# Maximem Synap's Agent Memory Now Available for LangChain

> Maximem Synap's persistent agent memory now integrates with LangChain.

_Maximem Team · May 20, 2026_

## **Maximem Synap's Agent Memory Now Available for LangChain**

Maximem Team

May 20, 2026

LangChain has 100,000-plus GitHub stars and the deepest integration ecosystem of any agent framework. If you are building an agent that chains LLM calls or orchestrates multi-step reasoning, LangChain is where you start.

Today, [Maximem Synap](https://www.maximem.ai/synap) extends LangChain with persistent per-user memory. Native memory stores conversation state within a process. Synap makes that state survive restarts, resolve entities across sessions, and retrieve relevant context without manual wiring.

## Where LangChain Excels

LangChain taught developers how to build agents. Composable chains and tool routing. Prompt templating and output parsing. Vector store [integrations](https://www.maximem.ai/glossary/integrations) and output [guardrails](https://www.maximem.ai/glossary/guardrails). If your agent needs to call APIs or reason across multiple steps, LangChain handles the logic and [orchestration](https://www.maximem.ai/glossary/orchestration) well.

The challenge is what happens when your user comes back tomorrow.

  

## How LangChain Memory Works Today

LangChain ships four built-in memory options. All store data within a single process lifetime. For a deeper look at how each works, see the LangChain memory documentation.

ConversationBufferMemory stores every message in a list, then dumps the entire list into your prompt. A scratchpad that grows forever. After twenty turns, your [context window](https://www.maximem.ai/glossary/context-window) fills with noise. The agent cannot see your actual instructions. On restart, the scratchpad clears. Every session starts fresh.

ConversationBufferWindowMemory keeps only the last K messages and discards everything older. The window slides. Yesterday's conversation is already gone. You are not solving memory. You are

accelerating forgetting.

ConversationSummaryMemory compresses history into summaries. Send a paragraph describing what happened instead of every message. But summarization is lossy. Stanford research found that a single summary pass drops accuracy from 66.7% to 57.1%. Each additional pass loses more. You trade recall for tokens.

VectorStoreRetrieverMemory stores messages in a [vector database](https://www.maximem.ai/glossary/vector-database) and retrieves by semantic similarity. Better than the first three, but still limited to similarity search. It has no concept of entities. No temporal awareness. It cannot resolve that "John from Acme" and "j.doe@acme.com" are the same person.

Cross-session persistence, [entity resolution](https://www.maximem.ai/blog/mastra-memory-synap-integration), compaction, and per-user scoping across restarts fall outside what these four classes were designed to handle. Those are infrastructure problems.

  

## What Synap Adds

Synap is agentic [context management](https://www.maximem.ai/glossary/context-management). It does not replace LangChain's memory. It extends it with a persistence layer.

We ship three components that plug into LangChain's native interfaces:

SynapCallbackHandler implements BaseCallbackHandler. Attach it to any chain. It captures every turn in the background and ingests it into Synap asynchronously. No code changes to your chain logic. No latency added to your execution. The chain keeps working. The agent starts remembering.

SynapChatMessageHistory implements BaseChatMessageHistory. Use it as your memory backend. Per-user, per-conversation scoping. State survives restarts. Prior messages replay on the next session without manual setup.

SynapRetriever implements BaseRetriever. Fetch user-scoped memories as standard LangChain Document objects. Two modes: fast (vector-only, 50 to 100ms) and accurate (graph traversal + reranking, 200 to 500ms).

The integration is a native package. Drop it in, replace the memory backend, and your chain picks up persistent memory without a rewrite.

  
We built this because agents kept stalling in production. Not from bad chain logic. From missing context that lived in a different session last Tuesday. Production testing hit 92% LongMemEval, 93.2% on LoCoMo. Fast mode retrieves in under 100ms.

For why context management is infrastructure and not a feature, read What Is Agentic Context Management? For build-versus-buy numbers, see The Real Cost of DIY Agent Memory.

  

## What Synap Adds to LangChain

  
PERSISTENCE

LangChain Native: In-process only. State clears on restart.

With Synap: Per-user memory survives across sessions and restarts.

  

ENTITY RESOLUTION

LangChain Native: Raw identifiers. No linking across sessions.

With Synap: "John" and "[j.doe@acme.com](mailto:j.doe@acme.com)" resolve to one canonical entity across every session.

  
COMPACTION

LangChain Native: Manual summarization. Lossy.

With Synap: Automatic and configurable. Accuracy-preserving compaction that does not drop critical facts.

  

RETRIEVAL LATENCY

LangChain Native: Depends on vector store setup.

With Synap: 50 to 100ms fast mode. 200 to 500ms accurate mode.

  

LONG-TERM RECALL

LangChain Native: Not benchmarked for cross-session recall.

With Synap: 92% on LongMemEval.

  

FAILURE HANDLING

LangChain Native: Retrieval failures crash the chain or return noise.

With Synap: Empty result and a logged error. Your chain keeps running.

  

USER SCOPING

LangChain Native: Session-scoped only.

With Synap: Built-in user\_id, conversation\_id, customer\_id scoping out of the box.

  

## What Production Teams Gain

  

Cross-session continuity. Your user chats on Monday, returns on Wednesday. SynapChatMessageHistory replays prior context. SynapRetriever surfaces relevant facts from last week. The agent treats every session as one continuous conversation. Native memory treats every session as a fresh start.

