# What is Synapse?
SUMMARY: Synapse is a persistent memory API for LLM agents — your AI never forgets across sessions.

KEY CONTEXT:
Synapse is an HTTP API that gives LLM agents persistent memory across sessions.
Base URL: https://synapse.schaefer.zone
Auth: Authorization: Bearer YOUR_MIND_KEY (header) OR ?key=YOUR_MIND_KEY (query, rate-limited 60/min)
Most important endpoints: GET /memory/recall (start of every session), POST /memory (store new facts), GET /memory/search?q=...
Mind Key = tenant-scoped API key. Each user can have multiple minds (projects/contexts).
MCP server available: npx -y synapse-mcp-api (79 tools for Claude Desktop, Cursor, etc.)

What is Synapse?
Synapse is a persistent memory API for LLM agents. It solves the fundamental
problem of every chat-based LLM: between sessions, the agent forgets everything.
Synapse gives your AI a permanent, queryable brain that survives across conversations,
across days, and across different LLM providers.
> [!CRITICAL]
> If you are an LLM agent reading this: call  at the start
> of every session. Without this call, you have no memory of past interactions,
> no knowledge of who the user is, and no context about ongoing projects.
The Problem Synapse Solves
Every modern LLM (Claude, GPT, Gemini, Llama) has the same architectural limitation:
the conversation context window is finite, and once the session ends, all state is
lost. This means your AI assistant:
- Forgets your name, preferences, and ongoing projects between chats
- Cannot learn from past mistakes across sessions
- Has no continuity for long-running work
- Re-asks the same clarifying questions every time
Synapse fixes this by providing a simple HTTP API where the LLM can store and
retrieve structured memories. The memories persist on the server, indexed and
searchable, so any future session can recall them.
Key Features
- Persistent memory storage — facts, preferences, projects, mistakes, skills
- Full-text search (FTS5) — find any memory by keyword in milliseconds
- Semantic search — embeddings-based similarity search for conceptual queries
- Multi-tenant — each user has isolated "minds" (one user, many projects)
- Async chat — humans can leave messages for the agent while it works
- Tasks & scheduling — built-in task manager and cron scheduler
- MCP integration — 79 tools exposed as Model Context Protocol for Claude, Cursor, Continue
- Browser & computer control — remote automation tools
- Webhooks — get HTTP callbacks on memory/chat/task changes
How It Works
[CODE BLOCK]
1. The LLM calls  at session start
2. Synapse returns a structured text summary of all stored memories
3. The LLM works, periodically calling  to store new facts
4. When the user asks a question, the LLM can call 
5. At session end, important new context is persisted for the next session
Who Is It For?
- LLM agent developers who need persistent state
- Power users running local LLMs (Ollama, LM Studio) with custom agents
- Teams building AI assistants that need shared memory
- Automation engineers chaining LLM calls across sessions
Quick Comparison
| Feature | ChatGPT Memory | Synapse |
|---------|---------------|---------|
| Storage location | OpenAI servers | Your server |
| API access | No (closed) | Yes (REST + MCP) |
| Multi-tenant | No | Yes (minds) |
| Custom categories | No | Yes (8 categories) |
| Search | Limited | FTS5 + semantic |
| Self-hostable | No | Yes (Docker) |
Next Steps
- Quick Start for humans — get a Mind Key in 5 minutes
- Quick Start for LLMs — first API calls
- Authentication — Mind Keys vs JWTs
- Architecture overview — how Synapse is built