lib 0.2.55

Speed up tests

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -7,35 +7,29 @@ body:
       value: >
         Thank you for taking the time to file a bug report. 
         
-        Use this to report bugs in LangChain. 
-        
-        If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
-        to ask for help with your issue.
+        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
         
         Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
         if there's another way to solve your problem:
         
-        [LangGraph documentation](https://langchain-ai.github.io/langgraph/).
+        [LangGraph Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
+        [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
         [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
         [GitHub search](https://github.com/langchain-ai/langgraph),
-        [LangChain Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
-        [LangChain Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        [LangChain ChatBot](https://chat.langchain.com/)
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
-      description: Please confirm and check all the following options.
+      description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
       options:
-        - label: I added a very descriptive title to this issue.
+        - label: This is a bug, not a usage question. For questions, please use GitHub Discussions.
           required: true
-        - label: I searched the [LangGraph](https://langchain-ai.github.io/langgraph/)/LangChain documentation with the integrated search.
+        - label: I added a clear and detailed title that summarizes the issue.
           required: true
-        - label: I used the GitHub search to find a similar question and didn't find it.
+        - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
           required: true
-        - label: I am sure that this is a bug in LangGraph/LangChain rather than my code.
-          required: true
-        - label: I am sure this is better as an issue [rather than a GitHub discussion](https://github.com/langchain-ai/langgraph/discussions/new/choose), since this is a LangGraph bug and not a design question.
+        - label: I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.
           required: true
   - type: textarea
     id: reproduction
@@ -45,14 +39,6 @@ body:
       label: Example Code
       description: |
         Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
-        
-        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
-        
-        **Important!** 
-        
-        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
-        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -92,25 +78,8 @@ body:
     attributes:
       label: System Info
       description: |
-        Please share your system info with us. 
-        
-        "pip freeze | grep langchain" 
-        platform (windows / linux / mac)
-        python version
-        
-        OR if you're on a recent version of langchain-core you can paste the output of:
-        
         python -m langchain_core.sys_info
       placeholder: |
-        "pip freeze | grep langgraph"
-        platform
-        python version
-        
-        Alternatively, if you're on a recent version of langchain-core you can paste the output of:
-        
         python -m langchain_core.sys_info
-        
-        These will only surface LangChain packages, don't forget to include any other relevant
-        packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
     validations:
       required: true

.github/scripts/run_langgraph_cli_test.py

@@ -22,8 +22,7 @@ def test(
         # check docker available
         capabilities = langgraph_cli.docker.check_capabilities(runner)
         # open config
-        with open(config) as f:
-            config_json = langgraph_cli.config.validate_config(json.load(f))
+        config_json = langgraph_cli.config.validate_config_file(config)
 
         set("Running...")
         args = [

.github/workflows/_test_langgraph.yml

@@ -60,7 +60,7 @@ jobs:
         env:
           LANGGRAPH_FF_SEND_V2: ${{ matrix.ff-send-v2 }}
         run: |
-          make test
+          make test_parallel
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/deploy_docs.yml

@@ -88,6 +88,7 @@ jobs:
               --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
               --check-links-ignore "https://github\.com/.*" \
+              --check-links-ignore "http://localhost:8123/.*" \
               --check-links-ignore "/.*\.(ipynb|html)$" \
               --check-links-ignore "https://python\.langchain\.com/.*" \
               --check-links-ignore "https://openai\.com/.*" \
@@ -104,6 +105,7 @@ jobs:
               echo "Running link check on HTML files matching changed notebook files..."
               poetry run pytest -v \
                 --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "http://localhost:8123/.*" \
                 --check-links-ignore "https://x.com/.*" \
                 --check-links-ignore "https://github\.com/.*" \
                 --check-links-ignore "/.*\.(ipynb|html)$" \

CONTRIBUTING.md

@@ -49,7 +49,7 @@ gain understanding of concepts and how they interact by showing one way to achie
 
 They should **avoid** giving
 multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
-be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction.
+be completely production-ready, it should be useful and practically satisfy the goal that you clearly stated in the tutorial's introduction.
 
 To quote the Diataxis website:
 

Makefile

@@ -13,7 +13,7 @@ serve-clean-docs: clean-docs
 	poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
 
 serve-docs: build-typedoc
-	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty
+	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph  -w ./libs/checkpoint --dirty
 
 clean-docs:
 	find ./docs/docs -name "*.ipynb" -type f -delete

README.md

@@ -238,7 +238,7 @@ final_state["messages"][-1].content
 * [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
 * [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
 * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
-* [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.
+* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
 
 ## Contributing
 

docs/_scripts/prepare_notebooks_for_ci.py

@@ -36,10 +36,11 @@ NOTEBOOKS_NO_EXECUTION = [
     "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
     # this loads a massive dataset from gcp
     "docs/docs/tutorials/usaco/usaco.ipynb",
+    # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
+    "docs/docs/how-tos/autogen-integration.ipynb",
     # TODO: need to update these notebooks to make sure they are runnable in CI
     "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
     "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
-    "docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb",  # taking a very long time to run
     "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
     "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
     "docs/docs/how-tos/map-reduce.ipynb"  # flakiness from structured output, only when running with VCR

docs/docs/cloud/deployment/semantic_search.md

@@ -0,0 +1,123 @@
+# How to add semantic search to your LangGraph deployment
+
+This guide explains how to add semantic search to your LangGraph deployment's cross-thread [store](../../concepts/persistence.md#memory-store), so that your agent can search for memories and other documents by semantic similarity.
+
+## Prerequisites
+
+- A LangGraph deployment (see [how to deploy](setup_pyproject.md))
+- API keys for your embedding provider (in this case, OpenAI)
+- `langchain >= 0.3.8` (if you specify using the string format below)
+
+## Steps
+
+1. Update your `langgraph.json` configuration file to include the store configuration:
+
+```json
+{
+    ...
+    "store": {
+        "index": {
+            "embed": "openai:text-embeddings-3-small",
+            "dims": 1536,
+            "fields": ["$"]
+        }
+    }
+}
+```
+
+This configuration:
+
+- Uses OpenAI's text-embeddings-3-small model for generating embeddings
+- Sets the embedding dimension to 1536 (matching the model's output)
+- Indexes all fields in your stored data (`["$"]` means index everything, or specify specific fields like `["text", "metadata.title"]`)
+
+2. To use the string embedding format above, make sure your dependencies include `langchain >= 0.3.8`:
+
+```toml
+# In pyproject.toml
+[project]
+dependencies = [
+    "langchain>=0.3.8"
+]
+```
+
+Or if using requirements.txt:
+
+```
+langchain>=0.3.8
+```
+
+## Usage
+
+Once configured, you can use semantic search in your LangGraph nodes. The store requires a namespace tuple to organize memories:
+
+```python
+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 = store.search(
+        namespace=("memory", "facts"),  # Organize memories by type
+        query="your search query",
+        limit=3  # number of results to return
+    )
+    return results
+```
+
+## Custom Embeddings
+
+If you want to use custom embeddings, you can pass a path to a custom embedding function:
+
+```json
+{
+    ...
+    "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:
+
+```python
+# 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]
+```
+
+## Querying via the API
+
+You can also query the store using the LangGraph SDK. Since the SDK uses async operations:
+
+```python
+from langgraph_sdk import get_client
+
+async def search_store():
+    client = get_client()
+    results = await client.store.search(
+        namespace=("memory", "facts"),
+        query="your search query",
+        limit=3  # number of results to return
+    )
+    return results
+
+# Use in an async context
+results = await search_store()
+```

docs/docs/cloud/deployment/test_locally.md

@@ -21,7 +21,7 @@ Install the proper packages:
 Ensure you have an API key, which you can create from the [LangSmith UI](https://smith.langchain.com) (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your `.env` file:
 
 ```python
-LANGCHAIN_API_KEY = *********
+LANGSMITH_API_KEY = *********
 ```
 
 ## Start the API server
@@ -54,7 +54,7 @@ You can either initialize by passing authentication or by setting an environment
     from langgraph_sdk import get_client
 
     # only pass the url argument to get_client() if you changed the default port when calling langgraph up
-    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGCHAIN_API_KEY>)
+    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGSMITH_API_KEY>)
     # Using the graph deployed with the name "agent"
     assistant_id = "agent"
     thread = await client.threads.create()
@@ -66,7 +66,7 @@ You can either initialize by passing authentication or by setting an environment
     import { Client } from "@langchain/langgraph-sdk";
 
     // only set the apiUrl if you changed the default port when calling langgraph up
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGCHAIN_API_KEY> });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGSMITH_API_KEY> });
     // Using the graph deployed with the name "agent"
     const assistantId = "agent";
     const thread = await client.threads.create();
@@ -78,13 +78,13 @@ You can either initialize by passing authentication or by setting an environment
     curl --request POST \
       --url <DEPLOYMENT_URL>/threads \
       --header 'Content-Type: application/json'
-      --header 'x-api-key: <LANGCHAIN_API_KEY>'
+      --header 'x-api-key: <LANGSMITH_API_KEY>'
     ```
   
 
 #### Initialize with environment variables
 
-If you have a `LANGCHAIN_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
+If you have a `LANGSMITH_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
 
 === "Python"
 
@@ -154,7 +154,7 @@ Now we can invoke our graph to ensure it is working. Make sure to change the inp
     }
     ```
 
-  === "CURL"
+=== "CURL"
 
     ```bash
     curl --request POST \

docs/docs/cloud/how-tos/configuration_cloud.md

@@ -83,7 +83,7 @@ We can now call `.get_schemas` to get schemas associated with this graph:
         assistant_id=assistant["assistant_id"]
     )
     # There are multiple types of schemas
-    # We can get the `config_schema` to look at the the configurable parameters
+    # We can get the `config_schema` to look at the configurable parameters
     print(schemas["config_schema"])
     ```
 
@@ -94,7 +94,7 @@ We can now call `.get_schemas` to get schemas associated with this graph:
       assistant["assistant_id"]
     );
     // There are multiple types of schemas
-    // We can get the `config_schema` to look at the the configurable parameters
+    // We can get the `config_schema` to look at the configurable parameters
     console.log(schemas.config_schema);
     ```
 

docs/docs/cloud/how-tos/interrupt_concurrent.md

@@ -94,6 +94,7 @@ Now we can start our two runs and join the second on euntil it has completed:
         assistant_id,
         input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
     )
+    # sleep a bit to get partial outputs from the first run
     await asyncio.sleep(2)
     run = await client.runs.create(
         thread["thread_id"],
@@ -114,6 +115,7 @@ Now we can start our two runs and join the second on euntil it has completed:
       assistantId,
       { input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
     );
+    // sleep a bit to get partial outputs from the first run
     await new Promise(resolve => setTimeout(resolve, 2000)); 
 
     let run = await client.runs.create(

docs/docs/cloud/how-tos/rollback_concurrent.md

@@ -95,7 +95,6 @@ Now let's run a thread with the multitask parameter set to "rollback":
         assistant_id,
         input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
     )
-    await asyncio.sleep(2)
     run = await client.runs.create(
         thread["thread_id"],
         assistant_id,
@@ -115,7 +114,6 @@ Now let's run a thread with the multitask parameter set to "rollback":
       assistantId,
       { input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
     );
-    await new Promise(resolve => setTimeout(resolve, 2000)); 
 
     let run = await client.runs.create(
       thread["thread_id"],
@@ -139,7 +137,7 @@ Now let's run a thread with the multitask parameter set to "rollback":
     --data "{
       \"assistant_id\": \"agent\",
       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
-    }" && sleep 2 && curl --request POST \
+    }" && curl --request POST \
     --url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
     --header 'Content-Type: application/json' \
     --data "{

docs/docs/cloud/quick_start.md

@@ -8,9 +8,9 @@ If you want to learn how to build an agent like this from scratch, take a look a
 
 This tutorial will use:
 
-- Anthropic for the LLM - sign up and get an API key [here](https://console.anthropic.com/)
-- Tavily for the search engine - sign up and get an API key [here](https://app.tavily.com/)
-- LangSmith for hosting - sign up and get an API key [here](https://smith.langchain.com/)
+- Anthropic for the LLM - sign up and get an API key [here](https://console.anthropic.com/).
+- Tavily for the search engine - sign up and get an API key [here](https://app.tavily.com/).
+- LangSmith for hosting - sign up and get an API key [here](https://smith.langchain.com/).
 
 ## Create and configure your app
 

docs/docs/cloud/reference/api/open_agent_api_ref.html

@@ -1,19 +0,0 @@
-<!doctype html>
-<html>
-  <head>
-    <title>Open Assistants API Specification</title>
-    <meta charset="utf-8" />
-    <meta
-      name="viewport"
-      content="width=device-width, initial-scale=1" />
-  </head>
-  <body>
-    <script id="api-reference" data-url="./open_agent_api.json"></script>
-    <script>
-      var configuration = {}
-      document.getElementById('api-reference').dataset.configuration =
-        JSON.stringify(configuration)
-    </script>
-    <script src="https://cdn.jsdelivr.net/npm/@scalar/api-reference"></script>
-  </body>
-</html>

docs/docs/cloud/reference/api/openapi.json

@@ -1557,8 +1557,11 @@
           "200": {
             "description": "Success",
             "content": {
-              "application/json": {
-                "schema": {}
+              "text/event-stream": {
+                "schema": {
+                  "type": "string",
+                  "description": "The server will send a stream of events in SSE format.\n\n**Example event**:\n\nid: 1\n\nevent: message\n\ndata: {}"
+                }
               }
             }
           },
@@ -1905,8 +1908,11 @@
           "200": {
             "description": "Success",
             "content": {
-              "application/json": {
-                "schema": {}
+              "text/event-stream": {
+                "schema": {
+                  "type": "string",
+                  "description": "The server will send a stream of events in SSE format.\n\n**Example event**:\n\nid: 1\n\nevent: message\n\ndata: {}"
+                }
               }
             }
           },
@@ -2143,8 +2149,11 @@
           "200": {
             "description": "Success",
             "content": {
-              "application/json": {
-                "schema": {}
+              "text/event-stream": {
+                "schema": {
+                  "type": "string",
+                  "description": "The server will send a stream of events in SSE format.\n\n**Example event**:\n\nid: 1\n\nevent: message\n\ndata: {}"
+                }
               }
             }
           },
@@ -2868,9 +2877,18 @@
             "description": "The cron schedule to execute this job on."
           },
           "assistant_id": {
-            "type": "string",
-            "format": "uuid",
-            "title": "Assistant Id"
+            "anyOf": [
+              {
+                "type": "string",
+                "format": "uuid",
+                "title": "Assistant Id"
+              },
+              {
+                "type": "string",
+                "title": "Graph Id"
+              }
+            ],
+            "description": "The assistant ID or graph name to run. If using graph name, will default to the assistant automatically created from that graph by the server."
           },
           "input": {
             "anyOf": [
@@ -3171,6 +3189,66 @@
         ],
         "title": "Run"
       },
+      "Send": {
+        "type": "object",
+        "title": "Send",
+        "description": "A message to send to a node.",
+        "properties": {
+          "node": {
+            "type": "string",
+            "title": "Node",
+            "description": "The node to send the message to."
+          },
+          "input": {
+            "type": "object",
+            "title": "Message",
+            "description": "The message to send."
+          }
+        },
+        "required": [
+          "node",
+          "input"
+        ]
+      },
+      "Command": {
+        "type": "object",
+        "title": "Command",
+        "description": "The command to run.",
+        "properties": {
+          "update": {
+            "type": "object",
+            "title": "Update",
+            "description": "An update to the state."
+          },
+          "resume": {
+            "type": [
+              "object",
+              "array",
+              "number",
+              "string",
+              "null"
+            ],
+            "title": "Resume",
+            "description": "A value to pass to an interrupted node."
+          },
+          "send": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/Send"
+              },
+              {
+                "type": "array",
+                "items": {
+                  "$ref": "#/components/schemas/Send"
+                }
+              },
+              {
+                "type": "null"
+              }
+            ]
+          }
+        }
+      },
       "RunCreateStateful": {
         "properties": {
           "assistant_id": {
@@ -3196,13 +3274,19 @@
           "input": {
             "anyOf": [
               {
-                "items": {
-                  "type": "object"
-                },
-                "type": "array"
+                "type": "object"
               },
               {
-                "type": "object"
+                "type": "null"
+              }
+            ],
+            "title": "Input",
+            "description": "The input to the graph."
+          },
+          "command": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/Command"
               },
               {
                 "type": "null"
@@ -3405,13 +3489,19 @@
           "input": {
             "anyOf": [
               {
-                "items": {
-                  "type": "object"
-                },
-                "type": "array"
+                "type": "object"
               },
               {
-                "type": "object"
+                "type": "null"
+              }
+            ],
+            "title": "Input",
+            "description": "The input to the graph."
+          },
+          "command": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/Command"
               },
               {
                 "type": "null"

docs/docs/cloud/reference/cli.md

@@ -26,10 +26,11 @@ The LangGraph command line interface includes commands to build and run a LangGr
 The LangGraph CLI requires a JSON configuration file with the following keys:
 
 | Key                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `dependencies`     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                  |
 | `graphs`           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
 | `env`              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| `store`            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, meaningto index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul>                                                                  |
 | `python_version`   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
 | `pip_config_file`  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
 | `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
@@ -41,33 +42,84 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
     </p>
 </div>
 
-Example:
+### Examples
+
+#### Basic Configuration
 
 ```json
 {
-  "dependencies": ["langchain_openai", "./your_package"],
+  "dependencies": ["."],
   "graphs": {
-    "my_graph_id": "./your_package/your_file.py:variable"
-  },
-  "env": "./.env"
+    "chat": "./chat/graph.py:graph"
+  }
 }
 ```
 
-Example:
+#### Adding semantic search to the store
+
+All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
+
+The `fields` configuration determines which parts of your documents to embed:
+- If omitted or set to `["$"]`, the entire document will be embedded
+- To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
+- Documents missing specified fields will still be stored but won't have embeddings for those fields
+- You can still override which fields to embed on a specific item at `put` time using the `index` parameter
 
 ```json
 {
-  "python_version": "3.11",
-  "dependencies": ["langchain_openai", "."],
+  "dependencies": ["."],
   "graphs": {
-    "my_graph_id": "./your_package/your_file.py:make_graph"
+    "memory_agent": "./agent/graph.py:graph"
   },
-  "env": {
-    "OPENAI_API_KEY": "secret-key"
+  "store": {
+    "index": {
+      "embed": "openai:text-embedding-3-small",
+      "dims": 1536,
+      "fields": ["$"]
+    }
   }
 }
 ```
 
+!!! note "Common model dimensions"
+        - openai:text-embedding-3-large: 3072
+        - openai:text-embedding-3-small: 1536
+        - openai:text-embedding-ada-002: 1536
+        - cohere:embed-english-v3.0: 1024
+        - cohere:embed-english-light-v3.0: 384
+        - cohere:embed-multilingual-v3.0: 1024
+        - cohere:embed-multilingual-light-v3.0: 384
+
+#### Semantic search with a custom embedding function
+
+If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
+
+```json
+{
+  "dependencies": ["."],
+  "graphs": {
+    "memory_agent": "./agent/graph.py:graph"
+  },
+  "store": {
+    "index": {
+      "embed": "./embeddings.py:embed_texts",
+      "dims": 768,
+      "fields": ["text", "summary"]
+    }
+  }
+}
+```
+
+The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
+
+```python
+# embeddings.py
+def embed_texts(texts: list[str]) -> list[list[float]]:
+    """Custom embedding function for semantic search."""
+    # Implementation using your preferred embedding model
+    return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors
+```
+
 ## Commands
 
 The base command for the LangGraph CLI is `langgraph`.
@@ -78,6 +130,37 @@ The base command for the LangGraph CLI is `langgraph`.
 langgraph [OPTIONS] COMMAND [ARGS]
 ```
 
+### `dev`
+
+Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+
+**Installation**
+
+This command requires the "inmem" extra to be installed:
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+```
+
+**Usage**
+
+```
+langgraph dev [OPTIONS]
+```
+
+**Options**
+
+| Option                        | Default          | Description                                                                         |
+| ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
+| `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
+| `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
+| `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
+| `--no-reload`                 |                  | Disable auto-reload                                                                 |
+| `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
+| `--no-browser`                |                  | Disable automatic browser opening                                                   |
+| `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+| `--help`                      |                  | Display command documentation                                                       |
+
 ### `build`
 
 Build LangGraph Cloud API server Docker image.
@@ -91,7 +174,7 @@ langgraph build [OPTIONS]
 **Options**
 
 | Option               | Default          | Description                                                                                                                  |
-|----------------------|------------------|------------------------------------------------------------------------------------------------------------------------------|
+| -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
 | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
 | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
 | `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
@@ -100,7 +183,7 @@ langgraph build [OPTIONS]
 
 ### `up`
 
-Start langgraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
 
 **Usage**
 
@@ -110,20 +193,20 @@ langgraph up [OPTIONS]
 
 **Options**
 
-| Option                       | Default                   | Description                                                                                                           |
-|------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------|
-| `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                         |
-| `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                 |
-| `--watch`                    |                           | Restart on file changes                                                                                               |
-| `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                     |
-| `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                    |
-| `--verbose`                  |                           | Show more output from the server logs.                                                                                |
-| `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                  |
-| `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                   |
-| `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph test --port 8000`                                                                 |
-| `--pull / --no-pull`         | `pull`                    | Pull latest images. Use --no-pull for running the server with locally-built images. Example: `langgraph up --no-pull` |
-| `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                             |
-| `--help`                     |                           | Display command documentation.                                                                                        |
+| Option                       | Default                   | Description                                                                                                             |
+| ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+| `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+| `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
+| `--watch`                    |                           | Restart on file changes                                                                                                 |
+| `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
+| `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                      |
+| `--verbose`                  |                           | Show more output from the server logs.                                                                                  |
+| `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
+| `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
+| `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
+| `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
+| `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                               |
+| `--help`                     |                           | Display command documentation.                                                                                          |
 
 ### `dockerfile`
 
@@ -138,7 +221,7 @@ langgraph dockerfile [OPTIONS] SAVE_PATH
 **Options**
 
 | Option              | Default          | Description                                                                                                     |
-|---------------------|------------------|-----------------------------------------------------------------------------------------------------------------|
+| ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
 | `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
 | `--help`            |                  | Show this message and exit.                                                                                     |
 
@@ -148,9 +231,9 @@ Example:
 langgraph dockerfile -c langgraph.json Dockerfile
 ```
 
-Would generate something like the following:
+This generates a Dockerfile that looks similar to:
 
-```text
+```dockerfile
 FROM langchain/langgraph-api:3.11
 
 ADD ./pipconf.txt /pipconfig.txt
@@ -170,6 +253,3 @@ RUN set -ex && \
 RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 
 ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
-```
-
-You can then customize, build images, push, and deploy from this file.

docs/docs/concepts/double_texting.md

@@ -15,10 +15,12 @@ If you do not want to use LangGraph Platform, we describe the options we have im
 ![](img/double_texting.png)
 
 ## Reject
+
 This is the simplest option, this just rejects any follow up runs and does not allow double texting. 
 See the [how-to guide](../cloud/how-tos/reject_concurrent.md) for configuring the reject double text option.
 
 ## Enqueue
+
 This is a relatively simple option which continues the first run until it completes the whole run, then sends the new input as a separate run. 
 See the [how-to guide](../cloud/how-tos/enqueue_concurrent.md) for configuring the enqueue double text option.
 
@@ -35,10 +37,6 @@ See the [how-to guide](../cloud/how-tos/interrupt_concurrent.md) for configuring
 
 ## Rollback
 
-This option rolls back all work done up until that point. 
-It then sends the user input in, basically as if it just followed the original run input. 
-
-This may create some weird states - for example, you may have two `User` messages in a row, with no `Asssitant` message in between them. 
-You will need to make sure the LLM you are calling can handle that, or combine those into a single `User` message.
+This option interrupts the current execution AND rolls back all work done up until that point, including the original run input. It then sends the new user input in, basically as if it was the original input.
 
 See the [how-to guide](../cloud/how-tos/rollback_concurrent.md) for configuring the rollback double text option.

docs/docs/concepts/high_level.md

@@ -2,13 +2,13 @@
 
 LLMs are extremely powerful, particularly when connected to other systems such as a retriever or APIs. This is why many LLM applications use a control flow of steps before and / or after LLM calls. As an example [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of relevant documents to a question, and passes those documents to an LLM in order to ground the response. Often a control flow of steps before and / or after an LLM is called a "chain." Chains are a popular paradigm for programming with LLMs and offer a high degree of reliability; the same set of steps runs with each chain invocation.
 
-However, we often want LLM systems that can pick their own control flow! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent given an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:
+However, we often want LLM systems that can pick their own control flow! This is one definition of an [agent](https://blog.langchain.dev/what-is-an-agent/): an agent is a system that uses an LLM to decide the control flow of an application. Unlike a chain, an agent gives an LLM some degree of control over the sequence of steps in the application. Examples of using an LLM to decide the control of an application:
 
 - Using an LLM to route between two potential paths
 - Using an LLM to decide which of many tools to call
 - Using an LLM to decide whether the generated answer is sufficient or more work is need
 
-There are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/) to consider, which given an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem. 
+There are many different types of [agent architectures](https://blog.langchain.dev/what-is-a-cognitive-architecture/) to consider, which give an LLM varying levels of control. On one extreme, a router allows an LLM to select a single step from a specified set of options and, on the other extreme, a fully autonomous long-running agent may have complete freedom to select any sequence of steps that it wants for a given problem. 
 
 ![Agent Types](img/agent_types.png)
 

docs/docs/concepts/human_in_the_loop.md

@@ -27,8 +27,8 @@ Adding a [breakpoint](./low_level.md#breakpoints) a specific location in the gra
 Here, we compile our graph with a checkpointer and a breakpoint at the node we want to interrupt before, `step_for_human_in_the_loop`. We then perform one of the above interaction patterns, which will create a new checkpoint if a human edits the graph state. The new checkpoint is saved to the `thread` and we can resume the graph execution from there by passing in `None` as the input.
 
 ```python
-# Compile our graph with a checkpoitner and a breakpoint before "step_for_human_in_the_loop"
-graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["step_for_human_in_the_loop"])
+# Compile our graph with a checkpointer and a breakpoint before "step_for_human_in_the_loop"
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["step_for_human_in_the_loop"])
 
 # Run the graph up to the breakpoint
 thread_config = {"configurable": {"thread_id": "1"}}
@@ -98,8 +98,8 @@ With persistence, we can surface the current agent state as well as the next ste
 If approved, the graph resumes execution from the last saved checkpoint, which is saved to the `thread`:
 
 ```python
-# Compile our graph with a checkpoitner and a breakpoint before the step to approve
-graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["node_2"])
+# Compile our graph with a checkpointer and a breakpoint before the step to approve
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
 
 # Run the graph up to the breakpoint
 for event in graph.stream(inputs, thread, stream_mode="values"):
@@ -120,7 +120,7 @@ See [our guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a detailed h
 
 Sometimes we want to review and edit the agent's state. 
  
-As with approval, we can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior the the step we want to check. 
+As with approval, we can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to the step we want to check. 
  
 We can surface the current state to a user and allow the user to edit the agent state.
  
@@ -131,8 +131,8 @@ We can edit the graph state by forking the current checkpoint, which is saved to
 We can then proceed with the graph from our forked checkpoint as done before. 
 
 ```python
-# Compile our graph with a checkpoitner and a breakpoint before the step to review
-graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["node_2"])
+# Compile our graph with a checkpointer and a breakpoint before the step to review
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
 
 # Run the graph up to the breakpoint
 for event in graph.stream(inputs, thread, stream_mode="values"):
@@ -170,11 +170,11 @@ With editing, the user makes a decision about whether or not to edit the graph s
 
 With input, we explicitly define a node in our graph for collecting human input!
 
-The the state update with the human input then runs *as this node*.
+The state update with the human input then runs *as this node*.
 
 ```python
-# Compile our graph with a checkpoitner and a breakpoint before the step to to collect human input
-graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["human_input"])
+# Compile our graph with a checkpointer and a breakpoint before the step to to collect human input
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_input"])
 
 # Run the graph up to the breakpoint
 for event in graph.stream(inputs, thread, stream_mode="values"):
@@ -211,8 +211,8 @@ Even if the tool call is correct, we may also want to apply discretion:
 With these points in mind, we can combine the above ideas to create a human-in-the-loop review of a tool call.
 
 ```python
-# Compile our graph with a checkpoitner and a breakpoint before the step to to review the tool call from the LLM 
-graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["human_review"])
+# Compile our graph with a checkpointer and a breakpoint before the step to to review the tool call from the LLM 
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_review"])
 
 # Run the graph up to the breakpoint
 for event in graph.stream(inputs, thread, stream_mode="values"):
@@ -319,4 +319,4 @@ for event in graph.stream(None, config, stream_mode="values"):
 
 See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#update-state) for related context on forking.
 
-See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
+See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!

docs/docs/concepts/index.md

@@ -30,7 +30,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - [Streaming](streaming.md): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs. 
 - [FAQ](faq.md): Frequently asked questions about LangGraph.
 
-## LangGraph Platform 
+## LangGraph Platform
 
 LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
 

docs/docs/concepts/langgraph_cli.md

@@ -28,9 +28,28 @@ The CLI provides the following core functionality:
 
 The `langgraph build` command builds a Docker image for the [LangGraph API server](./langgraph_server.md) that can be directly deployed.
 
+### `dev`
+
+!!! note "New in version 0.1.55"
+    The `langgraph dev` command was introduced in langgraph-cli version 0.1.55.
+
+The `langgraph dev` command starts a lightweight development server that requires no Docker installation. This server is ideal for rapid development and testing, with features like:
+
+- Hot reloading: Changes to your code are automatically detected and reloaded
+- Debugger support: Attach your IDE's debugger for line-by-line debugging
+- In-memory state with local persistence: Server state is stored in memory for speed but persisted locally between restarts
+
+To use this command, you need to install the CLI with the "inmem" extra:
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+```
+
+**Note**: This command is intended for local development and testing only. It is not recommended for production use. Since it does not use Docker, we recommend using virtual environments to manage your project's dependencies.
+
 ### `up`
 
-The `langgraph up` command starts an instance of the [LangGraph API server](./langgraph_server.md) locally. This requires docker to be installed and running locally. It also requires a LangSmith API key for local development or a license key for production use.
+The `langgraph up` command starts an instance of the [LangGraph API server](./langgraph_server.md) locally in a docker container. This requires thedocker server to be running locally. It also requires a LangSmith API key for local development or a license key for production use.
 
 The server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
 

docs/docs/concepts/langgraph_cloud.md

@@ -14,6 +14,13 @@ A **deployment** is an instance of a LangGraph API. A single deployment can have
 
 See the [how-to guide](../cloud/deployment/cloud.md#create-new-deployment) for creating a new deployment.
 
+## Resource Allocation
+
+| **Deployment Type** | **CPU** | **Memory** | **Scaling**         |
+|---------------------|---------|------------|---------------------|
+| Development         | 1 CPU   | 1 GB       | Up to 1 container   |
+| Production          | 1 CPU   | 2 GB       | Up to 10 containers |
+
 ## Revision
 
 A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
@@ -33,6 +40,7 @@ A high-level diagram of a Cloud SaaS deployment.
 
 ![diagram](img/langgraph_cloud_architecture.png)
 
+
 ## Related
 
 - [Deployment Options](./deployment_options.md)

docs/docs/concepts/langgraph_studio.md

@@ -35,6 +35,16 @@ While in Beta, LangGraph Studio is available for free to all [LangSmith](https:/
 
 If you have deployed your LangGraph application on LangGraph Platform (Cloud), you can access the studio as part of that
 
+### Development server
+
+LangGraph CLI also contains a command for running an in-memory development server that can be used to connect a local LangGraph app with the studio.
+See [instructions here](../cloud/reference/cli.md#dev) for more information.
+
+The way this works is that it runs inside your local environment.
+It will spin up an in-memory, development server to deploy the graph.
+You can then connect to the studio via the Cloud hosted version of LangGraph Platform.
+To be clear, the web studio will connect to your locally running server - your agent is still running locally and never leaves your device.
+
 ## Studio FAQs
 
 ### Why is my project failing to start?

docs/docs/concepts/low_level.md

@@ -391,7 +391,7 @@ Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-li
 
 It can often be useful to set breakpoints before or after certain nodes execute. This can be used to wait for human approval before continuing. These can be set when you ["compile" a graph](#compiling-your-graph). You can set breakpoints either _before_ a node executes (using `interrupt_before`) or after a node executes (using `interrupt_after`.)
 
-You **MUST** use a [checkpoiner](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
+You **MUST** use a [checkpointer](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
 
 In order to resume execution, you can just invoke your graph with `None` as the input.
 

docs/docs/concepts/memory.md

@@ -171,7 +171,7 @@ trim_messages(
 
 ## Long-term memory
 
-Long-term memory in LangGraph allows systems to retain information across different conversations or sessions. Unlike short-term memory, which is thread-scoped, long-term memory is saved within custom "namespaces."
+Long-term memory in LangGraph allows systems to retain information across different conversations or sessions. Unlike short-term memory, which is **thread-scoped**, long-term memory is saved within custom "namespaces."
 
 ### Storing memories
 
@@ -180,16 +180,34 @@ LangGraph stores long-term memories as JSON documents in a [store](persistence.m
 ```python
 from langgraph.store.memory import InMemoryStore
 
+
+def embed(texts: list[str]) -> list[list[float]]:
+    # Replace with an actual embedding function or LangChain embeddings object
+    return [[1.0, 2.0] * len(texts)]
+
+
 # InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production use.
-store = InMemoryStore()
+store = InMemoryStore(index={"embed": embed, "dims": 2})
 user_id = "my-user"
 application_context = "chitchat"
 namespace = (user_id, application_context)
-store.put(namespace, "a-memory", {"rules": ["User likes short, direct language", "User only speaks English & python"], "my-key": "my-value"})
+store.put(
+    namespace,
+    "a-memory",
+    {
+        "rules": [
+            "User likes short, direct language",
+            "User only speaks English & python",
+        ],
+        "my-key": "my-value",
+    },
+)
 # get the "memory" by ID
 item = store.get(namespace, "a-memory")
-# list "memories" within this namespace, filtering on content equivalence
-items = store.search(namespace, filter={"my-key": "my-value"})
+# search for "memories" within this namespace, filtering on content equivalence, sorted by vector similarity
+items = store.search(
+    namespace, filter={"my-key": "my-value"}, query="language preferences"
+)
 ```
 
 ### Framework for thinking about long-term memory
@@ -232,7 +250,7 @@ Alternatively, memories can be a collection of documents that are continuously u
 
 However, this shifts some complexity memory updating. The model must now _delete_ or _update_ existing items in the list, which can be tricky. In addition, some models may default to over-inserting and others may default to over-updating. See the [Trustcall](https://github.com/hinthornw/trustcall) package for one way to manage this and consider evaluation (e.g., with a tool like [LangSmith](https://docs.smith.langchain.com/tutorials/Developers/evaluation)) to help you tune the behavior.
 
-Working with document collections also shifts complexity to memory **search** over the list. The `Store` currently supports [filtering by metadata](https://langchain-ai.github.io/langgraph/reference/store/#storage) and will soon add [semantic search shortly](https://python.langchain.com/docs/concepts/vectorstores/), but selecting the most relevant documents can be tricky as the list grows.
+Working with document collections also shifts complexity to memory **search** over the list. The `Store` currently supports both [semantic search](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.SearchOp.query) and [filtering by content](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.SearchOp.filter).
 
 Finally, using a collection of memories can make it challenging to provide comprehensive context to the model. While individual memories may follow a specific schema, this structure might not capture the full context or relationships between memories. As a result, when using these memories to generate responses, the model may lack important contextual information that would be more readily available in a unified profile approach.
 

docs/docs/concepts/persistence.md

@@ -159,7 +159,7 @@ You must pass these when invoking the graph as part of the `configurable` portio
 # {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}  # also valid config
 
 config = {"configurable": {"thread_id": "1"}}
-graph.invoke(inputs, config=config)
+graph.invoke(None, config=config)
 ```
 
 Importantly, LangGraph knows whether a particular checkpoint has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
@@ -218,13 +218,16 @@ The final thing you can optionally specify when calling `update_state` is `as_no
 
 ## Memory Store
 
-![Update](img/persistence/shared_state.png)
+![Model of shared state](img/persistence/shared_state.png)
 
 A [state schema](low_level.md#schema) specifies a set of keys that are populated as a graph is executed. As discussed above, state can be written by a checkpointer to a thread at each graph step, enabling state persistence.
 
