io.github.MemoriLabs/memori-mcp
Memori MCP server — persistent AI memory with recall and augmentation tools
Open source Open in the app JSON README (API)
About
Memori MCP server — persistent AI memory with recall and augmentation tools
Details
- Kind
- MCP servers
- Topic
- AI, RAG & memory
- Publisher
- memorilabs
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 1.0.0
- Stars
- 2
- Forks
- 2
- Last push
- 2026-07-01T16:38:28Z
- Repository state
- ativo
- Language
- JavaScript
- License
- MIT
- Added
- 2026-08-29 03:02:06
- Updated
- 2026-08-29 03:02:06
- Origin id
io.github.MemoriLabs/memori-mcp
README
# Memori MCP > Persistent AI memory for any MCP-compatible agent — no SDK required. **memori-mcp** is the official [Memori](https://memorilabs.ai) MCP server. Connect it to your AI agent to give it long-term memory: recall relevant facts, retrieve broad state summaries, restore working state after context compaction, store durable preferences after responding, and maintain context across sessions. --- ## Why Memori MCP? Memori turns stateless agents into **stateful systems** by providing structured, persistent memory that works across sessions and workflows. 1. **Persistent state beyond prompts** — Most agents rely on prompt context and lose state between runs. Memori provides **durable, structured memory** so agents can retain facts, decisions, and outcomes over time. 2. **Memory from execution (not just natural language)** — Traditional systems extract memory from chat. Memori builds memory from **agent execution itself** — including tool calls, decisions, and results. This enables true **agent-native memory**, not just conversational recall. 3. **Lower cost, higher accuracy** — Instead of expanding prompt context, Memori retrieves only what matters. - Significantly reduced token usage - Faster responses - Improved accuracy vs long-context approaches 4. **Works with any MCP client and production-ready** - No SDK, no code changes, just config Memori is **state infrastructure for production agents** — enabling persistent memory, efficient retrieval, and structured context across both natural language and agent execution. ## LoCoMo Benchmark Memori was evaluated on the LoCoMo benchmark for long-conversation memory and achieved **81.95% overall accuracy** while using an average of **1,294 tokens per query**. That is just **4.97% of the full-context footprint**, showing that structured memory can preserve reasoning quality without forcing large prompts into every request. Compared with other retrieval-based memory systems, Memori outperformed Zep, LangMem, and Mem0 while reducing prompt size by roughly **67% vs. Zep** and lowering context cost by more than **20x vs. full-context prompting**. Read the [benchmark overview](https://memorilabs.ai/benchmark) or download the [paper](https://arxiv.org/abs/2603.19935). --- ## How It Works The server exposes seven tools: | Tool | When to call | What it does | |------|-------------|--------------| | `memori_recall` | Start of each user turn | Fetches relevant memories at the start of a user turn | | `memori_recall_summary` | Session starts, daily briefs, status updates, project overviews | Fetches broad memory state for session starts, daily briefs, status updates, and project overviews | | `memori_compaction` | After context compaction | Fetches a structured post-compaction brief so an agent can resume operational work | | `memori_advanced_augmentation` | After composing a response | Stores durable memory after the agent has drafted a response | | `memori_feedback` | When the user flags a memory issue or praises a result | Reports irrelevant, missing, stale, or especially useful memory behavior | | `memori_signup` | When the user explicitly asks and provides an email | Requests a Memori account/API key when the user explicitly asks | | `memori_quota` | When the user asks about usage or quota errors appear | Checks current memory usage and limits when the user asks or quota errors appear | ### Example Agent Flow Given the user message: *"I prefer Python and use uv for dependency management."* 1. Agent calls `memori_recall` with the user message as `query` 2. Agent composes a response using any returned facts 3. Agent sends the response to the user 4. Agent calls `memori_advanced_augmentation` with the `user_message` and `assistant_response` On a later turn like *"Write a hello world script"*, the agent recalls the Python + uv preference and personalizes its response. --- ## Prerequisites - A Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai) - An `entity_id` to identify the end user (e.g. `user_123`) - An optional `process_id` to identify the agent or workflow (e.g. `my_agent`) Export these in your shell or replace the placeholders directly in your config: ```bash export MEMORI_API_KEY="your-memori-api-key" export MEMORI_ENTITY_ID="user_123" export MEMORI_PROCESS_ID="my_agent" # optional ``` --- ## Server Details | Property | Value | |----------|-------| | **Server** | Memori MCP | | **Endpoint** | `https://api.memorilabs.ai/mcp/` | | **Transport** | Stateless HTTP | | **Auth** | API key via request headers | ### Headers | Header | Required | Description | |--------|----------|-------------| | `X-Memori-API-Key` | Yes | Your Memori API key from [app.memorilabs.ai](https://app.memorilabs.ai) | | `X-Memori-Entity-Id` | Yes | Stable end-user or entity identifier (e.g. `user_123`) | | `X-Memori-Process-Id` | No | Optional process, app, or workflow identifier (e.g. `my_agent`) for memory isolation | `session_id` is derived automatically as `<entity_id>-<UTC year-month-day:hour>`. You do not need to provide it. --- ## Verifying the Connection After configuring your client, verify the setup: - MCP server shows as connected and healthy in your client UI - Tools list includes `memori_recall`, `memori_recall_summary`, `memori_compaction`, and `memori_advanced_augmentation` - Calls return non-401 responses - `memori_recall` returns memories for known entities - `memori_advanced_augmentation` accepts durable user/assistant turn data If you receive `401` errors, double-check your `X-Memori-API-Key` value. See the [Troubleshooting guide](https://memorilabs.ai/docs/memori-cloud/support/troubleshooting) for more help. --- ## Links - [Memori Cloud](https://memorilabs.ai/docs/memori-cloud) - [Get an API key](https://app.memorilabs.ai) - [MCP Overview docs](https://memorilabs.ai/docs/memori-cloud/mcp/overview) - [Client Setup docs](https://memorilabs.ai/docs/memori-cloud/mcp/client-setup) - [Agent Skills docs](https://memorilabs.ai/docs/memori-cloud/mcp/agent-skills)