langgraph/how-tos/persistence_postgres/

[giscus[bot]]

langgraph/how-tos/persistence_postgres/

Build language agents as graphs

https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/

[vaibhavp4]

How do I pass config and recursion limit together while streaming the graph? Currently, I'm getting this error - TypeError: Pregel.stream() takes from 2 to 3 positional arguments but 4 were given

[nathan-vo810]

This can be done by adding the recursion_limit key in the config:

config = {"configurable": {"thread_id": "2"}, "recursion_limit": 10}
for event in app.stream(
    {"messages": [input_message]},
    config,
    stream_mode="values",
):
    event["messages"][-1].pretty_print()

[dangchinh25]

Can you also update this guide but for JS version, and maybe a seperate general guide to explain in detail how to create a custom checkpointer and each component a checkpointer should have? I feel like this is a really important feature to bring LangGraph to production and there should be more guidance for it.

[vbarda]

@dangchinh25 we've added a JS how-to guide here https://langchain-ai.github.io/langgraphjs/how-tos/persistence-postgres/

[dangchinh25]

can we update this, the guide seem to have been deleted

[PI-rampi]

Please add a specific section on accessing and modifying the state variables of nodes from outside the node functions.

[hinthornw]

You mean like this? https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/

[PI-rampi]

William Thanks for the reply, this is not exactly what I am looking for. Consider a simple scenario that an attribute in the state object has to be modified. I have a state object and want to modify a string parameter and set it to a value , for e.g. plan attribute . A simple document to modify state attributes with examples will be useful. class AgentState(TypedDict): task: str plan: str draft: str critique: str content: List[str] revision_number: int max_revisions: int workflow = StateGraph(AgentState) rampi

…

________________________________ From: William FH ***@***.***> Sent: 09 July 2024 03:47 To: langchain-ai/langgraph ***@***.***> Cc: Ramprasad ***@***.***>; Comment ***@***.***> Subject: Re: [langchain-ai/langgraph] langgraph/how-tos/persistence_postgres/ (Discussion #894) You mean like this? https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/edit-graph-state/<https://eu-central-1.protection.sophos.com/?d=langchain-ai.github.io&u=aHR0cHM6Ly9sYW5nY2hhaW4tYWkuZ2l0aHViLmlvL2xhbmdncmFwaC9ob3ctdG9zL2h1bWFuX2luX3RoZV9sb29wL2VkaXQtZ3JhcGgtc3RhdGUv&i=NWY1NzY4MDNhOGI2OTAwZWFlOGRmNDI2&t=QkNXVHhueXB5K0FEKzQ2eFNNSEpXelZJTCtEbCs5VGdscllybnBqZThGdz0=&h=afb31f07d9264eedb4c62fc9d0a21266&s=AVNPUEhUT0NFTkNSWVBUSVY6K0T94M0wPyDMzKN9BocAHrXlbSFJ3THbbw-AzQ3TEw> — Reply to this email directly, view it on GitHub<https://eu-central-1.protection.sophos.com/?d=github.com&u=aHR0cHM6Ly9naXRodWIuY29tL2xhbmdjaGFpbi1haS9sYW5nZ3JhcGgvZGlzY3Vzc2lvbnMvODk0I2Rpc2N1c3Npb25jb21tZW50LTk5OTIwOTM=&i=NWY1NzY4MDNhOGI2OTAwZWFlOGRmNDI2&t=V0pKTnpNS3A2clF5NG54MUJYeStraGNLOHoxcVZBRklqU3dQV0xMbGF5RT0=&h=afb31f07d9264eedb4c62fc9d0a21266&s=AVNPUEhUT0NFTkNSWVBUSVY6K0T94M0wPyDMzKN9BocAHrXlbSFJ3THbbw-AzQ3TEw>, or unsubscribe<https://eu-central-1.protection.sophos.com/?d=github.com&u=aHR0cHM6Ly9naXRodWIuY29tL25vdGlmaWNhdGlvbnMvdW5zdWJzY3JpYmUtYXV0aC9CRjVDSlQyVkNRUFJLTFFMWTZDVkdLVFpMTUZZSEFWQ05GU002QUFBQUFCS0U0N1RENlZISTJEU01WUVdJWDNMTVY0M1NSREpPTlJYSzQzVE5GWFc0UTNQTlZXV0szVFVITTRUU09KU0dBNFRH&i=NWY1NzY4MDNhOGI2OTAwZWFlOGRmNDI2&t=SDdWRUxYR1o5RmROWXNnNWhrdktVWGVvZW9rOXY0eDBlbWdsWXdjcVZUST0=&h=afb31f07d9264eedb4c62fc9d0a21266&s=AVNPUEhUT0NFTkNSWVBUSVY6K0T94M0wPyDMzKN9BocAHrXlbSFJ3THbbw-AzQ3TEw>. You are receiving this because you commented.Message ID: ***@***.***> This E-MAIL message is a property of Premjiinvest and is intended for use only by the individual or entity to which it is addressed. The information contained may be confidential and privileged. If this is a forwarded message, the content of this E-MAIL may not have been sent with the authority of the company. If you have received this message by mistake, kindly notify the sender by return E-MAIL and delete this message. Premjiinvest will not be responsible for any viruses or defects or any forwarded attachments emanating either from within or outside. Premjiinvest reserves the right to monitor and review the content of all messages sent to or from Premjiinvest E-MAIL addresses. Messages sent to or from this e-mail address may be stored on the Premjiinvest E-MAIL system or elsewhere.