 But, what if we want to retrain some information *across threads*? Consider the case of a chatbot where we want to retain specific information about the user across *all* chat conversations (e.g., threads) with that user!
 
-With checkpointers alone, we cannot share information across threads. This motivates the need for the `Store` interface. As an illustration, we can define an `InMemoryStore` to store information about a user across threads. We simply compile our graph with a checkpointer, as before, and will our new `in_memory_store`.
+With checkpointers alone, we cannot share information across threads. This motivates the need for the [`Store`](../reference/store.md#langgraph.store.base.BaseStore) interface. As an illustration, we can define an `InMemoryStore` to store information about a user across threads. We simply compile our graph with a checkpointer, as before, and with our new `in_memory_store` variable.
+
+### Basic Usage
+
 First, let's showcase this in isolation without using LangGraph.
 
 ```python
@@ -239,7 +242,7 @@ user_id = "1"
 namespace_for_memory = (user_id, "memories")
 ```
 
-We use the `store.put` to save memories to our namespace in the store. When we do this, we specify the namespace, as defined above, and a key-value pair for the memory: the key is simply a unique identifier for the memory (`memory_id`) and the value (a dictionary) is the memory itself.
+We use the `store.put` method to save memories to our namespace in the store. When we do this, we specify the namespace, as defined above, and a key-value pair for the memory: the key is simply a unique identifier for the memory (`memory_id`) and the value (a dictionary) is the memory itself.
 
 ```python
 memory_id = str(uuid.uuid4())
@@ -247,7 +250,7 @@ memory = {"food_preference" : "I like pizza"}
 in_memory_store.put(namespace_for_memory, memory_id, memory)
 ```
 
-We can read out memories in our namespace using `store.search`, which will return all memories for a given user as a list. The most recent memory is the last in the list.
+We can read out memories in our namespace using the `store.search` method, which will return all memories for a given user as a list. The most recent memory is the last in the list.
 
 ```python
 memories = in_memory_store.search(namespace_for_memory)
@@ -259,16 +262,69 @@ memories[-1].dict()
  'updated_at': '2024-10-02T17:22:31.590605+00:00'}
 ```
 
-Each memory type is a Python class with certain attributes. We can access it as a dictionary by converting via `.dict` as above.
+Each memory type is a Python class ([`Item`](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.Item)) with certain attributes. We can access it as a dictionary by converting via `.dict` as above.
 The attributes it has are:
 
 - `value`: The value (itself a dictionary) of this memory
-- `key`: The UUID for this memory in this namespace
+- `key`: A unique key for this memory in this namespace
 - `namespace`: A list of strings, the namespace of this memory type
 - `created_at`: Timestamp for when this memory was created
 - `updated_at`: Timestamp for when this memory was updated
 
-With this all in place, we use the `in_memory_store` in LangGraph. The `in_memory_store` works hand-in-hand with the checkpointer: the checkpointer saves state to threads, as discussed above, and the the `in_memory_store` allows us to store arbitrary information for access *across* threads. We compile the graph with both the checkpointer and the `in_memory_store` as follows. 
+### Semantic Search
+
+Beyond simple retrieval, the store also supports semantic search, allowing you to find memories based on meaning rather than exact matches. To enable this, configure the store with an embedding model:
+
+```python
+from langchain.embeddings import init_embeddings
+
+store = InMemoryStore(
+    index={
+        "embed": init_embeddings("openai:text-embedding-3-small"),  # Embedding provider
+        "dims": 1536,                              # Embedding dimensions
+        "fields": ["food_preference", "$"]              # Fields to embed
+    }
+)
+```
+
+Now when searching, you can use natural language queries to find relevant memories:
+
+```python
+# Find memories about food preferences
+# (This can be done after putting memories into the store)
+memories = store.search(
+    namespace_for_memory,
+    query="What does the user like to eat?",
+    limit=3  # Return top 3 matches
+)
+```
+
+You can control which parts of your memories get embedded by configuring the `fields` parameter or by specifying the `index` parameter when storing memories:
+
+```python
+# Store with specific fields to embed
+store.put(
+    namespace_for_memory,
+    str(uuid.uuid4()),
+    {
+        "food_preference": "I love Italian cuisine",
+        "context": "Discussing dinner plans"
+    },
+    index=["food_preference"]  # Only embed "food_preferences" field
+)
+
+# Store without embedding (still retrievable, but not searchable)
+store.put(
+    namespace_for_memory,
+    str(uuid.uuid4()),
+    {"system_info": "Last updated: 2024-01-01"},
+    index=False
+)
+```
+
+### Using in LangGraph
+
+With this all in place, we use the `in_memory_store` in LangGraph. The `in_memory_store` works hand-in-hand with the checkpointer: the checkpointer saves state to threads, as discussed above, and the `in_memory_store` allows us to store arbitrary information for access *across* threads. We compile the graph with both the checkpointer and the `in_memory_store` as follows. 
 
 ```python
 from langgraph.checkpoint.memory import MemorySaver
@@ -296,7 +352,7 @@ for update in graph.stream(
     print(update)
 ```
 
-We can access the `in_memory_store` and the `user_id` in *any node* by passing `store: BaseStore` and `config: RunnableConfig` as node arguments. Just as we saw above, simply use the `put` method to save memories to the store.
+We can access the `in_memory_store` and the `user_id` in *any node* by passing `store: BaseStore` and `config: RunnableConfig` as node arguments. Here's how we might use semantic search in a node to find relevant memories:
 
 ```python
 def update_memory(state: MessagesState, config: RunnableConfig, *, store: BaseStore):
@@ -317,7 +373,7 @@ def update_memory(state: MessagesState, config: RunnableConfig, *, store: BaseSt
 
 ```
 
-As we showed above, we can also access the store in any node and use `search` to get memories. Recall the the memories are returned as a list of objects that can be converted to a dictionary.
+As we showed above, we can also access the store in any node and use the `store.search` method to get memories. Recall the the memories are returned as a list of objects that can be converted to a dictionary.
 
 ```python
 memories[-1].dict()
@@ -332,12 +388,15 @@ We can access the memories and use them in our model call.
 
 ```python
 def call_model(state: MessagesState, config: RunnableConfig, *, store: BaseStore):
-    
     # Get the user id from the config
     user_id = config["configurable"]["user_id"]
     
-    # Get the memories for the user from the store
-    memories = store.search(("memories", user_id))
+    # Search based on the most recent message
+    memories = store.search(
+        namespace,
+        query=state["messages"][-1].content,
+        limit=3
+    )
     info = "\n".join([d.value["memory"] for d in memories])
     
