Memwyre

Memwyre

Persistent Shared Memory for Every AI.

[Memwyre Logo]

Website · Documentation · Console / App · Twitter / X

[FastAPI] [Vue 3] [Chrome Extension] [MCP] [LoCoMo Accuracy] [License: Apache 2.0]

Memwyre is an open-source, universal memory infrastructure and persistent knowledge retrieval layer for Large Language Models (LLMs), AI agents, and custom applications.

Rather than treating AI as stateless and losing context every time you switch between ChatGPT, Claude, Cursor, or different agent environments, Memwyre sits externally as a unified personal brain. It securely ingests, chunks, and structures your documents, web pages, conversations, and workflows—making them instantly retrievable across your entire AI toolchain.

Quickstart

npx -y install-memwyre

Table of Contents

1. Quickstart
2. Core Features
3. Ecosystem Tiers
4. System Architecture & Core Workflows
5. The LoCoMo Benchmark Evaluation
6. Database Schema & Multi-Tenancy
7. Installation & Setup
8. Environment Configuration
9. License

Core Features

• Decoupled Persistent Memory: Acts as an external, LLM-agnostic memory layer. Your knowledge base follows you whether you are using OpenAI, Google Gemini, Anthropic Claude, or local model configurations.
• Asynchronous Enrichment: Automatically refines memories, stripping filler text, generating summaries, extracting key entities, and producing atomic factual associations.
• Dynamic Context Pruning & Recency Decay: Utilizes an Ebbinghaus-inspired logarithmic decay to deprecate outdated or contradictory user preferences chronologically, keeping context sizes optimized.
• Approval-Based Inbox Flow: Introduces a memory dashboard inbox, allowing you to review, edit, approve, or reject auto-captured memories before committing them to long-term vector indexes.
• Project-Scoped Containerization: Restricts vector searches and factual associations to specific workspaces or project scopes, providing robust multi-tenant containerization.

Ecosystem Tiers

Memwyre provides multiple ways to ingest and retrieve information:

• 1. Web Application & Dashboard (Deployed on memwyre.tech): The main web app written in Vue 3 (Vite + Tailwind CSS), incorporating an onboarding tour, Monaco Editor for document management, billing integration, and a visual retrieval simulator to debug and verify vector rankings.
• 2. Chrome Extension (Manifest V3) (Available on Chrome Web Store): Auto-injects context into web chat clients, maps authentication tokens, and allows users to save articles, code snippets, or conversational logs directly to their vault with a single click.
• 3. Model Context Protocol (MCP) Server: A Python server mapping memory tools (search_memory, save_memory, get_document) directly into IDEs like Cursor and VS Code, or desktop assistants like Claude Desktop.
• 4. CLI Tool: A Node-based Command Line Interface (cli/) providing terminal-level interaction, query testing, and batch document uploads.
• 5. OpenClaw Plugin: A dedicated integration module (openclaw-plugin/) allowing multi-agent platforms to interface directly with the Memwyre memory vault.

System Architecture & Core Workflows

High-Level Components

Memwyre connects user clients to local or cloud vector search services and AI providers:

flowchart TB    subgraph Client_Side ["Client Side"]        Browser["WebApp (Vue 3 / Vite)"]        Extension["Chrome Extension (MV3)"]        CLI["CLI Client (Node.js)"]    end
    subgraph Load_Balancer ["Ingress"]        Nginx["Nginx Reverse Proxy"]    end
    subgraph Backend_Core ["Backend API (FastAPI)"]        Auth_Mod["Auth & Users Module"]        Mem_Mod["Memory Management"]        Ret_Mod["Retrieval Engine"]        LLM_Mod["LLM Service (V1/V2)"]    end
    subgraph Background_Workers ["Celery Workers"]        Ingest_Worker["Ingestion & Chunking Worker"]        Dedupe_Worker["Deduplication Worker"]    end
    subgraph Data_Persistence ["Data Layer"]        Postgres[("PostgreSQL / SQLite")]        Pinecone[("Pinecone / ChromaDB")]        Redis[("Redis Message Broker")]    end        subgraph External_Services ["AI Inference"]        NVIDIA["NVIDIA NIM (Kimi K2.6)"]        Azure["Azure OpenAI (GPT-4o-mini)"]        Gemini["Google Gemini API"]    end
    Browser -->|HTTPS| Nginx    Extension -->|HTTPS| Nginx    CLI -->|HTTPS| Nginx    Nginx --> Backend_Core        Auth_Mod --> Postgres    Mem_Mod --> Postgres    Mem_Mod --> Ingest_Worker        Ret_Mod --> Pinecone    Ret_Mod --> Postgres    Ret_Mod --> External_Services        Ingest_Worker --> External_Services    Ingest_Worker --> Pinecone    Ingest_Worker --> Postgres