[nathan-vo810]

You can modify the attribute by using a node and return the new value.

def modify_plan(state):
      current_plan = state['plan']
      new_plan = current_plan + " expired"
      return {'plan': new_plan}

workflow.add_node('modify_plan', modify_plan)
workflow.set_entry_point('modify_plan')
workflow.add_edge('modify_plan', END)

app = workflow.compile()

[merchana]

Hello, How can I limit the memory, avoiding the graph to continuously have more history on its prompt?

E.g., by limiting to the last 10 messages

[DLOVRIC2]

In my usecase I only need the latest 3 questions and answers from the agents, so I have a method something like this:

    def _get_conversation_history(self, checkpoint: Checkpoint | None) -> list[str]:
        """
        Extract and format conversation history from checkpoint.

        Args:
            checkpoint (dict): The checkpoint containing conversation state

        Returns:
            list[str]: Interleaved list of queries and responses, up to MAX_HISTORY_LENGTH
        """
        if not checkpoint:
            return []

        user_queries = checkpoint.get("channel_values", {}).get("query", [])
        agent_responses = checkpoint.get("channel_values", {}).get("response", [])

        # Create interleaved conversation history from recent interactions
        conversation_history = []
        for q, r in zip(user_queries[-self.MAX_HISTORY_LENGTH :], agent_responses[-self.MAX_HISTORY_LENGTH :]):
            conversation_history.extend([q, r])

        logger.info("Current conversation state:")
        logger.info(f"Latest queries: {user_queries[-self.MAX_HISTORY_LENGTH:]}")
        logger.info(f"Latest responses: {agent_responses[-self.MAX_HISTORY_LENGTH:]}")
        logger.info(f"Conversation history: {conversation_history}")

        return conversation_history

Note that in my state I have both 'query' and 'response' args that I use to store the ongoing conversation. Note that input arg is the checkpoint which can be retrieved from Postgres, Redis, MongoDB or any other. Hope that helps

[Brainsoft-Raxat]

I am currently implementing a customer support bot and have been exploring the use of persistent memory to manage user interactions. My initial approach was to load the memory for a specific user from persistent storage, ensuring that all interactions from different users remain isolated. This seemed straightforward for maintaining user-specific contexts across sessions.

However, I am a bit confused about the specific utility of threads in this context. I understand that threads can provide additional granularity by keeping separate interactions within the same user session, but I am struggling to come up with concrete use cases where this granularity would be beneficial.

One example I am considering is within a single chat interaction. Even with persistent memory loaded through a checkpointer, it seems possible to add more granularity by using threads to manage multiple sub-interactions within a single user session. For instance, in a customer support bot scenario, a user might request multiple tasks, and these could be managed as separate threads within the same interaction.

My question is about the practical management of this granularity. How does the agent determine which thread to use at any given time within an ongoing interaction? Are there specific strategies or examples where combining both checkpointers and threads has proven particularly effective? I would greatly appreciate insights from those with more experience in this area.

[lakshaytalkstomachines]

How are you maintining "This seemed straightforward for maintaining user-specific contexts across sessions." without threads? Have you implemented it yourself?

[emarco177]

Any plans to add to the documentation an explanation of the "state' structure once its persisted?

e.g:
b'{"v": 1, "ts": "2024-07-13T05:59:18.599275+00:00", "id": "1ef40dd0-add3-62c2-8000-c6d57b7034eb", "channel_values": {"input": "hello world", "start:step_1": "start"}, "channel_versions": {"start": 2, "input": 2, "start:step_1": 2}, "versions_seen": {"start": {"start": 1}, "step_1": {}, "human_feedback": {}, "step_3": {}}, "pending_sends": []}'

To understand what each of those values here represent :)

[lakshaytalkstomachines]

We need more documentation on "channels" data structure in langraph.

[HoangNguyen689]

Why we need the writes table?

[vbarda]

see below!

[HoangNguyen689]

It should have docs about the way checkpointer work.
What purposes the writes table is used for?

[HoangNguyen689]

cc: @vbarda

[vbarda]

thanks for the feedback -- we'll be updating the documents on this to add more comments / explanations soon!

re: writes table / checkpointer.put_writes method -- it's needed for storing pending writes: if a node fails mid-execution, we store pending checkpoint writes from other successful nodes at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the nodes that completed successfully previously. hope this helps!