     # ... Use memories in the model call
@@ -356,7 +415,22 @@ for update in graph.stream(
     print(update)
 ```
 
-When we use the LangGraph API, either locally (e.g., in LangGraph Studio) or with LangGraph Cloud, the memory store is available to use by default and does not need to be specified during graph compilation.
+When we use the LangGraph Platform, either locally (e.g., in LangGraph Studio) or with LangGraph Cloud, the base store is available to use by default and does not need to be specified during graph compilation. To enable semantic search, however, you **do** need to configure the indexing settings in your `langgraph.json` file. For example:
+
+```json
+{
+    ...
+    "store": {
+        "index": {
+            "embed": "openai:text-embeddings-3-small",
+            "dims": 1536,
+            "fields": ["$"]
+        }
+    }
+}
+```
+
+See the [deployment guide](../cloud/deployment/semantic_search.md) for more details and configuration options.
 
 ## Checkpointer libraries
 
@@ -405,4 +479,4 @@ Lastly, checkpointing also provides fault-tolerance and error recovery: if one o
 
 #### Pending writes
 
-Additionally, 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.
+Additionally, 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.

docs/docs/concepts/self_hosted.md

@@ -7,7 +7,7 @@
 
 ## Versions
 
-There are two versions of the self hosted deployment: [Self-Hosted Enterprise](./deployment_options.md#self-hosted-enterprise) and [Self-Hosted Lite](./deployment_options.md#self-hosted-lite).
+There are two versions of the self-hosted deployment: [Self-Hosted Enterprise](./deployment_options.md#self-hosted-enterprise) and [Self-Hosted Lite](./deployment_options.md#self-hosted-lite).
 
 ### Self-Hosted Lite
 
@@ -34,6 +34,10 @@ To use the Self-Hosted Enterprise version, you must acquire a license key that y
 
 For step-by-step instructions, see [How to set up a self-hosted deployment of LangGraph](../how-tos/deploy-self-hosted.md).
 
+## Helm Chart
+
+If you would like to deploy LangGraph Cloud on Kubernetes, you can use this [Helm chart](https://github.com/langchain-ai/helm/blob/main/charts/langgraph-cloud/README.md).
+
 ## Related
 
 - [How to set up a self-hosted deployment of LangGraph](../how-tos/deploy-self-hosted.md).

docs/docs/concepts/template_applications.md

@@ -6,22 +6,14 @@
 
 Templates are open source reference applications designed to help you get started quickly when building with LangGraph. They provide working examples of common agentic workflows that can be customized to your needs.
 
-Templates can be accessed via [LangGraph Studio](langgraph_studio.md), or cloned directly from Github. You can download LangGraph Studio and see available templates [here](https://studio.langchain.com/).
+Templates can be accessed via [LangGraph Studio (macOS only)](langgraph_studio.md), or cloned directly from Github. You can download LangGraph Studio and see available templates [here](https://studio.langchain.com/).
 
 ## Available templates
 
-- **New LangGraph Project**: A simple, minimal chatbot with memory.
-  - [Python](https://github.com/langchain-ai/new-langgraph-project)
-  - [JS/TS](https://github.com/langchain-ai/new-langgraphjs-project)
-- **ReAct Agent**: A simple agent that can be flexibly extended to many tools.
-  - [Python](https://github.com/langchain-ai/react-agent)
-  - [JS/TS](https://github.com/langchain-ai/react-agent-js)
-- **Memory Agent**: A ReAct-style agent with an additional tool to store memories for use across conversational threads.
-  - [Python](https://github.com/langchain-ai/memory-agent)
-  - [JS/TS](https://github.com/langchain-ai/memory-agent-js)
-- **Retrieval Agent**: An agent that includes a retrieval-based question-answering system.
-  - [Python](https://github.com/langchain-ai/retrieval-agent-template)
-  - [JS/TS](https://github.com/langchain-ai/retrieval-agent-template-js)
-- **Data-enrichment Agent**: An agent that performs web searches and organizes its findings into a structured format.
-  - [Python](https://github.com/langchain-ai/data-enrichment)
-  - [JS/TS](https://github.com/langchain-ai/data-enrichment-js)
+| Template                  | Description                                                                              | Python                                                           | JS/TS                                                               |
+|---------------------------|------------------------------------------------------------------------------------------|------------------------------------------------------------------|---------------------------------------------------------------------|
+| **New LangGraph Project** | A simple, minimal chatbot with memory.                                                   | [Repo](https://github.com/langchain-ai/new-langgraph-project)    | [Repo](https://github.com/langchain-ai/new-langgraphjs-project)     |
+| **ReAct Agent**           | A simple agent that can be flexibly extended to many tools.                              | [Repo](https://github.com/langchain-ai/react-agent)              | [Repo](https://github.com/langchain-ai/react-agent-js)              |
+| **Memory Agent**          | A ReAct-style agent with an additional tool to store memories for use across threads.    | [Repo](https://github.com/langchain-ai/memory-agent)             | [Repo](https://github.com/langchain-ai/memory-agent-js)             |
+| **Retrieval Agent**       | An agent that includes a retrieval-based question-answering system.                      | [Repo](https://github.com/langchain-ai/retrieval-agent-template) | [Repo](https://github.com/langchain-ai/retrieval-agent-template-js) |
+| **Data-Enrichment Agent** | An agent that performs web searches and organizes its findings into a structured format. | [Repo](https://github.com/langchain-ai/data-enrichment)          | [Repo](https://github.com/langchain-ai/data-enrichment-js)          |

docs/docs/how-tos/autogen-langgraph-platform.ipynb

@@ -0,0 +1,171 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8381b6e0-29a6-48c5-b451-5d2549351249",
+   "metadata": {},
+   "source": [
+    "# How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks\n",
+    "\n",
+    "[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) provides infrastructure for deploying agents. This integrates seamlessly with LangGraph, but can also work with other frameworks. The way to make this work is to wrap the agent in a single LangGraph node, and have that be the entire graph.\n",
+    "\n",
+    "Doing so will allow you to deploy to LangGraph Platform, and allows you to get a lot of the [benefits](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). You get horizontally scalable infrastructure, a task queue to handle bursty operations, a persistence layer to power short term memory, and long term memory support.\n",
+    "\n",
+    "In this guide we show how to do this with an AutoGen agent, but this method should work for agents defined in other frameworks like CrewAI, LlamaIndex, and others as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1113cb16-b538-448c-924c-85731ce96ebd",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f05993fa-9d03-4f45-bc13-0a8d87260d86",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "%pip install autogen langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4e0ca12-1714-4776-a30a-9527e519799b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3",
+   "metadata": {},
+   "source": [
+    "## Define autogen agent\n",
+    "\n",
+    "Here we define our AutoGen agent. From https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4a14dc7-d565-4207-8788-525f85b9fb27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import autogen\n",
+    "import os\n",
+    "\n",
+    "config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n",
+    "\n",
+    "llm_config = {\n",
+    "    \"timeout\": 600,\n",
+    "    \"cache_seed\": 42,\n",
+    "    \"config_list\": config_list,\n",
+    "    \"temperature\": 0,\n",
+    "}\n",
+    "\n",
+    "autogen_agent = autogen.AssistantAgent(\n",
+    "    name=\"assistant\",\n",
+    "    llm_config=llm_config,\n",
+    ")\n",
+    "\n",
+    "user_proxy = autogen.UserProxyAgent(\n",
+    "    name=\"user_proxy\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    max_consecutive_auto_reply=10,\n",
+    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
+    "    code_execution_config={\n",
+    "        \"work_dir\": \"web\",\n",
+    "        \"use_docker\": False,\n",
+    "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
+    "    llm_config=llm_config,\n",
+    "    system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1170836-f23e-4e4c-ab83-ce791cd7fbd2",
+   "metadata": {},
+   "source": [
+    "## Wrap in LangGraph\n",
+    "\n",
+    "We now wrap the AutoGen agent in a single LangGraph node, and make that the entire graph.\n",
+    "The main thing this involves is defining an Input and Output schema for the node, which you would need to do if deploying this manually, so it's no extra work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b417c16-ff4e-4d5c-a9a9-0aaeeef6ede5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import StateGraph, MessagesState\n",
+    "\n",
+    "\n",
+    "def call_autogen_agent(state: MessagesState):\n",
+    "    last_message = state[\"messages\"][-1]\n",
+    "    response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)\n",
+    "    # get the final response from the agent\n",
+    "    content = response.chat_history[-1][\"content\"]\n",
+    "    return {\"messages\": {\"role\": \"assistant\", \"content\": content}}\n",
+    "\n",
+    "\n",
+    "graph = StateGraph(MessagesState)\n",
+    "graph.add_node(call_autogen_agent)\n",
+    "graph.set_entry_point(\"call_autogen_agent\")\n",
+    "graph = graph.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6a18377-ac29-478f-a76a-b213f1a3c85d",
+   "metadata": {},
+   "source": [
+    "## Deploy with LangGraph Platform\n",
+    "\n",
+    "You can now deploy this as you normally would with LangGraph Platform. See [these instructions](https://langchain-ai.github.io/langgraph/concepts/deployment_options/) for more details."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/configuration.ipynb

@@ -345,7 +345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.1"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/cross-thread-persistence.ipynb

@@ -41,6 +41,9 @@
     "    <p>\n",
     "    Support for the <code><a href=\"https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore\">Store</a></code> API that is used in this guide was added in LangGraph <code>v0.2.32</code>.\n",
     "    </p>\n",
+    "    <p>\n",
+    "    Support for <b>index</b> and <b>query</b> arguments of the <code><a href=\"https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.BaseStore\">Store</a></code> API that is used in this guide was added in LangGraph <code>v0.2.54</code>.\n",
+    "    </p>\n",
     "</div>\n",
     "\n",
     "## Setup\n",
@@ -114,7 +117,7 @@
     "\n",
     "Importantly, to determine the user, we will be passing `user_id` via the config keyword argument of the node function.\n",
     "\n",
-    "Let's first define an `InMemoryStore` which is already populated with some memories about the users."
+    "Let's first define an `InMemoryStore` already populated with some memories about the users."
    ]
   },
   {
@@ -125,8 +128,14 @@
    "outputs": [],
    "source": [
     "from langgraph.store.memory import InMemoryStore\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
-    "in_memory_store = InMemoryStore()"
+    "in_memory_store = InMemoryStore(\n",
+    "    index={\n",
+    "        \"embed\": OpenAIEmbeddings(model=\"text-embedding-3-small\"),\n",
+    "        \"dims\": 1536,\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -163,7 +172,7 @@
     "def call_model(state: MessagesState, config: RunnableConfig, *, store: BaseStore):\n",
     "    user_id = config[\"configurable\"][\"user_id\"]\n",
     "    namespace = (\"memories\", user_id)\n",
-    "    memories = store.search(namespace)\n",
+    "    memories = store.search(namespace, query=str(state[\"messages\"][-1].content))\n",
     "    info = \"\\n\".join([d.value[\"data\"] for d in memories])\n",
     "    system_msg = f\"You are a helpful assistant talking to the user. User info: {info}\"\n",
     "\n",

docs/docs/how-tos/deploy-self-hosted.md

@@ -17,14 +17,18 @@ You will need to do the following:
 2. Build a docker image with the [LangGraph Server](../concepts/langgraph_server.md) using the [LangGraph CLI](../concepts/langgraph_cli.md).
 3. Deploy a web server that will run the docker image and pass in the necessary environment variables.
 
+## Helm Chart
+
+If you would like to deploy LangGraph Cloud on Kubernetes, you can use this [Helm chart](https://github.com/langchain-ai/helm/blob/main/charts/langgraph-cloud/README.md).
+
 ## Environment Variables
 
 You will eventually need to pass in the following environment variables to the LangGraph Deploy server:
 
 - `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs.
 - `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics.
-- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite]) LangSmith API key. This will be used to authenticate ONCE at server start up.
-- `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using Self-Hosted Enterprise) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
+- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
+- `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
 
 
 ## Build the Docker Image
@@ -70,7 +74,7 @@ If you want to run this quickly without setting up a separate Redis and Postgres
     * You need to replace `my-image` with the name of the image you built in the previous step (from `langgraph build`).
     and you should provide appropriate values for `REDIS_URI`, `DATABASE_URI`, and `LANGSMITH_API_KEY`.
     * If your application requires additional environment variables, you can pass them in a similar way.
-    * If using Self-Hosted Enterprise, you must provide `LANGGRAPH_CLOUD_LICENSE_KEY` as an additional environment variable.
+    * If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise), you must provide `LANGGRAPH_CLOUD_LICENSE_KEY` as an additional environment variable.
 
 
 ### Using Docker Compose

docs/docs/how-tos/index.md

@@ -39,6 +39,8 @@ LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) i
 - [How to manage conversation history](memory/manage-conversation-history.ipynb)
 - [How to delete messages](memory/delete-messages.ipynb)
 - [How to add summary conversation memory](memory/add-summary-conversation-history.ipynb)
+- [How to add long-term memory (cross-thread)](cross-thread-persistence.ipynb)
+- [How to use semantic search for long-term memory](memory/semantic-search.ipynb)
 
 ### Human-in-the-loop
 
@@ -70,7 +72,7 @@ you to involve humans in the decision-making process of your graph. These how-to
 
 ### Tool calling
 
-[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message. 
+[Tool calling](https://python.langchain.com/docs/concepts/tool_calling/) is a type of chat model API that accepts tool schemas, along with messages, as input and returns invocations of those tools as part of the output message.
 
 These how-to guides show common patterns for tool calling with LangGraph:
 
@@ -103,6 +105,7 @@ These how-to guides show common patterns for tool calling with LangGraph:
 - [How to force function calling agent to structure output](react-agent-structured-output.ipynb)
 - [How to pass custom LangSmith run ID for graph runs](run-id-langsmith.ipynb)
 - [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
+- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](autogen-integration.ipynb)
 
 ### Prebuilt ReAct Agent
 
@@ -122,7 +125,7 @@ These guides show how to use the prebuilt ReAct agent:
 
 This section includes how-to guides for LangGraph Platform.
 
-LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework. 
+LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
 
 The LangGraph Platform offers a few different deployment options described in the [deployment options guide](../concepts/deployment_options.md).
 
@@ -138,9 +141,11 @@ Learn how to set up your app for deployment to LangGraph Platform:
 - [How to set up app for deployment (requirements.txt)](../cloud/deployment/setup.md)
 - [How to set up app for deployment (pyproject.toml)](../cloud/deployment/setup_pyproject.md)
 - [How to set up app for deployment (JavaScript)](../cloud/deployment/setup_javascript.md)
+- [How to add semantic search](../cloud/deployment/semantic_search.md)
 - [How to customize Dockerfile](../cloud/deployment/custom_docker.md)
 - [How to test locally](../cloud/deployment/test_locally.md)
 - [How to rebuild graph at runtime](../cloud/deployment/graph_rebuild.md)
+- [How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks](autogen-langgraph-platform.ipynb)
 
 ### Deployment
 
@@ -148,7 +153,8 @@ LangGraph applications can be deployed using LangGraph Cloud, which provides a r
 
 - [How to deploy to LangGraph cloud](../cloud/deployment/cloud.md)
 - [How to deploy to a self-hosted environment](./deploy-self-hosted.md)
-- [How to interact with the deployment using RemoteGraph](./use-remote-graph.md) 
+- [How to interact with the deployment using RemoteGraph](./use-remote-graph.md)
+
 ### Assistants
 
 [Assistants](../concepts/assistants.md) is a configured instance of a template.
@@ -163,7 +169,7 @@ LangGraph applications can be deployed using LangGraph Cloud, which provides a r
 
 ### Runs
 
-LangGraph Cloud supports multiple types of runs besides streaming runs.
+LangGraph Platform supports multiple types of runs besides streaming runs.
 
 - [How to run an agent in the background](../cloud/how-tos/background_run.md)
 - [How to run multiple agents in the same thread](../cloud/how-tos/same-thread.md)
@@ -183,7 +189,7 @@ Streaming the results of your LLM application is vital for ensuring a good user
 
 ### Human-in-the-loop
 
-When creating complex graphs, leaving every decision up to the LLM can be dangerous, especially when the decisions involve invoking certain tools or accessing specific documents. To remedy this, LangGraph allows you to insert human-in-the-loop behavior to ensure your graph does not have undesired outcomes. Read more about the different ways you can add human-in-the-loop capabilities to your LangGraph Cloud projects in these how-to guides:
+When designing complex graphs, relying entirely on the LLM for decision-making can be risky, particularly when it involves tools that interact with files, APIs, or databases. These interactions may lead to unintended data access or modifications, depending on the use case. To mitigate these risks, LangGraph allows you to integrate human-in-the-loop behavior, ensuring your LLM applications operate as intended without undesirable outcomes.
 
 - [How to add a breakpoint](../cloud/how-tos/human_in_the_loop_breakpoint.md)
 - [How to wait for user input](../cloud/how-tos/human_in_the_loop_user_input.md)
@@ -193,7 +199,7 @@ When creating complex graphs, leaving every decision up to the LLM can be danger
 
 ### Double-texting
 
-Graph execution can take a while, and sometimes users may change their mind about the input they wanted to send before their original input has finished running. For example, a user might notice a typo in their original request and will edit the prompt and resend it. Deciding what to do in these cases is important for ensuring a smooth user experience and preventing your graphs from behaving in unexpected ways. The following how-to guides provide information on the various options LangGraph Cloud gives you for dealing with double-texting:
+Graph execution can take a while, and sometimes users may change their mind about the input they wanted to send before their original input has finished running. For example, a user might notice a typo in their original request and will edit the prompt and resend it. Deciding what to do in these cases is important for ensuring a smooth user experience and preventing your graphs from behaving in unexpected ways.
 
 - [How to use the interrupt option](../cloud/how-tos/interrupt_concurrent.md)
 - [How to use the rollback option](../cloud/how-tos/rollback_concurrent.md)
@@ -213,8 +219,9 @@ Graph execution can take a while, and sometimes users may change their mind abou
 LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.
 
 - [How to connect to a LangGraph Cloud deployment](../cloud/how-tos/test_deployment.md)
-- [How to connect to a local deployment](../cloud/how-tos/test_local_deployment.md)
-- [How to test your graph in LangGraph Studio](../cloud/how-tos/invoke_studio.md)
+- [How to connect to a local dev server](../how-tos/local-studio.md)
+- [How to connect to a local deployment (Docker)](../cloud/how-tos/test_local_deployment.md)
+- [How to test your graph in LangGraph Studio (MacOS only)](../cloud/how-tos/invoke_studio.md)
 - [How to interact with threads in LangGraph Studio](../cloud/how-tos/threads_studio.md)
 
 ## Troubleshooting
@@ -226,5 +233,3 @@ These are the guides for resolving common errors you may find while building wit
 - [INVALID_GRAPH_NODE_RETURN_VALUE](../troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md)
 - [MULTIPLE_SUBGRAPHS](../troubleshooting/errors/MULTIPLE_SUBGRAPHS.md)
 - [INVALID_CHAT_HISTORY](../troubleshooting/errors/INVALID_CHAT_HISTORY.md)
-
-

docs/docs/how-tos/local-studio.md

@@ -0,0 +1,86 @@
+# How to connect a local agent to LangGraph Studio
+
+This guide shows you how to connect your local agent to [LangGraph Studio](../concepts/langgraph_studio.md) for visualization, interaction, and debugging.
+
+## Connection Options
+
+There are two ways to connect your local agent to LangGraph Studio:
+
+- [LangGraph Desktop](../concepts/langgraph_studio.md#desktop-app): Application, Mac only, requires Docker
+- [Development Server](../concepts/langgraph_studio.md#dev-server): Python package, all platforms, no Docker
+
+In this guide we will cover how to use the development server as that is generally an easier and better experience.
+
+## Setup your application
+
+First, you will need to setup your application in the proper format.
+This means defining a `langgraph.json` file which contains paths to your agent(s).
+See [this guide](../concepts/application_structure.md) for information on how to do so.
+
+## Install langgraph-cli
+
+You will need to install [`langgraph-cli`](../cloud/reference/cli.md#langgraph-cli) (version `0.1.55` or higher).
+You will need to make sure to install the `inmem` extras.
+
+```shell
+pip install "langgraph-cli[inmem]==0.1.55"
+```
+
+## Run the development server
+
+1. Navigate to your project directory (where `langgraph.json` is located)
+
+2. Start the server:
+   ```bash
+   langgraph dev
+   ```
+
+This will look for the `langgraph.json` file in your current directory. 
+In there, it will find the paths to the graph(s), and start those up.
+It will then automatically connect to the cloud-hosted studio.
+
+## Use the studio
+
+After connecting to the studio, a browser window should automatically pop up.
+This will use the cloud hosted studio UI to connect to your local development server.
+Your graph is still running locally, the UI is connecting to visualizing the agent and threads that are defined locally.
+
+The graph will always use the most up-to-date code, so you will be able to change the underlying code and have it automatically reflected in the studio.
+This is useful for debugging workflows.
+You can run your graph in the UI until it messes up, go in and change your code, and then rerun from the node that failed.
+
+# (Optional) Attach a debugger
+
+For step-by-step debugging with breakpoints and variable inspection:
+
+```bash
+# Install debugpy package
+pip install debugpy
+
+# Start server with debugging enabled
+langgraph dev --debug-port 5678
+```
+
+Then attach your preferred debugger:
+
+=== "VS Code"
+    Add this configuration to `launch.json`:
+    ```json
+    {
+      "name": "Attach to LangGraph",
+      "type": "debugpy",
+      "request": "attach",
+      "connect": {
+        "host": "0.0.0.0",
+        "port": 5678
+      }
+    }
+    ```
+    Specify the port number you chose in the previous step.
+
+=== "PyCharm"
+    1. Go to Run → Edit Configurations
+    2. Click + and select "Python Debug Server"
+    3. Set IDE host name: `localhost`
+    4. Set port: `5678` (or the port number you chose in the previous step)
+    5. Click "OK" and start debugging

docs/docs/how-tos/memory/semantic-search.ipynb

@@ -0,0 +1,423 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to add semantic search to your agent's memory\n",
+    "\n",
+    "This guide shows how to enable semantic search in your agent's memory store. This lets search for items in the store by semantic similarity.\n",
+    "\n",
+    "First, install this guide's prerequisites."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai langchain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, create the store with an [index configuration](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.IndexConfig). By default, stores are configured without semantic/vector search. You can opt in to indexing items when creating the store by providing an [IndexConfig](https://langchain-ai.github.io/langgraph/reference/store/#langgraph.store.base.IndexConfig) to the store's constructor. If your store class does not implement this interface, or if you do not pass in an index configuration, semantic search is disabled, and all `index` arguments passed to `put` or `aput` will have no effect. Below is an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import init_embeddings\n",
+    "from langgraph.store.memory import InMemoryStore\n",
+    "\n",
+    "# Create store with semantic search enabled\n",
+    "embeddings = init_embeddings(\"openai:text-embedding-3-small\")\n",
+    "store = InMemoryStore(\n",
+    "    index={\n",
+    "        \"embed\": embeddings,\n",
+    "        \"dims\": 1536,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's store some memories:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store some memories\n",
+    "store.put((\"user_123\", \"memories\"), \"1\", {\"text\": \"I love pizza\"})\n",
+    "store.put((\"user_123\", \"memories\"), \"2\", {\"text\": \"I prefer Italian food\"})\n",
+    "store.put((\"user_123\", \"memories\"), \"3\", {\"text\": \"I don't like spicy food\"})\n",
+    "store.put((\"user_123\", \"memories\"), \"3\", {\"text\": \"I am studying econometrics\"})\n",
+    "store.put((\"user_123\", \"memories\"), \"3\", {\"text\": \"I am a plumber\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Search memories using natural language:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Memory: I prefer Italian food (similarity: 0.46482669521168163)\n",
+      "Memory: I love pizza (similarity: 0.35514845174380766)\n",
+      "Memory: I am a plumber (similarity: 0.155698702336571)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find memories about food preferences\n",
+    "memories = store.search((\"user_123\", \"memories\"), query=\"I like food?\", limit=5)\n",
+    "\n",
+    "for memory in memories:\n",
+    "    print(f'Memory: {memory.value[\"text\"]} (similarity: {memory.score})')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using in your agent\n",
+    "\n",
+    "Add semantic search to any node by injecting the store:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "from typing import Optional\n",
+    "\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "from langchain_core.tools import InjectedToolArg\n",
+    "from langgraph.store.base import BaseStore\n",
+    "from typing_extensions import Annotated\n",
+    "\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "\n",
+    "def add_memories(state, *, store: BaseStore):\n",
+    "    # Search based on user's last message\n",
+    "    items = store.search(\n",
+    "        (\"user_123\", \"memories\"), query=state[\"messages\"][-1].content, limit=2\n",
+    "    )\n",
+    "    memories = \"\\n\".join(item.value[\"text\"] for item in items)\n",
+    "    memories = f\"## Memories of user\\n{memories}\" if memories else \"\"\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": f\"You are a helpful assistant.\\n{memories}\"}\n",
+    "    ] + state[\"messages\"]\n",
+    "\n",
+    "\n",
+    "def upsert_memory(\n",
+    "    content: str,\n",
+    "    *,\n",
+    "    memory_id: Optional[uuid.UUID] = None,\n",
+    "    store: Annotated[BaseStore, InjectedToolArg],\n",
+    "):\n",
+    "    \"\"\"Upsert a memory in the database.\"\"\"\n",
+    "    mem_id = memory_id or uuid.uuid4()\n",
+    "    store.put(\n",
+    "        (\"user_123\", \"memories\"),\n",
+    "        key=str(mem_id),\n",
+    "        value={\"text\": content},\n",
+    "    )\n",
+    "    return f\"Stored memory {mem_id}\"\n",
+    "\n",
+    "\n",
+    "agent = create_react_agent(\n",
+    "    init_chat_model(\"openai:gpt-4o-mini\"),\n",
+    "    tools=[upsert_memory],\n",
+    "    state_modifier=add_memories,\n",
+    "    store=store,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What are you in the mood for? Since you love Italian food and pizza, would you like some recommendations for a delicious pizza or a different Italian dish?"
+     ]
+    }
+   ],
+   "source": [
+    "async for message, metadata in agent.astream(\n",
+    "    input={\"messages\": [{\"role\": \"user\", \"content\": \"I'm hungry\"}]},\n",
+    "    stream_mode=\"messages\",\n",
+    "):\n",
+    "    print(message.content, end=\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced Usage\n",
+    "\n",
+    "#### Multi-vector indexing\n",
+    "\n",
+    "Store and search different aspects of memories separately to improve recall or omit certain fields from being indexed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure store to embed both memory content and emotional context\n",
+    "store = InMemoryStore(\n",
+    "    index={\"embed\": embeddings, \"dims\": 1536, \"fields\": [\"memory\", \"emotional_context\"]}\n",
+    ")\n",
+    "# Store memories with different content/emotion pairs\n",
+    "store.put(\n",
+    "    (\"user_123\", \"memories\"),\n",
+    "    \"mem1\",\n",
+    "    {\n",
+    "        \"memory\": \"Had pizza with friends at Mario's\",\n",
+    "        \"emotional_context\": \"felt happy and connected\",\n",
+    "        \"this_isnt_indexed\": \"I prefer ravioli though\",\n",
+    "    },\n",
+    ")\n",
+    "store.put(\n",
+    "    (\"user_123\", \"memories\"),\n",
+    "    \"mem2\",\n",
+    "    {\n",
+    "        \"memory\": \"Ate alone at home\",\n",
+    "        \"emotional_context\": \"felt a bit lonely\",\n",
+    "        \"this_isnt_indexed\": \"I like pie\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Search focusing on emotional state - matches mem2\n",
+    "results = store.search(\n",
+    "    (\"user_123\", \"memories\"), query=\"times they felt isolated\", limit=1\n",
+    ")\n",
+    "print(\"Expect mem 2\")\n",
+    "for r in results:\n",
+    "    print(f\"Item: {r.key}; Score ({r.score})\")\n",
+    "    print(f\"Memory: {r.value['memory']}\")\n",
+    "    print(f\"Emotion: {r.value['emotional_context']}\\n\")\n",
+    "\n",
+    "# Search focusing on social eating - matches mem1\n",
+    "print(\"Expect mem1\")\n",
+    "results = store.search((\"user_123\", \"memories\"), query=\"fun pizza\", limit=1)\n",
+    "for r in results:\n",
+    "    print(f\"Item: {r.key}; Score ({r.score})\")\n",
+    "    print(f\"Memory: {r.value['memory']}\")\n",
+    "    print(f\"Emotion: {r.value['emotional_context']}\\n\")\n",
+    "\n",
+    "print(\"Expect random lower score (ravioli not indexed)\")\n",
+    "results = store.search((\"user_123\", \"memories\"), query=\"ravioli\", limit=1)\n",
+    "for r in results:\n",
+    "    print(f\"Item: {r.key}; Score ({r.score})\")\n",
+    "    print(f\"Memory: {r.value['memory']}\")\n",
+    "    print(f\"Emotion: {r.value['emotional_context']}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Override fields at storage time\n",
+    "You can override which fields to embed when storing a specific memory using `put(..., index=[...fields])`, regardless of the store's default configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expect mem1\n",
+      "Item: mem1; Score (0.3374698138722726)\n",
+      "Memory: I love spicy food\n",
+      "Context: At a Thai restaurant\n",
+      "\n",
+      "Expect mem2\n",
+      "Item: mem2; Score (0.3679447999059255)\n",
+      "Memory: The restaurant was too loud\n",
+      "Context: Dinner at an Italian place\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "store = InMemoryStore(\n",
+    "    index={\n",
+    "        \"embed\": embeddings,\n",
+    "        \"dims\": 1536,\n",
+    "        \"fields\": [\"memory\"],\n",
+    "    }  # Default to embed memory field\n",
+    ")\n",
+    "\n",
+    "# Store one memory with default indexing\n",
+    "store.put(\n",
+    "    (\"user_123\", \"memories\"),\n",
+    "    \"mem1\",\n",
+    "    {\"memory\": \"I love spicy food\", \"context\": \"At a Thai restaurant\"},\n",
+    ")\n",
+    "\n",
+    "# Store another overriding which fields to embed\n",
+    "store.put(\n",
+    "    (\"user_123\", \"memories\"),\n",
+    "    \"mem2\",\n",
+    "    {\"memory\": \"The restaurant was too loud\", \"context\": \"Dinner at an Italian place\"},\n",
+    "    index=[\"context\"],  # Override: only embed the context\n",
+    ")\n",
+    "\n",
+    "# Search about food - matches mem1 (using default field)\n",
+    "print(\"Expect mem1\")\n",
+    "results = store.search(\n",
+    "    (\"user_123\", \"memories\"), query=\"what food do they like\", limit=1\n",
+    ")\n",
+    "for r in results:\n",
+    "    print(f\"Item: {r.key}; Score ({r.score})\")\n",
+    "    print(f\"Memory: {r.value['memory']}\")\n",
+    "    print(f\"Context: {r.value['context']}\\n\")\n",
+    "\n",
+    "# Search about restaurant atmosphere - matches mem2 (using overridden field)\n",
+    "print(\"Expect mem2\")\n",
+    "results = store.search(\n",
+    "    (\"user_123\", \"memories\"), query=\"restaurant environment\", limit=1\n",
+    ")\n",
+    "for r in results:\n",
+    "    print(f\"Item: {r.key}; Score ({r.score})\")\n",
+    "    print(f\"Memory: {r.value['memory']}\")\n",
+    "    print(f\"Context: {r.value['context']}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Disable Indexing for Specific Memories\n",
+    "\n",
+    "Some memories shouldn't be searchable by content. You can disable indexing for these while still storing them using \n",
+    "`put(..., index=False)`. Example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "store = InMemoryStore(index={\"embed\": embeddings, \"dims\": 1536, \"fields\": [\"memory\"]})\n",
+    "\n",
+    "# Store a normal indexed memory\n",
+    "store.put(\n",
+    "    (\"user_123\", \"memories\"),\n",
+    "    \"mem1\",\n",
+    "    {\"memory\": \"I love chocolate ice cream\", \"type\": \"preference\"},\n",
+    ")\n",
+    "\n",
+    "# Store a system memory without indexing\n",
+    "store.put(\n",
+    "    (\"user_123\", \"memories\"),\n",
+    "    \"mem2\",\n",
+    "    {\"memory\": \"User completed onboarding\", \"type\": \"system\"},\n",
+    "    index=False,  # Disable indexing entirely\n",
+    ")\n",
+    "\n",
+    "# Search about food preferences - finds mem1\n",
+    "print(\"Expect mem1\")\n",
+    "results = store.search((\"user_123\", \"memories\"), query=\"what food preferences\", limit=1)\n",
+    "for r in results:\n",
+    "    print(f\"Item: {r.key}; Score ({r.score})\")\n",
+    "    print(f\"Memory: {r.value['memory']}\")\n",
+    "    print(f\"Type: {r.value['type']}\\n\")\n",
+    "\n",
+    "# Search about onboarding - won't find mem2 (not indexed)\n",
+    "print(\"Expect low score (mem2 not indexed)\")\n",
+    "results = store.search((\"user_123\", \"memories\"), query=\"onboarding status\", limit=1)\n",
+    "for r in results:\n",
+    "    print(f\"Item: {r.key}; Score ({r.score})\")\n",
+    "    print(f\"Memory: {r.value['memory']}\")\n",
+    "    print(f\"Type: {r.value['type']}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb

@@ -102,7 +102,7 @@
     "from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader\n",
     "\n",
     "# LCEL docs\n",
-    "url = \"https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel\"\n",
+    "url = \"https://python.langchain.com/docs/concepts/lcel/\"\n",
     "loader = RecursiveUrlLoader(\n",
     "    url=url, max_depth=20, extractor=lambda x: Soup(x, \"html.parser\").text\n",
     ")\n",

docs/docs/tutorials/index.md

@@ -6,25 +6,23 @@ title: Tutorials
 
 # Tutorials
 
-Welcome to the LangGraph Tutorials! These notebooks introduce LangGraph through building various language agents and applications.
+New to LangGraph or LLM app development? Read this material to get up and running building your first applications.
 
-## Quick Start
+## Get Started 🚀 {#quick-start}
 
-Learn the basics of LangGraph through a comprehensive quick start in which you will build an agent from scratch.
+- [LangGraph Quickstart](introduction.ipynb): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
+- [LangGraph Server Quickstart](langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using the REST API and LangGraph Studio Web UI.
+- [LangGraph Cloud QuickStart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Cloud.
 
-- [Quick Start](introduction.ipynb): In this tutorial, you will build a support chatbot using LangGraph.
-- [LangGraph Cloud Quick Start](../cloud/quick_start.md): In this tutorial, you will build and deploy an agent to LangGraph Cloud.
+## Use cases 🛠️
 
-## Use cases
-
-Learn from example implementations of graphs designed for specific scenarios and that implement common design patterns.
+Explore practical implementations tailored for specific scenarios:
 
 ### Chatbots
 
-- [Customer Support](customer-support/customer-support.ipynb): Build a customer support chatbot to manage flights, hotel reservations, car rentals, and other tasks
-- [Prompt Generation from User Requirements](chatbots/information-gather-prompting.ipynb): Build an information gathering chatbot
-- [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Build a code analysis and generation assistant
-
+- [Customer Support](customer-support/customer-support.ipynb): Build a multi-functional support bot for flights, hotels, and car rentals.
