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+---
+title: "Adding Long-Term Memory to LangGraph and LangChain Agents"
+description: Learn how to add long-term memory to LangGraph and LangChain agents using three integration patterns — tools, nodes, and BaseStore — with per-user memory banks and semantic recall.
+authors: [DK09876]
+date: 2026-03-17
+tags: [langgraph, langchain, integrations, agents, memory]
+image: /img/blog/langgraph-longterm-memory.png
+hide_table_of_contents: true
+---
+
+LangGraph agents are stateful by design — checkpointers save graph state between steps, and the Store API persists data across threads. But neither gives agents true long-term memory: the ability to extract meaning from conversations, build up knowledge over time, and recall it semantically when relevant.
+
+That's what Hindsight adds. Hindsight is a memory layer for LLM applications that automatically extracts facts from conversations, builds entity graphs, and retrieves relevant context using four parallel recall strategies. The `hindsight-langgraph` package brings that to LangGraph — and since the memory tools are standard LangChain `@tool` functions, they work with plain LangChain too.
+
+<!-- truncate -->
+
+## The problem
+
+LangGraph's built-in persistence is designed for graph state — checkpoints, intermediate values, cross-thread key-value storage. It's good at "what did this graph do last time?" but not at "what does this agent know about this user?"
+
+Consider a support agent that talks to the same customer across dozens of sessions. With checkpointers alone, each new thread starts cold. With `InMemoryStore` or `PostgresStore`, you can manually store and retrieve facts, but you're responsible for:
+
+- Deciding what to store (fact extraction)
+- Deciding what's relevant (semantic retrieval)
+- Handling contradictions and updates
+- Building knowledge graphs from raw conversations
+
+Hindsight does all of this automatically. You retain conversations, and it extracts facts, builds entity graphs, and retrieves relevant memories using four parallel strategies: **semantic** (embedding similarity), **BM25** (keyword overlap), **graph traversal** (entity relationships), and **temporal** (recency weighting). Each strategy catches different things — semantic recall finds conceptually similar memories, graph traversal finds memories linked through shared entities, and temporal weighting surfaces recent context before older facts. Together they substantially outperform single-strategy retrieval.
+
+## Three integration patterns
+
+We built three ways to add Hindsight memory to LangGraph, at different abstraction levels.
+
+### 1. Tools — the agent decides (LangChain & LangGraph)
+
+Give the agent retain/recall/reflect tools and let it decide when to use memory. These are standard LangChain `@tool` functions, so they work with both LangGraph (via `create_react_agent`) and plain LangChain (via `bind_tools()`).
+
+```python
+from hindsight_client import Hindsight
+from hindsight_langgraph import create_hindsight_tools
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+
+client = Hindsight(base_url="http://localhost:8888")
+tools = create_hindsight_tools(client=client, bank_id="user-123")
+
+# With LangGraph
+agent = create_react_agent(ChatOpenAI(model="gpt-4o"), tools=tools)
+
+# Or with plain LangChain
+model = ChatOpenAI(model="gpt-4o").bind_tools(tools)
+```
+
+The agent gets three tools:
+
+- **`hindsight_retain`** — stores the conversation and extracts facts from it
+- **`hindsight_recall`** — searches the memory bank for relevant context
+- **`hindsight_reflect`** — synthesizes across multiple memories to produce a summary or answer a question about what the agent knows (useful for questions like "what has this user told me about their stack?")
+
+The agent calls these based on conversation context — storing facts when the user shares something important, recalling when asked about past context, and reflecting when it needs to synthesize accumulated knowledge.
+
+**Best for**: ReAct agents that need to reason about when memory is relevant. Works with LangGraph for automatic tool execution loops or with plain LangChain if you manage the loop yourself.
+
+### 2. Nodes — memory as graph steps
+
+Add recall and retain as automatic nodes in your graph. No tool-calling required — memory runs on every turn.