[HoangNguyen689]

fyi: #1141

[HoangNguyen689]

@vbarda
Thanks.
Which resources I have to prepare for the writes? Is it the same as checkpoint table?

[vbarda]

thanks for starting the discussion -- we're working on improving the docs / developer experience around checkpointers and will post an update soon.

re: your question -- it really depends on what kind of checkpointer you're implementing, there isn't really a requirement for creating a specific table. the checkpointer just needs to conform to this interface: https://github.com/langchain-ai/langgraph/blob/main/libs/langgraph/langgraph/checkpoint/base.py#L152 and implement .put, .put_writes, .get_tuple, .list or their async versions if you need to run your graph asynchronously

if you use a SQL database, you can just use the same table schemas as the postgres tutorial (although you might have to adjust the field types since the ones in the tutorial are postgres-specific). see more built-in examples here:

https://github.com/langchain-ai/langgraph/blob/main/libs/langgraph/langgraph/checkpoint/sqlite.py#L64
https://github.com/langchain-ai/langgraph/blob/main/libs/langgraph/langgraph/checkpoint/aiosqlite.py#L46

[GaryFail]

I get this error from get_tuple:

CheckpointTuple.new() got an unexpected keyword argument 'pending_writes'

and sure enough it doesn't:

class CheckpointTuple(NamedTuple):
config: RunnableConfig
checkpoint: Checkpoint
metadata: CheckpointMetadata
parent_config: Optional[RunnableConfig] = None

Anybody any ideas?

[GaryFail]

This does now have pending_writes. I've tried to look back to see if it was updated or if it was a mistake on my behalf but I can't find where this has changed and can't find the file on the main branch so perhaps it's also since being refactored. One less thing to worry about though :-)

[vbarda]

Hi folks! We just released a new Postgres checkpointer library -- you can install it with pip install langgraph-checkpoint-postgres. This how-to guide has been updated to use the library directly. This release is part of the larger LangGraph v0.2 release -- see release notes here https://github.com/langchain-ai/langgraph/releases/tag/0.2.0

Please let me know if you run into any issues with the library!

[sataycat]

Here's how I use the lifespan now. I find that compiling the graph per invocation is rather quick in practice, compared to waiting for the first messages to stream out

@asynccontextmanager
async def lifespan(app: FastAPI):
    connection_kwargs = {
        "autocommit": True,
        "prepare_threshold": 0,
        "row_factory": dict_row,
    }
    async with AsyncConnectionPool(settings.DB_URI, kwargs=connection_kwargs) as pool:
        await pool.wait()  # optional
        yield {"pool": pool}


app = FastAPI(lifespan=lifespan)


# elsewhere

@router.post("/message")
async def message(request: Request):
    async with request.state.pool.connection() as conn:
        checkpointer = AsyncPostgresSaver(conn)
        graph = graph_builder.compile(checkpointer=checkpointer)
        async for event in graph.astream_events(input, config=config, version="v2"):
            # do your thing
            pass

[vbarda]

by the way, we added support for passing pool to PostgresSaver directly in langgraph-checkpoint-postgres==1.0.4

[Gontna]

if i want to operate the database to complete some operations,such as view all conversation history. what should i do to get the pool that defined in lifespan

[danielemaasit]

Thanks for pointing that out.

I guess I'm now questioning my implementation. I have a FastAPI in front of this so when somebody calls an endpoint I invoke the graph for the given thread. What I wasn't planning on doing was to compile the graph each time the endpoint was hit, instead compile the graph once and use that throughout the lifetime of the application using a connection pool to pull the connections out of upon each request.

Is there any guidance on whether what I am attempting to do is correct or am I way off the mark?

@GaryFail Did you ever get a response to this Question? Did you find a solution? If so, please share. I have tried implementing this for 10 straight hours and failed.

[GaryFail]

@danielemaasit I'm not sure exactly which point it is you are asking for clarification on. However we don't compile the graph on each request and we use a database connection pool which is initialised in the FastAPI lifespan event.

we have a connection pool set up like this:

connection_kwargs = {
    "autocommit": True,
    "prepare_threshold": 0,
    "row_factory": dict_row,
}

database_connection_pool = AsyncConnectionPool(
    conninfo=os.environ["STATE_CONNECTION_STRING"],
    max_size=20,
    open=False,
    kwargs=connection_kwargs,
)

main.py is something along the lines of this:

@asynccontextmanager
async def lifespan(app: FastAPI):
    await database_connection_pool.open()
    try:
        async with database_connection_pool.connection() as conn:
            await AsyncPostgresSaver(conn).setup()
    except Exception as e:
        logging.error(f"Error setting up database: {type(e).__name__} - {e}")
        raise
    yield
    await database_connection_pool.close()

The checkpointer then uses the connection pool:

def get_checkpointer() -> BaseCheckpointSaver:
    """
    Return an implementation of BaseCheckpointSaver to be passed to a graph to store state.
    """
    return AsyncPostgresSaver(database_connection_pool)

And that is used to compile the graph and we return the compiled graph whenever we want to use it:

saver = get_checkpointer()

graph = builder.compile(checkpointer=saver)


def get_graph() -> CompiledStateGraph:
    return graph

Just ensure you have the updates to the libraries etc.