+- [Prompt Generation from User Requirements](chatbots/information-gather-prompting.ipynb): Build an information gathering chatbot.
+- [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Build a code analysis and generation assistant.
 
 ### RAG
 

docs/docs/tutorials/introduction.ipynb

@@ -5,21 +5,21 @@
    "id": "4a1aae78-88a6-4133-b905-7e46c8e3772f",
    "metadata": {},
    "source": [
-    "# LangGraph Quick Start\n",
+    "# 🚀 LangGraph Quick Start\n",
     "\n",
-    "In this comprehensive quick start, we will build a support chatbot in LangGraph that can:\n",
+    "In this tutorial, we will build a support chatbot in LangGraph that can:\n",
     "\n",
-    "- Answer common questions by searching the web\n",
-    "- Maintain conversation state across calls\n",
-    "- Route complex queries to a human for review\n",
-    "- Use custom state to control its behavior\n",
-    "- Rewind and explore alternative conversation paths\n",
+    "✅ **Answer common questions** by searching the web  \n",
+    "✅ **Maintain conversation state** across calls  \n",
+    "✅ **Route complex queries** to a human for review  \n",
+    "✅ **Use custom state** to control its behavior  \n",
+    "✅ **Rewind and explore** alternative conversation paths  \n",
     "\n",
-    "We'll start with a basic chatbot and progressively add more sophisticated capabilities, introducing key LangGraph concepts along the way.\n",
+    "We'll start with a **basic chatbot** and progressively add more sophisticated capabilities, introducing key LangGraph concepts along the way. Let’s dive in! 🌟\n",
     "\n",
     "## Setup\n",
     "\n",
-    "First, install the required packages:"
+    "First, install the required packages and configure your environment:"
    ]
   },
   {
@@ -33,14 +33,6 @@
     "%pip install -U langgraph langsmith langchain_anthropic"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "a6d1e870-1bc0-4d44-86c0-96681ccf6113",
-   "metadata": {},
-   "source": [
-    "Next, set your API keys:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -120,27 +112,24 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "31c755cd-8994-4867-bdff-96a55d7beae7",
+   "id": "c08c41da-0855-49d3-9a3d-b7eb94413367",
    "metadata": {},
    "source": [
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Note</p>\n",
-    "    <p>\n",
-    "    The first thing you do when you define a graph is define the <code>State</code> of the graph. The <code>State</code> consists of the schema of the graph as well as <a href=\"https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers\">reducer functions</a> which specify how to apply updates to the state. In our example <code>State</code> is a <code>TypedDict</code> with a single key: <code>messages</code>. The <code>messages</code> key is annotated with the <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages\"><code>add_messages</code></a> reducer function, which tells LangGraph to append new messages to the existing list, rather than overwriting it. State keys without an annotation will be overwritten by each update, storing the most recent value. Check out <a href=\"https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages\">this conceptual guide</a> to learn more about state, reducers and other low-level concepts.\n",
-    "    </p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4137feed-746e-4c72-a34a-f7a699ad5dcf",
-   "metadata": {},
-   "source": [
-    "So now our graph knows two things:\n",
+    "Our graph can now handle two key tasks:\n",
+    "\n",
+    "1. Each `node` can receive the current `State` as input and output an update to the state.\n",
+    "2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function used with the `Annotated` syntax.\n",
+    "\n",
+    "------\n",
+    "\n",
+    "!!! tip \"Concept\"\n",
+    "\n",
+    "    When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a `TypedDict` with one key: `messages`. The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values. Learn more about state, reducers, and related concepts in [this guide](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).\n",
+    "\n",
+    "---------\n",
     "\n",
-    "1. Every `node` we define will receive the current `State` as input and return a value that updates that state.\n",
-    "2. `messages` will be _appended_ to the current list, rather than directly overwritten. This is communicated via the prebuilt [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function in the `Annotated` syntax.\n",
     "\n",
     "Next, add a \"`chatbot`\" node. Nodes represent units of work. They are typically regular python functions."
    ]
@@ -365,7 +354,7 @@
    "id": "f22c5d4a-3134-413c-81fe-dd9752fbeb66",
    "metadata": {},
    "source": [
-    "## Part 2: Enhancing the Chatbot with Tools\n",
+    "## Part 2: 🛠️ Enhancing the Chatbot with Tools\n",
     "\n",
     "To handle queries our chatbot can't answer \"from memory\", we'll integrate a web search tool. Our bot can use this tool to find relevant information and provide better responses.\n",
     "\n",
@@ -2046,7 +2035,7 @@
     "\n",
     "So far, we've relied on a simple state (it's just a list of messages!). You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can add additional fields to the state. In this section, we will extend our chat bot with a new node to illustrate this.\n",
     "\n",
-    "In the examples above, we involved a human deterministically: the graph __always__ interrupted whenever an tool was invoked. Suppose we wanted our chat bot to have the choice of relying on a human.\n",
+    "In the examples above, we involved a human deterministically: the graph __always__ interrupted whenever a tool was invoked. Suppose we wanted our chat bot to have the choice of relying on a human.\n",
     "\n",
     "One way to do this is to create a passthrough \"human\" node, before which the graph will always stop. We will only execute this node if the LLM invokes a \"human\" tool. For our convenience, we will include an \"ask_human\" flag in our graph state that we will flip if the LLM calls this tool.\n",
     "\n",
@@ -3136,11 +3125,29 @@
    "id": "e584d57f-5aad-4507-815f-0b2e4b64b791",
    "metadata": {},
    "source": [
-    "## Conclusion\n",
+    "## Next Steps\n",
     "\n",
-    "Congrats! You've completed the intro tutorial and built a chat bot in LangGraph that supports tool calling, persistent memory, human-in-the-loop interactivity, and even time-travel!\n",
+    "Take your journey further by exploring deployment and advanced features:\n",
     "\n",
-    "The [LangGraph documentation](https://langchain-ai.github.io/langgraph/) is a great resource for diving deeper into the library's capabilities."
+    "### Server Quickstart\n",
+    "\n",
+    "- **[LangGraph Server Quickstart](../langgraph-platform/local-server)**: Launch a LangGraph server locally and interact with it using the REST API and LangGraph Studio Web UI.\n",
+    "\n",
+    "### LangGraph Cloud\n",
+    "\n",
+    "- **[LangGraph Cloud QuickStart](../../cloud/quick_start)**: Deploy your LangGraph app using LangGraph Cloud.\n",
+    "\n",
+    "### LangGraph Framework\n",
+    "\n",
+    "- **[LangGraph Concepts](../../concepts)**: Learn the foundational concepts of LangGraph.  \n",
+    "- **[LangGraph How-to Guides](../../how-tos)**: Guides for common tasks with LangGraph.\n",
+    "\n",
+    "### LangGraph Platform\n",
+    "\n",
+    "Expand your knowledge with these resources:\n",
+    "\n",
+    "- **[LangGraph Platform Concepts](../../concepts#langgraph-platform)**: Understand the foundational concepts of the LangGraph Platform.  \n",
+    "- **[LangGraph Platform How-to Guides](../../how-tos#langgraph-platform)**: Guides for common tasks with LangGraph Platform. "
    ]
   }
  ],
@@ -3160,7 +3167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/langgraph-platform/local-server.md

@@ -0,0 +1,253 @@
+# Quick Start: Launch Local LangGraph Server
+
+This is a quick start guide to help you get a LangGraph app up and running locally.
+
+!!! info "Requirements"
+
+    - Python >= 3.11
+    - [LangGraph CLI](https://langchain-ai.github.io/langgraph/cloud/reference/cli/): Requires langchain-cli[inmem] >= 0.1.58
+
+## Install the LangGraph CLI
+
+```bash
+pip install "langgraph-cli[inmem]==0.1.58" python-dotenv
+```
+
+## 🌱 Create a LangGraph App
+
+Create a new app from the `react-agent` template. This template is a simple agent that can be flexibly extended to many tools.
+
+=== "Python Server"
+
+    ```shell
+    langgraph new path/to/your/app --template react-agent-python 
+    ```
+
+=== "Node Server"
+
+    ```shell
+    langgraph new path/to/your/app --template react-agent-js
+    ```
+
+!!! tip "Additional Templates"
+
+    If you use `langgraph new` without specifying a template, you will be presented with an interactive menu that will allow you to choose from a list of available templates.
+
+## Install Dependencies
+
+In the root of your new LangGraph app, install the dependencies in `edit` mode so your local changes are used by the server:
+
+```shell
+pip install -e .
+```
+
+## Create a `.env` file
+
+You will find a `.env.example` in the root of your new LangGraph app. Create
+a `.env` file in the root of your new LangGraph app and copy the contents of the `.env.example` file into it, filling in the necessary API keys:
+
+```bash
+LANGSMITH_API_KEY=lsv2...
+TAVILY_API_KEY=tvly-...
+ANTHROPIC_API_KEY=sk-
+OPENAI_API_KEY=sk-...
+```
+
+<details><summary>Get API Keys</summary>
+    <ul>
+        <li> <b>LANGSMITH_API_KEY</b>: Go to the <a href="https://smith.langchain.com/settings">LangSmith Settings page</a>. Then clck <b>Create API Key</b>.
+        </li>
+        <li>
+            <b>ANTHROPIC_API_KEY</b>: Get an API key from <a href="https://console.anthropic.com/">Anthropic</a>.
+        </li>
+        <li>
+            <b>OPENAI_API_KEY</b>: Get an API key from <a href="https://openai.com/">OpenAI</a>.
+        </li>
+        <li>
+            <b>TAVILY_API_KEY</b>: Get an API key on the <a href="https://app.tavily.com/">Tavily website</a>.
+        </li>
+    </ul>
+</details>
+
+## 🚀 Launch LangGraph Server
+
+```shell
+langgraph dev
+```
+
+This will start up the LangGraph API server locally. If this runs successfully, you should see something like:
+
+>    Ready!
+> 
+>    - API: [http://localhost:8123](http://localhost:8123/)
+>     
+>    - Docs: http://localhost:8123/docs
+>     
+>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:8123
+
+
+!!! note "In-Memory Mode"
+
+    The `langgraph dev` command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, you should deploy LangGraph Server with access to a persistent storage backend.
+
+    If you want to test your application with a persistent storage backend, you can use the `langgraph up` command instead of `langgraph dev`. You will
+    need to have `docker` installed on your machine to use this command.
+
+## LangGraph Studio Web UI
+
+Test your graph in the LangGraph Studio Web UI by visiting the URL provided in the output of the `langgraph up` command.
+
+>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:8123
+
+!!! warning "Safari Compatibility"
+    
+    Currently, LangGraph Studio Web does not support Safari when running a server locally.
+
+## Test the API
+
+=== "Python SDK (Async)"
+
+    **Install the LangGraph Python SDK**
+
+    ```shell
+    pip install langgraph-sdk
+    ```
+
+    **Send a message to the assistant (threadless run)**
+
+    ```python
+    from langgraph_sdk import get_client
+
+    client = get_client(url="http://localhost:8123")
+
+    async for chunk in client.runs.stream(
+        None,  # Threadless run
+        "agent", # Name of assistant. Defined in langgraph.json.
+        input={
+            "messages": [{
+                "role": "human",
+                "content": "What is LangGraph?",
+            }],
+        },
+        stream_mode="updates",
+    ):
+        print(f"Receiving new event of type: {chunk.event}...")
+        print(chunk.data)
+        print("\n\n")
+    ```
+
+=== "Python SDK (Sync)"
+
+    **Install the LangGraph Python SDK**
+
+    ```shell
+    pip install langgraph-sdk
+    ```
+
+    **Send a message to the assistant (threadless run)**
+
+    ```python
+    from langgraph_sdk import get_sync_client
+
+    client = get_sync_client(url="http://localhost:8123")
+
+    for chunk in client.runs.stream(
+        None,  # Threadless run
+        "agent", # Name of assistant. Defined in langgraph.json.
+        input={
+            "messages": [{
+                "role": "human",
+                "content": "What is LangGraph?",
+            }],
+        },
+        stream_mode="updates",
+    ):
+        print(f"Receiving new event of type: {chunk.event}...")
+        print(chunk.data)
+        print("\n\n")
+    ```
+
+=== "Javascript SDK"
+
+    **Install the LangGraph JS SDK**
+
+    ```shell
+    npm install @langchain/langgraph-sdk
+    ```
+
+    **Send a message to the assistant (threadless run)**
+
+    ```js
+    const { Client } = await import("@langchain/langgraph-sdk");
+
+    // only set the apiUrl if you changed the default port when calling langgraph up
+    const client = new Client({ apiUrl: "http://localhost:8123"});
+
+    const streamResponse = client.runs.stream(
+        null, // Threadless run
+        "agent", // Assistant ID
+        {
+            input: {
+                "messages": [
+                    { "role": "user", "content": "What is LangGraph?"}
+                ]
+            },
+            streamMode: "messages",
+        }
+    );
+
+    for await (const chunk of streamResponse) {
+        console.log(`Receiving new event of type: ${chunk.event}...`);
+        console.log(JSON.stringify(chunk.data));
+        console.log("\n\n");
+    }
+    ```
+
+=== "Rest API"
+
+    ```bash
+    curl -s --request POST \
+        --url "http://localhost:8123/runs/stream" \
+        --header 'Content-Type: application/json' \
+        --data "{
+            \"assistant_id\": \"agent\",
+            \"input\": {
+                \"messages\": [
+                    {
+                        \"role\": \"human\",
+                        \"content\": \"What is LangGraph?\"
+                    }
+                ]
+            },
+            \"stream_mode\": \"updates\"
+        }" 
+    ```
+
+!!! tip "Auth"
+
+    If you're connecting to a remote server, you will need to provide a LangSmith
+    API Key for authorization. Please see the API Reference for the clients
+    for more information.
+
+## Next Steps
+
+Now that you have a LangGraph app running locally, take your journey further by exploring deployment and advanced features:
+
+### 🌐 Deploy to LangGraph Cloud
+
+- **[LangGraph Cloud QuickStart](../../cloud/quick_start.md)**: Deploy your LangGraph app using LangGraph Cloud.
+
+### 📚 Learn More about LangGraph Platform
+
+Expand your knowledge with these resources:
+
+- **[LangGraph Platform Concepts](../../concepts/index.md#langgraph-platform)**: Understand the foundational concepts of the LangGraph Platform.  
+- **[LangGraph Platform How-to Guides](../../how-tos/index.md#langgraph-platform)**: Discover step-by-step guides to build and deploy applications.
+
+### 🛠️ Developer References
+
+Access detailed documentation for development and API usage:
+
+- **[LangGraph Server API Reference](../../cloud/reference/api/api_ref.html)**: Explore the LangGraph Server API documentation.  
+- **[Python SDK Reference](../../cloud/reference/sdk/python_sdk_ref.md)**: Explore the Python SDK API Reference.
+- **[JS/TS SDK Reference](../../cloud/reference/sdk/js_ts_sdk_ref.md)**: Explore the Python SDK API Reference.

docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb

@@ -934,7 +934,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb

@@ -112,7 +112,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_set_env(\"LANGCHAIN_API_KEY\")\n",
+    "_set_env(\"LANGSMITH_API_KEY\")\n",
     "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "os.environ[\"LANGCHAIN_PROJECT\"] = \"local-llama32-rag\""
    ]

docs/mkdocs.yml

@@ -94,6 +94,7 @@ nav:
       - Quick Start:
         - Quick Start: tutorials#quick-start
         - tutorials/introduction.ipynb
+        - tutorials/langgraph-platform/local-server.md
         - cloud/quick_start.md
       - Chatbots:
           - Chatbots: tutorials#chatbots
@@ -163,6 +164,7 @@ nav:
             - how-tos/memory/manage-conversation-history.ipynb
             - how-tos/memory/delete-messages.ipynb
             - how-tos/memory/add-summary-conversation-history.ipynb
+            - how-tos/memory/semantic-search.ipynb
         - Human-in-the-loop:
             - Human-in-the-loop: how-tos#human-in-the-loop
             - how-tos/human_in_the_loop/breakpoints.ipynb
@@ -224,6 +226,7 @@ nav:
               - cloud/deployment/setup.md
               - cloud/deployment/setup_pyproject.md
               - cloud/deployment/setup_javascript.md
+              - cloud/deployment/semantic_search.md
               - cloud/deployment/custom_docker.md
               - cloud/deployment/test_locally.md
               - cloud/deployment/graph_rebuild.md
@@ -438,4 +441,4 @@ validation:
   # and those anchors are not available in the actual doc
   anchors: info
   # this is needed to handle headers with anchors for nav
-  not_found: info
+  not_found: info

libs/checkpoint-duckdb/langgraph/checkpoint/duckdb/__init__.py

@@ -42,7 +42,7 @@ class DuckDBSaver(BaseDuckDBSaver):
             DuckDBSaver: A new DuckDBSaver instance.
         """
         with duckdb.connect(conn_string) as conn:
-            yield DuckDBSaver(conn)
+            yield cls(conn)
 
     def setup(self) -> None:
         """Set up the checkpoint database asynchronously.

libs/checkpoint-duckdb/langgraph/checkpoint/duckdb/aio.py

@@ -45,7 +45,7 @@ class AsyncDuckDBSaver(BaseDuckDBSaver):
             AsyncDuckDBSaver: A new AsyncDuckDBSaver instance.
         """
         with duckdb.connect(conn_string) as conn:
-            yield AsyncDuckDBSaver(conn)
+            yield cls(conn)
 
     async def setup(self) -> None:
         """Set up the checkpoint database asynchronously.

libs/checkpoint-duckdb/langgraph/store/duckdb/aio.py

@@ -156,7 +156,7 @@ class AsyncDuckDBStore(AsyncBatchedBaseStore, BaseDuckDBStore):
             AsyncDuckDBStore: A new AsyncDuckDBStore instance.
         """
         with duckdb.connect(conn_string) as conn:
-            yield AsyncDuckDBStore(conn)
+            yield cls(conn)
 
     async def setup(self) -> None:
         """Set up the store database asynchronously.

libs/checkpoint-duckdb/langgraph/store/duckdb/base.py

@@ -23,6 +23,7 @@ from langgraph.store.base import (
     Op,
     PutOp,
     Result,
+    SearchItem,
     SearchOp,
 )
 
@@ -283,7 +284,7 @@ class DuckDBStore(BaseStore, BaseDuckDBStore[duckdb.DuckDBPyConnection]):
 
         for cur, idx in cursors:
             rows = cur.fetchall()
-            items = [_row_to_item(_convert_ns(row[0]), row) for row in rows]
+            items = [_row_to_search_item(_convert_ns(row[0]), row) for row in rows]
             results[idx] = items
 
     def _batch_list_namespaces_ops(
@@ -376,6 +377,22 @@ def _row_to_item(
     )
 
 
+def _row_to_search_item(
+    namespace: tuple[str, ...],
+    row: tuple,
+) -> SearchItem:
+    """Convert a row from the database into an SearchItem."""
+    # TODO: Add support for search
+    _, key, val, created_at, updated_at = row
+    return SearchItem(
+        value=val if isinstance(val, dict) else json.loads(val),
+        key=key,
+        namespace=namespace,
+        created_at=created_at,
+        updated_at=updated_at,
+    )
+
+
 def _group_ops(ops: Iterable[Op]) -> tuple[dict[type, list[tuple[int, Op]]], int]:
     grouped_ops: dict[type, list[tuple[int, Op]]] = defaultdict(list)
     tot = 0

libs/checkpoint-postgres/Makefile

@@ -5,7 +5,11 @@
 ######################
 
 start-postgres:
-	POSTGRES_VERSION=${POSTGRES_VERSION:-16} docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait
+	POSTGRES_VERSION=${POSTGRES_VERSION:-16} docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait || ( \
+		echo "Failed to start PostgreSQL, printing logs..."; \
+		docker compose -f tests/compose-postgres.yml logs; \
+		exit 1 \
+	)
 
 stop-postgres:
 	docker compose -f tests/compose-postgres.yml down

libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py

@@ -1,10 +1,10 @@
 import threading
+from collections.abc import Iterator, Sequence
 from contextlib import contextmanager
-from typing import Any, Iterator, Optional, Sequence, Union
+from typing import Any, Optional
 
 from langchain_core.runnables import RunnableConfig
-from psycopg import Connection, Cursor, Pipeline
-from psycopg.errors import UndefinedTable
+from psycopg import Capabilities, Connection, Cursor, Pipeline
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import ConnectionPool
@@ -17,21 +17,11 @@ from langgraph.checkpoint.base import (
     CheckpointTuple,
     get_checkpoint_id,
 )
+from langgraph.checkpoint.postgres import _internal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
 from langgraph.checkpoint.serde.base import SerializerProtocol
 
-Conn = Union[Connection[DictRow], ConnectionPool[Connection[DictRow]]]
-
-
-@contextmanager
-def _get_connection(conn: Conn) -> Iterator[Connection[DictRow]]:
-    if isinstance(conn, Connection):
-        yield conn
-    elif isinstance(conn, ConnectionPool):
-        with conn.connection() as conn:
-            yield conn
-    else:
-        raise TypeError(f"Invalid connection type: {type(conn)}")
+Conn = _internal.Conn  # For backward compatibility
 
 
 class PostgresSaver(BasePostgresSaver):
@@ -39,7 +29,7 @@ class PostgresSaver(BasePostgresSaver):
 
     def __init__(
         self,
-        conn: Conn,
+        conn: _internal.Conn,
         pipe: Optional[Pipeline] = None,
         serde: Optional[SerializerProtocol] = None,
     ) -> None:
@@ -52,6 +42,7 @@ class PostgresSaver(BasePostgresSaver):
         self.conn = conn
         self.pipe = pipe
         self.lock = threading.Lock()
+        self.supports_pipeline = Capabilities().has_pipeline()
 
     @classmethod
     @contextmanager
@@ -72,9 +63,9 @@ class PostgresSaver(BasePostgresSaver):
         ) as conn:
             if pipeline:
                 with conn.pipeline() as pipe:
-                    yield PostgresSaver(conn, pipe)
+                    yield cls(conn, pipe)
             else:
-                yield PostgresSaver(conn)
+                yield cls(conn)
 
     def setup(self) -> None:
         """Set up the checkpoint database asynchronously.
@@ -84,16 +75,15 @@ class PostgresSaver(BasePostgresSaver):
         the first time checkpointer is used.
         """
         with self._cursor() as cur:
-            try:
-                row = cur.execute(
-                    "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
-                ).fetchone()
-                if row is None:
-                    version = -1
-                else:
-                    version = row["v"]
-            except UndefinedTable:
+            cur.execute(self.MIGRATIONS[0])
+            results = cur.execute(
+                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+            )
+            row = results.fetchone()
+            if row is None:
                 version = -1
+            else:
+                version = row["v"]
             for v, migration in zip(
                 range(version + 1, len(self.MIGRATIONS)),
                 self.MIGRATIONS[version + 1 :],
@@ -365,7 +355,14 @@ class PostgresSaver(BasePostgresSaver):
 
     @contextmanager
     def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor[DictRow]]:
-        with _get_connection(self.conn) as conn:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline (bool): whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the PostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        with _internal.get_connection(self.conn) as conn:
             if self.pipe:
                 # a connection in pipeline mode can be used concurrently
                 # in multiple threads/coroutines, but only one cursor can be
@@ -379,13 +376,24 @@ class PostgresSaver(BasePostgresSaver):
             elif pipeline:
                 # a connection not in pipeline mode can only be used by one
                 # thread/coroutine at a time, so we acquire a lock
-                with self.lock, conn.pipeline(), conn.cursor(
-                    binary=True, row_factory=dict_row
-                ) as cur:
-                    yield cur
+                if self.supports_pipeline:
+                    with (
+                        self.lock,
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    with (
+                        self.lock,
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
             else:
                 with self.lock, conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
 
-__all__ = ["PostgresSaver", "Conn"]
+__all__ = ["PostgresSaver", "BasePostgresSaver", "Conn"]

libs/checkpoint-postgres/langgraph/checkpoint/postgres/_ainternal.py

@@ -0,0 +1,24 @@
+"""Shared async utility functions for the Postgres checkpoint & storage classes."""
+
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+from typing import Union
+
+from psycopg import AsyncConnection
+from psycopg.rows import DictRow
+from psycopg_pool import AsyncConnectionPool
+
+Conn = Union[AsyncConnection[DictRow], AsyncConnectionPool[AsyncConnection[DictRow]]]
+
+
+@asynccontextmanager
+async def get_connection(
+    conn: Conn,
+) -> AsyncIterator[AsyncConnection[DictRow]]:
+    if isinstance(conn, AsyncConnection):
+        yield conn
+    elif isinstance(conn, AsyncConnectionPool):
+        async with conn.connection() as conn:
+            yield conn
+    else:
+        raise TypeError(f"Invalid connection type: {type(conn)}")

libs/checkpoint-postgres/langgraph/checkpoint/postgres/_internal.py

@@ -0,0 +1,22 @@
+"""Shared utility functions for the Postgres checkpoint & storage classes."""
+
+from collections.abc import Iterator
+from contextlib import contextmanager
+from typing import Union
+
+from psycopg import Connection
+from psycopg.rows import DictRow
+from psycopg_pool import ConnectionPool
+
+Conn = Union[Connection[DictRow], ConnectionPool[Connection[DictRow]]]
+
+
+@contextmanager
+def get_connection(conn: Conn) -> Iterator[Connection[DictRow]]:
+    if isinstance(conn, Connection):
+        yield conn
+    elif isinstance(conn, ConnectionPool):
+        with conn.connection() as conn:
+            yield conn
+    else:
+        raise TypeError(f"Invalid connection type: {type(conn)}")

libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py

@@ -1,10 +1,10 @@
 import asyncio
+from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager
-from typing import Any, AsyncIterator, Iterator, Optional, Sequence, Union
+from typing import Any, Optional
 
 from langchain_core.runnables import RunnableConfig
-from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline
-from psycopg.errors import UndefinedTable
+from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
 from psycopg.rows import DictRow, dict_row
 from psycopg.types.json import Jsonb
 from psycopg_pool import AsyncConnectionPool
@@ -17,23 +17,11 @@ from langgraph.checkpoint.base import (
     CheckpointTuple,
     get_checkpoint_id,
 )
+from langgraph.checkpoint.postgres import _ainternal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
 from langgraph.checkpoint.serde.base import SerializerProtocol
 
-Conn = Union[AsyncConnection[DictRow], AsyncConnectionPool[AsyncConnection[DictRow]]]
-
-
-@asynccontextmanager
-async def _get_connection(
-    conn: Conn,
-) -> AsyncIterator[AsyncConnection[DictRow]]:
-    if isinstance(conn, AsyncConnection):
-        yield conn
-    elif isinstance(conn, AsyncConnectionPool):
-        async with conn.connection() as conn:
-            yield conn
-    else:
-        raise TypeError(f"Invalid connection type: {type(conn)}")
+Conn = _ainternal.Conn  # For backward compatibility
 
 
 class AsyncPostgresSaver(BasePostgresSaver):
@@ -41,7 +29,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
 
     def __init__(
         self,
-        conn: Conn,
+        conn: _ainternal.Conn,
         pipe: Optional[AsyncPipeline] = None,
         serde: Optional[SerializerProtocol] = None,
     ) -> None:
@@ -55,6 +43,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         self.pipe = pipe
         self.lock = asyncio.Lock()
         self.loop = asyncio.get_running_loop()
+        self.supports_pipeline = Capabilities().has_pipeline()
 
     @classmethod
     @asynccontextmanager
@@ -65,7 +54,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         pipeline: bool = False,
         serde: Optional[SerializerProtocol] = None,
     ) -> AsyncIterator["AsyncPostgresSaver"]:
-        """Create a new PostgresSaver instance from a connection string.
+        """Create a new AsyncPostgresSaver instance from a connection string.
 
         Args:
             conn_string (str): The Postgres connection info string.
@@ -79,9 +68,9 @@ class AsyncPostgresSaver(BasePostgresSaver):
         ) as conn:
             if pipeline:
                 async with conn.pipeline() as pipe:
-                    yield AsyncPostgresSaver(conn=conn, pipe=pipe, serde=serde)
+                    yield cls(conn=conn, pipe=pipe, serde=serde)
             else:
-                yield AsyncPostgresSaver(conn=conn, serde=serde)
+                yield cls(conn=conn, serde=serde)
 
     async def setup(self) -> None:
         """Set up the checkpoint database asynchronously.
@@ -91,17 +80,15 @@ class AsyncPostgresSaver(BasePostgresSaver):
         the first time checkpointer is used.
         """
         async with self._cursor() as cur:
-            try:
-                results = await cur.execute(
-                    "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
-                )
-                row = await results.fetchone()
-                if row is None:
-                    version = -1
-                else:
-                    version = row["v"]
-            except UndefinedTable:
+            await cur.execute(self.MIGRATIONS[0])
+            results = await cur.execute(
+                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+            )
+            row = await results.fetchone()
+            if row is None:
                 version = -1
+            else:
+                version = row["v"]
             for v, migration in zip(
                 range(version + 1, len(self.MIGRATIONS)),
                 self.MIGRATIONS[version + 1 :],
@@ -156,15 +143,17 @@ class AsyncPostgresSaver(BasePostgresSaver):
                         value["pending_sends"],
                     ),
                     self._load_metadata(value["metadata"]),
-                    {
-                        "configurable": {
-                            "thread_id": value["thread_id"],
-                            "checkpoint_ns": value["checkpoint_ns"],
-                            "checkpoint_id": value["parent_checkpoint_id"],
+                    (
+                        {
+                            "configurable": {
+                                "thread_id": value["thread_id"],
+                                "checkpoint_ns": value["checkpoint_ns"],
+                                "checkpoint_id": value["parent_checkpoint_id"],
+                            }
                         }
-                    }
-                    if value["parent_checkpoint_id"]
-                    else None,
+                        if value["parent_checkpoint_id"]
+                        else None
+                    ),
                     await asyncio.to_thread(self._load_writes, value["pending_writes"]),
                 )
 
@@ -215,15 +204,17 @@ class AsyncPostgresSaver(BasePostgresSaver):
                         value["pending_sends"],
                     ),
                     self._load_metadata(value["metadata"]),
-                    {
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": value["parent_checkpoint_id"],
+                    (
+                        {
+                            "configurable": {
+                                "thread_id": thread_id,
+                                "checkpoint_ns": checkpoint_ns,
+                                "checkpoint_id": value["parent_checkpoint_id"],
+                            }
                         }
-                    }
-                    if value["parent_checkpoint_id"]
-                    else None,
+                        if value["parent_checkpoint_id"]
+                        else None
+                    ),
                     await asyncio.to_thread(self._load_writes, value["pending_writes"]),
                 )
 
@@ -323,7 +314,14 @@ class AsyncPostgresSaver(BasePostgresSaver):
     async def _cursor(
         self, *, pipeline: bool = False
     ) -> AsyncIterator[AsyncCursor[DictRow]]:
-        async with _get_connection(self.conn) as conn:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline (bool): whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the AsyncPostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        async with _ainternal.get_connection(self.conn) as conn:
             if self.pipe:
                 # a connection in pipeline mode can be used concurrently
                 # in multiple threads/coroutines, but only one cursor can be
@@ -337,14 +335,26 @@ class AsyncPostgresSaver(BasePostgresSaver):
             elif pipeline:
                 # a connection not in pipeline mode can only be used by one
                 # thread/coroutine at a time, so we acquire a lock
-                async with self.lock, conn.pipeline(), conn.cursor(
-                    binary=True, row_factory=dict_row
-                ) as cur:
-                    yield cur
+                if self.supports_pipeline:
+                    async with (
+                        self.lock,
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    async with (
+                        self.lock,
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
             else:
-                async with self.lock, conn.cursor(
-                    binary=True, row_factory=dict_row
-                ) as cur:
+                async with (
+                    self.lock,
+                    conn.cursor(binary=True, row_factory=dict_row) as cur,
+                ):
                     yield cur
 
     def list(
@@ -373,7 +383,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         while True:
             try:
                 yield asyncio.run_coroutine_threadsafe(
-                    anext(aiter_),
+                    anext(aiter_),  # noqa: F821
                     self.loop,
                 ).result()
             except StopAsyncIteration:
@@ -452,3 +462,6 @@ class AsyncPostgresSaver(BasePostgresSaver):
         return asyncio.run_coroutine_threadsafe(
             self.aput_writes(config, writes, task_id), self.loop
         ).result()
+
+
+__all__ = ["AsyncPostgresSaver", "Conn"]

libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py

@@ -1,5 +1,6 @@
 import random
-from typing import Any, List, Optional, Sequence, Tuple, cast
+from collections.abc import Sequence
+from typing import Any, Optional, cast
 
 from langchain_core.runnables import RunnableConfig
 from psycopg.types.json import Jsonb
@@ -133,6 +134,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
     INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
 
     jsonplus_serde = JsonPlusSerializer()
+    supports_pipeline: bool
 
     def _load_checkpoint(
         self,
@@ -248,7 +250,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
         config: Optional[RunnableConfig],
         filter: MetadataInput,
         before: Optional[RunnableConfig] = None,
-    ) -> Tuple[str, List[Any]]:
+    ) -> tuple[str, list[Any]]:
         """Return WHERE clause predicates for alist() given config, filter, before.
 
         This method returns a tuple of a string and a tuple of values. The string

libs/checkpoint-postgres/langgraph/store/postgres/aio.py

@@ -1,110 +1,310 @@
 import asyncio
 import logging
+from collections.abc import AsyncIterator, Iterable, Sequence
 from contextlib import asynccontextmanager
-from typing import (
-    Any,
-    AsyncIterator,
-    Callable,
-    Iterable,
-    Optional,
-    Sequence,
-    Union,
-    cast,
-)
+from typing import Any, Callable, Optional, Union, cast
 
 import orjson
-from psycopg import AsyncConnection, AsyncCursor
+from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
 from psycopg.errors import UndefinedTable
-from psycopg.rows import dict_row
+from psycopg.rows import DictRow, dict_row
+from psycopg_pool import AsyncConnectionPool
 
-from langgraph.store.base import GetOp, ListNamespacesOp, Op, PutOp, Result, SearchOp
+from langgraph.checkpoint.postgres import _ainternal
+from langgraph.store.base import (
+    GetOp,
+    ListNamespacesOp,
+    Op,
+    PutOp,
+    Result,
+    SearchOp,
+)
 from langgraph.store.base.batch import AsyncBatchedBaseStore
 from langgraph.store.postgres.base import (
+    _PLACEHOLDER,
     BasePostgresStore,
+    PoolConfig,
+    PostgresIndexConfig,
     Row,
     _decode_ns_bytes,
+    _ensure_index_config,
     _group_ops,
     _row_to_item,
+    _row_to_search_item,
 )
 
 logger = logging.getLogger(__name__)
 
 
-class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[AsyncConnection]):
-    __slots__ = ("_deserializer",)
+class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Conn]):
+    """Asynchronous Postgres-backed store with optional vector search using pgvector.
+
+    !!! example "Examples"
+        Basic setup and key-value storage:
+        ```python
+        from langgraph.store.postgres import AsyncPostgresStore
+
+        async with AsyncPostgresStore.from_conn_string(
+            "postgresql://user:pass@localhost:5432/dbname"
+        ) as store:
+            await store.setup()
+
+            # Store and retrieve data
+            await store.aput(("users", "123"), "prefs", {"theme": "dark"})
+            item = await store.aget(("users", "123"), "prefs")
+        ```
+
+        Vector search using LangChain embeddings:
+        ```python
+        from langchain.embeddings import init_embeddings
+        from langgraph.store.postgres import AsyncPostgresStore
+
+        async with AsyncPostgresStore.from_conn_string(
+            "postgresql://user:pass@localhost:5432/dbname",
+            index={
+                "dims": 1536,