Ingestion Pipeline

Ingesting a memory triggers background worker tasks to process, embed, and structure raw data asynchronously:

sequenceDiagram    participant User    participant API as FastAPI API    participant Worker as Celery Worker    participant LLM as LLM/Embedding Provider    participant Vector as Pinecone/ChromaDB    participant DB as PostgreSQL/SQLite
    User->>API: POST /memory (Raw Text Content)    API->>DB: Save Memory (Status: Pending)    API->>Worker: Dispatch Ingest Task    API-->>User: 202 Accepted (In progress)        Note over Worker: Asynchronous Processing    Worker->>LLM: Metadata Extraction (Titles, Tags)    Worker->>Worker: Semantic Chunking (Overlapping Splits)        loop Parallel Enrichment        Worker->>LLM: Enrich Chunk (Q&A Pairs, Summaries)        Worker->>LLM: Extract SPO Facts (Subject-Predicate-Object)    end        Worker->>Vector: Batch Upsert Embeddings (Chunks + Facts)    Worker->>DB: Write Chunks & Facts (Linked to Memory)    Worker->>DB: Update Memory Status (Approved/Active)

Parallelized Retrieval (RAG)

Retrieval queries run exact relational Fact lookups and fuzzy Semantic Search in parallel to feed LLM contexts with ultra-low latency:

sequenceDiagram    participant User    participant API as FastAPI API    participant RetSvc as RetrievalService    participant Vector as Vector Store    participant DB as Relational DB    participant LLM as GenAI Model
    User->>API: Chat Query / RAG Trigger    API->>RetSvc: search_memories(Query, project_id)        par State Fact Lookups        RetSvc->>Vector: Vector Search (Factual matches)        RetSvc->>DB: SQL Filter (Valid & Non-superseded Facts)    and Semantic Search        RetSvc->>Vector: Vector Search (Chunk embeddings)        RetSvc->>RetSvc: MMR Re-ranking (Filter redundant chunks)    end        RetSvc->>RetSvc: Merge Results (State Facts + Chunk text)    RetSvc-->>API: Ranked Top-K Context Items        API->>LLM: Generate Answer (Prompt + Merged Context)    LLM-->>User: Streaming Response

The LoCoMo Benchmark Evaluation

The LoCoMo-10 (Long Conversational Memory) benchmark, introduced by Snap Research in "Evaluating Very Long-Term Conversational Memory of LLM Agents" (2024), evaluates AI agent systems on long-term memory, factual consistency, temporal alignment, and multi-hop reasoning over lengthy, multi-session dialog flows (up to 32 sessions and 26,000 tokens per conversation).

Performance Metrics (Memwyre vs. Flat Vector Systems)

[!TIP] Context Compression: Memwyre achieves a 88.5% context length reduction (retrieving 3,000 tokens instead of the 26,000-token raw conversational dialog) while significantly outperforming flat vector indexing in accuracy.

Architectural Enablers of LoCoMo Performance

1. Dynamic Context Pruning: Strips out conversational noise (filler words, greetings, and distractors) during chunk enrichment.
2. Two-Stage Re-ranking: Employs a broad, high-recall vector fetch stage followed by a Cross-Encoder re-ranker to pick only the most contextually relevant memory items.
3. Ebbinghaus Logarithmic Recency Decay: Automatically deprecates older user preferences or conflicting facts chronologically when newer entries override them.
4. Adversarial Immunity: Utilizes strict semantic containment, causing the retriever to fail cleanly and refuse hallucinations when queried on non-existent information.