[gbaian10]

from psycopg_pool import AsyncConnectionPool

async with AsyncConnectionPool(
    # Example configuration
    conninfo=DB_URI,
    max_size=20,
    kwargs=connection_kwargs
) as pool, pool.connection() as conn:
    checkpointer = AsyncPostgresSaver(conn)

    # NOTE: you need to call .setup() the first time you're using your checkpointer
    # await checkpointer.setup()

    graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
    config = {"configurable": {"thread_id": "4"}}
    res = await graph.ainvoke(
        {"messages": [("human", "what's the weather in nyc")]}, config
    )

    checkpoint = await checkpointer.aget(config)

Regarding this piece of pool code, the pool has 20 connections available, but the code only takes one connection from the pool and assigns it to AsyncPostgresSaver.

Does this mean that an AsyncPostgresSaver will always have only one connection?
It seems that the pool's intended effect is not being utilized?

Additionally, I have a similar question to @GaryFail: how exactly should I compile the graph and the checkpointer in the API?

[vbarda]

great question. you can use something like FastAPI lifespan in the code snippet above to initialize the pool. we'll also look into passing the connection pool directly into the PostgresSaver!

[vbarda]

by the way, we added support for passing pool to PostgresSaver directly in langgraph-checkpoint-postgres==1.0.4

[sutharzan-ch]

@vbarda I am trying to figure out how to avoid compiling the graph each time I need to invoke it. Even if I pass the connection pool to PostgresSaver directly, I still have to create a checkpointer using a connection from the pool before compiling the graph. This means I end up compiling the graph for each Postgres DB thread. Is there a way to compile the graph once and reuse it across multiple invocations while still using the connection pool for database operations? Or am I misunderstanding the intended usage here?

[viplazylmht]

@sutharzan-ch You can compile graph when initialize the app (it can be FastAPI lifespan or your singleton instance). Then you can reuse the complied one to handle requests.

[sutharzan-ch]

@viplazylmht Thanks for the clarifications!

[vigneshmj1997]

Any idea on which implementation works the best for the production environment ..?

• is it best to compile the graph on every request
• or is it better to have a complied version and use that object on every request

[GaryFail]

I guess we need to know if the compiled graph object is thread safe.

[vbarda]

yes, compiled graph object is thread safe

[robocanic]

I think the second is better. There are usually several initialize steps before the graph can be complied. These steps may cost a long time. So in order to reduce the whole inference costs, I believe the single pattern is better.

[kvkenyon]

Hey when I run my agent outside of LangGraph studio it's accessing the DB fine. I've even used ngrok to expose to TCP port. But from the langgraph studio docker container it won't connect. Any ideas?

[jhachirag7]

i am getting error on imports
from langgraph.checkpoint.postgres import PostgresSaver

error:
ImportError no pq wrapper available.
Attempts made:

• couldn't import psycopg 'c' implementation: No module named 'psycopg_c'
• couldn't import psycopg 'binary' implementation: No module named 'psycopg_binary'
• couldn't import psycopg 'python' implementation: expected str, bytes or os.PathLike object, not NoneType

[jhachirag7]

got solved after installing pip install "psycopg[binary,pool]"

[DLOVRIC2]

thanks!

[joelbolz]

Hello everyone!
I am currently building an application and wanted to implement persistent memory using the async approach of the postgresmemorysaver. It works as expected, no hassle at all.

However, after prototyping with a simple graph (chatnode powered by ChatAgent + ToolNode), we now want to build a multi agent system. To get into that, Ive created a simple Agent Team (Supervisor, + 2 Worker Nodes with create_react_agent() Agents). When using the in-RAM MemorySaver() on the outer graph, it works absolutely fine. The Supervisor works and the Workers do their work and are able to recall the previous messages.

When trying to use the AsyncPostgresSaver however (meaning I literally just gave the outer graph that one instead of the MemorySaver), the supervisor fires, but after that nothing happens. No Worker is fired, nor do I get any Errors or Exceptions of any kind.

Anyone got an Idea what the problem is here?
Ive also tried giving the workers the same memory object as checkpointers, to no avail.

