
Gemdex
io.github.nikshipsPublished by GitHub account nikshipsv1.0.47Updated Oct 24, 2026
Persistent memory layer for AI coding agents: save, recall, update via Gemini plus LanceDB
Overview
Gemdex is a persistent memory layer for AI coding agents: it saves, recalls and updates memories across repos and sessions using embeddings plus keyword search.
- What you can do
- Save memories with text and small text attachments, returning an id and title, and update them by full replacement or literal edits, or delete them permanently on the local stdio surface only. Recall by text query to get a short list of titles and ids, then open a full memory body with its age, attachments and track record. Record whether a recalled memory worked, failed or became stale, and optionally weight ranking by those outcomes. Read attachment bytes back as UTF-8 or base64. Ingest chat history from coding agents into digest memories, and run hygiene passes that cluster and judge candidates for cleanup.
- When to use it
- Useful when you want an assistant to carry reusable findings, conventions or workflow notes between sessions and repositories instead of re-explaining them. Best suited to developers running coding agents on Apple Silicon macOS who want memory kept on-device, or teams self-hosting a shared pool.
- Requirements
- Runs locally via npx as a stdio process; local mode needs Apple Silicon, macOS 14+ and native arm64 Node 24 or newer, and the install downloads about 1.3 GB of managed Python/MLX and embedding weights. GEMINI_API_KEY is required in local mode unless set to the value 'local' for on-device embeddings. Remote mode needs GEMDEX_REMOTE_URL and the GEMDEX_REMOTE_TOKEN bearer token. Chat-history ingestion needs Claude Code and a logged-in account.
Installation
In SourceWeft
- Open Gemdex in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
Persistent memory for AI coding agents
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
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
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:
Save and recall
Ask your agent to save something durable:
Then, in another session or repo using that pool:
Tell your agent when to use memory in its instructions:
MCP tools
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:
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)
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.
This example targets a Google OAuth deployment; the client handles login.
Packages and library use
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:
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
Source: README.md at commit 366991b
Tools
0Version history
1- v1.0.47LatestSep 23, 2026
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