Wuniq

Wuniq is a knowledge engine for AI-assisted work. It stores decisions, constraints, intent, and human reasoning in.wuniqfiles beside your project files, so any MCP-compatible AI can read and maintain the knowledge exactly where the work lives.

Wuniq is local-first: no account, no cloud storage, and no telemetry.

The launcher has zero dependencies. It connects your MCP client to the MCP server included with the installed Wuniq desktop app:

MCP-compatible AI or IDE | v npx @wuniq/mcp | v Installed Wuniq desktop app | v Local projects and .wuniq knowledge

OpenCode uses a project-levelopencode.jsonfile. Add Wuniq under itsmcpkey, keeping any providers, permissions, or other MCP servers already present:

The repository root is also a Claude plugin. It does not contain a second MCP server:.mcp.jsonstarts the published@wuniq/mcplauncher, which connects Claude to the MCP server inside the installed Wuniq app.

The plugin includes one compact skill that teaches Claude the safe Wuniq workflow: discover visible projects first, read their context before working, preserve confirmed decisions, and synchronize after changes. It does not pre-approve tools or elevate permissions.

Before marketplace submission, the plugin can be checked and loaded locally with Claude Code:

claude plugin validate --strict . claude --plugin-dir .

The same repository is also a Codex plugin. Its.codex-plugin/plugin.jsonreuses the same skill and.mcp.json; it starts@wuniq/mcplocally and does not create a hosted service or upload project data.

Add the public Wuniq marketplace from Codex:

codex plugin marketplace add Wuniq/wuniq-mcp --ref main

Then open thePluginsdirectory in the ChatGPT desktop app, select theWuniqmarketplace, and installWuniq. Start a new Codex session after installation so the skill and MCP server are loaded.

ChatGPT on the web cannot run this localstdioMCP server. Wuniq's full integration is therefore for local Codex clients: the ChatGPT desktop app, Codex CLI, and the Codex IDE extension.

The repository root is a native Antigravity plugin. Itsmcp_config.jsonstarts the published@wuniq/mcplauncher, whileskills/wuniq/SKILL.mdsupplies the safe Wuniq workflow. It does not contain a second MCP server or upload project data.

Install it in Antigravity CLI from the public repository:

agy plugin install https://github.com/Wuniq/wuniq-mcp

In Antigravity 2.0, Wuniq can also be configured as a custom MCP server with the samenpx -y @wuniq/mcpcommand while its MCP Store listing is under review.

The repository root is also a Gemini CLI extension. Itsgemini-extension.jsonstarts the same published@wuniq/mcplauncher, whileskills/wuniq/SKILL.mdsupplies the safe Wuniq workflow as an Agent Skill. It does not contain a second MCP server or upload project data.

Install it directly from the public repository:

gemini extensions install https://github.com/Wuniq/wuniq-mcp

Restart Gemini CLI after installation so the extension and its MCP tools are loaded.

Google has transitioned personal, Pro, and Ultra users from Gemini CLI to Antigravity CLI. The Gemini CLI extension remains available for supported Enterprise, Google Cloud, and API-based environments.

- "Open the Wuniq projects visible to you and summarize the important context for this task."
- "Before changing the authentication module, read its Wuniq context and preserve the constraints already decided."
- "Record the decision we just made, implement the change, and run Wuniq sync when you finish."

The launcher passes the MCP client's standard input and output directly to the MCP server included with the installed app. If Wuniq is not installed, it exposes only a minimal MCP response explaining how to obtain the official app.

It does not configure IDEs, contain Wuniq product logic, upload project data, or provide a second Knowledge Engine.

Local-first agent memory: a plain-Markdown Obsidian vault is the source of truth, with a rebuildable DuckDB index for hybrid BM25 + vector + graph recall.

Persistent memory and semantic search for AI coding assistants across sessions

Give your agent a memory: shared, cited, tenant-isolated knowledge-graph memory for any MCP host. Grounded answers from a local-first June endpoint — abstains rather than guesses.

Decentralized persistent memory for AI agents — encrypted vault storage built on Walrus and Sui.

Persistent memory for AI assistants and coding agents across ChatGPT, Claude, Cursor, and other MCP-compatible tools.

Your portable AI memory vault — memories, skills & configs, shared across every AI tool.

Local Work Model for AI agents that learns from real outcomes.

Adaptive MCP memory system for AI applications. Learns which retrieval strategies work for your data, scores results using cognitive science models, builds a knowledge graph automatically, and validates every parameter change against real query history before adopting it. Patent pending.

Auditable, self-improving knowledge & memory for AI agents over MCP — citation-enforced answers and a replayable why-trace, self-hosted on Postgres.