Any and all help is greatly appreciated!
For simplicity, all relevant code is shown together below:

async def build_testteam_graph() -> StateGraph:
    from langchain_core.messages import HumanMessage
    from langchain_anthropic import ChatAnthropic
    from langchain_openai import ChatOpenAI
    from langgraph.prebuilt import create_react_agent
    from langchain_core.tools import tool

    from typing import List, Optional, Literal, TypedDict
    from langchain_core.language_models.chat_models import BaseChatModel

    from langgraph.graph import StateGraph, MessagesState, START, END
    from langgraph.types import Command
    global pool
    async with pool.connection() as conn:
        # Initialize persistent chat memory
        memory = AsyncPostgresSaver(conn)

        await memory.setup()
        class State(MessagesState):
            next: str

        def make_supervisor_node(llm: BaseChatModel, members: list[str]) -> str:
            options = ["FINISH"] + members
            print(options)
            system_prompt = (
                "You are a supervisor tasked with completing the task from the User below. As the first step, you will assign a task to analyze the request and then based on the returned workplan, assign the next workers from"
                f" one of the following workers: {members}. They can write Poems,or analyze user requests accordingly. They will fullfill their task and respond with their"
                " respond with the worker to act next. Each worker will perform a"
                " results and status. When you think the task is completely fullfilled,"
                " respond with FINISH."
            )

            class Router(TypedDict):
                """Worker to route to next. If no workers needed, route to FINISH."""

                next: Literal[*options]

            def supervisor_node(state: State) -> Command[Literal[*members, "__end__"]]:
                """An LLM-based router."""
                messages = [
                    {"role": "system", "content": system_prompt},
                ] + state["messages"]
                response = llm.with_structured_output(Router).invoke(messages)
                goto = response["next"]
                if goto == "FINISH":
                    goto = END

                return Command(goto=goto, update={"next": goto})

            return supervisor_node

        @tool
        def create_poem() -> str:
            """Use this to return the poem. There is only one poem, it is always the same."""
            return "ich mag mein haus, das sieht gut aus und in ihm wohnt ne' maus!"


        llm = ChatOpenAI(model="gpt-4o", streaming=True, stream_usage=True)


        create_poem_agent = create_react_agent(llm, tools=[create_poem], state_modifier="Your task is to create a poem! After that you are done, do not create anything else!", checkpointer=memory)
        def create_poem_node(state: State) -> Command[Literal["supervisor"]]:
            result = create_poem_agent.invoke(state)
            return Command(
                update={
                    "messages": [
                        HumanMessage(content=result["messages"][-1].content, name="poem_creator")
                    ]
                },
                # We want our workers to ALWAYS "report back" to the supervisor when done
                goto="supervisor",
            )

        @tool
        def analyze_request(list_of_steps: List) -> str:
            """Use this tool to pack the request into a list of tuples of required steps with instructuions and the respective worker from these: [poem_creator, DKR_Searcher].! After that you are done, do not create anything else!"""
            return list_of_steps

        analyze_request_agent = create_react_agent(llm, tools=[analyze_request], state_modifier=f"Your task is to analyze the request and put it into a list of tuples of required steps with instructuions and the respective worker from these: [poem_creator].! After that you are done, do not create anything else!", checkpointer=memory)
        def analyze_request_node(state: State) -> Command[Literal["supervisor"]]:
            result = analyze_request_agent.invoke(state)
            return Command(
                update={
                    "messages": [
                        HumanMessage(content=result["messages"][-1].content, name="request_analyzer")
                    ]
                },
                goto="supervisor",
            )
        supervisor_node = make_supervisor_node(llm, ["poem_creator", "request_analyzer"])

        builder = StateGraph(State)
        builder.add_edge(START, "supervisor")
        builder.add_node("supervisor", supervisor_node)
        builder.add_node("poem_creator", create_poem_node)
        builder.add_node("request_analyzer", analyze_request_node)
        graph = builder.compile(checkpointer=memory)

        from PIL import Image
        import io
        im = Image.open(io.BytesIO(graph.get_graph().draw_mermaid_png()))
        im.show()

        return graph

[joelbolz]

Ok I have tried out the synchronous PostgresSaver and with that it works, however only when using stream (compared to graph.astream that we used before).
Am I overlooking something here?

[jhachirag7]

currently i have been getting this below error

  File "d:\NeolenSearchtool\multi-agent\ai-agents\api\agent_api\src\langgraph_agent.py", line 281, in _get_checkpoints
    return checkpointer.get(configdb)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "d:\NeolenSearchtool\multi-agent\ai-agents\env\Lib\site-packages\langgraph\checkpoint\base\__init__.py", line 236, in get
    if value := self.get_tuple(config):
                ^^^^^^^^^^^^^^^^^^^^^^^
  File "d:\NeolenSearchtool\multi-agent\ai-agents\env\Lib\site-packages\langgraph\checkpoint\postgres\__init__.py", line 219, in get_tuple
    cur.execute(
  File "d:\NeolenSearchtool\multi-agent\ai-agents\env\Lib\site-packages\psycopg\cursor.py", line 97, in execute
    raise ex.with_traceback(None)
psycopg.errors.UndefinedColumn: column cw.task_path does not exist
LINE 27: ...array_agg(array[cw.type::bytea, cw.blob] order by cw.task_pa...

def _get_checkpoints(self, configdb: dict):
        """
        Retrieve database checkpoints with connection pooling.
        