+
+```python
+from hindsight_langgraph import create_recall_node, create_retain_node
+from langgraph.graph import StateGraph, MessagesState, START, END
+
+recall = create_recall_node(client=client, bank_id_from_config="user_id")
+retain = create_retain_node(client=client, bank_id_from_config="user_id")
+
+builder = StateGraph(MessagesState)
+builder.add_node("recall", recall)
+builder.add_node("agent", agent_node)
+builder.add_node("retain", retain)
+builder.add_edge(START, "recall")
+builder.add_edge("recall", "agent")
+builder.add_edge("agent", "retain")
+builder.add_edge("retain", END)
+```
+
+The recall node runs before the LLM, searches Hindsight for memories relevant to the user's message, and injects them as a `SystemMessage`. The retain node runs after, storing the conversation. Both resolve per-user bank IDs from `RunnableConfig` at runtime.
+
+**Best for**: Agents where you always want memory context injected automatically, without relying on the LLM to decide when to use memory tools.
+
+### 3. BaseStore — drop-in backend
+
+Replace LangGraph's `InMemoryStore` with Hindsight as the storage backend. If your team already uses LangGraph's store patterns, this is the lowest-friction path.
+
+```python
+from hindsight_langgraph import HindsightStore
+
+store = HindsightStore(client=client)
+graph = builder.compile(checkpointer=checkpointer, store=store)
+```
+
+Namespace tuples map to Hindsight bank IDs (`("user", "123")` → bank `user.123`), banks are auto-created, and `search()` uses Hindsight's full semantic recall instead of basic vector similarity.
+
+**Best for**: Teams already using LangGraph's `store` patterns who want better retrieval without restructuring their graph.
+
+---
+
+### Which pattern fits your use case?
+
+| | Tools | Nodes | BaseStore |
+|---|---|---|---|
+| Works with plain LangChain | Yes | No | No |
+| Memory runs automatically | No (LLM decides) | Yes | Yes |
+| Uses existing store interface | No | No | Yes |
+| LLM controls when to remember | Yes | No | No |
+| Lowest migration cost | — | Low | Lowest |
+
+---
+
+## Complete working example
+
+Here's a full support agent that remembers each user across sessions using the nodes pattern. This is copy-pasteable and runnable against either self-hosted Hindsight or Hindsight Cloud.
+
+```python
+import asyncio
+from hindsight_client import Hindsight
+from hindsight_langgraph import create_recall_node, create_retain_node
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage, SystemMessage
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.checkpoint.memory import MemorySaver
+
+# --- Setup ---
+
+client = Hindsight(base_url="http://localhost:8888")
+# For Hindsight Cloud:
+# client = Hindsight(base_url="https://api.hindsight.vectorize.io", api_key="...")
+
+llm = ChatOpenAI(model="gpt-4o")
+checkpointer = MemorySaver()
+
+# --- Memory nodes ---
+# bank_id_from_config pulls the user ID from RunnableConfig at runtime,
+# so one graph definition serves all users with isolated memory banks.
+
+recall = create_recall_node(client=client, bank_id_from_config="user_id")
+retain = create_retain_node(client=client, bank_id_from_config="user_id")
+
+# --- Agent node ---
+
+async def agent_node(state: MessagesState):
+    system = SystemMessage(content=(
+        "You are a helpful support agent. "
+        "Relevant memories about this user have been injected above. "
+        "Use them to personalize your response."
