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This guide explains how to add semantic search to your deployment’s cross-thread store, so that your agent can search for memories and other documents by semantic similarity.

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Prerequisites

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Steps

  1. Update your langgraph.json configuration file to include the store configuration:
{
    ...
    "store": {
        "index": {
            "embed": "openai:text-embedding-3-small",
            "dims": 1536,
            "fields": ["$"]
        }
    }
}
This configuration:
  • Uses OpenAI’s text-embedding-3-small model for generating embeddings
  • Sets the embedding dimension to 1536 (matching the model’s output)
  • Indexes all fields in your stored data (["$"] means index everything, or specify specific fields like ["text", "metadata.title"])
Each deployment supports a single embedding model. Configuring multiple embedding models is not supported, as it would cause ambiguity in /store endpoints and result in mixed-index issues.
  1. To use the string embedding format above, make sure your dependencies include langchain >= 0.3.8:
# In pyproject.toml
[project]
dependencies = [
    "langchain>=0.3.8"
]
Or if using requirements.txt: 
langchain>=0.3.8

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Usage

Once configured, you can use semantic search in your nodes. The store requires a namespace tuple to organize memories: 
async def search_memory(state: State, *, store: BaseStore):
    # Search the store using semantic similarity
    # The namespace tuple helps organize different types of memories
    # e.g., ("user_facts", "preferences") or ("conversation", "summaries")
    results = await store.asearch(
        namespace=("memory", "facts"),  # Organize memories by type
        query="your search query",
        limit=3  # number of results to return
    )
    return results
Each result is a SearchItem (extends Item with an additional score field). When semantic search is configured, score contains the similarity score: 
results[0].key       # "07e0caf4-1631-47b7-b15f-65515d4c1843"
results[0].value     # {"text": "User prefers dark mode"}
results[0].namespace # ("memory", "facts")
results[0].score     # 0.92 (similarity score, present when semantic search is configured)

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Changing your embedding model

Changing the embedding model or dimensions requires re-embedding all existing data. There is currently no automated migration tooling for this. Plan accordingly if you need to switch models.

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Custom embeddings

If you want to use custom embeddings, you can pass a path to a custom embedding function: 
{
    ...
    "store": {
        "index": {
            "embed": "path/to/embedding_function.py:embed",
            "dims": 1536,
            "fields": ["$"]
        }
    }
}
The deployment will look for the function in the specified path. The function must be async and accept a list of strings: 
# path/to/embedding_function.py
from openai import AsyncOpenAI

client = AsyncOpenAI()

async def aembed_texts(texts: list[str]) -> list[list[float]]:
    """Custom embedding function that must:
    1. Be async
    2. Accept a list of strings
    3. Return a list of float arrays (embeddings)
    """
    response = await client.embeddings.create(
        model="text-embedding-3-small",
        input=texts
    )
    return [e.embedding for e in response.data]

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Querying via the API

You can also query the store using the LangGraph SDK. Since the SDK uses async operations: 
from langgraph_sdk import get_client

async def search_store():
    client = get_client()
    results = await client.store.search_items(
        ("memory", "facts"),
        query="your search query",
        limit=3  # number of results to return
    )
    return results

# Use in an async context
results = await search_store()
Each result item includes a score field when semantic search is configured: 
results["items"][0]["key"]       # "07e0caf4-1631-47b7-b15f-65515d4c1843"
results["items"][0]["value"]     # {"text": "User prefers dark mode"}
results["items"][0]["namespace"] # ["memory", "facts"]
results["items"][0]["score"]     # 0.92 (similarity score)
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