        Args:
            configdb: Configuration dictionary
        
        Returns:
            Checkpoint data
        """
        with ConnectionPool(conninfo=self.db_uri, kwargs=self.connection_kwargs) as pool:
            checkpointer = PostgresSaver(pool)
            return checkpointer.get(configdb)

[kunal646]

If a graph have thread level persistence , then for the chatbot agent do I still need to pass previous messages every time? What’s the best way to manage memory without unnecessary repetition?

[ritik7jain]

I guess yes, i am too passing previous message each time a conv. history, you have to parse state snapshot and create history

[Don-peter-joseph]

InvalidSchemaName: no schema has been selected to create in
LINE 1: CREATE TABLE IF NOT EXISTS checkpoint_migrations

how to mention the schema

[ritik7jain]

have you found any solution to use specific schema instead of public

[ritik7jain]

db_uri_with_schema = f"{DB_URI}?options=-csearch_path%3D{schema}"
with PostgresSaver.from_conn_string(db_uri_with_schema) as checkpointer:

This resolved issue for me

[itsorivera]

Hello, thank you for your solution regarding a custom schema. Have you found any solution for a custom table in PostgreSQL beyond the default table checkpoints?

[ritik7jain]

Is there any way to clear postgress table, once one chat session end

[mail2chethankarur]

Hi, I am using AsyncConnectionPool just like how it is shown in the documentation above.

But for some reason, it works only for first two times with one thread_id. If I hit the endpoint for the third time on, it throws an error (put below). Basically, it is just terminating the connection with Postgres without any specific reason.

I'm building an agentic framework which contains subgraphs as nodes in the main_graph

 config = {"configurable": {"thread_id": session_id}}
from psycopg_pool import AsyncConnectionPool

        async with AsyncConnectionPool(
            conninfo=DB_URI,
            max_size=20,
            kwargs=connection_kwargs,
        ) as pool:
            checkpointer = AsyncPostgresSaver(pool)
            await checkpointer.setup()

            compiled_main = main_graph.compile(checkpointer=checkpointer)
            
            async for event in compiled_main.astream_events(
                initial_state, config, version="v2"
            ): 
                    <rest of code>

Error:
| psycopg.OperationalError: sending prepared query failed: server closed the connection unexpectedly
| This probably means the server terminated abnormally
| before or while processing the request.

📝 title
>
> - l'm using langgraph version 0.3.16 and in the subgraphs, I have added (checkpointer=True)

[Trapti04]

I am facing an issue with AsyncPostgresSaver when any state key in my State has a NaN value.. the default implementation does not handle conversion of state data which maybe NaN during serailization input postgresql. what is the best way to make this work. I am on langgraph version 0.3.5. and langgraph-checkpoint-postgres==2.0.15. generally the error occurs when one of my Sub Agents is raising a GrpahInterrupt for user info, I think the checkpointer tries to serailize in db what is currently in State and this exception is raised.

I have tried this-> but does not work :

lass CustomJsonPlusSerializer(JsonPlusSerializer):
"""Custom serializer that handles NaN values and rounds numeric values"""

def default(self, obj):
    """Handle NaN values and round numeric values during serialization"""
    try:
        if pd.isna(obj):
            return None
        if isinstance(obj, (np.number, float, int)):
            return float(round(obj, 2))
        if isinstance(obj, dict):
            return {k: self._default(v) for k, v in obj.items()}
        if isinstance(obj, list):
            return [self._default(item) for item in obj]
        return super()._default(obj)
    except Exception as e:
        self.logger.error(f"Error in _default: {str(e)}")
        raise


def encode(self, obj: Any) -> str:
    """Ensures serialization allows NaN values by using orjson options"""
    return orjson.dumps(
        obj, 
        option=orjson.OPT_NAIVE_UTC | orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_ALLOW_NAN
    ).decode("utf-8")

[Trapti04]

error I encounter File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/checkpoint/postgres/aio.py", line 345, in _cursor
conn.pipeline(),
^^^^^^^^^^^^^^^
File "/Users/traptikalra/.pyenv/versions/3.12.8/lib/python3.12/contextlib.py", line 217, in aexit
await anext(self.gen)
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/psycopg/connection_async.py", line 416, in pipeline
async with pipeline:
^^^^^^^^
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/psycopg/_pipeline.py", line 266, in aexit
raise exc2.with_traceback(None)
psycopg.errors.InvalidTextRepresentation: invalid input syntax for type json
DETAIL: Token "NaN" is invalid.
CONTEXT: JSON data, line 1: ...3786, 43690.320158519615, 44859.282252098456, NaN...
unnamed portal parameter $6 = '...'
During task with name 'analyst' and id '38f96f06-c667-edff-9ef7-3672a0fabbcc'

[Trapti04]

then I decided to write a custom Serde from SerializerProtocol

class NaNSafeSerializer(SerializerProtocol):
"""Custom serializer that converts NaN, Infinity, and -Infinity to None for JSON compatibility."""