+    ))
+    response = await llm.ainvoke([system] + state["messages"])
+    return {"messages": [response]}
+
+# --- Graph ---
+
+builder = StateGraph(MessagesState)
+builder.add_node("recall", recall)
+builder.add_node("agent", agent_node)
+builder.add_node("retain", retain)
+builder.add_edge(START, "recall")
+builder.add_edge("recall", "agent")
+builder.add_edge("agent", "retain")
+builder.add_edge("retain", END)
+
+graph = builder.compile(checkpointer=checkpointer)
+
+# --- Run ---
+
+async def chat(user_id: str, thread_id: str, message: str):
+    config = {
+        "configurable": {
+            "user_id": user_id,
+            "thread_id": thread_id,
+        }
+    }
+    result = await graph.ainvoke(
+        {"messages": [HumanMessage(content=message)]},
+        config=config,
+    )
+    return result["messages"][-1].content
+
+
+async def main():
+    # Session 1: user shares context
+    print("Session 1")
+    print(await chat("user-42", "thread-1", "Hi! I'm running into issues with our Postgres connection pool. We're on SQLAlchemy 2.0."))
+    print(await chat("user-42", "thread-1", "We're using async sessions with asyncpg. The pool keeps exhausting under load."))
+
+    # Session 2: new thread, same user — agent remembers
+    print("\nSession 2 (new thread)")
+    print(await chat("user-42", "thread-2", "Hey, back again. Still fighting the connection pool issue."))
+    # Agent recalls SQLAlchemy 2.0, asyncpg, and the pool exhaustion context
+    # without the user having to repeat themselves.
+
+
+asyncio.run(main())
+```
+
+What Hindsight extracts from Session 1 and stores in `user-42`'s memory bank:
+
+```
+- Uses SQLAlchemy 2.0 with async sessions
+- Uses asyncpg driver
+- Experiencing connection pool exhaustion under load
+- Running Postgres
+```
+
+When Session 2 starts on a fresh thread, the recall node searches the memory bank for context relevant to "Still fighting the connection pool issue" and injects those facts as a `SystemMessage` before the LLM responds. The agent picks up exactly where the last session ended.
+
+---
+
+## Per-user memory in one line
+
+All three patterns support dynamic bank IDs. Instead of hardcoding a bank, resolve it from the graph's config at runtime:
+
+```python
+recall = create_recall_node(client=client, bank_id_from_config="user_id")
+
+# Each invocation gets its own isolated memory bank
+await graph.ainvoke(
+    {"messages": [...]},
+    config={"configurable": {"user_id": "user-456"}},
+)
+```
+
+One graph definition serves all users. Memory banks are created automatically and kept fully isolated.
+
+## Getting started
+
+```bash
+pip install hindsight-langgraph
+```
+
+Works with both self-hosted Hindsight and [Hindsight Cloud](https://ui.hindsight.vectorize.io/signup). For cloud, pass your API key when creating the client:
+
+```python
+client = Hindsight(base_url="https://api.hindsight.vectorize.io", api_key="your-key")
+# or
+from hindsight_client import configure
+configure(api_key="your-key")  # defaults to the cloud URL
+```
+
+## What to build with this
+
+Long-term memory unlocks a different class of agent behavior. A few patterns we've seen work well:
+
+- **Support agents** that remember each customer's history, preferences, and past issues across sessions
+- **Sales assistants** that accumulate context about prospects over multiple touchpoints
+- **Personal productivity agents** that build up a model of a user's work style, priorities, and decisions
+
+In all three cases, the agent gets meaningfully better the longer it runs — not just because of a longer context window, but because Hindsight distills conversations into structured knowledge it can retrieve precisely when relevant.
+
+Full docs: [LangGraph integration](/docs/sdks/integrations/langgraph) | [GitHub](https://github.com/vectorize-io/hindsight/tree/main/hindsight-integrations/langgraph)
diff --git a/hindsight-docs/blog/authors.yml b/hindsight-docs/blog/authors.yml
index f2dbf11b..1b34314b 100644
--- a/hindsight-docs/blog/authors.yml
+++ b/hindsight-docs/blog/authors.yml
@@ -19,3 +19,9 @@ chrislatimer:
   title: Hindsight Team
   url: https://github.com/chrislatimer
   image_url: https://github.com/chrislatimer.png
+
+DK09876:
+  name: DK09876
+  title: Hindsight Team
+  url: https://github.com/DK09876
+  image_url: https://github.com/DK09876.png
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