+                "embed": init_embeddings("openai:text-embedding-3-small"),
+                "fields": ["text"]  # specify which fields to embed. Default is the whole serialized value
+            }
+        ) as store:
+            await store.setup()  # Do this once to run migrations
+
+            # Store documents
+            await store.aput(("docs",), "doc1", {"text": "Python tutorial"})
+            await store.aput(("docs",), "doc2", {"text": "TypeScript guide"})
+            # Don't index the following
+            await store.aput(("docs",), "doc3", {"text": "Other guide"}, index=False)
+
+            # Search by similarity
+            results = await store.asearch(("docs",), query="python programming")
+        ```
+
+        Using connection pooling for better performance:
+        ```python
+        from langgraph.store.postgres import AsyncPostgresStore, PoolConfig
+
+        async with AsyncPostgresStore.from_conn_string(
+            "postgresql://user:pass@localhost:5432/dbname",
+            pool_config=PoolConfig(
+                min_size=5,
+                max_size=20
+            )
+        ) as store:
+            await store.setup()
+            # Use store with connection pooling...
+        ```
+
+    Warning:
+        Make sure to:
+        1. Call `setup()` before first use to create necessary tables and indexes
+        2. Have the pgvector extension available to use vector search
+        3. Use Python 3.10+ for async functionality
+
+    Note:
+        Semantic search is disabled by default. You can enable it by providing an `index` configuration
+        when creating the store. Without this configuration, all `index` arguments passed to
+        `put` or `aput`will have no effect.
+    """
+
+    __slots__ = (
+        "_deserializer",
+        "pipe",
+        "lock",
+        "supports_pipeline",
+        "index_config",
+        "embeddings",
+    )
 
     def __init__(
         self,
-        conn: AsyncConnection[Any],
+        conn: _ainternal.Conn,
         *,
+        pipe: Optional[AsyncPipeline] = None,
         deserializer: Optional[
             Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]
         ] = None,
+        index: Optional[PostgresIndexConfig] = None,
     ) -> None:
+        if isinstance(conn, AsyncConnectionPool) and pipe is not None:
+            raise ValueError(
+                "Pipeline should be used only with a single AsyncConnection, not AsyncConnectionPool."
+            )
         super().__init__()
         self._deserializer = deserializer
         self.conn = conn
+        self.pipe = pipe
+        self.lock = asyncio.Lock()
         self.loop = asyncio.get_running_loop()
+        self.supports_pipeline = Capabilities().has_pipeline()
+        self.index_config = index
+        if self.index_config:
+            self.embeddings, self.index_config = _ensure_index_config(self.index_config)
+
+        else:
+            self.embeddings = None
 
     async def abatch(self, ops: Iterable[Op]) -> list[Result]:
         grouped_ops, num_ops = _group_ops(ops)
         results: list[Result] = [None] * num_ops
 
-        async with self.conn.pipeline():
-            tasks = []
-
-            if GetOp in grouped_ops:
-                tasks.append(
-                    self._batch_get_ops(
-                        cast(Sequence[tuple[int, GetOp]], grouped_ops[GetOp]), results
-                    )
-                )
-
-            if PutOp in grouped_ops:
-                tasks.append(
-                    self._batch_put_ops(
-                        cast(Sequence[tuple[int, PutOp]], grouped_ops[PutOp])
-                    )
-                )
-
-            if SearchOp in grouped_ops:
-                tasks.append(
-                    self._batch_search_ops(
-                        cast(Sequence[tuple[int, SearchOp]], grouped_ops[SearchOp]),
-                        results,
-                    )
-                )
-
-            if ListNamespacesOp in grouped_ops:
-                tasks.append(
-                    self._batch_list_namespaces_ops(
-                        cast(
-                            Sequence[tuple[int, ListNamespacesOp]],
-                            grouped_ops[ListNamespacesOp],
-                        ),
-                        results,
-                    )
-                )
-
-            await asyncio.gather(*tasks)
+        async with _ainternal.get_connection(self.conn) as conn:
+            if self.pipe:
+                async with self.pipe:
+                    await self._execute_batch(grouped_ops, results, conn)
+            else:
+                await self._execute_batch(grouped_ops, results, conn)
 
         return results
 
     def batch(self, ops: Iterable[Op]) -> list[Result]:
         return asyncio.run_coroutine_threadsafe(self.abatch(ops), self.loop).result()
 
+    @classmethod
+    @asynccontextmanager
+    async def from_conn_string(
+        cls,
+        conn_string: str,
+        *,
+        pipeline: bool = False,
+        pool_config: Optional[PoolConfig] = None,
+        index: Optional[PostgresIndexConfig] = None,
+    ) -> AsyncIterator["AsyncPostgresStore"]:
+        """Create a new AsyncPostgresStore instance from a connection string.
+
+        Args:
+            conn_string (str): The Postgres connection info string.
+            pipeline (bool): Whether to use AsyncPipeline (only for single connections)
+            pool_config (Optional[PoolConfig]): Configuration for the connection pool.
+                If provided, will create a connection pool and use it instead of a single connection.
+                This overrides the `pipeline` argument.
+            index (Optional[PostgresIndexConfig]): The embedding config.
+
+        Returns:
+            AsyncPostgresStore: A new AsyncPostgresStore instance.
+        """
+        if pool_config is not None:
+            pc = pool_config.copy()
+            async with cast(
+                AsyncConnectionPool[AsyncConnection[DictRow]],
+                AsyncConnectionPool(
+                    conn_string,
+                    min_size=pc.pop("min_size", 1),
+                    max_size=pc.pop("max_size", None),
+                    kwargs={
+                        "autocommit": True,
+                        "prepare_threshold": 0,
+                        "row_factory": dict_row,
+                        **(pc.pop("kwargs", None) or {}),
+                    },
+                    **cast(dict, pc),
+                ),
+            ) as pool:
+                yield cls(conn=pool, index=index)
+        else:
+            async with await AsyncConnection.connect(
+                conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+            ) as conn:
+                if pipeline:
+                    async with conn.pipeline() as pipe:
+                        yield cls(conn=conn, pipe=pipe, index=index)
+                else:
+                    yield cls(conn=conn, index=index)
+
+    async def setup(self) -> None:
+        """Set up the store database asynchronously.
+
+        This method creates the necessary tables in the Postgres database if they don't
+        already exist and runs database migrations. It MUST be called directly by the user
+        the first time the store is used.
+        """
+
+        async def _get_version(cur: AsyncCursor[DictRow], table: str) -> int:
+            try:
+                await cur.execute(f"SELECT v FROM {table} ORDER BY v DESC LIMIT 1")
+                row = await cur.fetchone()
+                if row is None:
+                    version = -1
+                else:
+                    version = row["v"]
+            except UndefinedTable:
+                version = -1
+                await cur.execute(
+                    f"""
+                    CREATE TABLE IF NOT EXISTS {table} (
+                        v INTEGER PRIMARY KEY
+                    )
+                """
+                )
+            return version
+
+        async with self._cursor() as cur:
+            version = await _get_version(cur, table="store_migrations")
+            for v, sql in enumerate(self.MIGRATIONS[version + 1 :], start=version + 1):
+                await cur.execute(sql)
+                await cur.execute("INSERT INTO store_migrations (v) VALUES (%s)", (v,))
+
+            if self.index_config:
+                version = await _get_version(cur, table="vector_migrations")
+                for v, migration in enumerate(
+                    self.VECTOR_MIGRATIONS[version + 1 :], start=version + 1
+                ):
+                    sql = migration.sql
+                    if migration.params:
+                        params = {
+                            k: v(self) if v is not None and callable(v) else v
+                            for k, v in migration.params.items()
+                        }
+                        sql = sql % params
+                    await cur.execute(sql)
+                    await cur.execute(
+                        "INSERT INTO vector_migrations (v) VALUES (%s)", (v,)
+                    )
+
+    async def _execute_batch(
+        self,
+        grouped_ops: dict,
+        results: list[Result],
+        conn: AsyncConnection[DictRow],
+    ) -> None:
+        async with self._cursor(pipeline=True) as cur:
+            if GetOp in grouped_ops:
+                await self._batch_get_ops(
+                    cast(Sequence[tuple[int, GetOp]], grouped_ops[GetOp]),
+                    results,
+                    cur,
+                )
+
+            if SearchOp in grouped_ops:
+                await self._batch_search_ops(
+                    cast(Sequence[tuple[int, SearchOp]], grouped_ops[SearchOp]),
+                    results,
+                    cur,
+                )
+
+            if ListNamespacesOp in grouped_ops:
+                await self._batch_list_namespaces_ops(
+                    cast(
+                        Sequence[tuple[int, ListNamespacesOp]],
+                        grouped_ops[ListNamespacesOp],
+                    ),
+                    results,
+                    cur,
+                )
+
+            if PutOp in grouped_ops:
+                await self._batch_put_ops(
+                    cast(Sequence[tuple[int, PutOp]], grouped_ops[PutOp]),
+                    cur,
+                )
+
     async def _batch_get_ops(
         self,
         get_ops: Sequence[tuple[int, GetOp]],
         results: list[Result],
+        cur: AsyncCursor[DictRow],
     ) -> None:
-        cursors = []
         for query, params, namespace, items in self._get_batch_GET_ops_queries(get_ops):
-            cur = self.conn.cursor(binary=True)
             await cur.execute(query, params)
-            cursors.append((cur, namespace, items))
-
-        for cur, namespace, items in cursors:
             rows = cast(list[Row], await cur.fetchall())
             key_to_row = {row["key"]: row for row in rows}
             for idx, key in items:
@@ -119,29 +319,59 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[AsyncConnectio
     async def _batch_put_ops(
         self,
         put_ops: Sequence[tuple[int, PutOp]],
+        cur: AsyncCursor[DictRow],
     ) -> None:
-        queries = self._get_batch_PUT_queries(put_ops)
+        queries, embedding_request = self._prepare_batch_PUT_queries(put_ops)
+        if embedding_request:
+            if self.embeddings is None:
+                # Should not get here since the embedding config is required
+                # to return an embedding_request above
+                raise ValueError(
+                    "Embedding configuration is required for vector operations "
+                    f"(for semantic search). "
+                    f"Please provide an EmbeddingConfig when initializing the {self.__class__.__name__}."
+                )
+            query, txt_params = embedding_request
+            vectors = await self.embeddings.aembed_documents(
+                [param[-1] for param in txt_params]
+            )
+            queries.append(
+                (
+                    query,
+                    [
+                        p
+                        for (ns, k, pathname, _), vector in zip(txt_params, vectors)
+                        for p in (ns, k, pathname, vector)
+                    ],
+                )
+            )
+
         for query, params in queries:
-            cur = self.conn.cursor(binary=True)
             await cur.execute(query, params)
 
     async def _batch_search_ops(
         self,
         search_ops: Sequence[tuple[int, SearchOp]],
         results: list[Result],
+        cur: AsyncCursor[DictRow],
     ) -> None:
-        queries = self._get_batch_search_queries(search_ops)
-        cursors: list[tuple[AsyncCursor[Any], int]] = []
+        queries, embedding_requests = self._prepare_batch_search_queries(search_ops)
 
-        for (query, params), (idx, _) in zip(queries, search_ops):
-            cur = self.conn.cursor(binary=True)
+        if embedding_requests and self.embeddings:
+            vectors = await self.embeddings.aembed_documents(
+                [query for _, query in embedding_requests]
+            )
+            for (idx, _), vector in zip(embedding_requests, vectors):
+                _paramslist = queries[idx][1]
+                for i in range(len(_paramslist)):
+                    if _paramslist[i] is _PLACEHOLDER:
+                        _paramslist[i] = vector
+
+        for (idx, _), (query, params) in zip(search_ops, queries):
             await cur.execute(query, params)
-            cursors.append((cur, idx))
-
-        for cur, idx in cursors:
             rows = cast(list[Row], await cur.fetchall())
             items = [
-                _row_to_item(
+                _row_to_search_item(
                     _decode_ns_bytes(row["prefix"]), row, loader=self._deserializer
                 )
                 for row in rows
@@ -152,67 +382,57 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[AsyncConnectio
         self,
         list_ops: Sequence[tuple[int, ListNamespacesOp]],
         results: list[Result],
+        cur: AsyncCursor[DictRow],
     ) -> None:
         queries = self._get_batch_list_namespaces_queries(list_ops)
-        cursors: list[tuple[AsyncCursor[Any], int]] = []
         for (query, params), (idx, _) in zip(queries, list_ops):
-            cur = self.conn.cursor(binary=True)
             await cur.execute(query, params)
-            cursors.append((cur, idx))
-
-        for cur, idx in cursors:
             rows = cast(list[dict], await cur.fetchall())
             namespaces = [_decode_ns_bytes(row["truncated_prefix"]) for row in rows]
             results[idx] = namespaces
 
-    @classmethod
     @asynccontextmanager
-    async def from_conn_string(
-        cls,
-        conn_string: str,
-    ) -> AsyncIterator["AsyncPostgresStore"]:
-        """Create a new AsyncPostgresStore instance from a connection string.
+    async def _cursor(
+        self, *, pipeline: bool = False
+    ) -> AsyncIterator[AsyncCursor[DictRow]]:
+        """Create a database cursor as a context manager.
 
         Args:
-            conn_string (str): The Postgres connection info string.
-
-        Returns:
-            AsyncPostgresStore: A new AsyncPostgresStore instance.
+            pipeline: whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the PostgresStore instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
         """
-        async with await AsyncConnection.connect(
-            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
-        ) as conn:
-            yield cls(conn=conn)
-
-    async def setup(self) -> None:
-        """Set up the store database asynchronously.
-
-        This method creates the necessary tables in the Postgres database if they don't
-        already exist and runs database migrations. It MUST be called directly by the user
-        the first time the store is used.
-        """
-        async with self.conn.cursor() as cur:
-            try:
-                await cur.execute(
-                    "SELECT v FROM store_migrations ORDER BY v DESC LIMIT 1"
-                )
-                row = cast(dict, await cur.fetchone())
-                if row is None:
-                    version = -1
+        async with _ainternal.get_connection(self.conn) as conn:
+            if self.pipe:
+                # a connection in pipeline mode can be used concurrently
+                # in multiple threads/coroutines, but only one cursor can be
+                # used at a time
+                try:
+                    async with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                        yield cur
+                finally:
+                    if pipeline:
+                        await self.pipe.sync()
+            elif pipeline:
+                # a connection not in pipeline mode can only be used by one
+                # thread/coroutine at a time, so we acquire a lock
+                if self.supports_pipeline:
+                    async with (
+                        self.lock,
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
                 else:
-                    version = row["v"]
-            except UndefinedTable:
-                version = -1
-                # Create store_migrations table if it doesn't exist
-                await cur.execute(
-                    """
-                    CREATE TABLE IF NOT EXISTS store_migrations (
-                        v INTEGER PRIMARY KEY
-                    )
-                    """
-                )
-            for v, migration in enumerate(
-                self.MIGRATIONS[version + 1 :], start=version + 1
-            ):
-                await cur.execute(migration)
-                await cur.execute("INSERT INTO store_migrations (v) VALUES (%s)", (v,))
+                    async with (
+                        self.lock,
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+            else:
+                async with (
+                    self.lock,
+                    conn.cursor(binary=True) as cur,
+                ):
+                    yield cur

libs/checkpoint-postgres/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.2"
+version = "2.0.7"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 license = "MIT"
@@ -10,10 +10,10 @@ packages = [{ include = "langgraph" }]
 
 [tool.poetry.dependencies]
 python = "^3.9.0,<4.0"
-langgraph-checkpoint = "^2.0.2"
+langgraph-checkpoint = "^2.0.7"
 orjson = ">=3.10.1"
-psycopg = "^3.0.0"
-psycopg-pool = "^3.0.0"
+psycopg = "^3.2.0"
+psycopg-pool = "^3.2.0"
 
 [tool.poetry.group.dev.dependencies]
 ruff = "^0.6.2"

libs/checkpoint-postgres/tests/compose-postgres.yml

@@ -1,12 +1,13 @@
 services:
   postgres-test:
-    image: postgres:${POSTGRES_VERSION:-16}
+    image: pgvector/pgvector:pg${POSTGRES_VERSION:-16}
     ports:
       - "5441:5432"
     environment:
       POSTGRES_DB: postgres
       POSTGRES_USER: postgres
       POSTGRES_PASSWORD: postgres
+    command: ["postgres", "-c", "shared_preload_libraries=vector"]
     healthcheck:
       test: pg_isready -U postgres
       start_period: 10s

libs/checkpoint-postgres/tests/conftest.py

@@ -1,10 +1,13 @@
-from typing import AsyncIterator
+from collections.abc import AsyncIterator
 
 import pytest
 from psycopg import AsyncConnection
 from psycopg.errors import UndefinedTable
 from psycopg.rows import DictRow, dict_row
 
+from tests.embed_test_utils import CharacterEmbeddings
+
+DEFAULT_POSTGRES_URI = "postgres://postgres:postgres@localhost:5441/"
 DEFAULT_URI = "postgres://postgres:postgres@localhost:5441/postgres?sslmode=disable"
 
 
@@ -24,6 +27,18 @@ async def clear_test_db(conn: AsyncConnection[DictRow]) -> None:
         await conn.execute("DELETE FROM checkpoint_blobs")
         await conn.execute("DELETE FROM checkpoint_writes")
         await conn.execute("DELETE FROM checkpoint_migrations")
-        await conn.execute("DELETE FROM store_migrations")
     except UndefinedTable:
         pass
+    try:
+        await conn.execute("DELETE FROM store_migrations")
+        await conn.execute("DELETE FROM store")
+    except UndefinedTable:
+        pass
+
+
+@pytest.fixture
+def fake_embeddings() -> CharacterEmbeddings:
+    return CharacterEmbeddings(dims=500)
+
+
+VECTOR_TYPES = ["vector", "halfvec"]

libs/checkpoint-postgres/tests/embed_test_utils.py

@@ -0,0 +1,55 @@
+"""Embedding utilities for testing."""
+
+import math
+import random
+from collections import Counter, defaultdict
+from typing import Any
+
+from langchain_core.embeddings import Embeddings
+
+
+class CharacterEmbeddings(Embeddings):
+    """Simple character-frequency based embeddings using random projections."""
+
+    def __init__(self, dims: int = 50, seed: int = 42):
+        """Initialize with embedding dimensions and random seed."""
+        self._rng = random.Random(seed)
+        self.dims = dims
+        # Create projection vector for each character lazily
+        self._char_projections: defaultdict[str, list[float]] = defaultdict(
+            lambda: [
+                self._rng.gauss(0, 1 / math.sqrt(self.dims)) for _ in range(self.dims)
+            ]
+        )
+
+    def _embed_one(self, text: str) -> list[float]:
+        """Embed a single text."""
+        counts = Counter(text)
+        total = sum(counts.values())
+
+        if total == 0:
+            return [0.0] * self.dims
+
+        embedding = [0.0] * self.dims
+        for char, count in counts.items():
+            weight = count / total
+            char_proj = self._char_projections[char]
+            for i, proj in enumerate(char_proj):
+                embedding[i] += weight * proj
+
+        norm = math.sqrt(sum(x * x for x in embedding))
+        if norm > 0:
+            embedding = [x / norm for x in embedding]
+
+        return embedding
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Embed a list of documents."""
+        return [self._embed_one(text) for text in texts]
+
+    def embed_query(self, text: str) -> list[float]:
+        """Embed a query string."""
+        return self._embed_one(text)
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, CharacterEmbeddings) and self.dims == other.dims

libs/checkpoint-postgres/tests/test_async.py

@@ -1,8 +1,14 @@
+# type: ignore
+
+from contextlib import asynccontextmanager
 from typing import Any
+from uuid import uuid4
 
 import pytest
-from conftest import DEFAULT_URI  # type: ignore
 from langchain_core.runnables import RunnableConfig
+from psycopg import AsyncConnection
+from psycopg.rows import dict_row
+from psycopg_pool import AsyncConnectionPool
 
 from langgraph.checkpoint.base import (
     Checkpoint,
@@ -11,103 +17,212 @@ from langgraph.checkpoint.base import (
     empty_checkpoint,
 )
 from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+from tests.conftest import DEFAULT_POSTGRES_URI
 
 
-class TestAsyncPostgresSaver:
-    @pytest.fixture(autouse=True)
-    async def setup(self) -> None:
-        # objects for test setup
-        self.config_1: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-1",
-                # for backwards compatibility testing
-                "thread_ts": "1",
-                "checkpoint_ns": "",
-            }
-        }
-        self.config_2: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_id": "2",
-                "checkpoint_ns": "",
-            }
-        }
-        self.config_3: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_id": "2-inner",
-                "checkpoint_ns": "inner",
-            }
-        }
+@asynccontextmanager
+async def _pool_saver():
+    """Fixture for pool mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncConnectionPool(
+            DEFAULT_POSTGRES_URI + database,
+            max_size=10,
+            kwargs={"autocommit": True, "row_factory": dict_row},
+        ) as pool:
+            checkpointer = AsyncPostgresSaver(pool)
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
 
-        self.chkpnt_1: Checkpoint = empty_checkpoint()
-        self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1)
-        self.chkpnt_3: Checkpoint = empty_checkpoint()
 
-        self.metadata_1: CheckpointMetadata = {
-            "source": "input",
-            "step": 2,
-            "writes": {},
-            "score": 1,
+@asynccontextmanager
+async def _pipe_saver():
+    """Fixture for pipeline mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            async with conn.pipeline() as pipe:
+                checkpointer = AsyncPostgresSaver(conn, pipe=pipe)
+                await checkpointer.setup()
+            async with conn.pipeline() as pipe:
+                checkpointer = AsyncPostgresSaver(conn, pipe=pipe)
+                yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _base_saver():
+    """Fixture for regular connection mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = AsyncPostgresSaver(conn)
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _saver(name: str):
+    if name == "base":
+        async with _base_saver() as saver:
+            yield saver
+    elif name == "pool":
+        async with _pool_saver() as saver:
+            yield saver
+    elif name == "pipe":
+        async with _pipe_saver() as saver:
+            yield saver
+
+
+@pytest.fixture
+def test_data():
+    """Fixture providing test data for checkpoint tests."""
+    config_1: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-1",
+            # for backwards compatibility testing
+            "thread_ts": "1",
+            "checkpoint_ns": "",
         }
-        self.metadata_2: CheckpointMetadata = {
-            "source": "loop",
+    }
+    config_2: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-2",
+            "checkpoint_id": "2",
+            "checkpoint_ns": "",
+        }
+    }
+    config_3: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-2",
+            "checkpoint_id": "2-inner",
+            "checkpoint_ns": "inner",
+        }
+    }
+
+    chkpnt_1: Checkpoint = empty_checkpoint()
+    chkpnt_2: Checkpoint = create_checkpoint(chkpnt_1, {}, 1)
+    chkpnt_3: Checkpoint = empty_checkpoint()
+
+    metadata_1: CheckpointMetadata = {
+        "source": "input",
+        "step": 2,
+        "writes": {},
+        "score": 1,
+    }
+    metadata_2: CheckpointMetadata = {
+        "source": "loop",
+        "step": 1,
+        "writes": {"foo": "bar"},
+        "score": None,
+    }
+    metadata_3: CheckpointMetadata = {}
+
+    return {
+        "configs": [config_1, config_2, config_3],
+        "checkpoints": [chkpnt_1, chkpnt_2, chkpnt_3],
+        "metadata": [metadata_1, metadata_2, metadata_3],
+    }
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+async def test_asearch(request, saver_name: str, test_data) -> None:
+    async with _saver(saver_name) as saver:
+        configs = test_data["configs"]
+        checkpoints = test_data["checkpoints"]
+        metadata = test_data["metadata"]
+
+        await saver.aput(configs[0], checkpoints[0], metadata[0], {})
+        await saver.aput(configs[1], checkpoints[1], metadata[1], {})
+        await saver.aput(configs[2], checkpoints[2], metadata[2], {})
+
+        # call method / assertions
+        query_1 = {"source": "input"}  # search by 1 key
+        query_2 = {
             "step": 1,
             "writes": {"foo": "bar"},
-            "score": None,
-        }
-        self.metadata_3: CheckpointMetadata = {}
-        async with AsyncPostgresSaver.from_conn_string(DEFAULT_URI) as saver:
-            await saver.setup()
+        }  # search by multiple keys
+        query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
+        query_4 = {"source": "update", "step": 1}  # no match
 
-    async def test_asearch(self) -> None:
-        async with AsyncPostgresSaver.from_conn_string(DEFAULT_URI) as saver:
-            await saver.aput(self.config_1, self.chkpnt_1, self.metadata_1, {})
-            await saver.aput(self.config_2, self.chkpnt_2, self.metadata_2, {})
-            await saver.aput(self.config_3, self.chkpnt_3, self.metadata_3, {})
+        search_results_1 = [c async for c in saver.alist(None, filter=query_1)]
+        assert len(search_results_1) == 1
+        assert search_results_1[0].metadata == metadata[0]
 
-            # call method / assertions
-            query_1 = {"source": "input"}  # search by 1 key
-            query_2 = {
-                "step": 1,
-                "writes": {"foo": "bar"},
-            }  # search by multiple keys
-            query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
-            query_4 = {"source": "update", "step": 1}  # no match
+        search_results_2 = [c async for c in saver.alist(None, filter=query_2)]
+        assert len(search_results_2) == 1
+        assert search_results_2[0].metadata == metadata[1]
 
-            search_results_1 = [c async for c in saver.alist(None, filter=query_1)]
-            assert len(search_results_1) == 1
-            assert search_results_1[0].metadata == self.metadata_1
+        search_results_3 = [c async for c in saver.alist(None, filter=query_3)]
+        assert len(search_results_3) == 3
 
-            search_results_2 = [c async for c in saver.alist(None, filter=query_2)]
-            assert len(search_results_2) == 1
-            assert search_results_2[0].metadata == self.metadata_2
+        search_results_4 = [c async for c in saver.alist(None, filter=query_4)]
+        assert len(search_results_4) == 0
 
-            search_results_3 = [c async for c in saver.alist(None, filter=query_3)]
-            assert len(search_results_3) == 3
+        # search by config (defaults to checkpoints across all namespaces)
+        search_results_5 = [
+            c async for c in saver.alist({"configurable": {"thread_id": "thread-2"}})
+        ]
+        assert len(search_results_5) == 2
+        assert {
+            search_results_5[0].config["configurable"]["checkpoint_ns"],
+            search_results_5[1].config["configurable"]["checkpoint_ns"],
+        } == {"", "inner"}
 
-            search_results_4 = [c async for c in saver.alist(None, filter=query_4)]
-            assert len(search_results_4) == 0
 
-            # search by config (defaults to checkpoints across all namespaces)
-            search_results_5 = [
-                c
-                async for c in saver.alist({"configurable": {"thread_id": "thread-2"}})
-            ]
-            assert len(search_results_5) == 2
-            assert {
-                search_results_5[0].config["configurable"]["checkpoint_ns"],
-                search_results_5[1].config["configurable"]["checkpoint_ns"],
-            } == {"", "inner"}
-
-            # TODO: test before and limit params
-
-    async def test_null_chars(self) -> None:
-        async with AsyncPostgresSaver.from_conn_string(DEFAULT_URI) as saver:
-            config = await saver.aput(
-                self.config_1, self.chkpnt_1, {"my_key": "\x00abc"}, {}
-            )
-            assert (await saver.aget_tuple(config)).metadata["my_key"] == "abc"  # type: ignore
-            assert [c async for c in saver.alist(None, filter={"my_key": "abc"})][
-                0
-            ].metadata["my_key"] == "abc"
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+async def test_null_chars(request, saver_name: str, test_data) -> None:
+    async with _saver(saver_name) as saver:
+        config = await saver.aput(
+            test_data["configs"][0],
+            test_data["checkpoints"][0],
+            {"my_key": "\x00abc"},
+            {},
+        )
+        assert (await saver.aget_tuple(config)).metadata["my_key"] == "abc"  # type: ignore
+        assert [c async for c in saver.alist(None, filter={"my_key": "abc"})][
+            0
+        ].metadata["my_key"] == "abc"

libs/checkpoint-postgres/tests/test_async_store.py

@@ -1,114 +1,84 @@
 # type: ignore
+import itertools
+import sys
 import uuid
-from datetime import datetime
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+from typing import Any, Optional
 
 import pytest
-from conftest import DEFAULT_URI  # type: ignore
+from langchain_core.embeddings import Embeddings
+from psycopg import AsyncConnection
 
 from langgraph.store.base import GetOp, Item, ListNamespacesOp, PutOp, SearchOp
 from langgraph.store.postgres import AsyncPostgresStore
+from tests.conftest import (
+    DEFAULT_URI,
+    VECTOR_TYPES,
+    CharacterEmbeddings,
+)
 
 
-class MockAsyncCursor:
-    def __init__(self, fetch_result: Any) -> None:
-        self.fetch_result = fetch_result
-        self.execute = AsyncMock()
-        self.fetchall = AsyncMock(return_value=self.fetch_result)
+@pytest.fixture(scope="function", params=["default", "pipe", "pool"])
+async def store(request) -> AsyncIterator[AsyncPostgresStore]:
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
 
+    database = f"test_{uuid.uuid4().hex[:16]}"
+    uri_parts = DEFAULT_URI.split("/")
+    uri_base = "/".join(uri_parts[:-1])
+    query_params = ""
+    if "?" in uri_parts[-1]:
+        db_name, query_params = uri_parts[-1].split("?", 1)
+        query_params = "?" + query_params
 
-class MockAsyncConnection:
-    def __init__(self) -> None:
-        self.cursor = MagicMock()
-        self.pipeline = MagicMock(
-            return_value=AsyncMock(__aenter__=AsyncMock(), __aexit__=AsyncMock())
-        )
+    conn_string = f"{uri_base}/{database}{query_params}"
+    admin_conn_string = DEFAULT_URI
 
+    async with await AsyncConnection.connect(
+        admin_conn_string, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(conn_string) as store:
+            await store.setup()
 
-@pytest.fixture
-def mock_connection() -> MockAsyncConnection:
-    return MockAsyncConnection()
-
-
-@pytest.fixture
-async def store(mock_connection: MockAsyncConnection) -> AsyncPostgresStore:
-    return AsyncPostgresStore(mock_connection)
+        if request.param == "pipe":
+            async with AsyncPostgresStore.from_conn_string(
+                conn_string, pipeline=True
+            ) as store:
+                yield store
+        elif request.param == "pool":
+            async with AsyncPostgresStore.from_conn_string(
+                conn_string, pool_config={"min_size": 1, "max_size": 10}
+            ) as store:
+                yield store
+        else:  # default
+            async with AsyncPostgresStore.from_conn_string(conn_string) as store:
+                yield store
+    finally:
+        async with await AsyncConnection.connect(
+            admin_conn_string, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
 
 
 async def test_abatch_order(store: AsyncPostgresStore) -> None:
-    mock_connection = store.conn
-    mock_get_cursor = MockAsyncCursor(
-        [
-            {
-                "key": "key1",
-                "value": '{"data": "value1"}',
-                "created_at": datetime.now(),
-                "updated_at": datetime.now(),
-                "prefix": "test.foo",
-            },
-            {
-                "key": "key2",
-                "value": '{"data": "value2"}',
-                "created_at": datetime.now(),
-                "updated_at": datetime.now(),
-                "prefix": "test.bar",
-            },
-        ]
-    )
-    mock_search_cursor = MockAsyncCursor(
-        [
-            {
-                "key": "key1",
-                "value": '{"data": "value1"}',
-                "created_at": datetime.now(),
-                "updated_at": datetime.now(),
-                "prefix": "test.foo",
-            },
-        ]
-    )
-    mock_list_namespaces_cursor = MockAsyncCursor(
-        [
-            {"truncated_prefix": b"\x01test"},
-        ]
-    )
-
-    failures = []
-
-    def cursor_side_effect(binary: bool = False) -> Any:
-        cursor = MagicMock()
-
-        async def execute_side_effect(query: str, *params: Any) -> None:
-            # My super sophisticated database.
-            if "SELECT prefix, key," in query:
-                cursor.fetchall = mock_search_cursor.fetchall
-            elif "SELECT DISTINCT ON (truncated_prefix)" in query:
-                cursor.fetchall = mock_list_namespaces_cursor.fetchall
-            elif "WHERE prefix = %s AND key" in query:
-                cursor.fetchall = mock_get_cursor.fetchall
-            elif "INSERT INTO " in query:
-                pass
-            else:
-                e = ValueError(f"Unmatched query: {query}")
-                failures.append(e)
-                raise e
-
-        cursor.execute = AsyncMock(side_effect=execute_side_effect)
-        return cursor
-
-    mock_connection.cursor.side_effect = cursor_side_effect  # type: ignore
+    # Setup test data
+    await store.aput(("test", "foo"), "key1", {"data": "value1"})
+    await store.aput(("test", "bar"), "key2", {"data": "value2"})
 
     ops = [
-        GetOp(namespace=("test",), key="key1"),
-        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
+        GetOp(namespace=("test", "foo"), key="key1"),
+        PutOp(namespace=("test", "bar"), key="key2", value={"data": "value2"}),
         SearchOp(
             namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
         ),
         ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
         GetOp(namespace=("test",), key="key3"),
     ]
+
     results = await store.abatch(ops)
-    assert not failures
     assert len(results) == 5
     assert isinstance(results[0], Item)
     assert isinstance(results[0].value, dict)
@@ -118,27 +88,29 @@ async def test_abatch_order(store: AsyncPostgresStore) -> None:
     assert isinstance(results[2], list)
     assert len(results[2]) == 1
     assert isinstance(results[3], list)
-    assert results[3] == [("test",)]
+    assert ("test", "foo") in results[3] and ("test", "bar") in results[3]
     assert results[4] is None
 
     ops_reordered = [
         SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
-        GetOp(namespace=("test",), key="key2"),
+        GetOp(namespace=("test", "bar"), key="key2"),
         ListNamespacesOp(match_conditions=None, max_depth=None, limit=5, offset=0),
         PutOp(namespace=("test",), key="key3", value={"data": "value3"}),
-        GetOp(namespace=("test",), key="key1"),
+        GetOp(namespace=("test", "foo"), key="key1"),
     ]
 
     results_reordered = await store.abatch(ops_reordered)
-    assert not failures
     assert len(results_reordered) == 5
     assert isinstance(results_reordered[0], list)
-    assert len(results_reordered[0]) == 1
+    assert len(results_reordered[0]) == 2
     assert isinstance(results_reordered[1], Item)