        @staticmethod
        def serialize(obj) -> str:
            """Convert object to JSON string, replacing NaN with None."""
            def replace_nan(val):
                if isinstance(val, float) and (np.isnan(val) or np.isinf(val)):
                    return None  # Convert NaN, Infinity, -Infinity to JSON null
                return val

            # Recursively replace NaN in lists/dictionaries
            def clean_data(data):
                if isinstance(data, dict):
                    return {k: clean_data(v) for k, v in data.items()}
                elif isinstance(data, list):
                    return [clean_data(v) for v in data]
                return replace_nan(data)

            safe_obj = clean_data(obj)
            return json.dumps(safe_obj)

        @staticmethod
        def deserialize(data: str):
            """Convert JSON string back to object, handling NaN conversion."""
            if not data:  # Handle empty data case
                return {}

            parsed_data = json.loads(data)

            # Ensure returned object is iterable (dict, list, or at least an empty dict)
            if parsed_data is None:
                return {}  # Return an empty dict instead of None

            def replace_none(val):
                if val is None:
                    return np.nan  # Optionally convert back to NaN
                return val

            #parsed_data = json.loads(data)
            if isinstance(parsed_data, list):
                return [replace_none(v) for v in parsed_data]
            elif isinstance(parsed_data, dict):
                return {k: replace_none(v) for k, v in parsed_data.items()}
            return replace_none(parsed_data)

But I am getting another issue. please suggest what is to be done and if I can show you this issue, it will be great. this is the error here File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/utils/runnable.py", line 583, in ainvoke
input = await step.ainvoke(input, config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/pregel/init.py", line 2420, in ainvoke
async for chunk in self.astream(
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/pregel/init.py", line 2258, in astream
async with AsyncPregelLoop(
^^^^^^^^^^^^^^^^
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/pregel/loop.py", line 1109, in aexit
return await exit_task
^^^^^^^^^^^^^^^
File "/Users/traptikalra/.pyenv/versions/3.12.8/lib/python3.12/contextlib.py", line 754, in aexit
raise exc_details[1]
File "/Users/traptikalra/.pyenv/versions/3.12.8/lib/python3.12/contextlib.py", line 737, in aexit
cb_suppress = await cb(*exc_details)
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/pregel/executor.py", line 206, in aexit
raise exc
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/pregel/loop.py", line 1023, in _checkpointer_put_after_previous
await prev
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/pregel/loop.py", line 1025, in _checkpointer_put_after_previous
await cast(BaseCheckpointSaver, self.checkpointer).aput(
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/checkpoint/postgres/aio.py", line 263, in aput
await asyncio.to_thread(
File "/Users/traptikalra/.pyenv/versions/3.12.8/lib/python3.12/asyncio/threads.py", line 25, in to_thread
return await loop.run_in_executor(None, func_call)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/traptikalra/.pyenv/versions/3.12.8/lib/python3.12/concurrent/futures/thread.py", line 59, in run
result = self.fn(*self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/traptikalra/backend-ai/AnalyticsAI/chat_ai_service/agent_service/.agentvenv/lib/python3.12/site-packages/langgraph/checkpoint/postgres/base.py", line 191, in _dump_blobs
(
TypeError: Value after * must be an iterable, not NoneType

[WangXL1024]

When I execute the code for the "With a connection pool" and "With a connection" sections, the following error occurs:

PS C:\01_Work\01_Project\MyPYProject\LangGraph\Persistence> python .\use_Postgres_checkpointer_for_persistence.py
Traceback (most recent call last):
File "C:\01_Work\01_Project\MyPYProject\LangGraph\Persistence\use_Postgres_checkpointer_for_persistence.py", line 5, in
from langgraph.prebuilt import create_react_agent
File "C:\01_Work\04_Soft\01_Programs\Python312\Lib\site-packages\langgraph\prebuilt_init_.py", line 3, in
from langgraph.prebuilt.chat_agent_executor import create_react_agent
File "C:\01_Work\04_Soft\01_Programs\Python312\Lib\site-packages\langgraph\prebuilt\chat_agent_executor.py", line 33, in
from langgraph.graph import END, StateGraph
File "C:\01_Work\04_Soft\01_Programs\Python312\Lib\site-packages\langgraph\graph_init_.py", line 1, in
from langgraph.graph.graph import END, START, Graph
File "C:\01_Work\04_Soft\01_Programs\Python312\Lib\site-packages\langgraph\graph\graph.py", line 33, in
from langgraph.graph.branch import Branch
File "C:\01_Work\04_Soft\01_Programs\Python312\Lib\site-packages\langgraph\graph\branch.py", line 33, in
from langgraph.pregel.write import ChannelWrite
File "C:\01_Work\04_Soft\01_Programs\Python312\Lib\site-packages\langgraph\pregel_init_.py", line 93, in
from langgraph.pregel.checkpoint import create_checkpoint
File "C:\01_Work\04_Soft\01_Programs\Python312\Lib\site-packages\langgraph\pregel\checkpoint.py", line 5, in
from langgraph.checkpoint.base import LATEST_VERSION, Checkpoint
ImportError: cannot import name 'LATEST_VERSION' from 'langgraph.checkpoint.base' (C:\01_Work\04_Soft\01_Programs\Python312\Lib\site-packages\langgraph\checkpoint\base_init_.py)

