Synap Memory

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The memory layer for production AI agents

Your AI agents forget everything between conversations. Synap fixes that with a production-grade memory layer built for applications that serve real users at scale. #1 on LongMemEval (92%) and LoCoMo (93.2%), sub-15ms anticipatory retrieval, and native integrations with every major AI framework.

LangChain · LangGraph · LlamaIndex · CrewAI · AutoGen · Haystack · Google ADK · OpenAI Agents · Semantic Kernel · Pydantic AI · Agno · LiveKit · Pipecat · Claude Agent · Mastra · Vercel AI SDK · NeMo Agent Toolkit · Microsoft Agent Framework

What's in this repo: the open-source Python and JavaScript SDKs plus all framework integrations, licensed under Apache 2.0. The Synap memory engine itself (ingestion, entity resolution, retrieval, anticipation) runs as a fully managed cloud service operated by Maximem and is not open source. The SDKs in this repo are clients for that service; there is nothing to self-host, and an API key is required.

Benchmarks

Synap leads the field on the two standard long-term memory benchmarks, evaluated on identical hardware with an open-source harness.

Synap outperforms leading published memory systems, run through the same open-source evaluation harness on identical hardware and configs.

"Longer conversations make Synap better, not worse." Richer entity graphs and stronger pattern recognition at scale.

Full methodology and reproduction instructions → maximem.ai/blog/synap-benchmark-results

Install

# Python
pip install maximem-synap

# JavaScript / TypeScript
npm install @maximem/synap-js-sdk

60-second quickstart

Your agent forgets. Synap remembers across conversations, sessions, and devices.

The SDK connects to the hosted Synap cloud service, so you'll need an API key. There's no local server to run: memory processing happens in Synap's cloud, not in this package.

import asyncio
from maximem_synap import MaximemSynapSDK

sdk = MaximemSynapSDK(api_key="your-api-key")

async def main():
    await sdk.initialize()

    # Monday's standup
    await sdk.conversation.record_message(
        conversation_id="mon-standup",
        user_id="alice",
        role="user",
        content="I'm migrating our auth service to OAuth2 this sprint.",
    )

    # Friday: completely different conversation, same user
    context = await sdk.fetch(
        conversation_id="fri-review",
        user_id="alice",
        search_query=["what is alice working on?"],
    )

    print(context.formatted_context)
    # → "Alice is migrating the auth service to OAuth2 this sprint."

asyncio.run(main())

const { createClient } = require('@maximem/synap-js-sdk');

const client = createClient({ apiKey: 'your-api-key' });
await client.init();

// Record
await client.conversation.recordMessage({
    conversationId: 'mon-standup',
    userId: 'alice',
    role: 'user',
    content: "I'm migrating our auth service to OAuth2 this sprint.",
});

// Fetch later, anywhere
const context = await client.fetchUserContext({
    userId: 'alice',
    query: 'what is alice working on?',
});

console.log(context.formattedContext);

What makes Synap different

🎯 Anticipatory Retrieval

Synap pre-fetches context before your agent requests it. 15ms P50 latency in production. For voice AI agents, this is the difference between natural conversation and awkward pauses.

🔗 Entity Resolution

When a user says "my manager" in turn 3 and "Sarah" in turn 12, Synap resolves them automatically. Cross-session, cross-conversation, without the agent doing any work.

⏳ Temporal Awareness

Context from 30 minutes ago and context from 30 days ago should not carry equal weight. Synap applies temporal decay and relevance scoring so your agent surfaces the right information at the right time.

🧠 Conscious Forgetting

When a user says "ignore what I said about the budget," Synap processes that as a retraction, not just more context to store. Contradiction handling is built into the pipeline.

🏗️ Custom Memory Architectures

No universal memory model. Synap builds customized memory architectures per use case. Customer support agents and voice AI agents need different context strategies. Synap handles both.

🏢 Multi-Tenant Scoping

Built for B2B from day one. Memory is scoped across a four-level hierarchy:

client          → shared knowledge across your entire platform
  └── customer  → per-company context (multi-tenant B2B)
        └── user       → per-user memory and preferences
              └── conversation → in-session history

One fetch() call merges all relevant scopes in parallel.

Framework integrations

Installable packages, not code snippets. Deep framework surfaces with callbacks, graph nodes, retrievers, memories, and plugins.

LangChain

pip install maximem-synap maximem-synap-langchain

from maximem_synap import MaximemSynapSDK
from synap_langchain import SynapChatMessageHistory
from langchain_openai import ChatOpenAI
from langchain_core.runnables.history import RunnableWithMessageHistory

sdk = MaximemSynapSDK(api_key="your-api-key")
await sdk.initialize()

chain = RunnableWithMessageHistory(
    ChatOpenAI(),
    lambda session_id: SynapChatMessageHistory(
        sdk=sdk, conversation_id=session_id, user_id="alice",
    ),
)

CrewAI

pip install maximem-synap maximem-synap-crewai

from synap_crewai import SynapStorageBackend

crew = Crew(
    agents=[...], tasks=[...],
    memory=True,
    storage=SynapStorageBackend(sdk=sdk, user_id="alice"),
)

LlamaIndex

pip install maximem-synap maximem-synap-llamaindex

from synap_llamaindex import SynapChatMemory

memory = SynapChatMemory(sdk=sdk, user_id="alice")
agent = ReActAgent.from_tools(tools, memory=memory)

All integrations