Gemdex

io.github.nikships由 GitHub 帳號 nikships 發布v1.0.47更新於 Oct 24, 2026

Persistent memory layer for AI coding agents: save, recall, update via Gemini plus LanceDB

已驗證STDIO僅桌面知識與記憶開發者工具

概覽

AI 產生的概覽

Gemdex 是給 AI 程式開發代理用的持久記憶層,透過向量與關鍵字檢索跨儲存庫、跨工作階段保存、召回與更新記憶。

可以做什麼
儲存含文字與小型文字附件的記憶,回傳 id 與標題;可整段替換或逐條編輯更新記憶,永久刪除僅在本地 stdio 介面提供。以文字查詢召回,先取得標題與 id 的簡短清單,再開啟完整記憶內容及其時間、附件與成效紀錄。記錄某條記憶是有效、失敗或過時,並可選擇依這些結果加權排序。以 UTF-8 或 base64 讀回附件內容。從程式開發代理匯入對話歷史產生摘要記憶,並執行整理流程對候選記憶分群與判定。
適用情境
適合希望助理在工作階段與儲存庫之間延續可重用結論、慣例或工作流程說明、不必反覆交代的情境。最適用於在 Apple Silicon macOS 上執行程式開發代理並希望記憶留在本機的開發者,或自架共用記憶池的團隊。
執行需求
透過 npx 以 stdio 本地程序執行;本地模式需要 Apple Silicon、macOS 14 以上與原生 arm64 的 Node 24 以上,安裝會下載約 1.3 GB 的託管 Python/MLX 與嵌入模型權重。本地模式需要 GEMINI_API_KEY,除非設為值 local 以使用裝置端嵌入。遠端模式需要 GEMDEX_REMOTE_URL 與 GEMDEX_REMOTE_TOKEN 權杖。匯入對話歷史需要 Claude Code 及已登入帳號。
安裝前請注意
記憶文字與附件以明文儲存,且不做機密資訊遮蔽,不要儲存憑證。本地匯入與整理會透過你的帳號經 Claude Code 推論傳送所選對話內容,會計入方案額度或產生相當於 Haiku 的費用。自架伺服器會用其金鑰把嵌入內容與工作階段摘要傳送給 Gemini。刪除為永久操作且僅在本地 stdio 介面提供;自架部署若超出回環位址需設定 HTTPS 等安全措施。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Gemdex,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Save a useful memory once and recall it across repos and sessions. Gemdex indexes small chunks for precise hybrid semantic + BM25 matching, then resolves matches to whole parent memories. MCP agents scan a cheap title index and open only the memories they need.

Two ways to run it

LocalSelf-hosted
Agent connectionnpx gemdex-mcp, stdioStreamable HTTP /mcp
EmbeddingsOn-device EmbeddingGemma 2 via MLXServer-owned Gemini, including multimodal
StorageLanceDB and blobs under ~/.gemdexPostgres/pgvector and file/S3 blobs
PlatformApple Silicon, macOS 14+, native arm64 Node ≥24Docker host
Human managementNative macOS app via localhost sidecarWeb manager
PoolOne per machineShared across machines
MCP toolsSeven, including deleteSix, without delete

These are separate pools. The npx package and desktop sidecar are local-only; connect directly to HTTP MCP for a self-hosted pool.

Local quickstart

bash
npx gemdex-mcp installclaude mcp add gemdex -- npx -y gemdex-mcp@latest

The explicit install downloads managed Python/MLX and pinned mlx-community/embeddinggemma-2-8bit weights, about 1.45 GB to download and 1.9 GB on disk. No Python, uv, Homebrew, HF CLI, or compiler setup is needed. Embeddings run offline after installation. There is no API key, sentinel value, or provider switch. Rosetta Node is unsupported. See MLX model and runtime requirements.

All seven tools remain discoverable before installation and return setup guidance. Retry a tool after installation, or reconnect your MCP client.

For another MCP client:

json
{  "mcpServers": {    "gemdex": {      "command": "npx",      "args": ["-y", "gemdex-mcp@latest"]    }  }}

Save and recall

Ask your agent to save something durable:

text
Save how we set up the review workflow to memory.

Then, in another session or repo using that pool:

text
Check memory for our review workflow and use it here.

Tell your agent when to use memory in its instructions:

markdown
## Memory
Use Gemdex recall to find relevant memories before starting work.Save reusable findings and update existing memories when correcting them.Report worked/failed/stale outcomes after acting on a fetched memory.Use read_attachment for transcript bytes.Delete only when a memory should be permanently removed and the connectedsurface provides delete_memory.

MCP tools

ToolBehavior
save_memorySave content and/or attachments (images, audio, video, text files); return id and title
recallText query and/or example media files, top 10 titles + ids, never full bodies
get_memoryFull parent body, age, attachments and track record
update_memoryFull content replacement or literal edits, optional title/attachments
report_outcomeRecord worked, failed, or stale, with optional note
read_attachmentAttachment as UTF-8 text, a viewable image, or base64
delete_memoryStdio only, permanent delete and client-stat cleanup

get_memory, not title-index recall, increments recall counts. Stats live in a separate ledger (~/.gemdex/stats.json by default). GEMDEX_TRUST_RANKING=true enables outcome-weighted title ranking; otherwise ranking is relevance-only.