     assert results_reordered[1].value == {"data": "value2"}
     assert results_reordered[1].key == "key2"
     assert isinstance(results_reordered[2], list)
-    assert results_reordered[2] == [("test",)]
+    assert ("test", "foo") in results_reordered[2] and (
+        "test",
+        "bar",
+    ) in results_reordered[2]
     assert results_reordered[3] is None
     assert isinstance(results_reordered[4], Item)
     assert results_reordered[4].value == {"data": "value1"}
@@ -146,26 +118,9 @@ async def test_abatch_order(store: AsyncPostgresStore) -> None:
 
 
 async def test_batch_get_ops(store: AsyncPostgresStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockAsyncCursor(
-        [
-            {
-                "key": "key1",
-                "value": '{"data": "value1"}',
-                "created_at": datetime.now(),
-                "updated_at": datetime.now(),
-                "prefix": "test.foo",
-            },
-            {
-                "key": "key2",
-                "value": '{"data": "value2"}',
-                "created_at": datetime.now(),
-                "updated_at": datetime.now(),
-                "prefix": "test.bar",
-            },
-        ]
-    )
-    mock_connection.cursor.return_value = mock_cursor
+    # Setup test data
+    await store.aput(("test",), "key1", {"data": "value1"})
+    await store.aput(("test",), "key2", {"data": "value2"})
 
     ops = [
         GetOp(namespace=("test",), key="key1"),
@@ -184,10 +139,6 @@ async def test_batch_get_ops(store: AsyncPostgresStore) -> None:
 
 
 async def test_batch_put_ops(store: AsyncPostgresStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockAsyncCursor([])
-    mock_connection.cursor.return_value = mock_cursor
-
     ops = [
         PutOp(namespace=("test",), key="key1", value={"data": "value1"}),
         PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
@@ -198,30 +149,16 @@ async def test_batch_put_ops(store: AsyncPostgresStore) -> None:
 
     assert len(results) == 3
     assert all(result is None for result in results)
-    assert mock_cursor.execute.call_count == 2
+
+    # Verify the puts worked
+    items = await store.asearch(["test"], limit=10)
+    assert len(items) == 2  # key3 had None value so wasn't stored
 
 
 async def test_batch_search_ops(store: AsyncPostgresStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockAsyncCursor(
-        [
-            {
-                "key": "key1",
-                "value": '{"data": "value1"}',
-                "created_at": datetime.now(),
-                "updated_at": datetime.now(),
-                "prefix": "test.foo",
-            },
-            {
-                "key": "key2",
-                "value": '{"data": "value2"}',
-                "created_at": datetime.now(),
-                "updated_at": datetime.now(),
-                "prefix": "test.bar",
-            },
-        ]
-    )
-    mock_connection.cursor.return_value = mock_cursor
+    # Setup test data
+    await store.aput(("test", "foo"), "key1", {"data": "value1"})
+    await store.aput(("test", "bar"), "key2", {"data": "value2"})
 
     ops = [
         SearchOp(
@@ -233,297 +170,338 @@ async def test_batch_search_ops(store: AsyncPostgresStore) -> None:
     results = await store.abatch(ops)
 
     assert len(results) == 2
-    assert len(results[0]) == 2
-    assert len(results[1]) == 2
+    assert len(results[0]) == 1  # Filtered results
+    assert len(results[1]) == 2  # All results
 
 
 async def test_batch_list_namespaces_ops(store: AsyncPostgresStore) -> None:
-    mock_connection = store.conn
-    mock_cursor = MockAsyncCursor(
-        [
-            {"truncated_prefix": b"\x01test.namespace1"},
-            {"truncated_prefix": b"\x01test.namespace2"},
-        ]
-    )
-    mock_connection.cursor.return_value = mock_cursor
+    # Setup test data
+    await store.aput(("test", "namespace1"), "key1", {"data": "value1"})
+    await store.aput(("test", "namespace2"), "key2", {"data": "value2"})
 
     ops = [ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0)]
 
     results = await store.abatch(ops)
 
     assert len(results) == 1
-    assert results[0] == [("test", "namespace1"), ("test", "namespace2")]
+    assert len(results[0]) == 2
+    assert ("test", "namespace1") in results[0]
+    assert ("test", "namespace2") in results[0]
 
 
-# The following use the actual DB connection
+@asynccontextmanager
+async def _create_vector_store(
+    vector_type: str,
+    distance_type: str,
+    fake_embeddings: CharacterEmbeddings,
+    text_fields: Optional[list[str]] = None,
+) -> AsyncIterator[AsyncPostgresStore]:
+    """Create a store with vector search enabled."""
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
 
+    database = f"test_{uuid.uuid4().hex[:16]}"
+    uri_parts = DEFAULT_URI.split("/")
+    uri_base = "/".join(uri_parts[:-1])
+    query_params = ""
+    if "?" in uri_parts[-1]:
+        db_name, query_params = uri_parts[-1].split("?", 1)
+        query_params = "?" + query_params
 
-class TestAsyncPostgresStore:
-    @pytest.fixture(autouse=True)
-    async def setup(self) -> None:
-        async with AsyncPostgresStore.from_conn_string(DEFAULT_URI) as store:
+    conn_string = f"{uri_base}/{database}{query_params}"
+    admin_conn_string = DEFAULT_URI
+
+    index_config = {
+        "dims": fake_embeddings.dims,
+        "embed": fake_embeddings,
+        "ann_index_config": {
+            "vector_type": vector_type,
+        },
+        "distance_type": distance_type,
+        "text_fields": text_fields,
+    }
+
+    async with await AsyncConnection.connect(
+        admin_conn_string, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            conn_string,
+            index=index_config,
+        ) as store:
             await store.setup()
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            admin_conn_string, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
 
-    async def test_basic_store_ops(self) -> None:
-        async with AsyncPostgresStore.from_conn_string(DEFAULT_URI) as store:
-            namespace = ("test", "documents")
-            item_id = "doc1"
-            item_value = {"title": "Test Document", "content": "Hello, World!"}
 
-            await store.aput(namespace, item_id, item_value)
-            item = await store.aget(namespace, item_id)
+@pytest.fixture(
+    scope="function",
+    params=[
+        (vector_type, distance_type)
+        for vector_type in VECTOR_TYPES
+        for distance_type in (
+            ["hamming"] if vector_type == "bit" else ["l2", "inner_product", "cosine"]
+        )
+    ],
+    ids=lambda p: f"{p[0]}_{p[1]}",
+)
+async def vector_store(
+    request,
+    fake_embeddings: CharacterEmbeddings,
+) -> AsyncIterator[AsyncPostgresStore]:
+    """Create a store with vector search enabled."""
+    vector_type, distance_type = request.param
+    async with _create_vector_store(
+        vector_type, distance_type, fake_embeddings
+    ) as store:
+        yield store
 
-            assert item
-            assert item.namespace == namespace
-            assert item.key == item_id
-            assert item.value == item_value
 
-            updated_value = {
-                "title": "Updated Test Document",
-                "content": "Hello, LangGraph!",
-            }
-            await store.aput(namespace, item_id, updated_value)
-            updated_item = await store.aget(namespace, item_id)
+async def test_vector_store_initialization(
+    vector_store: AsyncPostgresStore, fake_embeddings: CharacterEmbeddings
+) -> None:
+    """Test store initialization with embedding config."""
+    assert vector_store.index_config is not None
+    assert vector_store.index_config["dims"] == fake_embeddings.dims
+    if isinstance(vector_store.index_config["embed"], Embeddings):
+        assert vector_store.index_config["embed"] == fake_embeddings
 
-            assert updated_item.value == updated_value
-            assert updated_item.updated_at > item.updated_at
-            different_namespace = ("test", "other_documents")
-            item_in_different_namespace = await store.aget(different_namespace, item_id)
-            assert item_in_different_namespace is None
 
-            new_item_id = "doc2"
-            new_item_value = {"title": "Another Document", "content": "Greetings!"}
-            await store.aput(namespace, new_item_id, new_item_value)
+async def test_vector_insert_with_auto_embedding(
+    vector_store: AsyncPostgresStore,
+) -> None:
+    """Test inserting items that get auto-embedded."""
+    docs = [
+        ("doc1", {"text": "short text"}),
+        ("doc2", {"text": "longer text document"}),
+        ("doc3", {"text": "longest text document here"}),
+        ("doc4", {"description": "text in description field"}),
+        ("doc5", {"content": "text in content field"}),
+        ("doc6", {"body": "text in body field"}),
+    ]
 
-            search_results = await store.asearch(["test"], limit=10)
-            items = search_results
-            assert len(items) == 2
-            assert any(item.key == item_id for item in items)
-            assert any(item.key == new_item_id for item in items)
+    for key, value in docs:
+        await vector_store.aput(("test",), key, value)
 
-            namespaces = await store.alist_namespaces(prefix=["test"])
-            assert ("test", "documents") in namespaces
+    results = await vector_store.asearch(("test",), query="long text")
+    assert len(results) > 0
 
-            await store.adelete(namespace, item_id)
-            await store.adelete(namespace, new_item_id)
-            deleted_item = await store.aget(namespace, item_id)
-            assert deleted_item is None
+    doc_order = [r.key for r in results]
+    assert "doc2" in doc_order
+    assert "doc3" in doc_order
 
-            deleted_item = await store.aget(namespace, new_item_id)
-            assert deleted_item is None
 
-            empty_search_results = await store.asearch(["test"], limit=10)
-            assert len(empty_search_results) == 0
+async def test_vector_update_with_embedding(vector_store: AsyncPostgresStore) -> None:
+    """Test that updating items properly updates their embeddings."""
+    await vector_store.aput(("test",), "doc1", {"text": "zany zebra Xerxes"})
+    await vector_store.aput(("test",), "doc2", {"text": "something about dogs"})
+    await vector_store.aput(("test",), "doc3", {"text": "text about birds"})
 
-    async def test_list_namespaces(self) -> None:
-        async with AsyncPostgresStore.from_conn_string(DEFAULT_URI) as store:
-            test_pref = str(uuid.uuid4())
-            test_namespaces = [
-                (test_pref, "test", "documents", "public", test_pref),
-                (test_pref, "test", "documents", "private", test_pref),
-                (test_pref, "test", "images", "public", test_pref),
-                (test_pref, "test", "images", "private", test_pref),
-                (test_pref, "prod", "documents", "public", test_pref),
-                (
-                    test_pref,
-                    "prod",
-                    "documents",
-                    "some",
-                    "nesting",
-                    "public",
-                    test_pref,
-                ),
-                (test_pref, "prod", "documents", "private", test_pref),
-            ]
+    results_initial = await vector_store.asearch(("test",), query="Zany Xerxes")
+    assert len(results_initial) > 0
+    assert results_initial[0].key == "doc1"
+    initial_score = results_initial[0].score
 
-            for namespace in test_namespaces:
-                await store.aput(namespace, "dummy", {"content": "dummy"})
+    await vector_store.aput(("test",), "doc1", {"text": "new text about dogs"})
 
-            prefix_result = await store.alist_namespaces(prefix=[test_pref, "test"])
-            assert len(prefix_result) == 4
-            assert all([ns[1] == "test" for ns in prefix_result])
+    results_after = await vector_store.asearch(("test",), query="Zany Xerxes")
+    after_score = next((r.score for r in results_after if r.key == "doc1"), 0.0)
+    assert after_score < initial_score
 
-            specific_prefix_result = await store.alist_namespaces(
-                prefix=[test_pref, "test", "documents"]
-            )
-            assert len(specific_prefix_result) == 2
-            assert all(
-                [ns[1:3] == ("test", "documents") for ns in specific_prefix_result]
-            )
+    results_new = await vector_store.asearch(("test",), query="new text about dogs")
+    for r in results_new:
+        if r.key == "doc1":
+            assert r.score > after_score
 
-            suffix_result = await store.alist_namespaces(suffix=["public", test_pref])
-            assert len(suffix_result) == 4
-            assert all(ns[-2] == "public" for ns in suffix_result)
+    # Don't index this one
+    await vector_store.aput(
+        ("test",), "doc4", {"text": "new text about dogs"}, index=False
+    )
+    results_new = await vector_store.asearch(
+        ("test",), query="new text about dogs", limit=3
+    )
+    assert not any(r.key == "doc4" for r in results_new)
 
-            prefix_suffix_result = await store.alist_namespaces(
-                prefix=[test_pref, "test"], suffix=["public", test_pref]
-            )
-            assert len(prefix_suffix_result) == 2
-            assert all(
-                ns[1] == "test" and ns[-2] == "public" for ns in prefix_suffix_result
-            )
 
-            wildcard_prefix_result = await store.alist_namespaces(
-                prefix=[test_pref, "*", "documents"]
-            )
-            assert len(wildcard_prefix_result) == 5
-            assert all(ns[2] == "documents" for ns in wildcard_prefix_result)
+async def test_vector_search_with_filters(vector_store: AsyncPostgresStore) -> None:
+    """Test combining vector search with filters."""
+    docs = [
+        ("doc1", {"text": "red apple", "color": "red", "score": 4.5}),
+        ("doc2", {"text": "red car", "color": "red", "score": 3.0}),
+        ("doc3", {"text": "green apple", "color": "green", "score": 4.0}),
+        ("doc4", {"text": "blue car", "color": "blue", "score": 3.5}),
+    ]
 
-            wildcard_suffix_result = await store.alist_namespaces(
-                suffix=["*", "public", test_pref]
-            )
-            assert len(wildcard_suffix_result) == 4
-            assert all(ns[-2] == "public" for ns in wildcard_suffix_result)
-            wildcard_single = await store.alist_namespaces(
-                suffix=["some", "*", "public", test_pref]
-            )
-            assert len(wildcard_single) == 1
-            assert wildcard_single[0] == (
-                test_pref,
-                "prod",
-                "documents",
-                "some",
-                "nesting",
-                "public",
-                test_pref,
-            )
+    for key, value in docs:
+        await vector_store.aput(("test",), key, value)
 
-            max_depth_result = await store.alist_namespaces(max_depth=3)
-            assert all([len(ns) <= 3 for ns in max_depth_result])
-            max_depth_result = await store.alist_namespaces(
-                max_depth=4, prefix=[test_pref, "*", "documents"]
-            )
-            assert (
-                len(set(tuple(res) for res in max_depth_result))
-                == len(max_depth_result)
-                == 5
-            )
+    results = await vector_store.asearch(
+        ("test",), query="apple", filter={"color": "red"}
+    )
+    assert len(results) == 2
+    assert results[0].key == "doc1"
 
-            limit_result = await store.alist_namespaces(prefix=[test_pref], limit=3)
-            assert len(limit_result) == 3
+    results = await vector_store.asearch(
+        ("test",), query="car", filter={"color": "red"}
+    )
+    assert len(results) == 2
+    assert results[0].key == "doc2"
 
-            offset_result = await store.alist_namespaces(prefix=[test_pref], offset=3)
-            assert len(offset_result) == len(test_namespaces) - 3
+    results = await vector_store.asearch(
+        ("test",), query="bbbbluuu", filter={"score": {"$gt": 3.2}}
+    )
+    assert len(results) == 3
+    assert results[0].key == "doc4"
 
-            empty_prefix_result = await store.alist_namespaces(prefix=[test_pref])
-            assert len(empty_prefix_result) == len(test_namespaces)
-            assert set(tuple(ns) for ns in empty_prefix_result) == set(
-                tuple(ns) for ns in test_namespaces
-            )
+    results = await vector_store.asearch(
+        ("test",), query="apple", filter={"score": {"$gte": 4.0}, "color": "green"}
+    )
+    assert len(results) == 1
+    assert results[0].key == "doc3"
 
-            for namespace in test_namespaces:
-                await store.adelete(namespace, "dummy")
 
-    async def test_search(self):
-        async with AsyncPostgresStore.from_conn_string(DEFAULT_URI) as store:
-            test_namespaces = [
-                ("test_search", "documents", "user1"),
-                ("test_search", "documents", "user2"),
-                ("test_search", "reports", "department1"),
-                ("test_search", "reports", "department2"),
-            ]
-            test_items = [
-                {"title": "Doc 1", "author": "John Doe", "tags": ["important"]},
-                {"title": "Doc 2", "author": "Jane Smith", "tags": ["draft"]},
-                {"title": "Report A", "author": "John Doe", "tags": ["final"]},
-                {"title": "Report B", "author": "Alice Johnson", "tags": ["draft"]},
-            ]
-            empty = await store.asearch(
-                (
-                    "scoped",
-                    "assistant_id",
-                    "shared",
-                    "6c5356f6-63ab-4158-868d-cd9fd14c736e",
-                ),
-                limit=10,
-                offset=0,
-            )
-            assert len(empty) == 0
+async def test_vector_search_pagination(vector_store: AsyncPostgresStore) -> None:
+    """Test pagination with vector search."""
+    for i in range(5):
+        await vector_store.aput(
+            ("test",), f"doc{i}", {"text": f"test document number {i}"}
+        )
 
-            for namespace, item in zip(test_namespaces, test_items):
-                await store.aput(namespace, f"item_{namespace[-1]}", item)
+    results_page1 = await vector_store.asearch(("test",), query="test", limit=2)
+    results_page2 = await vector_store.asearch(
+        ("test",), query="test", limit=2, offset=2
+    )
 
-            docs_result = await store.asearch(["test_search", "documents"])
-            assert len(docs_result) == 2
-            assert all([item.namespace[1] == "documents" for item in docs_result]), [
-                item.namespace for item in docs_result
-            ]
+    assert len(results_page1) == 2
+    assert len(results_page2) == 2
+    assert results_page1[0].key != results_page2[0].key
 
-            reports_result = await store.asearch(["test_search", "reports"])
-            assert len(reports_result) == 2
-            assert all(item.namespace[1] == "reports" for item in reports_result)
+    all_results = await vector_store.asearch(("test",), query="test", limit=10)
+    assert len(all_results) == 5
 
-            limited_result = await store.asearch(["test_search"], limit=2)
-            assert len(limited_result) == 2
-            offset_result = await store.asearch(["test_search"])
-            assert len(offset_result) == 4
 
-            offset_result = await store.asearch(["test_search"], offset=2)
-            assert len(offset_result) == 2
-            assert all(item not in limited_result for item in offset_result)
+async def test_vector_search_edge_cases(vector_store: AsyncPostgresStore) -> None:
+    """Test edge cases in vector search."""
+    await vector_store.aput(("test",), "doc1", {"text": "test document"})
 
-            john_doe_result = await store.asearch(
-                ["test_search"], filter={"author": "John Doe"}
-            )
-            assert len(john_doe_result) == 2
-            assert all(item.value["author"] == "John Doe" for item in john_doe_result)
+    perfect_match = await vector_store.asearch(("test",), query="text test document")
+    perfect_score = perfect_match[0].score
 
-            draft_result = await store.asearch(
-                ["test_search"], filter={"tags": ["draft"]}
-            )
-            assert len(draft_result) == 2
-            assert all("draft" in item.value["tags"] for item in draft_result)
+    results = await vector_store.asearch(("test",), query="")
+    assert len(results) == 1
+    assert results[0].score is None
 
-            page1 = await store.asearch(["test_search"], limit=2, offset=0)
-            page2 = await store.asearch(["test_search"], limit=2, offset=2)
-            all_items = page1 + page2
-            assert len(all_items) == 4
-            assert len(set(item.key for item in all_items)) == 4
-            empty = await store.asearch(
-                (
-                    "scoped",
-                    "assistant_id",
-                    "shared",
-                    "again",
-                    "maybe",
-                    "some-long",
-                    "6be5cb0e-2eb4-42e6-bb6b-fba3c269db25",
-                ),
-                limit=10,
-                offset=0,
-            )
-            assert len(empty) == 0
+    results = await vector_store.asearch(("test",), query=None)
+    assert len(results) == 1
+    assert results[0].score is None
 
-            # Test with a namespace beginning with a number (like a UUID)
-            uuid_namespace = (str(uuid.uuid4()), "documents")
-            uuid_item_id = "uuid_doc"
-            uuid_item_value = {
-                "title": "UUID Document",
-                "content": "This document has a UUID namespace.",
-            }
+    long_query = "foo " * 100
+    results = await vector_store.asearch(("test",), query=long_query)
+    assert len(results) == 1
+    assert results[0].score < perfect_score
 
-            # Insert the item with the UUID namespace
-            await store.aput(uuid_namespace, uuid_item_id, uuid_item_value)
+    special_query = "test!@#$%^&*()"
+    results = await vector_store.asearch(("test",), query=special_query)
+    assert len(results) == 1
+    assert results[0].score < perfect_score
 
-            # Retrieve the item to verify it was stored correctly
-            retrieved_item = await store.aget(uuid_namespace, uuid_item_id)
-            assert retrieved_item is not None
-            assert retrieved_item.namespace == uuid_namespace
-            assert retrieved_item.key == uuid_item_id
-            assert retrieved_item.value == uuid_item_value
 
-            # Search for the item using the UUID namespace
-            search_result = await store.asearch([uuid_namespace[0]])
-            assert len(search_result) == 1
-            assert search_result[0].key == uuid_item_id
-            assert search_result[0].value == uuid_item_value
+@pytest.mark.parametrize(
+    "vector_type,distance_type",
+    [
+        *itertools.product(["vector", "halfvec"], ["cosine", "inner_product", "l2"]),
+    ],
+)
+async def test_embed_with_path(
+    request: Any,
+    fake_embeddings: CharacterEmbeddings,
+    vector_type: str,
+    distance_type: str,
+) -> None:
+    """Test vector search with specific text fields in Postgres store."""
+    async with _create_vector_store(
+        vector_type,
+        distance_type,
+        fake_embeddings,
+        text_fields=["key0", "key1", "key3"],
+    ) as store:
+        # This will have 2 vectors representing it
+        doc1 = {
+            # Omit key0 - check it doesn't raise an error
+            "key1": "xxx",
+            "key2": "yyy",
+            "key3": "zzz",
+        }
+        # This will have 3 vectors representing it
+        doc2 = {
+            "key0": "uuu",
+            "key1": "vvv",
+            "key2": "www",
+            "key3": "xxx",
+        }
+        await store.aput(("test",), "doc1", doc1)
+        await store.aput(("test",), "doc2", doc2)
 
-            # Clean up: delete the item with the UUID namespace
-            await store.adelete(uuid_namespace, uuid_item_id)
+        # doc2.key3 and doc1.key1 both would have the highest score
+        results = await store.asearch(("test",), query="xxx")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        ascore = results[0].score
+        bscore = results[1].score
+        assert ascore == pytest.approx(bscore, abs=1e-3)
 
-            # Verify the item was deleted
-            deleted_item = await store.aget(uuid_namespace, uuid_item_id)
-            assert deleted_item is None
+        results = await store.asearch(("test",), query="uuu")
+        assert len(results) == 2
+        assert results[0].key != results[1].key
+        assert results[0].key == "doc2"
+        assert results[0].score > results[1].score
+        assert ascore == pytest.approx(results[0].score, abs=1e-3)
 
-            for namespace in test_namespaces:
-                await store.adelete(namespace, f"item_{namespace[-1]}")
+        # Un-indexed - will have low results for both. Not zero (because we're projecting)
+        # but less than the above.
+        results = await store.asearch(("test",), query="www")
+        assert len(results) == 2
+        assert results[0].score < ascore
+        assert results[1].score < ascore
+
+
+@pytest.mark.parametrize(
+    "vector_type,distance_type",
+    [
+        *itertools.product(["vector", "halfvec"], ["cosine", "inner_product", "l2"]),
+    ],
+)
+async def test_search_sorting(
+    request: Any,
+    fake_embeddings: CharacterEmbeddings,
+    vector_type: str,
+    distance_type: str,
+) -> None:
+    """Test operation-level field configuration for vector search."""
+    async with _create_vector_store(
+        vector_type,
+        distance_type,
+        fake_embeddings,
+        text_fields=["key1"],  # Default fields that won't match our test data
+    ) as store:
+        amatch = {
+            "key1": "mmm",
+        }
+
+        await store.aput(("test", "M"), "M", amatch)
+        N = 100
+        for i in range(N):
+            await store.aput(("test", "A"), f"A{i}", {"key1": "no"})
+        for i in range(N):
+            await store.aput(("test", "Z"), f"Z{i}", {"key1": "no"})
+
+        results = await store.asearch(("test",), query="mmm", limit=10)
+        assert len(results) == 10
+        assert len(set(r.key for r in results)) == 10
+        assert results[0].key == "M"
+        assert results[0].score > results[1].score

libs/checkpoint-postgres/tests/test_sync.py

@@ -1,8 +1,14 @@
+# type: ignore
+
+from contextlib import contextmanager
 from typing import Any
+from uuid import uuid4
 
 import pytest
-from conftest import DEFAULT_URI  # type: ignore
 from langchain_core.runnables import RunnableConfig
+from psycopg import Connection
+from psycopg.rows import dict_row
+from psycopg_pool import ConnectionPool
 
 from langgraph.checkpoint.base import (
     Checkpoint,
@@ -11,102 +17,199 @@ from langgraph.checkpoint.base import (
     empty_checkpoint,
 )
 from langgraph.checkpoint.postgres import PostgresSaver
+from tests.conftest import DEFAULT_POSTGRES_URI
 
 
-class TestPostgresSaver:
-    @pytest.fixture(autouse=True)
-    def setup(self) -> None:
-        # objects for test setup
-        self.config_1: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-1",
-                # for backwards compatibility testing
-                "thread_ts": "1",
-                "checkpoint_ns": "",
-            }
-        }
-        self.config_2: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_id": "2",
-                "checkpoint_ns": "",
-            }
-        }
-        self.config_3: RunnableConfig = {
-            "configurable": {
-                "thread_id": "thread-2",
-                "checkpoint_id": "2-inner",
-                "checkpoint_ns": "inner",
-            }
-        }
+@contextmanager
+def _pool_saver():
+    """Fixture for pool mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with ConnectionPool(
+            DEFAULT_POSTGRES_URI + database,
+            max_size=10,
+            kwargs={"autocommit": True, "row_factory": dict_row},
+        ) as pool:
+            checkpointer = PostgresSaver(pool)
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
 
-        self.chkpnt_1: Checkpoint = empty_checkpoint()
-        self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1)
-        self.chkpnt_3: Checkpoint = empty_checkpoint()
 
-        self.metadata_1: CheckpointMetadata = {
-            "source": "input",
-            "step": 2,
-            "writes": {},
-            "score": 1,
+@contextmanager
+def _pipe_saver():
+    """Fixture for pipeline mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        with Connection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            with conn.pipeline() as pipe:
+                checkpointer = PostgresSaver(conn, pipe=pipe)
+                checkpointer.setup()
+            with conn.pipeline() as pipe:
+                checkpointer = PostgresSaver(conn, pipe=pipe)
+                yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@contextmanager
+def _base_saver():
+    """Fixture for regular connection mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        with Connection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = PostgresSaver(conn)
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@contextmanager
+def _saver(name: str):
+    if name == "base":
+        with _base_saver() as saver:
+            yield saver
+    elif name == "pool":
+        with _pool_saver() as saver:
+            yield saver
+    elif name == "pipe":
+        with _pipe_saver() as saver:
+            yield saver
+
+
+@pytest.fixture
+def test_data():
+    """Fixture providing test data for checkpoint tests."""
+    config_1: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-1",
+            # for backwards compatibility testing
+            "thread_ts": "1",
+            "checkpoint_ns": "",
         }
-        self.metadata_2: CheckpointMetadata = {
-            "source": "loop",
+    }
+    config_2: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-2",
+            "checkpoint_id": "2",
+            "checkpoint_ns": "",
+        }
+    }
+    config_3: RunnableConfig = {
+        "configurable": {
+            "thread_id": "thread-2",
+            "checkpoint_id": "2-inner",
+            "checkpoint_ns": "inner",
+        }
+    }
+
+    chkpnt_1: Checkpoint = empty_checkpoint()
+    chkpnt_2: Checkpoint = create_checkpoint(chkpnt_1, {}, 1)
+    chkpnt_3: Checkpoint = empty_checkpoint()
+
+    metadata_1: CheckpointMetadata = {
+        "source": "input",
+        "step": 2,
+        "writes": {},
+        "score": 1,
+    }
+    metadata_2: CheckpointMetadata = {
+        "source": "loop",
+        "step": 1,
+        "writes": {"foo": "bar"},
+        "score": None,
+    }
+    metadata_3: CheckpointMetadata = {}
+
+    return {
+        "configs": [config_1, config_2, config_3],
+        "checkpoints": [chkpnt_1, chkpnt_2, chkpnt_3],
+        "metadata": [metadata_1, metadata_2, metadata_3],
+    }
+
+
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+def test_search(saver_name: str, test_data) -> None:
+    with _saver(saver_name) as saver:
+        configs = test_data["configs"]
+        checkpoints = test_data["checkpoints"]
+        metadata = test_data["metadata"]
+
+        saver.put(configs[0], checkpoints[0], metadata[0], {})
+        saver.put(configs[1], checkpoints[1], metadata[1], {})
+        saver.put(configs[2], checkpoints[2], metadata[2], {})
+
+        # call method / assertions
+        query_1 = {"source": "input"}  # search by 1 key
+        query_2 = {
             "step": 1,
             "writes": {"foo": "bar"},
-            "score": None,
-        }
-        self.metadata_3: CheckpointMetadata = {}
-        with PostgresSaver.from_conn_string(DEFAULT_URI) as saver:
-            saver.setup()
+        }  # search by multiple keys
+        query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
+        query_4 = {"source": "update", "step": 1}  # no match
 
-    def test_search(self) -> None:
-        with PostgresSaver.from_conn_string(DEFAULT_URI) as saver:
-            # save checkpoints
-            saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {})
-            saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {})
-            saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {})
+        search_results_1 = list(saver.list(None, filter=query_1))
+        assert len(search_results_1) == 1
+        assert search_results_1[0].metadata == metadata[0]
 
-            # call method / assertions
-            query_1 = {"source": "input"}  # search by 1 key
-            query_2 = {
-                "step": 1,
-                "writes": {"foo": "bar"},
-            }  # search by multiple keys
-            query_3: dict[str, Any] = {}  # search by no keys, return all checkpoints
-            query_4 = {"source": "update", "step": 1}  # no match
+        search_results_2 = list(saver.list(None, filter=query_2))
+        assert len(search_results_2) == 1
+        assert search_results_2[0].metadata == metadata[1]
 
-            search_results_1 = list(saver.list(None, filter=query_1))
-            assert len(search_results_1) == 1
-            assert search_results_1[0].metadata == self.metadata_1
+        search_results_3 = list(saver.list(None, filter=query_3))
+        assert len(search_results_3) == 3
 
-            search_results_2 = list(saver.list(None, filter=query_2))
-            assert len(search_results_2) == 1
-            assert search_results_2[0].metadata == self.metadata_2
+        search_results_4 = list(saver.list(None, filter=query_4))
+        assert len(search_results_4) == 0
 
-            search_results_3 = list(saver.list(None, filter=query_3))
-            assert len(search_results_3) == 3
+        # search by config (defaults to checkpoints across all namespaces)
+        search_results_5 = list(saver.list({"configurable": {"thread_id": "thread-2"}}))
+        assert len(search_results_5) == 2
+        assert {
+            search_results_5[0].config["configurable"]["checkpoint_ns"],
+            search_results_5[1].config["configurable"]["checkpoint_ns"],
+        } == {"", "inner"}
 
-            search_results_4 = list(saver.list(None, filter=query_4))
-            assert len(search_results_4) == 0
 
-            # search by config (defaults to checkpoints across all namespaces)
-            search_results_5 = list(
-                saver.list({"configurable": {"thread_id": "thread-2"}})
-            )
-            assert len(search_results_5) == 2
-            assert {
-                search_results_5[0].config["configurable"]["checkpoint_ns"],
-                search_results_5[1].config["configurable"]["checkpoint_ns"],
-            } == {"", "inner"}
-
-            # TODO: test before and limit params
-
-    def test_null_chars(self) -> None:
-        with PostgresSaver.from_conn_string(DEFAULT_URI) as saver:
-            config = saver.put(self.config_1, self.chkpnt_1, {"my_key": "\x00abc"}, {})
-            assert saver.get_tuple(config).metadata["my_key"] == "abc"  # type: ignore
-            assert (
-                list(saver.list(None, filter={"my_key": "abc"}))[0].metadata["my_key"]  # type: ignore
-                == "abc"
-            )
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+def test_null_chars(saver_name: str, test_data) -> None:
+    with _saver(saver_name) as saver:
+        config = saver.put(
+            test_data["configs"][0],
+            test_data["checkpoints"][0],
+            {"my_key": "\x00abc"},
+            {},
+        )
+        assert saver.get_tuple(config).metadata["my_key"] == "abc"  # type: ignore
+        assert (
+            list(saver.list(None, filter={"my_key": "abc"}))[0].metadata["my_key"]
+            == "abc"
+        )

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py

@@ -110,7 +110,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
                 check_same_thread=False,
             )
         ) as conn:
-            yield SqliteSaver(conn)
+            yield cls(conn)
 
     def setup(self) -> None:
         """Set up the checkpoint database.

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py

@@ -137,7 +137,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
             AsyncSqliteSaver: A new AsyncSqliteSaver instance.
         """
         async with aiosqlite.connect(conn_string) as conn:
-            yield AsyncSqliteSaver(conn)
+            yield cls(conn)
 
     def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
         """Get a checkpoint tuple from the database.

libs/checkpoint/Makefile

@@ -4,11 +4,13 @@
 # TESTING AND COVERAGE
 ######################
 
+TEST ?= .
+
 test:
-	poetry run pytest tests
+	poetry run pytest $(TEST)
 
 test_watch:
-	poetry run ptw .
+	poetry run ptw $(TEST)
 
 ######################
 # LINTING AND FORMATTING

libs/checkpoint/langgraph/checkpoint/base/__init__.py

@@ -39,12 +39,13 @@ PendingWrite = Tuple[str, str, Any]
 class CheckpointMetadata(TypedDict, total=False):
     """Metadata associated with a checkpoint."""
 
-    source: Literal["input", "loop", "update"]
+    source: Literal["input", "loop", "update", "fork"]
     """The source of the checkpoint.
 
     - "input": The checkpoint was created from an input to invoke/stream/batch.
     - "loop": The checkpoint was created from inside the pregel loop.
     - "update": The checkpoint was created from a manual state update.
+    - "fork": The checkpoint was created as a copy of another checkpoint.
     """
     step: int
     """The step number of the checkpoint.

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -1,10 +1,14 @@
 import asyncio
+import logging
+import os
+import pickle
 import random
+import shutil
 from collections import defaultdict
-from contextlib import AbstractAsyncContextManager, AbstractContextManager
+from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from functools import partial
 from types import TracebackType
-from typing import Any, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple
+from typing import Any, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple, Type
 
 from langchain_core.runnables import RunnableConfig
 
@@ -20,6 +24,8 @@ from langgraph.checkpoint.base import (
