flashback-memory

flashback-memory

Enables conversation memory for LLMs by storing chat history and retrieving relevant memories via embedding-based semantic search, supporting tools like store_turn and flashback_memory.

Category
Visit Server

README

Flashback Memory

Conversation memory system untuk LLM — simpan histori chat, lalu saat lo tanya, sistem cari memory yang relevan lewat embedding + semantic search (bukan cuma keyword). Dibangun sebagai MCP server supaya bisa dipasang ke klien mana pun (LibreChat, Claude Desktop, dll).

Cara kerja

CHAT ─▶ store_turn (user + AI)
          └─ HotBuffer akumulasi token
               └─ [OFFLOAD] kalau lewat hot window:
                    ├─ [SATPPAM]  LLM (NVIDIA) baca chunk → judul/topik/kategori
                    ├─ [EMBED]    NVIDIA nemotron → vektor 2048-dim
                    └─ [SAVE]     simpan block + vektor ke LanceDB

CHAT ─▶ flashback_memory (query)
          ├─ [EMBED]   query → vektor
          ├─ [SEARCH]  LanceDB cari kandidat mirip
          └─ [RESULT]  balikin memory / klarifikasi kalau ambigu

Setup

1. Prerequisites

  • Python 3.13 (disarankan di proot-distro Debian, karena mcp/lancedb butuh glibc — native Termux Python 3.14 gak kompatibel)
  • venv dengan dependency (lihat requirements.txt)
python3 -m venv /root/.venv_fbn_deb
. /root/.venv_fbn_deb/bin/activate
pip install -r requirements.txt

2. Config & API key

Copy .env.example.env, isi NVIDIA_API_KEY lo:

cp .env.example .env
nano .env   # isi: NVIDIA_API_KEY=***

config.yaml sudah siap pakai. Edit kalau perlu (embedding.dim, hot_window_tokens, dsb).

3. Jalankan

SSE mode (untuk LibreChat / remote client):

bash scripts/start.sh

Server jalan di http://localhost:8001/sse. Log di /root/flashback_sse.log.

Stdio mode (untuk MCP client lokal):

bash scripts/start_stdio.sh

4. Monitoring

Lihat apa yang terjadi di dalam MCP (SATPPAM / EMBED / SEARCH / offload):

bash scripts/logs.sh          # tail -f log
bash scripts/status.sh        # cek server jalan atau tidak

Pasang ke LibreChat

Di librechat.yaml:

mcpServers:
  FlashbackMemory:
    type: sse
    url: http://<IP-DEBIAN-PROOT>:8001/sse

mcpSettings:
  allowedAddresses:
    - <IP-DEBIAN-PROOT>:8001
  allowedDomains: []

⚠️ Pakai IP private (bukan 127.0.0.1) karena LibreChat jalan di container proot berbeda. Cek IP dengan hostname -I di Debian proot.

Tools

  • store_turn(user_msg, model_output, conv_id, conv_key) — simpan 1 turn
  • flashback_memory(query, conv_id, conv_key) — cari memory relevan

Struktur

flashback-memory/
├── README.md
├── .env.example
├── .gitignore
├── config.yaml
├── requirements.txt
├── server.py              # MCP stdio
├── server_sse.py          # MCP SSE (LibreChat)
├── core/                  # store, chunker, retriever, ambiguity
├── models/                # OpenAI-compatible client
├── prompts/               # SATPPAM prompt
├── scripts/               # start / logs / status
└── tests/                 # unit + e2e

Env vars

Var Default Keterangan
NVIDIA_API_KEY (dari .env) API key NVIDIA (embedding + SATPPAM)
FLASHBACK_PORT 8001 Port SSE server

Catatan

  • DB (/root/flashback_memory_db) dan .env gak di-commit (lihat .gitignore).
  • Embedding: nvidia/nemotron-3-embed-1b (dim 2048).

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured