Power your agent
URL ของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
https://synap-mcp.maximem.ai/mcpปุ่ม Claude ด้านบนจะเปิด Claude พร้อมกรอกชื่อและ URL ให้แล้ว คุณยังต้องยืนยันที่นั่นเอง
claude mcp add --transport http ai.maximem-synap https://synap-mcp.maximem.ai/mcpRun in a terminal.
{ "mcpServers": { "ai.maximem-synap": { "command": "npx", "args": [ "-y", "mcp-remote", "https://synap-mcp.maximem.ai/mcp" ] } }}claude_desktop_config.json
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
Synap gives your agents memory that lasts across conversations, sessions and devices. It scores 92.0% on LongMemEval and 93.2% on LoCoMo (gpt-5-mini answer and judge, reproduce with our open harness), fetches context in ~15ms P50 (internal measurement) before your agent asks, and ships native packages for 24 agent frameworks.
Persistent memory for AI agents — log and recall conversation context over MCP.
Every plan, including the free one, gets every Synap capability. Plans differ on volume and support, not features.
Synap's scores on the two standard long-term memory benchmarks, produced with our open-source harness on the official dataset releases.
Other vendors publish their own numbers under different answer models and judges. Our harness ships adapters for Mem0, Zep and Supermemory, so you can run them side by side yourself. How scores differ across setups →
Category breakdown, methodology and every scope decision → eval_benchmark_runs_output
# Python
pip install maximem-synap
# JavaScript / TypeScript
npm install @maximem/synap-js-sdk
Paste this into Claude Code, Codex or Cursor:
Add Synap memory to this project. Run
pip install maximem-synap(ornpm i @maximem/synap-js-sdk), then install the Synap skill from https://github.com/maximem-ai/maximem_synap_sdk/tree/main/skills/synap and follow it to wire memory into the agent framework this repo uses. Ask me for my Synap API key when you need it.
git clone --depth 1 https://github.com/maximem-ai/maximem_synap_sdk /tmp/synap && cp -r /tmp/synap/skills/synap ~/.claude/skills/synap
git clone --depth 1 https://github.com/maximem-ai/maximem_synap_sdk /tmp/synap && mkdir -p ~/.agents/skills && cp -r /tmp/synap/skills/synap-codex ~/.agents/skills/synap
Add the Synap MCP server URL and your API key from the dashboard. See packages/mcps/synap-mcp-server/.
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);
Most memory layers wait for the agent to ask. Synap works out what the agent will need next and has it ready. Six parts make that work.
Synap pre-fetches context before your agent requests it. ~15ms P50 latency in production (Maximem internal measurement). For voice AI agents, this is the difference between natural conversation and awkward pauses.
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.
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.
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.
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.
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.
Installable packages, not code snippets. Deep framework surfaces with callbacks, graph nodes, retrievers, memories, and plugins.
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",
),
)
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"),
)
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)
Give no-code and low-code agents (Gumloop, n8n, and any MCP-compatible client) persistent memory with nothing but an MCP URL and your Synap API key. No SDK, no code. The server exposes Synap's memory as four MCP tools (log_exchange, check_memory_status, recall_context, list_recent_memories) over Streamable HTTP.
It's a stateless adapter over the hosted Synap API: each call maps to one REST operation and your Bearer token is forwarded verbatim, so there's no separate backend to run. Use the managed endpoint (grab the URL and token from your dashboard), or self-host the adapter from source.
→ Source & self-hosting: packages/mcps/synap-mcp-server/
Understand the system before building on it:
Drop-in instructions that teach coding agents how to wire Synap into your codebase. Install steps are in Using a coding agent?.
~/.agents/skills/synap).This repo is a published mirror of Maximem's monorepo, so everything under packages/ is overwritten on each sync and pull requests against it are closed. Bug reports, gaps, and new-framework requests are very welcome as issues, and questions go to Discussions. See CONTRIBUTING.md for how to run the code locally and what a new integration needs.
Apache 2.0 (see LICENSE). Each package declares its own license in its metadata; the core Python and JS SDKs and the Vercel AI SDK integration are MIT.
Persistent memory for AI agents — log and recall conversation context over MCP.
{ "mcpServers": { "ai.maximem-synap": { "url": "https://synap-mcp.maximem.ai/mcp", "headers": {} } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก http สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Synap Memory เข้าถึงได้ผ่าน HTTP
ตรวจสอบล่าสุดเมื่อ 2026-11-07 Persistent memory for AI agents — log and recall conversation context over MCP.