 )
 from langgraph.checkpoint.serde.types import TASKS, ChannelProtocol
 
+logger = logging.getLogger(__name__)
+
 
 class MemorySaver(
     BaseCheckpointSaver[str], AbstractContextManager, AbstractAsyncContextManager
@@ -68,13 +74,18 @@ class MemorySaver(
         self,
         *,
         serde: Optional[SerializerProtocol] = None,
+        factory: Type[defaultdict] = defaultdict,
     ) -> None:
         super().__init__(serde=serde)
-        self.storage = defaultdict(lambda: defaultdict(dict))
-        self.writes = defaultdict(dict)
+        self.storage = factory(lambda: defaultdict(dict))
+        self.writes = factory(dict)
+        self.stack = ExitStack()
+        if factory is not defaultdict:
+            self.stack.enter_context(self.storage)  # type: ignore[arg-type]
+            self.stack.enter_context(self.writes)  # type: ignore[arg-type]
 
     def __enter__(self) -> "MemorySaver":
-        return self
+        return self.stack.__enter__()
 
     def __exit__(
         self,
@@ -82,10 +93,10 @@ class MemorySaver(
         exc_value: Optional[BaseException],
         traceback: Optional[TracebackType],
     ) -> Optional[bool]:
-        return
+        return self.stack.__exit__(exc_type, exc_value, traceback)
 
     async def __aenter__(self) -> "MemorySaver":
-        return self
+        return self.stack.__enter__()
 
     async def __aexit__(
         self,
@@ -93,7 +104,7 @@ class MemorySaver(
         __exc_value: Optional[BaseException],
         __traceback: Optional[TracebackType],
     ) -> Optional[bool]:
-        return
+        return self.stack.__exit__(__exc_type, __exc_value, __traceback)
 
     def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
         """Get a checkpoint tuple from the in-memory storage.
@@ -361,7 +372,7 @@ class MemorySaver(
             RunnableConfig: The updated config containing the saved writes' timestamp.
         """
         thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
         checkpoint_id = config["configurable"]["checkpoint_id"]
         outer_key = (thread_id, checkpoint_ns, checkpoint_id)
         outer_writes_ = self.writes.get(outer_key)
@@ -478,3 +489,76 @@ class MemorySaver(
         next_v = current_v + 1
         next_h = random.random()
         return f"{next_v:032}.{next_h:016}"
+
+
+class PersistentDict(defaultdict):
+    """Persistent dictionary with an API compatible with shelve and anydbm.
+
+    The dict is kept in memory, so the dictionary operations run as fast as
+    a regular dictionary.
+
+    Write to disk is delayed until close or sync (similar to gdbm's fast mode).
+
+    Input file format is automatically discovered.
+    Output file format is selectable between pickle, json, and csv.
+    All three serialization formats are backed by fast C implementations.
+
+    Adapted from https://code.activestate.com/recipes/576642-persistent-dict-with-multiple-standard-file-format/
+
+    """
+
+    def __init__(self, *args: Any, filename: str, **kwds: Any) -> None:
+        self.flag = "c"  # r=readonly, c=create, or n=new
+        self.mode = None  # None or an octal triple like 0644
+        self.format = "pickle"  # 'csv', 'json', or 'pickle'
+        self.filename = filename
+        super().__init__(*args, **kwds)
+
+    def sync(self) -> None:
+        "Write dict to disk"
+        if self.flag == "r":
+            return
+        tempname = self.filename + ".tmp"
+        fileobj = open(tempname, "wb" if self.format == "pickle" else "w")
+        try:
+            self.dump(fileobj)
+        except Exception:
+            os.remove(tempname)
+            raise
+        finally:
+            fileobj.close()
+        shutil.move(tempname, self.filename)  # atomic commit
+        if self.mode is not None:
+            os.chmod(self.filename, self.mode)
+
+    def close(self) -> None:
+        self.sync()
+        self.clear()
+
+    def __enter__(self) -> "PersistentDict":
+        return self
+
+    def __exit__(self, *exc_info: Any) -> None:
+        self.close()
+
+    def dump(self, fileobj: Any) -> None:
+        if self.format == "pickle":
+            pickle.dump(dict(self), fileobj, 2)
+        else:
+            raise NotImplementedError("Unknown format: " + repr(self.format))
+
+    def load(self) -> None:
+        # try formats from most restrictive to least restrictive
+        if self.flag == "n":
+            return
+        with open(self.filename, "rb" if self.format == "pickle" else "r") as fileobj:
+            for loader in (pickle.load,):
+                fileobj.seek(0)
+                try:
+                    return self.update(loader(fileobj))
+                except EOFError:
+                    return
+                except Exception:
+                    logging.error(f"Failed to load file: {fileobj.name}")
+                    raise
+            raise ValueError("File not in a supported f ormat")

libs/checkpoint/langgraph/store/base/__init__.py

@@ -1,12 +1,27 @@
 """Base classes and types for persistent key-value stores.
 
-Stores enable persistence and memory that can be shared across threads,
-scoped to user IDs, assistant IDs, or other arbitrary namespaces.
+Stores provide long-term memory that persists across threads and conversations.
+Supports hierarchical namespaces, key-value storage, and optional vector search.
+
+Core types:
+    - BaseStore: Store interface with sync/async operations
+    - Item: Stored key-value pairs with metadata
+    - Op: Get/Put/Search/List operations
 """
 
 from abc import ABC, abstractmethod
 from datetime import datetime
-from typing import Any, Iterable, Literal, NamedTuple, Optional, Union, cast
+from typing import Any, Iterable, Literal, NamedTuple, Optional, TypedDict, Union, cast
+
+from langchain_core.embeddings import Embeddings
+
+from langgraph.store.base.embed import (
+    AEmbeddingsFunc,
+    EmbeddingsFunc,
+    ensure_embeddings,
+    get_text_at_path,
+    tokenize_path,
+)
 
 
 class Item:
@@ -73,112 +88,510 @@ class Item:
         }
 
 
+class SearchItem(Item):
+    """Represents an item returned from a search operation with additional metadata."""
+
+    __slots__ = ("score",)
+
+    def __init__(
+        self,
+        namespace: tuple[str, ...],
+        key: str,
+        value: dict[str, Any],
+        created_at: datetime,
+        updated_at: datetime,
+        score: Optional[float] = None,
+    ) -> None:
+        """Initialize a result item.
+
+        Args:
+            namespace: Hierarchical path to the item.
+            key: Unique identifier within the namespace.
+            value: The stored value.
+            created_at: When the item was first created.
+            updated_at: When the item was last updated.
+            score: Relevance/similarity score if from a ranked operation.
+        """
+        super().__init__(
+            value=value,
+            key=key,
+            namespace=namespace,
+            created_at=created_at,
+            updated_at=updated_at,
+        )
+        self.score = score
+
+    def dict(self) -> dict:
+        result = super().dict()
+        result["score"] = self.score
+        return result
+
+
 class GetOp(NamedTuple):
-    """Operation to retrieve an item by namespace and key."""
+    """Operation to retrieve a specific item by its namespace and key.
+
+    This operation allows precise retrieval of stored items using their full path
+    (namespace) and unique identifier (key) combination.
+
+    ???+ example "Examples"
+
+        Basic item retrieval:
+        ```python
+        GetOp(namespace=("users", "profiles"), key="user123")
+        GetOp(namespace=("cache", "embeddings"), key="doc456")
+        ```
+    """
 
     namespace: tuple[str, ...]
-    """Hierarchical path for the item."""
+    """Hierarchical path that uniquely identifies the item's location.
+
+    ???+ example "Examples"
+
+        ```python
+        ("users",)  # Root level users namespace
+        ("users", "profiles")  # Profiles within users namespace
+        ```
+    """
+
     key: str
-    """Unique identifier within the namespace."""
+    """Unique identifier for the item within its specific namespace.
+
+    ???+ example "Examples"
+
+        ```python
+        "user123"  # For a user profile
+        "doc456"  # For a document
+        ```
+    """
 
 
 class SearchOp(NamedTuple):
-    """Operation to search for items within a namespace prefix."""
+    """Operation to search for items within a specified namespace hierarchy.
+
+    This operation supports both structured filtering and natural language search
+    within a given namespace prefix. It provides pagination through limit and offset
+    parameters.
+
+    Note:
+        Natural language search support depends on your store implementation.
+
+    ???+ example "Examples"
+        Search with filters and pagination:
+        ```python
+        SearchOp(
+            namespace_prefix=("documents",),
+            filter={"type": "report", "status": "active"},
+            limit=5,
+            offset=10
+        )
+        ```
+
+        Natural language search:
+        ```python
+        SearchOp(
+            namespace_prefix=("users", "content"),
+            query="technical documentation about APIs",
+            limit=20
+        )
+        ```
+    """
 
     namespace_prefix: tuple[str, ...]
-    """Hierarchical path prefix to search within."""
+    """Hierarchical path prefix defining the search scope.
+
+    ???+ example "Examples"
+
+        ```python
+        ()  # Search entire store
+        ("documents",)  # Search all documents
+        ("users", "content")  # Search within user content
+        ```
+    """
+
     filter: Optional[dict[str, Any]] = None
-    """Key-value pairs to filter results."""
+    """Key-value pairs for filtering results based on exact matches or comparison operators.
+
+    The filter supports both exact matches and operator-based comparisons.
+
+    Supported Operators:
+        - $eq: Equal to (same as direct value comparison)
+        - $ne: Not equal to
+        - $gt: Greater than
+        - $gte: Greater than or equal to
+        - $lt: Less than
+        - $lte: Less than or equal to
+
+    ???+ example "Examples"
+        Simple exact match:
+
+        ```python
+        {"status": "active"}
+        ```
+
+        Comparison operators:
+
+        ```python
+        {"score": {"$gt": 4.99}}  # Score greater than 4.99
+        ```
+
+        Multiple conditions:
+
+        ```python
+        {
+            "score": {"$gte": 3.0},
+            "color": "red"
+        }
+        ```
+    """
+
     limit: int = 10
-    """Maximum number of items to return."""
+    """Maximum number of items to return in the search results."""
+
     offset: int = 0
-    """Number of items to skip before returning results."""
+    """Number of matching items to skip for pagination."""
 
+    query: Optional[str] = None
+    """Natural language search query for semantic search capabilities.
 
-class PutOp(NamedTuple):
-    """Operation to store, update, or delete an item."""
-
-    namespace: tuple[str, ...]
-    """Hierarchical path for the item.
-    
-    Represented as a tuple of strings, allowing for nested categorization.
-    For example: ("documents", "user123")
-    """
-
-    key: str
-    """Unique identifier for the document.
-    
-    Should be distinct within its namespace.
-    """
-
-    value: Optional[dict[str, Any]]
-    """Data to be stored, or None to delete the item.
-    
-    Schema:
-    - Should be a dictionary where:
-      - Keys are strings representing field names
-      - Values can be of any serializable type
-    - If None, it indicates that the item should be deleted
+    ???+ example "Examples"
+        - "technical documentation about REST APIs"
+        - "machine learning papers from 2023"
     """
 
 
-NameSpacePath = tuple[Union[str, Literal["*"]], ...]
+# Type representing a namespace path that can include wildcards
+NamespacePath = tuple[Union[str, Literal["*"]], ...]
+"""A tuple representing a namespace path that can include wildcards.
 
+???+ example "Examples"
+    ```python
+    ("users",)  # Exact users namespace
+    ("documents", "*")  # Any sub-namespace under documents
+    ("cache", "*", "v1")  # Any cache category with v1 version
+    ```
+"""
+
+# Type for specifying how to match namespaces
 NamespaceMatchType = Literal["prefix", "suffix"]
+"""Specifies how to match namespace paths.
+
+Values:
+    "prefix": Match from the start of the namespace
+    "suffix": Match from the end of the namespace
+"""
 
 
 class MatchCondition(NamedTuple):
-    """Represents a single match condition."""
+    """Represents a pattern for matching namespaces in the store.
+
+    This class combines a match type (prefix or suffix) with a namespace path
+    pattern that can include wildcards to flexibly match different namespace
+    hierarchies.
+
+    ???+ example "Examples"
+        Prefix matching:
+        ```python
+        MatchCondition(match_type="prefix", path=("users", "profiles"))
+        ```
+
+        Suffix matching with wildcard:
+        ```python
+        MatchCondition(match_type="suffix", path=("cache", "*"))
+        ```
+
+        Simple suffix matching:
+        ```python
+        MatchCondition(match_type="suffix", path=("v1",))
+        ```
+    """
 
     match_type: NamespaceMatchType
-    path: NameSpacePath
+    """Type of namespace matching to perform."""
+
+    path: NamespacePath
+    """Namespace path pattern that can include wildcards."""
 
 
 class ListNamespacesOp(NamedTuple):
-    """Operation to list namespaces with optional match conditions."""
+    """Operation to list and filter namespaces in the store.
+
+    This operation allows exploring the organization of data, finding specific
+    collections, and navigating the namespace hierarchy.
+
+    ???+ example "Examples"
+
+        List all namespaces under the "documents" path:
+        ```python
+        ListNamespacesOp(
+            match_conditions=(MatchCondition(match_type="prefix", path=("documents",)),),
+            max_depth=2
+        )
+        ```
+
+        List all namespaces that end with "v1":
+        ```python
+        ListNamespacesOp(
+            match_conditions=(MatchCondition(match_type="suffix", path=("v1",)),),
+            limit=50
+        )
+        ```
+
+    """
 
     match_conditions: Optional[tuple[MatchCondition, ...]] = None
-    """A tuple of match conditions to apply to namespaces."""
+    """Optional conditions for filtering namespaces.
+
+    ???+ example "Examples"
+        All user namespaces:
+        ```python
+        (MatchCondition(match_type="prefix", path=("users",)),)
+        ```
+
+        All namespaces that start with "docs" and end with "draft":
+        ```python
+        (
+            MatchCondition(match_type="prefix", path=("docs",)),
+            MatchCondition(match_type="suffix", path=("draft",))
+        ) 
+        ```
+    """
 
     max_depth: Optional[int] = None
-    """Return namespaces up to this depth in the hierarchy."""
+    """Maximum depth of namespace hierarchy to return.
+
+    Note:
+        Namespaces deeper than this level will be truncated.
+    """
 
     limit: int = 100
     """Maximum number of namespaces to return."""
 
     offset: int = 0
-    """Number of namespaces to skip before returning results."""
+    """Number of namespaces to skip for pagination."""
+
+
+class PutOp(NamedTuple):
+    """Operation to store, update, or delete an item in the store.
+
+    This class represents a single operation to modify the store's contents,
+    whether adding new items, updating existing ones, or removing them.
+    """
+
+    namespace: tuple[str, ...]
+    """Hierarchical path that identifies the location of the item.
+
+    The namespace acts as a folder-like structure to organize items.
+    Each element in the tuple represents one level in the hierarchy.
+
+    ???+ example "Examples"
+        Root level documents
+        ```python
+        ("documents",)
+        ```
+        
+        User-specific documents
+        ```python
+        ("documents", "user123")
+        ```
+        
+        Nested cache structure
+        ```python
+        ("cache", "embeddings", "v1")
+        ```
+    """
+
+    key: str
+    """Unique identifier for the item within its namespace.
+
+    The key must be unique within the specific namespace to avoid conflicts.
+    Together with the namespace, it forms a complete path to the item.
+
+    Example:
+        If namespace is ("documents", "user123") and key is "report1",
+        the full path would effectively be "documents/user123/report1"
+    """
+
+    value: Optional[dict[str, Any]]
+    """The data to store, or None to mark the item for deletion.
+
+    The value must be a dictionary with string keys and JSON-serializable values.
+    Setting this to None signals that the item should be deleted.
+
+    Example:
+        {
+            "field1": "string value",
+            "field2": 123,
+            "nested": {"can": "contain", "any": "serializable data"}
+        }
+    """
+
+    index: Optional[Union[Literal[False], list[str]]] = None  # type: ignore[assignment]
+    """Controls how the item's fields are indexed for search operations.
+
+    Indexing configuration determines how the item can be found through search:
+        - None (default): Uses the store's default indexing configuration (if provided)
+        - False: Disables indexing for this item
+        - list[str]: Specifies which json path fields to index for search
+
+    The item remains accessible through direct get() operations regardless of indexing.
+    When indexed, fields can be searched using natural language queries through
+    vector similarity search (if supported by the store implementation).
+
+    Path Syntax:
+        - Simple field access: "field"
+        - Nested fields: "parent.child.grandchild"
+        - Array indexing:
+          - Specific index: "array[0]"
+          - Last element: "array[-1]"
+          - All elements (each individually): "array[*]"
+
+    ???+ example "Examples"
+        - None - Use store defaults (whole item)
+        - list[str] - List of fields to index
+        
+        ```python
+        [
+            "metadata.title",                    # Nested field access
+            "context[*].content",                # Index content from all context as separate vectors
+            "authors[0].name",                   # First author's name
+            "revisions[-1].changes",             # Most recent revision's changes
+            "sections[*].paragraphs[*].text",    # All text from all paragraphs in all sections
+            "metadata.tags[*]",                  # All tags in metadata
+        ]
+        ```
+    """
 
 
 Op = Union[GetOp, SearchOp, PutOp, ListNamespacesOp]
-Result = Union[Item, list[Item], list[tuple[str, ...]], None]
+Result = Union[Item, list[Item], list[SearchItem], list[tuple[str, ...]], None]
 
 
 class InvalidNamespaceError(ValueError):
     """Provided namespace is invalid."""
 
 
-def _validate_namespace(namespace: tuple[str, ...]) -> None:
-    if not namespace:
-        raise InvalidNamespaceError("Namespace cannot be empty.")
-    for label in namespace:
-        if not isinstance(label, str):
-            raise InvalidNamespaceError(
-                f"Invalid namespace label '{label}' found in {namespace}. Namespace labels"
-                f" must be strings, but got {type(label).__name__}."
-            )
-        if "." in label:
-            raise InvalidNamespaceError(
-                f"Invalid namespace label '{label}' found in {namespace}. Namespace labels cannot contain periods ('.')."
-            )
-        elif not label:
-            raise InvalidNamespaceError(
-                f"Namespace labels cannot be empty strings. Got {label} in {namespace}"
-            )
-    if namespace[0] == "langgraph":
-        raise InvalidNamespaceError(
-            f'Root label for namespace cannot be "langgraph". Got: {namespace}'
+class IndexConfig(TypedDict, total=False):
+    """Configuration for indexing documents for semantic search in the store.
+
+    If not provided to the store, the store will not support vector search.
+    In that case, all `index` arguments to put() and `aput()` operations will be ignored.
+    """
+
+    dims: int
+    """Number of dimensions in the embedding vectors.
+    
+    Common embedding models have the following dimensions:
+        - openai:text-embedding-3-large: 3072
+        - openai:text-embedding-3-small: 1536
+        - openai:text-embedding-ada-002: 1536
+        - cohere:embed-english-v3.0: 1024
+        - cohere:embed-english-light-v3.0: 384
+        - cohere:embed-multilingual-v3.0: 1024
+        - cohere:embed-multilingual-light-v3.0: 384
+    """
+
+    embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc]
+    """Optional function to generate embeddings from text.
+    
+    Can be specified in three ways:
+        1. A LangChain Embeddings instance
+        2. A synchronous embedding function (EmbeddingsFunc)
+        3. An asynchronous embedding function (AEmbeddingsFunc)
+    
+    ???+ example "Examples"
+        Using LangChain's initialization with InMemoryStore:
+        ```python
+        from langchain.embeddings import init_embeddings
+        from langgraph.store.memory import InMemoryStore
+        
+        store = InMemoryStore(
+            index={
+                "dims": 1536,
+                "embed": init_embeddings("openai:text-embedding-3-small")
+            }
         )
+        ```
+        
+        Using a custom embedding function with InMemoryStore:
+        ```python
+        from openai import OpenAI
+        from langgraph.store.memory import InMemoryStore
+        
+        client = OpenAI()
+        
+        def embed_texts(texts: list[str]) -> list[list[float]]:
+            response = client.embeddings.create(
+                model="text-embedding-3-small",
+                input=texts
+            )
+            return [e.embedding for e in response.data]
+            
+        store = InMemoryStore(
+            index={
+                "dims": 1536,
+                "embed": embed_texts
+            }
+        )
+        ```
+        
+        Using an asynchronous embedding function with InMemoryStore:
+        ```python
+        from openai import AsyncOpenAI
+        from langgraph.store.memory import InMemoryStore
+        
+        client = AsyncOpenAI()
+        
+        async def aembed_texts(texts: list[str]) -> list[list[float]]:
+            response = await client.embeddings.create(
+                model="text-embedding-3-small",
+                input=texts
+            )
+            return [e.embedding for e in response.data]
+            
+        store = InMemoryStore(
+            index={
+                "dims": 1536,
+                "embed": aembed_texts
+            }
+        )
+        ```
+    """
+
+    fields: Optional[list[str]]
+    """Fields to extract text from for embedding generation.
+    
+    Controls which parts of stored items are embedded for semantic search. Follows JSON path syntax:
+
+        - ["$"]: Embeds the entire JSON object as one vector  (default)
+        - ["field1", "field2"]: Embeds specific top-level fields
+        - ["parent.child"]: Embeds nested fields using dot notation
+        - ["array[*].field"]: Embeds field from each array element separately
+    
+    Note:
+        You can always override this behavior when storing an item using the
+        `index` parameter in the `put` or `aput` operations.
+    
+    ???+ example "Examples"
+        ```python
+        # Embed entire document (default)
+        fields=["$"]
+        
+        # Embed specific fields
+        fields=["text", "summary"]
+        
+        # Embed nested fields
+        fields=["metadata.title", "content.body"]
+        
+        # Embed from arrays
+        fields=["messages[*].content"]  # Each message content separately
+        fields=["context[0].text"]      # First context item's text
+        ```
+    
+    Note:
+        - Fields missing from a document are skipped
+        - Array notation creates separate embeddings for each element
+        - Complex nested paths are supported (e.g., "a.b[*].c.d")
+    """
 
 
 class BaseStore(ABC):
@@ -186,6 +599,15 @@ class BaseStore(ABC):
 
     Stores enable persistence and memory that can be shared across threads,
     scoped to user IDs, assistant IDs, or other arbitrary namespaces.
+    Some implementations may support semantic search capabilities through
+    an optional `index` configuration.
+
+    Note:
+        Semantic search capabilities vary by implementation and are typically
+        disabled by default. Stores that support this feature can be configured
+        by providing an `index` configuration at creation time. Without this
+        configuration, semantic search is disabled and any `index` arguments
+        to storage operations will have no effect.
     """
 
     __slots__ = ("__weakref__",)
@@ -231,33 +653,109 @@ class BaseStore(ABC):
         namespace_prefix: tuple[str, ...],
         /,
         *,
+        query: Optional[str] = None,
         filter: Optional[dict[str, Any]] = None,
         limit: int = 10,
         offset: int = 0,
-    ) -> list[Item]:
+    ) -> list[SearchItem]:
         """Search for items within a namespace prefix.
 
         Args:
             namespace_prefix: Hierarchical path prefix to search within.
+            query: Optional query for natural language search.
             filter: Key-value pairs to filter results.
             limit: Maximum number of items to return.
             offset: Number of items to skip before returning results.
 
         Returns:
             List of items matching the search criteria.
-        """
-        return self.batch([SearchOp(namespace_prefix, filter, limit, offset)])[0]
 
-    def put(self, namespace: tuple[str, ...], key: str, value: dict[str, Any]) -> None:
-        """Store or update an item.
+        ???+ example "Examples"
+            Basic filtering:
+            ```python
+            # Search for documents with specific metadata
+            results = store.search(
+                ("docs",),
+                filter={"type": "article", "status": "published"}
+            )
+            ```
+
+            Natural language search (requires vector store implementation):
+            ```python
+            # Initialize store with embedding configuration
+            store = YourStore( # e.g., InMemoryStore, AsyncPostgresStore
+                index={
+                    "dims": 1536,  # embedding dimensions
+                    "embed": your_embedding_function,  # function to create embeddings
+                    "fields": ["text"]  # fields to embed. Defaults to ["$"]
+                }
+            )
+
+            # Search for semantically similar documents
+            results = store.search(
+                ("docs",),
+                query="machine learning applications in healthcare",
+                filter={"type": "research_paper"},
+                limit=5
+            )
+            ```
+
+            Note: Natural language search support depends on your store implementation
+            and requires proper embedding configuration.
+        """
+        return self.batch([SearchOp(namespace_prefix, filter, limit, offset, query)])[0]
+
+    def put(
+        self,
+        namespace: tuple[str, ...],
+        key: str,
+        value: dict[str, Any],
+        index: Optional[Union[Literal[False], list[str]]] = None,
+    ) -> None:
+        """Store or update an item in the store.
 
         Args:
-            namespace: Hierarchical path for the item.
-            key: Unique identifier within the namespace.
-            value: Dictionary containing the item's data.
+            namespace: Hierarchical path for the item, represented as a tuple of strings.
+                Example: ("documents", "user123")
+            key: Unique identifier within the namespace. Together with namespace forms
+                the complete path to the item.
+            value: Dictionary containing the item's data. Must contain string keys
+                and JSON-serializable values.
+            index: Controls how the item's fields are indexed for search:
+
+                - None (default): Use `fields` you configured when creating the store (if any)
+                    If you do not initialize the store with indexing capabilities,
+                    the `index` parameter will be ignored
+                - False: Disable indexing for this item
+                - list[str]: List of field paths to index, supporting:
+                    - Nested fields: "metadata.title"
+                    - Array access: "chapters[*].content" (each indexed separately)
+                    - Specific indices: "authors[0].name"
+
+        Note:
+            Indexing support depends on your store implementation.
+            If you do not initialize the store with indexing capabilities,
+            the `index` parameter will be ignored.
+
+        ???+ example "Examples"
+            Store item. Indexing depends on how you configure the store.
+            ```python
+            store.put(("docs",), "report", {"memory": "Will likes ai"})
+            ```
+
+            Do not index item for semantic search. Still accessible through get()
+            and search() operations but won't have a vector representation.
+            ```python
+            store.put(("docs",), "report", {"memory": "Will likes ai"}, index=False)
+            ```
+
+            Index specific fields for search.
+            ```python
+            store.put(("docs",), "report", {"memory": "Will likes ai"}, index=["memory"])
+            ```
         """
         _validate_namespace(namespace)
-        self.batch([PutOp(namespace, key, value)])
+        self.batch([PutOp(namespace, key, value, index=index)])
 
     def delete(self, namespace: tuple[str, ...], key: str) -> None:
         """Delete an item.
@@ -271,8 +769,8 @@ class BaseStore(ABC):
     def list_namespaces(
         self,
         *,
-        prefix: Optional[NameSpacePath] = None,
-        suffix: Optional[NameSpacePath] = None,
+        prefix: Optional[NamespacePath] = None,
+        suffix: Optional[NamespacePath] = None,
         max_depth: Optional[int] = None,
         limit: int = 100,
         offset: int = 0,
@@ -286,7 +784,7 @@ class BaseStore(ABC):
             prefix (Optional[Tuple[str, ...]]): Filter namespaces that start with this path.
             suffix (Optional[Tuple[str, ...]]): Filter namespaces that end with this path.
             max_depth (Optional[int]): Return namespaces up to this depth in the hierarchy.
-                Namespaces deeper than this level will be truncated to this depth.
+                Namespaces deeper than this level will be truncated.
             limit (int): Maximum number of namespaces to return (default 100).
             offset (int): Number of namespaces to skip for pagination (default 0).
 
@@ -294,16 +792,18 @@ class BaseStore(ABC):
             List[Tuple[str, ...]]: A list of namespace tuples that match the criteria.
             Each tuple represents a full namespace path up to `max_depth`.
 
-        Examples:
-
+        ???+ example "Examples":
             Setting max_depth=3. Given the namespaces:
-                # ("a", "b", "c")
-                # ("a", "b", "d", "e")
-                # ("a", "b", "d", "i")
-                # ("a", "b", "f")
-                # ("a", "c", "f")
-                store.list_namespaces(prefix=("a", "b"), max_depth=3)
-                # [("a", "b", "c"), ("a", "b", "d"), ("a", "b", "f")]
+            ```python
+            # Example if you have the following namespaces:
+            # ("a", "b", "c")
+            # ("a", "b", "d", "e")
+            # ("a", "b", "d", "i")
+            # ("a", "b", "f")
+            # ("a", "c", "f")
+            store.list_namespaces(prefix=("a", "b"), max_depth=3)
+            # [("a", "b", "c"), ("a", "b", "d"), ("a", "b", "f")]
+            ```
         """
         match_conditions = []
         if prefix:
@@ -336,37 +836,121 @@ class BaseStore(ABC):
         namespace_prefix: tuple[str, ...],
         /,
         *,
+        query: Optional[str] = None,
         filter: Optional[dict[str, Any]] = None,
         limit: int = 10,
         offset: int = 0,
-    ) -> list[Item]:
+    ) -> list[SearchItem]:
         """Asynchronously search for items within a namespace prefix.
 
         Args:
             namespace_prefix: Hierarchical path prefix to search within.
+            query: Optional query for natural language search.
             filter: Key-value pairs to filter results.
             limit: Maximum number of items to return.
             offset: Number of items to skip before returning results.
 
         Returns:
             List of items matching the search criteria.
+
+        ???+ example "Examples"
+            Basic filtering:
+            ```python
+            # Search for documents with specific metadata
+            results = await store.asearch(
+                ("docs",),
+                filter={"type": "article", "status": "published"}
+            )
+            ```
+
+            Natural language search (requires vector store implementation):
+            ```python
+            # Initialize store with embedding configuration
+            store = YourStore( # e.g., InMemoryStore, AsyncPostgresStore
+                index={
+                    "dims": 1536,  # embedding dimensions
+                    "embed": your_embedding_function,  # function to create embeddings
+                    "fields": ["text"]  # fields to embed
+                }
+            )
+
+            # Search for semantically similar documents
+            results = await store.asearch(
+                ("docs",),
+                query="machine learning applications in healthcare",
+                filter={"type": "research_paper"},
+                limit=5
+            )
+            ```
+
+            Note: Natural language search support depends on your store implementation
+            and requires proper embedding configuration.
         """
-        return (await self.abatch([SearchOp(namespace_prefix, filter, limit, offset)]))[
-            0
-        ]
+        return (
+            await self.abatch(
+                [SearchOp(namespace_prefix, filter, limit, offset, query)]
+            )
+        )[0]
 
     async def aput(
-        self, namespace: tuple[str, ...], key: str, value: dict[str, Any]
+        self,
+        namespace: tuple[str, ...],
+        key: str,
+        value: dict[str, Any],
+        index: Optional[Union[Literal[False], list[str]]] = None,
     ) -> None:
-        """Asynchronously store or update an item.
+        """Asynchronously store or update an item in the store.
 
         Args:
-            namespace: Hierarchical path for the item.
-            key: Unique identifier within the namespace.
-            value: Dictionary containing the item's data.
+            namespace: Hierarchical path for the item, represented as a tuple of strings.
+                Example: ("documents", "user123")
+            key: Unique identifier within the namespace. Together with namespace forms
+                the complete path to the item.
+            value: Dictionary containing the item's data. Must contain string keys
+                and JSON-serializable values.
+            index: Controls how the item's fields are indexed for search:
+
+                - None (default): Use `fields` you configured when creating the store (if any)
+                    If you do not initialize the store with indexing capabilities,
+                    the `index` parameter will be ignored
+                - False: Disable indexing for this item
+                - list[str]: List of field paths to index, supporting:
+                    - Nested fields: "metadata.title"
+                    - Array access: "chapters[*].content" (each indexed separately)
+                    - Specific indices: "authors[0].name"
+
+        Note:
+            Indexing support depends on your store implementation.
+            If you do not initialize the store with indexing capabilities,
+            the `index` parameter will be ignored.
+
+        ???+ example "Examples"
+            Store item. Indexing depends on how you configure the store.
+            ```python
+            await store.aput(("docs",), "report", {"memory": "Will likes ai"})
+            ```
+
+            Do not index item for semantic search. Still accessible through get()
+            and search() operations but won't have a vector representation.
+            ```python
+            await store.aput(("docs",), "report", {"memory": "Will likes ai"}, index=False)
+            ```
+
+            Index specific fields for search (if store configured to index items):
+            ```python
+            await store.aput(
+                ("docs",),
+                "report",
+                {
+                    "memory": "Will likes ai",
+                    "context": [{"content": "..."}, {"content": "..."}]
+                },
+                index=["memory", "context[*].content"]
+            )
+            ```
         """
         _validate_namespace(namespace)
-        await self.abatch([PutOp(namespace, key, value)])
+        await self.abatch([PutOp(namespace, key, value, index=index)])
 
     async def adelete(self, namespace: tuple[str, ...], key: str) -> None:
         """Asynchronously delete an item.
@@ -380,8 +964,8 @@ class BaseStore(ABC):
     async def alist_namespaces(
         self,
         *,
-        prefix: Optional[NameSpacePath] = None,
-        suffix: Optional[NameSpacePath] = None,
+        prefix: Optional[NamespacePath] = None,
+        suffix: Optional[NamespacePath] = None,
         max_depth: Optional[int] = None,
         limit: int = 100,
         offset: int = 0,
@@ -403,16 +987,19 @@ class BaseStore(ABC):
             List[Tuple[str, ...]]: A list of namespace tuples that match the criteria.
             Each tuple represents a full namespace path up to `max_depth`.
 
-        Examples:
+        ???+ example "Examples"
+            Setting max_depth=3 with existing namespaces:
+            ```python
+            # Given the following namespaces:
+            # ("a", "b", "c")
+            # ("a", "b", "d", "e")
+            # ("a", "b", "d", "i")
+            # ("a", "b", "f")
+            # ("a", "c", "f")
 