  

Accuracy that ships. 92% LongMemEval, 93.2% on LoCoMo measures whether agents recall facts across long, multi-turn conversations spanning multiple sessions.

  

Token efficiency. Synap's compaction trims conversation history without dropping critical context. Most teams see 60 to 70% fewer tokens shipped to the LLM per turn. At scale, that is the difference between profit and burn.

  

Latency that does not block. Fast retrieval: 50 to 100ms. Accurate mode with graph traversal and reranking: 200 to 500ms. Both degrade without crashing. A failure returns empty results and a log line, not a broken chain.

  

Entity resolution. "John from Acme," "[j.doe@acme.com](mailto:j.doe@acme.com)," and "user\_4829" resolve to one person across every session. Synap handles this at the memory layer so your chain nodes do not have to.

  

Production resilience. The callback handler captures turns in the background without adding latency. The retriever returns empty results on failure instead of crashing. The message history replays prior messages per session. All three components implement standard LangChain interfaces. No wrappers. No adapters.

  

## How to Get Started

## **Setup**

Install the package alongside LangChain and your model provider:

```
pip install maximem-synap-langchain langchain langchain-openai
```

Configure your API key. Generate one from the [**Synap Dashboard**](https://synap.maximem.ai/).

**.env**

```
SYNAP_API_KEY=synap_your_key_here
OPENAI_API_KEY=your-openai-api-key
```

Then initialize the SDK once at application startup:

```
from maximem_synap import MaximemSynapSDK

sdk = MaximemSynapSDK()  # picks up SYNAP_API_KEY from env
await sdk.initialize()
```

See [**SDK Initialization**](https://docs.maximem.ai/sdk/initialization) for the full lifecycle and configuration options.

## [**​**](https://docs.maximem.ai/integrations/langchain#basic-integration)

**Basic integration** The smallest useful integration is a chain with**automatic memory**via`SynapCallbackHandler`. Every turn is ingested without changing your chain logic:

```
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from synap_langchain import SynapCallbackHandler

llm = ChatOpenAI(model="gpt-4o")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with long-term memory."),
    ("human", "{question}"),
])

chain = prompt | llm

handler = SynapCallbackHandler(
    sdk=sdk,
    conversation_id="conv-123",
    user_id="alice",
    customer_id="acme",   # optional — required for B2B instances
)

response = await chain.ainvoke(
    {"question": "Remind me what we agreed on for the Q2 roadmap."},
    config={"callbacks": [handler]},
)
```

The handler observes the chain via LangChain’s callback system, captures the user/assistant pair, and ingests it into Synap asynchronously.**Ingestion failures are logged at**`ERROR`**level and never propagate to your chain**— your application keeps running even if Synap is unreachable.This is the smallest viable setup. To make the model_aware_of memory at inference time, combine the callback with`SynapChatMessageHistory`and/or`SynapRetriever`below.  

  

## Memory Is Infrastructure

LangChain gave the world a standard for building agents. Real value. The memory layer it ships handles in-session state well. Making that state persist across sessions, resolve entities, and retrieveintelligently is a different problem.

The teams that ship production agents discover this around month three. They either build memory infrastructure themselves, or they plug in a system built for the problem.  
This is why memory is infrastructure, not a feature.  
Start building LangChain agents that remember across sessions: [https://synap.maximem.ai](https://synap.maximem.ai)

Synap pricing is usage-based. You pay for memory operations: storage, retrieval, compaction. No per-seat or per-framework surcharge. Starter plan: $49/month. Every new account gets $25 in free credits to test before committing. See full pricing at [https://synap.maximem.ai/pricing](https://synap.maximem.ai/pricing).

  

## Related Posts

  

  - [What Is Agentic Context Management?](https://www.maximem.ai/blog/what-is-agentic-context-management)

  - [The Real Cost of DIY Agent Memory](https://www.maximem.ai/blog/real-cost-diy-agent-memory)

  - [Skills Are the New Microservices](https://www.maximem.ai/blog/skills-new-microservices)

  

## **Frequently Asked Questions**

  

Does LangChain have built-in memory?

-   Yes. Four classes. All process-local. Nothing survives a restart. See the LangChain memory documentation for the full list.
    

How is Synap different from VectorStoreRetrieverMemory?

-   VectorStoreRetrieverMemory is similarity search over vectors. Synap adds entity linking, temporal awareness, automatic compaction, and a graph layer for relationship queries. Same interface. Different depth.
    

  
What is the latency overhead?

-   50 to 100ms for fast mode. 200 to 500ms for accurate mode. Callback ingestion runs in the background and adds zero latency to your chain.
    

  

Can I use this in production today?

-   Yes. The package implements BaseCallbackHandler, BaseChatMessageHistory, and BaseRetriever. Read failures return empty results. Write failures raise SynapIntegrationError so your agent knows
    

  persistence missed.

  

How does pricing work?

-   Usage-based. Storage, retrieval, and compaction are metered separately. No per-seat or per-framework fee. Starter plan is $49/month with $25 in free credits for new accounts. See
    

  [https://synap.maximem.ai/pricing](https://synap.maximem.ai/pricing).

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Source: [https://www.maximem.ai/blog/langchain-maximem-synap-memory-integration](https://www.maximem.ai/blog/langchain-maximem-synap-memory-integration)