Database Schema & Multi-Tenancy

Memwyre uses a hybrid storage model: metadata, relational facts, and user credentials reside in SQL tables (PostgreSQL/SQLite), while document chunks and enriched fact strings are mirrored in vector databases (Pinecone/ChromaDB).

👉 View the Detailed Database Schema for a complete breakdown of the Core Entities (users, projects, memories, documents, chunks, facts) and the Entity Relationship Diagram.

Fact Supersession & Project Containerization

• Supersession: When a new fact matching the same subject and predicate is written (e.g. user moves from Berlin to Tokyo), the database marks the old record's is_superseded flag as true and updates valid_until to the current timestamp. This guarantees that temporal inquiries return state-accurate facts.
• Containerization: Every memory, document, chunk, and fact contains a project_id. When querying, filters strictly enforce containment matching the current workspace's project_id, preventing leakage across different project environments.

Installation & Setup

Prerequisites

• Python 3.11 or 3.12 (UV package manager recommended)
• Node.js 18+ & npm
• Redis server (for background Celery tasks)
• PostgreSQL database (Optional; SQLite is used by default)

1. Backend API Setup

Navigate to the backend directory, initialize the environment, install dependencies, and start the FastAPI server:

cd backend
# Create virtual environment and install packagesuv venvuv pip install -r requirements.txt
# Start the FastAPI serveruv run uvicorn app.main:app --reload

API Swagger UI documentation will be available at http://localhost:8000/docs.

2. Background Ingestion Worker

Ensure Redis is running, then start the Celery background worker to process ingestion queues:

cd backenduv run celery -A app.celery_app worker --loglevel=info -P solo

3. Frontend Dashboard Setup

Install frontend packages and spin up the Vite development server:

cd frontendnpm installnpm run dev

The web dashboard runs at http://localhost:5173.

4. Chrome Extension Installation

1. Open Chrome and navigate to chrome://extensions/
2. Enable Developer mode (top-right toggle)
3. Click Load unpacked (top-left)
4. Select the extension/ folder from the root of this project.
5. In the Web App, go to Settings -> Copy Extension Token and paste it into the Extension popup to link your session.

5. MCP Server Integration

Claude Desktop

Add the following to your Claude Desktop config (located at %APPDATA%\Claude\claude_desktop_config.json on Windows or ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{  "mcpServers": {    "brain-vault": {      "command": "python",      "args": ["/path/to/memwyre/backend/mcp_server.py"]    }  }}

Cursor / VS Code

Configure your editor's MCP settings to run the server via command line:

python /path/to/memwyre/backend/mcp_server.py

6. Node CLI Setup

Install dependencies and run the command line tool globally:

cd clinpm installnode index.js --help

Environment Configuration

Copy the example environment file in the backend/ directory and configure the variables:

cp backend/.env.example backend/.env

Key environment parameters:

# --- Base Secrets & Database ---SECRET_KEY="your-strong-random-64-character-string"DATABASE_URL="postgresql://postgres:password@localhost/brain-vault"
# --- Redis & Celery ---CELERY_BROKER_URL="redis://localhost:6379/0"REDIS_URL="redis://localhost:6379/0"
# --- Vector Database (Pinecone) ---PINECONE_API_KEY="your-pinecone-api-key"PINECONE_HOST="https://your-pinecone-index-host"PINECONE_SPARSE_HOST="https://your-optional-sparse-index-host"
# --- LLM Providers & Embeddings ---MEMORY_ENGINE_VERSION="v2"  # "v1" for NVIDIA, "v2" for Azure/OpenAIAZURE_OPENAI_API_KEY="your-azure-key"AZURE_OPENAI_ENDPOINT="https://your-resource-name.cognitiveservices.azure.com/"AZURE_OPENAI_DEPLOYMENT="gpt-4o-mini"AZURE_OPENAI_EMBEDDING_DEPLOYMENT="text-embedding-3-small"
# --- V1 Compatibility (NVIDIA NIM) ---EMBEDDING_API_KEY="your-nvidia-embedding-key"LLM_API_KEY="your-nvidia-llm-key"

License

This project is licensed under the Apache License, Version 2.0 (Apache-2.0). See the LICENSE and NOTICE files for details.