-            Setting max_depth=3. Given the namespaces:
-                # ("a", "b", "c")
-                # ("a", "b", "d", "e")
-                # ("a", "b", "d", "i")
-                # ("a", "b", "f")
-                # ("a", "c", "f")
-                await store.alist_namespaces(prefix=("a", "b"), max_depth=3)
-                # [("a", "b", "c"), ("a", "b", "d"), ("a", "b", "f")]
+            await store.alist_namespaces(prefix=("a", "b"), max_depth=3)
+            # Returns: [("a", "b", "c"), ("a", "b", "d"), ("a", "b", "f")]
+            ```
         """
         match_conditions = []
         if prefix:
@@ -427,3 +1014,44 @@ class BaseStore(ABC):
             offset=offset,
         )
         return (await self.abatch([op]))[0]
+
+
+def _validate_namespace(namespace: tuple[str, ...]) -> None:
+    if not namespace:
+        raise InvalidNamespaceError("Namespace cannot be empty.")
+    for label in namespace:
+        if not isinstance(label, str):
+            raise InvalidNamespaceError(
+                f"Invalid namespace label '{label}' found in {namespace}. Namespace labels"
+                f" must be strings, but got {type(label).__name__}."
+            )
+        if "." in label:
+            raise InvalidNamespaceError(
+                f"Invalid namespace label '{label}' found in {namespace}. Namespace labels cannot contain periods ('.')."
+            )
+        elif not label:
+            raise InvalidNamespaceError(
+                f"Namespace labels cannot be empty strings. Got {label} in {namespace}"
+            )
+    if namespace[0] == "langgraph":
+        raise InvalidNamespaceError(
+            f'Root label for namespace cannot be "langgraph". Got: {namespace}'
+        )
+
+
+__all__ = [
+    "BaseStore",
+    "Item",
+    "Op",
+    "PutOp",
+    "GetOp",
+    "SearchOp",
+    "ListNamespacesOp",
+    "MatchCondition",
+    "NamespacePath",
+    "NamespaceMatchType",
+    "Embeddings",
+    "ensure_embeddings",
+    "tokenize_path",
+    "get_text_at_path",
+]

libs/checkpoint/langgraph/store/base/batch.py

@@ -1,13 +1,17 @@
 import asyncio
 import weakref
-from typing import Any, Optional
+from typing import Any, Literal, Optional, Union
 
 from langgraph.store.base import (
     BaseStore,
     GetOp,
     Item,
+    ListNamespacesOp,
+    MatchCondition,
+    NamespacePath,
     Op,
     PutOp,
+    SearchItem,
     SearchOp,
     _validate_namespace,
 )
@@ -40,12 +44,13 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace_prefix: tuple[str, ...],
         /,
         *,
+        query: Optional[str] = None,
         filter: Optional[dict[str, Any]] = None,
         limit: int = 10,
         offset: int = 0,
-    ) -> list[Item]:
+    ) -> list[SearchItem]:
         fut = self._loop.create_future()
-        self._aqueue[fut] = SearchOp(namespace_prefix, filter, limit, offset)
+        self._aqueue[fut] = SearchOp(namespace_prefix, filter, limit, offset, query)
         return await fut
 
     async def aput(
@@ -53,10 +58,11 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace: tuple[str, ...],
         key: str,
         value: dict[str, Any],
+        index: Optional[Union[Literal[False], list[str]]] = None,
     ) -> None:
         _validate_namespace(namespace)
         fut = self._loop.create_future()
-        self._aqueue[fut] = PutOp(namespace, key, value)
+        self._aqueue[fut] = PutOp(namespace, key, value, index)
         return await fut
 
     async def adelete(
@@ -68,6 +74,74 @@ class AsyncBatchedBaseStore(BaseStore):
         self._aqueue[fut] = PutOp(namespace, key, None)
         return await fut
 
+    async def alist_namespaces(
+        self,
+        *,
+        prefix: Optional[NamespacePath] = None,
+        suffix: Optional[NamespacePath] = None,
+        max_depth: Optional[int] = None,
+        limit: int = 100,
+        offset: int = 0,
+    ) -> list[tuple[str, ...]]:
+        fut = self._loop.create_future()
+        match_conditions = []
+        if prefix:
+            match_conditions.append(MatchCondition(match_type="prefix", path=prefix))
+        if suffix:
+            match_conditions.append(MatchCondition(match_type="suffix", path=suffix))
+
+        op = ListNamespacesOp(
+            match_conditions=tuple(match_conditions),
+            max_depth=max_depth,
+            limit=limit,
+            offset=offset,
+        )
+        self._aqueue[fut] = op
+        return await fut
+
+
+def _dedupe_ops(values: list[Op]) -> tuple[Optional[list[int]], list[Op]]:
+    """Dedupe operations while preserving order for results.
+
+    Args:
+        values: List of operations to dedupe
+
+    Returns:
+        Tuple of (listen indices, deduped operations)
+        where listen indices map deduped operation results back to original positions
+    """
+    if len(values) <= 1:
+        return None, list(values)
+
+    dedupped: list[Op] = []
+    listen: list[int] = []
+    puts: dict[tuple[tuple[str, ...], str], int] = {}
+
+    for op in values:
+        if isinstance(op, (GetOp, SearchOp, ListNamespacesOp)):
+            try:
+                listen.append(dedupped.index(op))
+            except ValueError:
+                listen.append(len(dedupped))
+                dedupped.append(op)
+        elif isinstance(op, PutOp):
+            putkey = (op.namespace, op.key)
+            if putkey in puts:
+                # Overwrite previous put
+                ix = puts[putkey]
+                dedupped[ix] = op
+                listen.append(ix)
+            else:
+                puts[putkey] = len(dedupped)
+                listen.append(len(dedupped))
+                dedupped.append(op)
+
+        else:  # Any new ops will be treated regularly
+            listen.append(len(dedupped))
+            dedupped.append(op)
+
+    return listen, dedupped
+
 
 async def _run(
     aqueue: dict[asyncio.Future, Op], store: weakref.ReferenceType[BaseStore]
@@ -81,7 +155,12 @@ async def _run(
             taken = aqueue.copy()
             # action each operation
             try:
-                results = await s.abatch(taken.values())
+                values = list(taken.values())
+                listen, dedupped = _dedupe_ops(values)
+                results = await s.abatch(dedupped)
+                if listen is not None:
+                    results = [results[ix] for ix in listen]
+
                 # set the results of each operation
                 for fut, result in zip(taken, results):
                     fut.set_result(result)

libs/checkpoint/langgraph/store/base/embed.py

@@ -0,0 +1,380 @@
+"""Utilities for working with embedding functions and LangChain's Embeddings interface.
+
+This module provides tools to wrap arbitrary embedding functions (both sync and async)
+into LangChain's Embeddings interface. This enables using custom embedding functions
+with LangChain-compatible tools while maintaining support for both synchronous and
+asynchronous operations.
+"""
+
+import asyncio
+import json
+from typing import Any, Awaitable, Callable, Optional, Sequence, Union
+
+from langchain_core.embeddings import Embeddings
+
+EmbeddingsFunc = Callable[[Sequence[str]], list[list[float]]]
+"""Type for synchronous embedding functions.
+
+The function should take a sequence of strings and return a list of embeddings,
+where each embedding is a list of floats. The dimensionality of the embeddings
+should be consistent for all inputs.
+"""
+
+AEmbeddingsFunc = Callable[[Sequence[str]], Awaitable[list[list[float]]]]
+"""Type for asynchronous embedding functions.
+
+Similar to EmbeddingsFunc, but returns an awaitable that resolves to the embeddings.
+"""
+
+
+def ensure_embeddings(
+    embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, None],
+) -> Embeddings:
+    """Ensure that an embedding function conforms to LangChain's Embeddings interface.
+
+    This function wraps arbitrary embedding functions to make them compatible with
+    LangChain's Embeddings interface. It handles both synchronous and asynchronous
+    functions.
+
+    Args:
+        embed: Either an existing Embeddings instance, or a function that converts
+            text to embeddings. If the function is async, it will be used for both
+            sync and async operations.
+
+    Returns:
+        An Embeddings instance that wraps the provided function(s).
+
+    ??? example "Examples"
+        Wrap a synchronous embedding function:
+        ```python
+        def my_embed_fn(texts):
+            return [[0.1, 0.2] for _ in texts]
+
+        embeddings = ensure_embeddings(my_embed_fn)
+        result = embeddings.embed_query("hello")  # Returns [0.1, 0.2]
+        ```
+
+        Wrap an asynchronous embedding function:
+        ```python
+        async def my_async_fn(texts):
+            return [[0.1, 0.2] for _ in texts]
+
+        embeddings = ensure_embeddings(my_async_fn)
+        result = await embeddings.aembed_query("hello")  # Returns [0.1, 0.2]
+        ```
+    """
+    if embed is None:
+        raise ValueError("embed must be provided")
+    if isinstance(embed, Embeddings):
+        return embed
+    return EmbeddingsLambda(embed)
+
+
+class EmbeddingsLambda(Embeddings):
+    """Wrapper to convert embedding functions into LangChain's Embeddings interface.
+
+    This class allows arbitrary embedding functions to be used with LangChain-compatible
+    tools. It supports both synchronous and asynchronous operations, and can handle:
+    1. A synchronous function for sync operations (async operations will use sync function)
+    2. An async function for both sync/async operations (sync operations will raise an error)
+
+    The embedding functions should convert text into fixed-dimensional vectors that
+    capture the semantic meaning of the text.
+
+    Args:
+        func: Function that converts text to embeddings. Can be sync or async.
+            If async, it will be used for async operations, but sync operations
+            will raise an error. If sync, it will be used for both sync and async operations.
+
+    ??? example "Examples"
+        With a sync function:
+        ```python
+        def my_embed_fn(texts):
+            # Return 2D embeddings for each text
+            return [[0.1, 0.2] for _ in texts]
+
+        embeddings = EmbeddingsLambda(my_embed_fn)
+        result = embeddings.embed_query("hello")  # Returns [0.1, 0.2]
+        await embeddings.aembed_query("hello")  # Also returns [0.1, 0.2]
+        ```
+
+        With an async function:
+        ```python
+        async def my_async_fn(texts):
+            return [[0.1, 0.2] for _ in texts]
+
+        embeddings = EmbeddingsLambda(my_async_fn)
+        await embeddings.aembed_query("hello")  # Returns [0.1, 0.2]
+        # Note: embed_query() would raise an error
+        ```
+    """
+
+    def __init__(
+        self,
+        func: Union[EmbeddingsFunc, AEmbeddingsFunc],
+    ) -> None:
+        if func is None:
+            raise ValueError("func must be provided")
+        if _is_async_callable(func):
+            self.afunc = func
+        else:
+            self.func = func
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Embed a list of texts into vectors.
+
+        Args:
+            texts: list of texts to convert to embeddings.
+
+        Returns:
+            list of embeddings, one per input text. Each embedding is a list of floats.
+
+        Raises:
+            ValueError: If the instance was initialized with only an async function.
+        """
+        func = getattr(self, "func", None)
+        if func is None:
+            raise ValueError(
+                "EmbeddingsLambda was initialized with an async function but no sync function. "
+                "Use aembed_documents for async operation or provide a sync function."
+            )
+        return func(texts)
+
+    def embed_query(self, text: str) -> list[float]:
+        """Embed a single piece of text.
+
+        Args:
+            text: Text to convert to an embedding.
+
+        Returns:
+            Embedding vector as a list of floats.
+
+        Note:
+            This is equivalent to calling embed_documents with a single text
+            and taking the first result.
+        """
+        return self.embed_documents([text])[0]
+
+    async def aembed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Asynchronously embed a list of texts into vectors.
+
+        Args:
+            texts: list of texts to convert to embeddings.
+
+        Returns:
+            list of embeddings, one per input text. Each embedding is a list of floats.
+
+        Note:
+            If no async function was provided, this falls back to the sync implementation.
+        """
+        afunc = getattr(self, "afunc", None)
+        if afunc is None:
+            return await super().aembed_documents(texts)
+        return await afunc(texts)
+
+    async def aembed_query(self, text: str) -> list[float]:
+        """Asynchronously embed a single piece of text.
+
+        Args:
+            text: Text to convert to an embedding.
+
+        Returns:
+            Embedding vector as a list of floats.
+
+        Note:
+            This is equivalent to calling aembed_documents with a single text
+            and taking the first result.
+        """
+        afunc = getattr(self, "afunc", None)
+        if afunc is None:
+            return await super().aembed_query(text)
+        return (await afunc([text]))[0]
+
+
+def get_text_at_path(obj: Any, path: Union[str, list[str]]) -> list[str]:
+    """Extract text from an object using a path expression or pre-tokenized path.
+
+    Args:
+        obj: The object to extract text from
+        path: Either a path string or pre-tokenized path list.
+
+    !!! info "Path types handled"
+        - Simple paths: "field1.field2"
+        - Array indexing: "[0]", "[*]", "[-1]"
+        - Wildcards: "*"
+        - Multi-field selection: "{field1,field2}"
+        - Nested paths in multi-field: "{field1,nested.field2}"
+    """
+    if not path or path == "$":
+        return [json.dumps(obj, sort_keys=True)]
+
+    tokens = tokenize_path(path) if isinstance(path, str) else path
+
+    def _extract_from_obj(obj: Any, tokens: list[str], pos: int) -> list[str]:
+        if pos >= len(tokens):
+            if isinstance(obj, (str, int, float, bool)):
+                return [str(obj)]
+            elif obj is None:
+                return []
+            elif isinstance(obj, (list, dict)):
+                return [json.dumps(obj, sort_keys=True)]
+            return []
+
+        token = tokens[pos]
+        results = []
+
+        if token.startswith("[") and token.endswith("]"):
+            if not isinstance(obj, list):
+                return []
+
+            index = token[1:-1]
+            if index == "*":
+                for item in obj:
+                    results.extend(_extract_from_obj(item, tokens, pos + 1))
+            else:
+                try:
+                    idx = int(index)
+                    if idx < 0:
+                        idx = len(obj) + idx
+                    if 0 <= idx < len(obj):
+                        results.extend(_extract_from_obj(obj[idx], tokens, pos + 1))
+                except (ValueError, IndexError):
+                    return []
+
+        elif token.startswith("{") and token.endswith("}"):
+            if not isinstance(obj, dict):
+                return []
+
+            fields = [f.strip() for f in token[1:-1].split(",")]
+            for field in fields:
+                nested_tokens = tokenize_path(field)
+                if nested_tokens:
+                    current_obj: Optional[dict] = obj
+                    for nested_token in nested_tokens:
+                        if (
+                            isinstance(current_obj, dict)
+                            and nested_token in current_obj
+                        ):
+                            current_obj = current_obj[nested_token]
+                        else:
+                            current_obj = None
+                            break
+                    if current_obj is not None:
+                        if isinstance(current_obj, (str, int, float, bool)):
+                            results.append(str(current_obj))
+                        elif isinstance(current_obj, (list, dict)):
+                            results.append(json.dumps(current_obj, sort_keys=True))
+
+        # Handle wildcard
+        elif token == "*":
+            if isinstance(obj, dict):
+                for value in obj.values():
+                    results.extend(_extract_from_obj(value, tokens, pos + 1))
+            elif isinstance(obj, list):
+                for item in obj:
+                    results.extend(_extract_from_obj(item, tokens, pos + 1))
+
+        # Handle regular field
+        else:
+            if isinstance(obj, dict) and token in obj:
+                results.extend(_extract_from_obj(obj[token], tokens, pos + 1))
+
+        return results
+
+    return _extract_from_obj(obj, tokens, 0)
+
+
+# Private utility functions
+
+
+def tokenize_path(path: str) -> list[str]:
+    """Tokenize a path into components.
+
+    !!! info "Types handled"
+        - Simple paths: "field1.field2"
+        - Array indexing: "[0]", "[*]", "[-1]"
+        - Wildcards: "*"
+        - Multi-field selection: "{field1,field2}"
+    """
+    if not path:
+        return []
+
+    tokens = []
+    current: list[str] = []
+    i = 0
+    while i < len(path):
+        char = path[i]
+
+        if char == "[":  # Handle array index
+            if current:
+                tokens.append("".join(current))
+                current = []
+            bracket_count = 1
+            index_chars = ["["]
+            i += 1
+            while i < len(path) and bracket_count > 0:
+                if path[i] == "[":
+                    bracket_count += 1
+                elif path[i] == "]":
+                    bracket_count -= 1
+                index_chars.append(path[i])
+                i += 1
+            tokens.append("".join(index_chars))
+            continue
+
+        elif char == "{":  # Handle multi-field selection
+            if current:
+                tokens.append("".join(current))
+                current = []
+            brace_count = 1
+            field_chars = ["{"]
+            i += 1
+            while i < len(path) and brace_count > 0:
+                if path[i] == "{":
+                    brace_count += 1
+                elif path[i] == "}":
+                    brace_count -= 1
+                field_chars.append(path[i])
+                i += 1
+            tokens.append("".join(field_chars))
+            continue
+
+        elif char == ".":  # Handle regular field
+            if current:
+                tokens.append("".join(current))
+                current = []
+        else:
+            current.append(char)
+        i += 1
+
+    if current:
+        tokens.append("".join(current))
+
+    return tokens
+
+
+def _is_async_callable(
+    func: Any,
+) -> bool:
+    """Check if a function is async.
+
+    This includes both async def functions and classes with async __call__ methods.
+
+    Args:
+        func: Function or callable object to check.
+
+    Returns:
+        True if the function is async, False otherwise.
+    """
+    return (
+        asyncio.iscoroutinefunction(func)
+        or hasattr(func, "__call__")  # noqa: B004
+        and asyncio.iscoroutinefunction(func.__call__)
+    )
+
+
+__all__ = [
+    "ensure_embeddings",
+    "EmbeddingsFunc",
+    "AEmbeddingsFunc",
+]

libs/checkpoint/langgraph/store/memory/__init__.py

@@ -1,79 +1,456 @@
+"""In-memory dictionary-backed store with optional vector search.
+
+!!! example "Examples"
+    Basic key-value storage:
+    ```python
+    from langgraph.store.memory import InMemoryStore
+
+    store = InMemoryStore()
+    store.put(("users", "123"), "prefs", {"theme": "dark"})
+    item = store.get(("users", "123"), "prefs")
+    ```
+
+    Vector search using LangChain embeddings:
+    ```python
+    from langchain.embeddings import init_embeddings
+    from langgraph.store.memory import InMemoryStore
+
+    store = InMemoryStore(
+        index={
+            "dims": 1536,
+            "embed": init_embeddings("openai:text-embedding-3-small")
+        }
+    )
+
+    # Store documents
+    store.put(("docs",), "doc1", {"text": "Python tutorial"})
+    store.put(("docs",), "doc2", {"text": "TypeScript guide"})
+
+    # Search by similarity
+    results = store.search(("docs",), query="python programming")
+    ```
+
+    Vector search using OpenAI SDK directly:
+    ```python
+    from openai import OpenAI
+    from langgraph.store.memory import InMemoryStore
+
+    client = OpenAI()
+
+    def embed_texts(texts: list[str]) -> list[list[float]]:
+        response = client.embeddings.create(
+            model="text-embedding-3-small",
+            input=texts
+        )
+        return [e.embedding for e in response.data]
+
+    store = InMemoryStore(
+        index={
+            "dims": 1536,
+            "embed": embed_texts
+        }
+    )
+
+    # Store documents
+    store.put(("docs",), "doc1", {"text": "Python tutorial"})
+    store.put(("docs",), "doc2", {"text": "TypeScript guide"})
+
+    # Search by similarity
+    results = store.search(("docs",), query="python programming")
+    ```
+
+    Async vector search using OpenAI SDK:
+    ```python
+    from openai import AsyncOpenAI
+    from langgraph.store.memory import InMemoryStore
+
+    client = AsyncOpenAI()
+
+    async def aembed_texts(texts: list[str]) -> list[list[float]]:
+        response = await client.embeddings.create(
+            model="text-embedding-3-small",
+            input=texts
+        )
+        return [e.embedding for e in response.data]
+
+    store = InMemoryStore(
+        index={
+            "dims": 1536,
+            "embed": aembed_texts
+        }
+    )
+
+    # Store documents
+    await store.aput(("docs",), "doc1", {"text": "Python tutorial"})
+    await store.aput(("docs",), "doc2", {"text": "TypeScript guide"})
+
+    # Search by similarity
+    results = await store.asearch(("docs",), query="python programming")
+    ```
+
+Warning:
+    This store keeps all data in memory. Data is lost when the process exits.
+    For persistence, use a database-backed store like PostgresStore.
+
+Tip:
+    For vector search, install numpy for better performance:
+    ```bash
+    pip install numpy
+    ```
+"""
+
+import asyncio
+import concurrent.futures as cf
+import functools
+import logging
 from collections import defaultdict
 from datetime import datetime, timezone
-from typing import Iterable
+from importlib import util
+from typing import Any, Iterable, Optional
+
+from langchain_core.embeddings import Embeddings
 
 from langgraph.store.base import (
     BaseStore,
     GetOp,
+    IndexConfig,
     Item,
     ListNamespacesOp,
     MatchCondition,
     Op,
     PutOp,
     Result,
+    SearchItem,
     SearchOp,
+    ensure_embeddings,
+    get_text_at_path,
+    tokenize_path,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class InMemoryStore(BaseStore):
-    """A KV store backed by an in-memory python dictionary.
+    """In-memory dictionary-backed store with optional vector search.
 
-    Useful for testing/experimentation and lightweight PoC's.
-    For actual persistence, use a Store backed by a proper database.
+    !!! example "Examples"
+        Basic key-value storage:
+            store = InMemoryStore()
+            store.put(("users", "123"), "prefs", {"theme": "dark"})
+            item = store.get(("users", "123"), "prefs")
+
+        Vector search with embeddings:
+            from langchain.embeddings import init_embeddings
+            store = InMemoryStore(index={
+                "dims": 1536,
+                "embed": init_embeddings("openai:text-embedding-3-small"),
+                "fields": ["text"],
+            })
+
+            # Store documents
+            store.put(("docs",), "doc1", {"text": "Python tutorial"})
+            store.put(("docs",), "doc2", {"text": "TypeScript guide"})
+
+            # Search by similarity
+            results = store.search(("docs",), query="python programming")
+
+    Note:
+        Semantic search is disabled by default. You can enable it by providing an `index` configuration
+        when creating the store. Without this configuration, all `index` arguments passed to
+        `put` or `aput`will have no effect.
+
+    Warning:
+        This store keeps all data in memory. Data is lost when the process exits.
+        For persistence, use a database-backed store like PostgresStore.
+
+    Tip:
+        For vector search, install numpy for better performance:
+        ```bash
+        pip install numpy
+        ```
     """
 
-    __slots__ = ("_data",)
+    __slots__ = (
+        "_data",
+        "_vectors",
+        "index_config",
+        "embeddings",
+    )
 
-    def __init__(self) -> None:
+    def __init__(self, *, index: Optional[IndexConfig] = None) -> None:
+        # Both _data and _vectors are wrapped in the In-memory API
+        # Do not change their names
         self._data: dict[tuple[str, ...], dict[str, Item]] = defaultdict(dict)
+        # [ns][key][path]
+        self._vectors: dict[tuple[str, ...], dict[str, dict[str, list[float]]]] = (
+            defaultdict(lambda: defaultdict(dict))
+        )
+        self.index_config = index
+        if self.index_config:
+            self.index_config = self.index_config.copy()
+            self.embeddings: Optional[Embeddings] = ensure_embeddings(
+                self.index_config.get("embed"),
+            )
+            self.index_config["__tokenized_fields"] = [
+                (p, tokenize_path(p)) if p != "$" else (p, p)
+                for p in (self.index_config.get("fields") or ["$"])
+            ]
+
+        else:
+            self.index_config = None
+            self.embeddings = None
 
     def batch(self, ops: Iterable[Op]) -> list[Result]:
+        # The batch/abatch methods are treated as internal.
+        # Users should access via put/search/get/list_namespaces/etc.
+        results, put_ops, search_ops = self._prepare_ops(ops)
+        if search_ops:
+            queryinmem_store = self._embed_search_queries(search_ops)
+            self._batch_search(search_ops, queryinmem_store, results)
+
+        to_embed = self._extract_texts(put_ops)
+        if to_embed and self.index_config and self.embeddings:
+            embeddings = self.embeddings.embed_documents(list(to_embed))
+            self._insertinmem_store(to_embed, embeddings)
+        self._apply_put_ops(put_ops)
+        return results
+
+    async def abatch(self, ops: Iterable[Op]) -> list[Result]:
+        # The batch/abatch methods are treated as internal.
+        # Users should access via put/search/get/list_namespaces/etc.
+        results, put_ops, search_ops = self._prepare_ops(ops)
+        if search_ops:
+            queryinmem_store = await self._aembed_search_queries(search_ops)
+            self._batch_search(search_ops, queryinmem_store, results)
+
+        to_embed = self._extract_texts(put_ops)
+        if to_embed and self.index_config and self.embeddings:
+            embeddings = await self.embeddings.aembed_documents(list(to_embed))
+            self._insertinmem_store(to_embed, embeddings)
+        self._apply_put_ops(put_ops)
+        return results
+
+    # Helpers
+
+    def _filter_items(self, op: SearchOp) -> list[tuple[Item, list[list[float]]]]:
+        """Filter items by namespace and filter function, return items with their embeddings."""
+        namespace_prefix = op.namespace_prefix
+
+        def filter_func(item: Item) -> bool:
+            if not op.filter:
+                return True
+
+            return all(
+                _compare_values(item.value.get(key), filter_value)
+                for key, filter_value in op.filter.items()
+            )
+
+        filtered = []
+        for namespace in self._data:
+            if not (
+                namespace[: len(namespace_prefix)] == namespace_prefix
+                if len(namespace) >= len(namespace_prefix)
+                else False
+            ):
+                continue
+
+            for key, item in self._data[namespace].items():
+                if filter_func(item):
+                    if op.query and (embeddings := self._vectors[namespace].get(key)):
+                        filtered.append((item, list(embeddings.values())))
+                    else:
+                        filtered.append((item, []))
+        return filtered
+
+    def _embed_search_queries(
+        self,
+        search_ops: dict[int, tuple[SearchOp, list[tuple[Item, list[list[float]]]]]],
+    ) -> dict[str, list[float]]:
+        queryinmem_store = {}
+        if self.index_config and self.embeddings and search_ops:
+            queries = {op.query for (op, _) in search_ops.values() if op.query}
+
+            if queries:
+                with cf.ThreadPoolExecutor() as executor:
+                    futures = {
+                        q: executor.submit(self.embeddings.embed_query, q)
+                        for q in list(queries)
+                    }
+                    for query, future in futures.items():
+                        queryinmem_store[query] = future.result()
+
+        return queryinmem_store
+
+    async def _aembed_search_queries(
+        self,
+        search_ops: dict[int, tuple[SearchOp, list[tuple[Item, list[list[float]]]]]],
+    ) -> dict[str, list[float]]:
+        queryinmem_store = {}
+        if self.index_config and self.embeddings and search_ops:
+            queries = {op.query for (op, _) in search_ops.values() if op.query}
+
+            if queries:
+                coros = [self.embeddings.aembed_query(q) for q in list(queries)]
+                results = await asyncio.gather(*coros)
+                queryinmem_store = dict(zip(queries, results))
+
+        return queryinmem_store
+
+    def _batch_search(
+        self,
+        ops: dict[int, tuple[SearchOp, list[tuple[Item, list[list[float]]]]]],
+        queryinmem_store: dict[str, list[float]],
+        results: list[Result],
+    ) -> None:
+        """Perform batch similarity search for multiple queries."""
+        for i, (op, candidates) in ops.items():
+            if not candidates:
+                results[i] = []
+                continue
+            if op.query and queryinmem_store:
+                query_embedding = queryinmem_store[op.query]
+                flat_items, flat_vectors = [], []
+                scoreless = []
+                for item, vectors in candidates:
+                    for vector in vectors:
+                        flat_items.append(item)
+                        flat_vectors.append(vector)
+                    if not vectors:
+                        scoreless.append(item)
+
+                scores = _cosine_similarity(query_embedding, flat_vectors)
+                sorted_results = sorted(
+                    zip(scores, flat_items), key=lambda x: x[0], reverse=True
+                )
+                # max pooling
+                seen: set[tuple[tuple[str, ...], str]] = set()
+                kept: list[tuple[Optional[float], Item]] = []
+                for score, item in sorted_results:
+                    key = (item.namespace, item.key)
+                    if key in seen:
+                        continue
+                    ix = len(seen)
+                    seen.add(key)
+                    if ix >= op.offset + op.limit:
+                        break
+                    if ix < op.offset:
+                        continue
+
+                    kept.append((score, item))
+                if scoreless and len(kept) < op.limit:
+                    # Corner case: if we request more items than what we have embedded,
+                    # fill the rest with non-scored items
+                    kept.extend(
+                        (None, item) for item in scoreless[: op.limit - len(kept)]
+                    )
+
+                results[i] = [
+                    SearchItem(
+                        namespace=item.namespace,
+                        key=item.key,
+                        value=item.value,
+                        created_at=item.created_at,
+                        updated_at=item.updated_at,
+                        score=float(score) if score is not None else None,
+                    )
+                    for score, item in kept
+                ]
+            else:
+                results[i] = [
+                    SearchItem(
+                        namespace=item.namespace,
+                        key=item.key,
+                        value=item.value,
+                        created_at=item.created_at,
+                        updated_at=item.updated_at,
+                    )
+                    for (item, _) in candidates[op.offset : op.offset + op.limit]
+                ]
+
+    def _prepare_ops(
+        self, ops: Iterable[Op]
+    ) -> tuple[
+        list[Result],
+        dict[tuple[tuple[str, ...], str], PutOp],
+        dict[int, tuple[SearchOp, list[tuple[Item, list[list[float]]]]]],
+    ]:
         results: list[Result] = []
-        for op in ops:
+        put_ops: dict[tuple[tuple[str, ...], str], PutOp] = {}
+        search_ops: dict[
+            int, tuple[SearchOp, list[tuple[Item, list[list[float]]]]]
+        ] = {}
+        for i, op in enumerate(ops):
             if isinstance(op, GetOp):
                 item = self._data[op.namespace].get(op.key)
                 results.append(item)
             elif isinstance(op, SearchOp):
-                candidates = [
-                    item
-                    for namespace, items in self._data.items()
-                    if (
-                        namespace[: len(op.namespace_prefix)] == op.namespace_prefix
-                        if len(namespace) >= len(op.namespace_prefix)
-                        else False
-                    )
-                    for item in items.values()
-                ]
-                if op.filter:
-                    candidates = [
-                        item
-                        for item in candidates
-                        if item.value.items() >= op.filter.items()
-                    ]
-                results.append(candidates[op.offset : op.offset + op.limit])
-            elif isinstance(op, PutOp):
-                if op.value is None:
-                    self._data[op.namespace].pop(op.key, None)
-                elif op.key in self._data[op.namespace]:
-                    self._data[op.namespace][op.key].value = op.value
-                    self._data[op.namespace][op.key].updated_at = datetime.now(
-                        timezone.utc
-                    )
-                else:
-                    self._data[op.namespace][op.key] = Item(
-                        value=op.value,
-                        key=op.key,
-                        namespace=op.namespace,
-                        created_at=datetime.now(timezone.utc),
-                        updated_at=datetime.now(timezone.utc),
-                    )
+                search_ops[i] = (op, self._filter_items(op))
                 results.append(None)
             elif isinstance(op, ListNamespacesOp):
                 results.append(self._handle_list_namespaces(op))
-        return results
+            elif isinstance(op, PutOp):
+                put_ops[(op.namespace, op.key)] = op
+                results.append(None)
+            else:
+                raise ValueError(f"Unknown operation type: {type(op)}")
 
-    async def abatch(self, ops: Iterable[Op]) -> list[Result]:
-        return self.batch(ops)
+        return results, put_ops, search_ops
+
+    def _apply_put_ops(self, put_ops: dict[tuple[tuple[str, ...], str], PutOp]) -> None:
+        for (namespace, key), op in put_ops.items():
+            if op.value is None:
+                self._data[namespace].pop(key, None)
+                self._vectors[namespace].pop(key, None)
+            else:
+                self._data[namespace][key] = Item(
+                    value=op.value,
+                    key=key,
+                    namespace=namespace,
+                    created_at=datetime.now(timezone.utc),
+                    updated_at=datetime.now(timezone.utc),
+                )
+
+    def _extract_texts(
+        self, put_ops: dict[tuple[tuple[str, ...], str], PutOp]
+    ) -> dict[str, list[tuple[tuple[str, ...], str, str]]]:
+        if put_ops and self.index_config and self.embeddings:
+            to_embed = defaultdict(list)
+
+            for op in put_ops.values():
+                if op.value is not None and op.index is not False:
+                    if op.index is None:
+                        paths = self.index_config["__tokenized_fields"]
+                    else:
+                        paths = [(ix, tokenize_path(ix)) for ix in op.index]
+                    for path, field in paths:
+                        texts = get_text_at_path(op.value, field)
+                        if texts:
+                            if len(texts) > 1:
+                                for i, text in enumerate(texts):
+                                    to_embed[text].append(
+                                        (op.namespace, op.key, f"{path}.{i}")
+                                    )
+
+                            else:
+                                to_embed[texts[0]].append((op.namespace, op.key, path))
+
+            return to_embed
+
+        return {}
+
+    def _insertinmem_store(
+        self,
+        to_embed: dict[str, list[tuple[tuple[str, ...], str, str]]],
+        embeddings: list[list[float]],
+    ) -> None:
+        indices = [index for indices in to_embed.values() for index in indices]
+        if len(indices) != len(embeddings):
+            raise ValueError(
+                f"Number of embeddings ({len(embeddings)}) does not"
+                f" match number of indices ({len(indices)})"
+            )
+        for embedding, (ns, key, path) in zip(embeddings, indices):
+            self._vectors[ns][key][path] = embedding
 
     def _handle_list_namespaces(self, op: ListNamespacesOp) -> list[tuple[str, ...]]:
         all_namespaces = list(
@@ -94,7 +471,54 @@ class InMemoryStore(BaseStore):
         return namespaces[op.offset : op.offset + op.limit]
 
 
+@functools.lru_cache(maxsize=1)
+def _check_numpy() -> bool:
+    if bool(util.find_spec("numpy")):
+        return True
+    logger.warning(
+        "NumPy not found in the current Python environment. "
+        "The InMemoryStore will use a pure Python implementation for vector operations, "
+        "which may significantly impact performance, especially for large datasets or frequent searches. "
+        "For optimal speed and efficiency, consider installing NumPy: "
+        "pip install numpy"
+    )
+    return False
+
+
+def _cosine_similarity(X: list[float], Y: list[list[float]]) -> list[float]:
+    """
+    Compute cosine similarity between a vector X and a matrix Y.
+    Lazy import numpy for efficiency.
+    """
+    if not Y:
+        return []
+    if _check_numpy():
+        import numpy as np  # type: ignore
+
+        X_arr = np.array(X) if not isinstance(X, np.ndarray) else X
+        Y_arr = np.array(Y) if not isinstance(Y, np.ndarray) else Y
+        X_norm = np.linalg.norm(X_arr)
+        Y_norm = np.linalg.norm(Y_arr, axis=1)
+
+        # Avoid division by zero
+        mask = Y_norm != 0
+        similarities = np.zeros_like(Y_norm)
+        similarities[mask] = np.dot(Y_arr[mask], X_arr) / (Y_norm[mask] * X_norm)
+        return similarities.tolist()
+
+    similarities = []
+    for y in Y:
+        dot_product = sum(a * b for a, b in zip(X, y))
+        norm1 = sum(a * a for a in X) ** 0.5
+        norm2 = sum(a * a for a in y) ** 0.5
+        similarity = dot_product / (norm1 * norm2) if norm1 > 0 and norm2 > 0 else 0.0
+        similarities.append(similarity)
+
+    return similarities
+
+
 def _does_match(match_condition: MatchCondition, key: tuple[str, ...]) -> bool:
+    """Whether a namespace key matches a match condition."""
     match_type = match_condition.match_type
     path = match_condition.path
 
@@ -117,3 +541,44 @@ def _does_match(match_condition: MatchCondition, key: tuple[str, ...]) -> bool:
         return True
     else:
         raise ValueError(f"Unsupported match type: {match_type}")
+
+
+def _compare_values(item_value: Any, filter_value: Any) -> bool:
+    """Compare values in a JSONB-like way, handling nested objects."""
+    if isinstance(filter_value, dict):
+        if any(k.startswith("$") for k in filter_value):
+            return all(
+                _apply_operator(item_value, op_key, op_value)
+                for op_key, op_value in filter_value.items()
+            )
+        if not isinstance(item_value, dict):
+            return False
+        return all(
+            _compare_values(item_value.get(k), v) for k, v in filter_value.items()
+        )
+    elif isinstance(filter_value, (list, tuple)):
+        return (
+            isinstance(item_value, (list, tuple))
+            and len(item_value) == len(filter_value)
+            and all(_compare_values(iv, fv) for iv, fv in zip(item_value, filter_value))
+        )
+    else:
+        return item_value == filter_value
+
+
+def _apply_operator(value: Any, operator: str, op_value: Any) -> bool:
+    """Apply a comparison operator, matching PostgreSQL's JSONB behavior."""
+    if operator == "$eq":
+        return value == op_value
+    elif operator == "$gt":
+        return float(value) > float(op_value)
+    elif operator == "$gte":
+        return float(value) >= float(op_value)
+    elif operator == "$lt":
+        return float(value) < float(op_value)
+    elif operator == "$lte":
+        return float(value) <= float(op_value)
+    elif operator == "$ne":
+        return value != op_value
+    else:
+        raise ValueError(f"Unsupported operator: {operator}")

libs/checkpoint/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.3"
+version = "2.0.8"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"

libs/checkpoint/tests/embed_test_utils.py

@@ -0,0 +1,55 @@
+"""Embedding utilities for testing."""
+
+import math
+import random
+from collections import Counter, defaultdict
+from typing import Any
+
+from langchain_core.embeddings import Embeddings
+
+
+class CharacterEmbeddings(Embeddings):
+    """Simple character-frequency based embeddings using random projections."""
+
+    def __init__(self, dims: int = 50, seed: int = 42):
+        """Initialize with embedding dimensions and random seed."""
+        self._rng = random.Random(seed)
+        self.dims = dims
+        # Create projection vector for each character lazily
+        self._char_projections: defaultdict[str, list[float]] = defaultdict(
+            lambda: [
+                self._rng.gauss(0, 1 / math.sqrt(self.dims)) for _ in range(self.dims)
+            ]
+        )
+
+    def _embed_one(self, text: str) -> list[float]:
+        """Embed a single text."""
+        counts = Counter(text)
+        total = sum(counts.values())
+
+        if total == 0:
+            return [0.0] * self.dims
+
+        embedding = [0.0] * self.dims
+        for char, count in counts.items():
+            weight = count / total
+            char_proj = self._char_projections[char]
+            for i, proj in enumerate(char_proj):
+                embedding[i] += weight * proj
+
+        norm = math.sqrt(sum(x * x for x in embedding))
+        if norm > 0:
+            embedding = [x / norm for x in embedding]
+
+        return embedding
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Embed a list of documents."""
+        return [self._embed_one(text) for text in texts]
+
+    def embed_query(self, text: str) -> list[float]:
+        """Embed a query string."""
+        return self._embed_one(text)
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, CharacterEmbeddings) and self.dims == other.dims

libs/checkpoint/tests/test_store.py

@@ -1,13 +1,104 @@
+# mypy: disable-error-code="operator"
 import asyncio
+import json
 from datetime import datetime
-from typing import Iterable
+from typing import Any, Iterable
 
 import pytest
 from pytest_mock import MockerFixture
 
-from langgraph.store.base import GetOp, InvalidNamespaceError, Item, Op, PutOp, Result
+from langgraph.store.base import (
+    GetOp,
+    InvalidNamespaceError,
+    Item,
+    Op,
+    PutOp,
+    Result,
+    get_text_at_path,
+)
 from langgraph.store.base.batch import AsyncBatchedBaseStore
 from langgraph.store.memory import InMemoryStore
+from tests.embed_test_utils import CharacterEmbeddings
+
+
+class MockAsyncBatchedStore(AsyncBatchedBaseStore):
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__()
+        self._store = InMemoryStore(**kwargs)
+
+    def batch(self, ops: Iterable[Op]) -> list[Result]:
+        return self._store.batch(ops)
+
+    async def abatch(self, ops: Iterable[Op]) -> list[Result]:
+        return self._store.batch(ops)
+
+
+def test_get_text_at_path() -> None:
+    nested_data = {
+        "name": "test",
+        "info": {
+            "age": 25,
+            "tags": ["a", "b", "c"],
+            "metadata": {"created": "2024-01-01", "updated": "2024-01-02"},
+        },
+        "items": [
+            {"id": 1, "value": "first", "tags": ["x", "y"]},
+            {"id": 2, "value": "second", "tags": ["y", "z"]},
+            {"id": 3, "value": "third", "tags": ["z", "w"]},
+        ],
+        "empty": None,
+        "zeros": [0, 0.0, "0"],
+        "empty_list": [],
+        "empty_dict": {},
+    }
+
+    assert get_text_at_path(nested_data, "$") == [
+        json.dumps(nested_data, sort_keys=True)
+    ]
+
+    assert get_text_at_path(nested_data, "name") == ["test"]
+    assert get_text_at_path(nested_data, "info.age") == ["25"]
+
+    assert get_text_at_path(nested_data, "info.metadata.created") == ["2024-01-01"]
+
+    assert get_text_at_path(nested_data, "items[0].value") == ["first"]
+    assert get_text_at_path(nested_data, "items[-1].value") == ["third"]
+    assert get_text_at_path(nested_data, "items[1].tags[0]") == ["y"]
+
+    values = get_text_at_path(nested_data, "items[*].value")
+    assert set(values) == {"first", "second", "third"}
+
+    metadata_dates = get_text_at_path(nested_data, "info.metadata.*")
+    assert set(metadata_dates) == {"2024-01-01", "2024-01-02"}
+    name_and_age = get_text_at_path(nested_data, "{name,info.age}")
+    assert set(name_and_age) == {"test", "25"}
+
+    item_fields = get_text_at_path(nested_data, "items[*].{id,value}")
+    assert set(item_fields) == {"1", "2", "3", "first", "second", "third"}
+
+    all_tags = get_text_at_path(nested_data, "items[*].tags[*]")
+    assert set(all_tags) == {"x", "y", "z", "w"}
+
+    assert get_text_at_path(None, "any.path") == []
+    assert get_text_at_path({}, "any.path") == []
+    assert get_text_at_path(nested_data, "") == [
+        json.dumps(nested_data, sort_keys=True)
+    ]
+    assert get_text_at_path(nested_data, "nonexistent") == []
+    assert get_text_at_path(nested_data, "items[99].value") == []
+    assert get_text_at_path(nested_data, "items[*].nonexistent") == []
+
+    assert get_text_at_path(nested_data, "empty") == []
+    assert get_text_at_path(nested_data, "empty_list") == ["[]"]
+    assert get_text_at_path(nested_data, "empty_dict") == ["{}"]
+
+    zeros = get_text_at_path(nested_data, "zeros[*]")
+    assert set(zeros) == {"0", "0.0"}
+
+    assert get_text_at_path(nested_data, "items[].value") == []
+    assert get_text_at_path(nested_data, "items[abc].value") == []
+    assert get_text_at_path(nested_data, "{unclosed") == []
+    assert get_text_at_path(nested_data, "nested[{invalid}]") == []
 
 
 async def test_async_batch_store(mocker: MockerFixture) -> None:
@@ -292,12 +383,14 @@ async def test_cannot_put_empty_namespace() -> None:
 
     await store.aput(("foo", "langgraph", "foo"), "bar", doc)
     assert (await store.aget(("foo", "langgraph", "foo"), "bar")).value == doc  # type: ignore[union-attr]
-    assert (await store.asearch(("foo", "langgraph", "foo")))[0].value == doc
+    assert (await store.asearch(("foo", "langgraph", "foo"), query="bar"))[
+        0
+    ].value == doc
     await store.adelete(("foo", "langgraph", "foo"), "bar")
     assert (await store.aget(("foo", "langgraph", "foo"), "bar")) is None
     store.put(("foo", "langgraph", "foo"), "bar", doc)
     assert store.get(("foo", "langgraph", "foo"), "bar").value == doc  # type: ignore[union-attr]
-    assert store.search(("foo", "langgraph", "foo"))[0].value == doc
+    assert store.search(("foo", "langgraph", "foo"), query="bar")[0].value == doc
     store.delete(("foo", "langgraph", "foo"), "bar")
     assert store.get(("foo", "langgraph", "foo"), "bar") is None
 
@@ -313,17 +406,6 @@ async def test_cannot_put_empty_namespace() -> None:
     store.delete(("langgraph", "foo"), "bar")
     assert store.get(("langgraph", "foo"), "bar") is None
 
-    class MockAsyncBatchedStore(AsyncBatchedBaseStore):
-        def __init__(self):
-            super().__init__()
-            self._store = InMemoryStore()
-
-        def batch(self, ops: Iterable[Op]) -> list[Result]:
-            return self._store.batch(ops)
-
-        async def abatch(self, ops: Iterable[Op]) -> list[Result]:
-            return self._store.batch(ops)
-
     async_store = MockAsyncBatchedStore()
     doc = {"foo": "bar"}
 
@@ -340,13 +422,528 @@ async def test_cannot_put_empty_namespace() -> None:
         await async_store.aput(("langgraph", "foo"), "bar", doc)
 
     await async_store.aput(("foo", "langgraph", "foo"), "bar", doc)
-    assert (await async_store.aget(("foo", "langgraph", "foo"), "bar")).value == doc
+    val = await async_store.aget(("foo", "langgraph", "foo"), "bar")
+    assert val is not None
+    assert val.value == doc
     assert (await async_store.asearch(("foo", "langgraph", "foo")))[0].value == doc
+    assert (await async_store.asearch(("foo", "langgraph", "foo"), query="bar"))[
+        0
+    ].value == doc
     await async_store.adelete(("foo", "langgraph", "foo"), "bar")
     assert (await async_store.aget(("foo", "langgraph", "foo"), "bar")) is None
 
     await async_store.abatch([PutOp(("valid", "namespace"), "key", doc)])
-    assert (await async_store.aget(("valid", "namespace"), "key")).value == doc
+    val = await async_store.aget(("valid", "namespace"), "key")
+    assert val is not None
+    assert val.value == doc
     assert (await async_store.asearch(("valid", "namespace")))[0].value == doc
     await async_store.adelete(("valid", "namespace"), "key")
     assert (await async_store.aget(("valid", "namespace"), "key")) is None
+
+
+async def test_async_batch_store_deduplication(mocker: MockerFixture) -> None:
+    abatch = mocker.spy(InMemoryStore, "batch")
+    store = MockAsyncBatchedStore()
+
+    same_doc = {"value": "same"}
+    diff_doc = {"value": "different"}
+    await asyncio.gather(
+        store.aput(namespace=("test",), key="same", value=same_doc),
+        store.aput(namespace=("test",), key="different", value=diff_doc),
+    )
+    abatch.reset_mock()
+
+    results = await asyncio.gather(
+        store.aget(namespace=("test",), key="same"),
+        store.aget(namespace=("test",), key="same"),
+        store.aget(namespace=("test",), key="different"),
+    )
+
+    assert len(results) == 3
+    assert results[0] == results[1]
+    assert results[0] != results[2]
+    assert results[0].value == same_doc  # type: ignore
+    assert results[2].value == diff_doc  # type: ignore
+    assert len(abatch.call_args_list) == 1
+    ops = list(abatch.call_args_list[0].args[1])
+    assert len(ops) == 2
+    assert GetOp(("test",), "same") in ops
+    assert GetOp(("test",), "different") in ops
+
+    abatch.reset_mock()
+
+    doc1 = {"value": 1}
+    doc2 = {"value": 2}
+    results = await asyncio.gather(
+        store.aput(namespace=("test",), key="key", value=doc1),
+        store.aput(namespace=("test",), key="key", value=doc2),
+    )
+    assert len(abatch.call_args_list) == 1
+    ops = list(abatch.call_args_list[0].args[1])
+    assert len(ops) == 1
+    assert ops[0] == PutOp(("test",), "key", doc2)
+    assert len(results) == 2
+    assert all(result is None for result in results)
+
+    result = await store.aget(namespace=("test",), key="key")
+    assert result is not None
+    assert result.value == doc2
+
+    abatch.reset_mock()
+
+    results = await asyncio.gather(
+        store.asearch(("test",), filter={"value": 2}),
+        store.asearch(("test",), filter={"value": 2}),
+    )
+    assert len(abatch.call_args_list) == 1
+    ops = list(abatch.call_args_list[0].args[1])
+    assert len(ops) == 1
+    assert len(results) == 2
+    assert results[0] == results[1]
+    assert len(results[0]) == 1
+    assert results[0][0].value == doc2
+
+    abatch.reset_mock()
+
+
+@pytest.fixture
+def fake_embeddings() -> CharacterEmbeddings:
+    return CharacterEmbeddings(dims=500)
+
+
+def test_vector_store_initialization(fake_embeddings: CharacterEmbeddings) -> None:
+    """Test store initialization with embedding config."""
+    store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    assert store.index_config is not None
+    assert store.index_config["dims"] == fake_embeddings.dims
+    assert store.index_config["embed"] == fake_embeddings
+
+
+def test_vector_insert_with_auto_embedding(
+    fake_embeddings: CharacterEmbeddings,
+) -> None:
+    """Test inserting items that get auto-embedded."""
+    store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    docs = [
+        ("doc1", {"text": "short text"}),
+        ("doc2", {"text": "longer text document"}),
+        ("doc3", {"text": "longest text document here"}),
+        ("doc4", {"description": "text in description field"}),
+        ("doc5", {"content": "text in content field"}),
+        ("doc6", {"body": "text in body field"}),
+    ]
+
+    for key, value in docs:
+        store.put(("test",), key, value)
+
+    results = store.search(("test",), query="long text")
+    assert len(results) > 0
+
+    doc_order = [r.key for r in results]
+    assert "doc2" in doc_order
+    assert "doc3" in doc_order
+
+
+async def test_async_vector_insert_with_auto_embedding(
+    fake_embeddings: CharacterEmbeddings,
+) -> None:
+    """Test inserting items that get auto-embedded using async methods."""
+    store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    docs = [
+        ("doc1", {"text": "short text"}),
+        ("doc2", {"text": "longer text document"}),
+        ("doc3", {"text": "longest text document here"}),
+        ("doc4", {"description": "text in description field"}),
+        ("doc5", {"content": "text in content field"}),
+        ("doc6", {"body": "text in body field"}),
+    ]
+
+    for key, value in docs:
+        await store.aput(("test",), key, value)
+
+    results = await store.asearch(("test",), query="long text")
+    assert len(results) > 0
+
+    doc_order = [r.key for r in results]
+    assert "doc2" in doc_order
+    assert "doc3" in doc_order
+
+
+def test_vector_update_with_embedding(fake_embeddings: CharacterEmbeddings) -> None:
+    """Test that updating items properly updates their embeddings."""
+    store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    store.put(("test",), "doc1", {"text": "zany zebra Xerxes"})
+    store.put(("test",), "doc2", {"text": "something about dogs"})
+    store.put(("test",), "doc3", {"text": "text about birds"})
+
+    results_initial = store.search(("test",), query="Zany Xerxes")
+    assert len(results_initial) > 0
+    assert results_initial[0].key == "doc1"
+    initial_score = results_initial[0].score
+    assert initial_score is not None
+
+    store.put(("test",), "doc1", {"text": "new text about dogs"})
+
+    results_after = store.search(("test",), query="Zany Xerxes")
+    after_score = next((r.score for r in results_after if r.key == "doc1"), 0.0)
+    assert after_score is not None
+    assert after_score < initial_score
+
+    results_new = store.search(("test",), query="new text about dogs")
+    for r in results_new:
+        if r.key == "doc1":
+            assert r.score > after_score
+
+    # Don't index this one
+    store.put(("test",), "doc4", {"text": "new text about dogs"}, index=False)
+    results_new = store.search(("test",), query="new text about dogs", limit=3)
+    assert not any(r.key == "doc4" for r in results_new)
+
+
+async def test_async_vector_update_with_embedding(
+    fake_embeddings: CharacterEmbeddings,
+) -> None:
+    """Test that updating items properly updates their embeddings using async methods."""
+    store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    await store.aput(("test",), "doc1", {"text": "zany zebra Xerxes"})
+    await store.aput(("test",), "doc2", {"text": "something about dogs"})
+    await store.aput(("test",), "doc3", {"text": "text about birds"})
+
+    results_initial = await store.asearch(("test",), query="Zany Xerxes")
+    assert len(results_initial) > 0
+    assert results_initial[0].key == "doc1"
+    initial_score = results_initial[0].score
+
+    await store.aput(("test",), "doc1", {"text": "new text about dogs"})
+
+    results_after = await store.asearch(("test",), query="Zany Xerxes")
+    after_score = next((r.score for r in results_after if r.key == "doc1"), 0.0)
+    assert after_score is not None
+    assert after_score < initial_score
+
+    results_new = await store.asearch(("test",), query="new text about dogs")
+    for r in results_new:
+        if r.key == "doc1":
+            assert r.score is not None
+            assert r.score > after_score
+
+    # Don't index this one
+    await store.aput(("test",), "doc4", {"text": "new text about dogs"}, index=False)
+    results_new = await store.asearch(("test",), query="new text about dogs", limit=3)
+    assert not any(r.key == "doc4" for r in results_new)
+
+
+def test_vector_search_with_filters(fake_embeddings: CharacterEmbeddings) -> None:
+    """Test combining vector search with filters."""
+    inmem_store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    # Insert test documents
+    docs = [
+        ("doc1", {"text": "red apple", "color": "red", "score": 4.5}),
+        ("doc2", {"text": "red car", "color": "red", "score": 3.0}),
+        ("doc3", {"text": "green apple", "color": "green", "score": 4.0}),
+        ("doc4", {"text": "blue car", "color": "blue", "score": 3.5}),
+    ]
+
+    for key, value in docs:
+        inmem_store.put(("test",), key, value)
+
+    results = inmem_store.search(("test",), query="apple", filter={"color": "red"})
+    assert len(results) == 2
+    assert results[0].key == "doc1"
+
+    results = inmem_store.search(("test",), query="car", filter={"color": "red"})
+    assert len(results) == 2
+    assert results[0].key == "doc2"
+
+    results = inmem_store.search(
+        ("test",), query="bbbbluuu", filter={"score": {"$gt": 3.2}}
+    )
+    assert len(results) == 3
+    assert results[0].key == "doc4"
+
+    # Multiple filters
+    results = inmem_store.search(
+        ("test",), query="apple", filter={"score": {"$gte": 4.0}, "color": "green"}
+    )
+    assert len(results) == 1
+    assert results[0].key == "doc3"
+
+
+async def test_async_vector_search_with_filters(
+    fake_embeddings: CharacterEmbeddings,
+) -> None:
+    """Test combining vector search with filters using async methods."""
+    store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    # Insert test documents
+    docs = [
+        ("doc1", {"text": "red apple", "color": "red", "score": 4.5}),
+        ("doc2", {"text": "red car", "color": "red", "score": 3.0}),
+        ("doc3", {"text": "green apple", "color": "green", "score": 4.0}),
+        ("doc4", {"text": "blue car", "color": "blue", "score": 3.5}),
+    ]
+
+    for key, value in docs:
+        await store.aput(("test",), key, value)
+
+    results = await store.asearch(("test",), query="apple", filter={"color": "red"})
+    assert len(results) == 2
+    assert results[0].key == "doc1"
+
+    results = await store.asearch(("test",), query="car", filter={"color": "red"})
+    assert len(results) == 2
+    assert results[0].key == "doc2"
+
+    results = await store.asearch(
+        ("test",), query="bbbbluuu", filter={"score": {"$gt": 3.2}}
+    )
+    assert len(results) == 3
+    assert results[0].key == "doc4"
+
+    # Multiple filters
+    results = await store.asearch(
+        ("test",), query="apple", filter={"score": {"$gte": 4.0}, "color": "green"}
+    )
+    assert len(results) == 1
+    assert results[0].key == "doc3"
+
+
+async def test_async_batched_vector_search_concurrent(
+    fake_embeddings: CharacterEmbeddings,
+) -> None:
+    """Test concurrent vector search operations using async batched store."""
+    store = MockAsyncBatchedStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+
+    colors = ["red", "blue", "green", "yellow", "purple"]
+    items = ["apple", "car", "house", "book", "phone"]
+    scores = [3.0, 3.5, 4.0, 4.5, 5.0]
+
+    docs = []
+    for i in range(50):
+        color = colors[i % len(colors)]
+        item = items[i % len(items)]
+        score = scores[i % len(scores)]
+        docs.append(
+            (
+                f"doc{i}",
+                {"text": f"{color} {item}", "color": color, "score": score, "index": i},
+            )
+        )
+    coros = [
+        *[store.aput(("test",), key, value) for key, value in docs],
+        *[store.adelete(("test",), key) for key, value in docs],
+        *[store.aput(("test",), key, value) for key, value in docs],
+    ]
+    await asyncio.gather(*coros)
+
+    # Prepare multiple search queries with different filters
+    search_queries: list[tuple[str, dict[str, Any]]] = [
+        ("apple", {"color": "red"}),
+        ("car", {"color": "blue"}),
+        ("house", {"color": "green"}),
+        ("phone", {"score": {"$gt": 4.99}}),
+        ("book", {"score": {"$lte": 3.5}}),
+        ("apple", {"score": {"$gte": 3.0}, "color": "red"}),
+        ("car", {"score": {"$lt": 5.1}, "color": "blue"}),
+        ("house", {"index": {"$gt": 25}}),
+        ("phone", {"index": {"$lte": 10}}),
+    ]
+
+    all_results = await asyncio.gather(
+        *[
+            store.asearch(("test",), query=query, filter=filter_)
+            for query, filter_ in search_queries
+        ]
+    )
+
+    for results, (query, filter_) in zip(all_results, search_queries):
+        assert len(results) > 0, f"No results for query '{query}' with filter {filter_}"
+
+        for result in results:
+            if "color" in filter_:
+                assert result.value["color"] == filter_["color"]
+
+            if "score" in filter_:
+                score = result.value["score"]
+                for op, value in filter_["score"].items():
+                    if op == "$gt":
+                        assert score > value
+                    elif op == "$gte":
+                        assert score >= value
+                    elif op == "$lt":
+                        assert score < value
+                    elif op == "$lte":
+                        assert score <= value
+
+            if "index" in filter_:
+                index = result.value["index"]
+                for op, value in filter_["index"].items():
+                    if op == "$gt":
+                        assert index > value
+                    elif op == "$gte":
+                        assert index >= value
+                    elif op == "$lt":
+                        assert index < value
+                    elif op == "$lte":
+                        assert index <= value
+
+
+def test_vector_search_pagination(fake_embeddings: CharacterEmbeddings) -> None:
+    """Test pagination with vector search."""
+    store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    for i in range(5):
+        store.put(("test",), f"doc{i}", {"text": f"test document number {i}"})
+
+    results_page1 = store.search(("test",), query="test", limit=2)
+    results_page2 = store.search(("test",), query="test", limit=2, offset=2)
+
+    assert len(results_page1) == 2
+    assert len(results_page2) == 2
+    assert results_page1[0].key != results_page2[0].key
+
+    all_results = store.search(("test",), query="test", limit=10)
+    assert len(all_results) == 5
+
+
+async def test_async_vector_search_pagination(
+    fake_embeddings: CharacterEmbeddings,
+) -> None:
+    """Test pagination with vector search using async methods."""
+    store = InMemoryStore(
+        index={"dims": fake_embeddings.dims, "embed": fake_embeddings}
+    )
+    for i in range(5):
+        await store.aput(("test",), f"doc{i}", {"text": f"test document number {i}"})
+
+    results_page1 = await store.asearch(("test",), query="test", limit=2)
+    results_page2 = await store.asearch(("test",), query="test", limit=2, offset=2)
+
+    assert len(results_page1) == 2
+    assert len(results_page2) == 2
+    assert results_page1[0].key != results_page2[0].key
+
+    all_results = await store.asearch(("test",), query="test", limit=10)
+    assert len(all_results) == 5
+
+
+async def test_embed_with_path(fake_embeddings: CharacterEmbeddings) -> None:
+    # Test store-level field configuration
+    store = InMemoryStore(
+        index={
+            "dims": fake_embeddings.dims,
+            "embed": fake_embeddings,
+            # Key 2 isn't included. Don't index it.
+            "fields": ["key0", "key1", "key3"],
+        }
+    )
+    # This will have 2 vectors representing it
+    doc1 = {
+        # Omit key0 - check it doesn't raise an error
+        "key1": "xxx",
+        "key2": "yyy",
+        "key3": "zzz",
+    }
+    # This will have 3 vectors representing it
+    doc2 = {
+        "key0": "uuu",
+        "key1": "vvv",
+        "key2": "www",
+        "key3": "xxx",
+    }
+    await store.aput(("test",), "doc1", doc1)
+    await store.aput(("test",), "doc2", doc2)
+
+    # doc2.key3 and doc1.key1 both would have the highest score
+    results = await store.asearch(("test",), query="xxx")
+    assert len(results) == 2
+    assert results[0].key != results[1].key
+    ascore = results[0].score
+    bscore = results[1].score
+    assert ascore == bscore
+    assert ascore is not None and bscore is not None
+
+    results = await store.asearch(("test",), query="uuu")
+    assert len(results) == 2
+    assert results[0].key != results[1].key
+    assert results[0].key == "doc2"
+    assert results[0].score is not None and results[0].score > results[1].score
+    assert ascore == pytest.approx(results[0].score, abs=1e-5)
+
+    # Un-indexed - will have low results for both. Not zero (because we're projecting)
+    # but less than the above.
+    results = await store.asearch(("test",), query="www")
+    assert len(results) == 2
+    assert results[0].score < ascore
+    assert results[1].score < ascore
+
+    # Test operation-level field configuration
+    store_no_defaults = InMemoryStore(
+        index={
+            "dims": fake_embeddings.dims,
+            "embed": fake_embeddings,
+            "fields": ["key17"],
+        }
+    )
+
+    doc3 = {
+        "key0": "aaa",
+        "key1": "bbb",
+        "key2": "ccc",
+        "key3": "ddd",
+    }
+    doc4 = {
+        "key0": "eee",
+        "key1": "bbb",  # Same as doc3.key1
+        "key2": "fff",
+        "key3": "ggg",
+    }
+
+    await store_no_defaults.aput(("test",), "doc3", doc3, index=["key0", "key1"])
+    await store_no_defaults.aput(("test",), "doc4", doc4, index=["key1", "key3"])
+
+    results = await store_no_defaults.asearch(("test",), query="aaa")
+    assert len(results) == 2
+    assert results[0].key == "doc3"
+    assert results[0].score is not None and results[0].score > results[1].score
+
+    results = await store_no_defaults.asearch(("test",), query="ggg")
+    assert len(results) == 2
+    assert results[0].key == "doc4"
+    assert results[0].score is not None and results[0].score > results[1].score
+
+    results = await store_no_defaults.asearch(("test",), query="bbb")
+    assert len(results) == 2
+    assert results[0].key != results[1].key
+    assert results[0].score == results[1].score
+
+    results = await store_no_defaults.asearch(("test",), query="ccc")
+    assert len(results) == 2
+    assert all(r.score < ascore for r in results)
+
+    doc5 = {
+        "key0": "hhh",
+        "key1": "iii",
+    }
+    await store_no_defaults.aput(("test",), "doc5", doc5, index=False)
+
+    results = await store_no_defaults.asearch(("test",), query="hhh")
+    assert len(results) == 3
+    doc5_result = next(r for r in results if r.key == "doc5")
+    assert doc5_result.score is None