[hinthornw]

Hm i think you may have to upgrade your langgraph-checkpoint version. We may need to update the min-bound of langraph so that upgrading locally doesn't run into this issue. We will fix this in the next version so you don't have to manuall yupdaet the langgraph-checkpoint version

[yu-rovikov]

Hi! I would like to have several PostgresSavers for different services in my application. However, they would all dump the data into the same 4 tables:
checkpoint_blobs
checkpoint_migrations
checkpoint_writes
checkpoints

Is this allowed? Do I need to worry about this? For example, do I need to ensure that different PostgresSavers obtain different thread_ids?

[rodpenna]

I'm having a problem using AsyncConnectionPool and AsyncPostgresSaver together with FastAPI, when I make the request the error appears, I'm creating the pool during the lifespan and getting the checkpoint when the request is created.

AsyncCursorProxy.execute() got an unexpected keyword argument 'binary'

"fastapi>=0.115.12",
"langgraph>=0.3.26",
"langgraph-checkpoint-postgres>=2.0.19",
"langgraph-supervisor>=0.0.16",
"psutil>=7.0.0",
"psycopg-binary>=3.2.6",
"psycopg-pool>=3.2.6",

[pakamona]

I have the same issue but only when adding ElasticAPM middleware (for observability purposes) as stated here https://www.elastic.co/docs/reference/apm/agents/python/starlette-support

[zakshyman]

Solution for this problem is to disable PSYCOPG_ASYNC instrument in elastic-apm.

        os.environ["SKIP_INSTRUMENT_PSYCOPG_ASYNC"] = "1"

You won't have postgres instrumentarium, but at least all other will work.

Other potential instruments that might be needed to disable:

       os.environ["SKIP_INSTRUMENT_PSYCOPG2"] = "1"
       os.environ["SKIP_INSTRUMENT_AIOPG"] = "1"
       os.environ["SKIP_INSTRUMENT_PSYCOPG"] = "1"
       os.environ["SKIP_INSTRUMENT_ASYNCPG"] = "1"

You can check all instruments by enabling DEBUG level for "elasticapm.instrument" logger.

[alberto-agudo]

Had the same issue, this solved it

Solution for this problem is to disable PSYCOPG_ASYNC instrument in elastic-apm.

        os.environ["SKIP_INSTRUMENT_PSYCOPG_ASYNC"] = "1"

You won't have postgres instrumentarium, but at least all other will work.

Other potential instruments that might be needed to disable:

       os.environ["SKIP_INSTRUMENT_PSYCOPG2"] = "1"
       os.environ["SKIP_INSTRUMENT_AIOPG"] = "1"
       os.environ["SKIP_INSTRUMENT_PSYCOPG"] = "1"
       os.environ["SKIP_INSTRUMENT_ASYNCPG"] = "1"

You can check all instruments by enabling DEBUG level for "elasticapm.instrument" logger.

[PovedaAqui]

It has been painfully. Any example?

[rajanskumarsoni]

got this error while executing above code:
ValueError: Asked to cache, but no cache found at langchain.cache.

[anamul94]

from psycopg_pool import ConnectionPool

with ConnectionPool(
    # Example configuration
    conninfo=DB_URI,
    max_size=20,
    kwargs=connection_kwargs,
) as pool:
    checkpointer = PostgresSaver(pool)

    # NOTE: you need to call .setup() the first time you're using your checkpointer
    checkpointer.setup()

    graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
    config = {"configurable": {"thread_id": "1"}}
    res = graph.invoke({"messages": [("human", "what's the weather in sf")]}, config)
    checkpoint = checkpointer.get(config)

Getting this error

UndefinedColumn: column "checkpoint_ns" does not exist
 LINE 5:     checkpoint_ns,
         ^
HINT:  Perhaps you meant to reference the column "checkpoints.checkpoint".

[itsorivera]

How can I change the name of my target postgres table to save my checkpoints and not use the checkpoint table by default?

[kishorkrishna007]

Whats the actual implementation for thread checkpoint rollback ?

i mean, if a specific thread gets an exception during execution which leads to a "broken" state i.e. ai_messages with tool calls without tool_messages. Whats the best way to have the last valid checkpoint being the last again ?

Did you get a solution to this?