

# ... build parent + subgraph as normal ...
checkpointer = CosmosDBSaver(database_name="mydb", container_name="checkpoints")
graph = parent_builder.compile(checkpointer=checkpointer)

for _, chunk in graph.stream({"foo": "hello"}, config, subgraphs=True):
    print(chunk)

Built-in Message Pruning

Long-running agents accumulate message history with every turn. Left unchecked this inflates checkpoint size, increases CosmosDB 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 CosmosDB. 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-cosmosdb[reducer]"

Usage

from agentstate_reducer import MessageReducer
from langgraph_checkpoint_cosmosdb import CosmosDBSaver

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

checkpointer = CosmosDBSaver(
    database_name="mydb",
    container_name="checkpoints",
    reducer=reducer,        # prune before each checkpoint save
    messages_key="messages" # state channel holding the message list (default)
)

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).

Data Model

Checkpoints and writes are stored as separate items in the same CosmosDB container, differentiated by a key prefix and partition key:

Item type	Partition key format	Item id format
Checkpoint	checkpoint$<thread_id>$<ns>$	checkpoint$<thread_id>$<ns>$<checkpoint_id>
Pending write	writes$<thread_id>$<ns>$<checkpoint_id>$	writes$<thread_id>$<ns>$<checkpoint_id>$<task_id>$<idx>

The container requires a partition key path of /partition_key.

License

MIT