SynaBun is an open-source, self-hosted AI workspace and persistent memory MCP server that keeps memory in SQLite on your machine with local embeddings. Mem0 is a memory layer for AI applications, an open-source library plus the hosted Mem0 Platform, that uses OpenAI by default.
Last updated
| Feature | SynaBun | Mem0 |
|---|---|---|
| What it is | AI workspace and memory MCP server for the coding agents you use | Memory layer for AI apps: open-source library and hosted platform |
| MCP tools | 158 (141 on by default) | 11, on Mem0's hosted MCP server |
| Default embeddings | all-MiniLM-L6-v2, computed on your machine | OpenAI text-embedding-3-small (needs an OpenAI API key) |
| Where memory lives | SQLite on your machine | Local Qdrant and SQLite (library), Postgres with pgvector (self-hosted server), or Mem0 Platform |
| Runs offline | Yes, by default | Once configured with local models, such as Ollama |
| Install | npm install -g synabun or the desktop app | pip install mem0ai, or Docker Compose for the server |
| Managed cloud | No | Mem0 Platform: free plan, paid plans from $19 a month |
| License | Apache 2.0 | Apache 2.0 (library) |
Mem0 facts checked on October 21, 2026 against its repository, documentation and pricing page (sources at the end). SynaBun facts are from its source code.
A hosted service. Mem0 Platform runs the memory for you, with a free plan and paid plans. SynaBun has no hosted service: it runs on your machine, and you keep and back up its data yourself.
Framework integrations. Mem0 documents integrations with LangGraph, LlamaIndex, AutoGen, CrewAI, LangChain and others. If your product is built on one of those frameworks, Mem0 fits into it directly.
Embedding quality at scale. OpenAI's text-embedding-3-small produces 1,536-dimension vectors, against 384 for all-MiniLM-L6-v2, and scores higher on public benchmarks such as MTEB. If retrieval quality over a very large corpus is your bottleneck, a hosted model can be worth the API calls.
Community. Mem0's repository has about 67,600 GitHub stars (October 2026), so there are more tutorials and third-party examples.
Local by default. SynaBun computes embeddings with all-MiniLM-L6-v2 on your machine and stores memory in SQLite, so recall needs no API key and never waits on a hosted embedding API. Mem0's library uses OpenAI for embeddings and for its LLM until you configure local models.
More than memory. SynaBun's MCP server has 158 tools: memory, plus the Assistant that dispatches Claude Code, Codex and OpenCode worker agents, browser automation (46 Playwright tools), social media tools for seven platforms, a whiteboard, autonomous loops and Discord tools. Mem0's MCP server has 11 tools, all about memory.
Setup. One command (npm install -g synabun, then synabun) or the desktop app. SQLite needs no server to run or maintain.
Built for coding agents. SynaBun configures itself for Claude Code, Codex, Gemini CLI and OpenCode, and ships Claude Code hooks that recall and save memories during a session without being asked. Memories have categories, project labels, an importance from 1 to 10 and an optional recency boost; recall combines keyword search and embeddings.
Pick Mem0 if: you want a hosted memory service, memory belongs to a product you build for many users, or your stack is built on LangGraph, LlamaIndex, AutoGen or CrewAI.
Pick SynaBun if: you use Claude Code, Codex, OpenCode or Gemini CLI every day, you want memory that stays on your machine without an API key, or you want memory, agents, a browser and automation in one install.
Yes: both are MCP servers, and an MCP client can connect to several at once. In practice you would keep one memory store, because two sets of memory tools (remember and add_memory) leave the agent two places to look.
To move from Mem0 to SynaBun, export your memories through Mem0's API, then save them with SynaBun's remember tool, which embeds them locally.
Checked on October 21, 2026.
One command. SQLite + local embeddings. 158 MCP tools.
Read the docs GitHub