Gemdex

io.github.nikships由 GitHub 账号 nikships 发布v1.0.47更新于 Oct 24, 2026

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

已验证STDIO仅桌面知识与记忆开发者工具

概览

AI 生成的概览

Gemdex 是面向 AI 编程代理的持久记忆层,借助向量与关键词检索跨仓库、跨会话保存、召回和更新记忆。

可以做什么
保存带文本和小型文本附件的记忆,返回 id 与标题;可按整体替换或逐条编辑更新记忆,永久删除仅在本地 stdio 端可用。按文本查询召回,先得到标题与 id 的简短列表,再打开完整记忆正文及其时间、附件和效果记录。记录某条记忆是有效、失败还是过时,并可选择按这些结果加权排序。以 UTF-8 或 base64 读回附件内容。从编程代理导入聊天历史生成摘要记忆,并运行整理流程对候选记忆聚类和判定。
适用场景
适合希望助手在会话与仓库之间延续可复用结论、约定或工作流说明、而不必反复交代的场景。最适用于在 Apple Silicon macOS 上运行编程代理并希望记忆留在本机的开发者,或自建共享记忆池的团队。
运行要求
通过 npx 以 stdio 本地进程运行;本地模式需要 Apple Silicon、macOS 14 及以上和原生 arm64 的 Node 24 及以上,安装会下载约 1.3 GB 的托管 Python/MLX 与嵌入模型权重。本地模式需要 GEMINI_API_KEY,除非设为值 local 以使用端侧嵌入。远程模式需要 GEMDEX_REMOTE_URL 和 GEMDEX_REMOTE_TOKEN 令牌。导入聊天历史需要 Claude Code 及已登录账号。
安装前请注意
记忆文本与附件以明文存储,且不做密钥脱敏,不要保存凭据。本地导入与整理会通过你的账号经 Claude Code 推理发送所选对话内容,会计入套餐额度或产生相当于 Haiku 的费用。自建服务器会用其密钥把嵌入内容和会话摘要发送给 Gemini。删除为永久操作且仅在本地 stdio 端提供;自建部署若超出回环地址需配置 HTTPS 等安全措施。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Gemdex,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

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

来源:README.md,提交 366991b

工具

0
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版本历史

1
  1. v1.0.47最新Sep 23, 2026

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