Local saves include an advisory when existing memories are similar, using already-computed vectors and centroid similarity (default threshold 0.93). The advisory does not block the save. Configure it with GEMDEX_SIMILAR_ON_SAVE and GEMDEX_SIMILAR_THRESHOLD.

Attachments

Local memories can carry images (PNG, JPEG, WebP, GIF), audio (WAV, MP3, M4A, FLAC, AAC, AIFF; up to 5 minutes) and video (MP4, MOV, M4V, WebM). EmbeddingGemma 2 embeds each file on-device into the same space as text, so the memory is found by what the image shows or the recording says. Recall also takes example files: "is there anything in memory about this screenshot?" works with or without a text query. Captions are optional; they add keyword matching and help most for audio. Text files (.txt, .json, .jsonl/.ndjson) are stored as non-embedded blobs. Per memory: 8 images, 4 audio files, 2 videos and 4 text files, 100 MiB each. PDFs are not supported locally. Use a local path or inline base64 data + mimeType; every attachment is readable through read_attachment.

The self-hosted server supports Gemini multimodal embedding and media recall through /v1. HTTP MCP attachment inputs are inline base64 only, never client filesystem paths. See the /v1 contract.

Chat history and memory hygiene

Local digestion and hygiene use your existing Claude Code login. Install Claude Code and run claude auth login, then:

bash
npx gemdex-mcp statusnpx gemdex-mcp ingest-history --source claude --dry-runnpx gemdex-mcp ingest-history --source claude

Sources include Claude Code, Factory CLI, Codex, Antigravity, or a folder path. Only never-before-ingested sessions are processed. Each produces a digest memory and a cleaned transcript attachment. backfill-transcripts attaches files referenced by older digest footers, with --dry-run and --force options; missing files are skipped with a message.

Inference runs as isolated claude -p --model haiku structured-JSON calls: no tools, settings, skills, MCP servers, hooks, CLAUDE.md, or session persistence, and a temporary working directory. Concurrency is four, with at most three total attempts and a five-minute timeout per call.

Cost estimates are Haiku API list-price equivalents ($1 input / $5 output per million tokens). With a Claude subscription, usage counts against plan limits, not a per-token bill. Local embeddings stay on-device, but ingestion transcripts and hygiene candidates go through Claude Code inference.

Hygiene first clusters local vectors, then judges candidates with Claude Code. Deleting findings requires human approval. Self-hosted uploads instead use the server's Gemini digestion. See chat-history paths.

The desktop app

The native macOS app manages the local pool: browse, search (by text or by file), and edit memories, attach and preview images, audio and video, ingest chat history, and review hygiene findings. It talks to gemdex serve, which binds only 127.0.0.1 and uses a per-launch token. Its contract exposes explicit MLX install/migration jobs and Claude Code readiness. Memory routes return 503 {needsInstall:true} until installation; ingestion/hygiene start requires Claude Code readiness. See the sidecar contract.

The web manager is the primary human surface for self-hosted deployments. The desktop app is not a client for that pool.

Self-host the whole stack (one command)

bash
curl -fsSL https://raw.githubusercontent.com/nikships/gemdex/main/scripts/install.sh | bash

The installer starts Postgres, gemdex-server, HTTP MCP and the web manager, generates secrets, waits for migrations, verifies a real save and recall, and prints Streamable HTTP client configuration. It asks for a Google AI Studio key, or reads GEMINI_API_KEY. This key belongs to the server, not the agent client.

The default is loopback-only. --lan exposes MCP and web to your trusted LAN with static MCP auth and login-free web access. For public access, use HTTPS, Google OAuth, and the deployment guide. Never publish Postgres or /v1.

json
{  "mcpServers": {    "gemdex": {      "type": "http",      "url": "https://gemdex.example.com/mcp"    }  }}

This example targets a Google OAuth deployment; the client handles login.

GuidePurpose
Self-host deployCompose, Google OAuth, HTTPS edge and exposure checks
OperationsStorage, backup/restore, upgrades and troubleshooting
Go furtherDNS, managed platforms, sizing and costs
SecurityEnforced boundaries and pre-launch checklist
Chat historyLocal ingestion, web upload and OAuth record import

Packages and library use

PackageResponsibility
coreMemoryStore, embedding providers, shared HTTP router and inference
mcpLocal stdio tools, CLI and desktop sidecar
serverBYOI /v1, Postgres/pgvector, Gemini and file/S3 blobs
mcp-httpSix-tool Streamable HTTP agent surface
webBrowser manager and session-upload BFF
appNative local manager

To use the engine directly, see the core library example. Local environment settings are in the MCP README; self-hosted settings are in the server README.

Privacy & safety

Memory text and attachment bytes are plaintext unless you add your own storage controls. Gemdex provides no secret redaction or hosted custody service. Local embeddings run on-device; local ingestion and hygiene send selected content through Claude Code using your account. The self-hosted server sends embedding payloads and uploaded-session digestion to Gemini using its key. See custody and self-host security.

Build from source

From a clone with Node ≥24 and pnpm ≥10:

bash
pnpm installpnpm build

The local MCP entry point is packages/mcp/dist/index.js. See CONTRIBUTING.md for checks and development guidance.

License

MIT. See LICENSE.

MCP Registry

mcp-name: io.github.nikships/gemdex

來源:README.md,提交 366991b

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版本歷史

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  1. v1.0.47最新Sep 23, 2026

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