How do I install Waggle?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Waggle need an API key?

The publisher lists these environment variables: WAGGLE_TRANSPORT, WAGGLE_BACKEND, WAGGLE_DB_PATH, WAGGLE_DEFAULT_TENANT_ID, WAGGLE_MODEL.

What transport does Waggle use?

Waggle runs locally over stdio.

When was Waggle last checked?

Last checked 2026-11-08. Persistent graph-backed conversational memory for AI agents.

Waggle MCP server

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx waggle-mcp==0.1.8

Credentials from the publisher

WAGGLE_TRANSPORT — Transport mode for the MCP server.
WAGGLE_BACKEND — Backend database type: sqlite for local use or neo4j for service deployments.
WAGGLE_DB_PATH — Path to the SQLite memory database when WAGGLE_BACKEND is sqlite.
WAGGLE_DEFAULT_TENANT_ID — Default tenant ID for local or shared memory isolation.
WAGGLE_MODEL — Sentence-transformers model used for local embeddings.

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1WAGGLE_TRANSPORT=<value> WAGGLE_BACKEND=<value> WAGGLE_DB_PATH=<value> WAGGLE_DEFAULT_TENANT_ID=<value> WAGGLE_MODEL=<value> claude mcp add io.github.abhigyan-shekhar-waggle-mcp -- uvx waggle-mcp==0.1.8

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "io.github.abhigyan-shekhar-waggle-mcp": {4      "command": "uvx",5      "args": [6        "waggle-mcp==0.1.8"7      ],8      "env": {9        "WAGGLE_TRANSPORT": "<value>",10        "WAGGLE_BACKEND": "<value>",11        "WAGGLE_DB_PATH": "<value>",12        "WAGGLE_DEFAULT_TENANT_ID": "<value>",13        "WAGGLE_MODEL": "<value>"14      }15    }16  }17}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Version

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

Keep the context, not just the conversation

Waggle is an open-source, local-first memory layer for AI agents. It stores project knowledge as a graph: what you decided, why it matters, what it depends on, and what has changed. Your next conversation can pick up from that context instead of starting over.

Use Waggle with your MCP client, inspect and edit memory in Graph Studio, or bring a portable memory graph into the browser workspace.

Continue across sessions. Recall project decisions, requirements, preferences, and open questions without repeatedly pasting context.
Understand the reasoning. Explore relationships, supporting evidence, contradictions, and the history behind a decision.
Keep control of corrections. In the WebMCP workspace, agents propose changes; humans approve the exact content before it becomes authoritative.
Take memory with you. Export and import .abhi files across supported workflows without tying your graph to one client.
Start locally. The default SQLite store lives on your machine. Local use does not require a Waggle account or an external database.

Quick Start

The Python package requires Python 3.11+ and pipx. On macOS, you can install pipx with brew install pipx; see the installation guide for client-specific options.

pipx install waggle-mcp
pipx ensurepath

Restart your terminal after the first pipx ensurepath, then run:

waggle-mcp setup --yes
waggle-mcp doctor

Restart your MCP client to load Waggle. Setup detects supported clients and writes their configuration; automatic memory behavior uses the client's installed hooks, skills, or project instructions.

To check continuity, ask your agent to remember a project decision, then open a fresh session in the same project and ask what was decided. Keep the same project identifier across sessions.

Choose your client

Client	Setup guide
Codex	Install the Waggle plugin or configure the MCP server
Claude Code	MCP server and automatic memory hooks
Claude Desktop	Desktop extension and manual configuration
VS Code	Waggle extension and workspace setup
Cursor	Connect the local MCP server
Antigravity	Client configuration
Other MCP clients	Standard MCP configuration
ChatGPT with Site tools	Use the browser workspace

For clients that accept an mcpServers configuration:

{
  "mcpServers": {
    "waggle": {
      "command": "waggle-mcp",
      "args": ["serve", "--transport", "stdio"]
    }
  }
}

If the command is not found, run pipx ensurepath and reopen your terminal. Use the troubleshooting guide for installation, startup, and client connection issues.

How memory works

Waggle separates durable project knowledge from the model's context window. An agent retrieves relevant memory when needed and records meaningful outcomes for later conversations.

Capture: record decisions, constraints, preferences, and supporting context.
Connect: link related memories and preserve contradictions and updates.
Recall: retrieve scoped context with evidence and provenance.
Continue: use that context in another session or supported client.

The core MCP workflow uses prime_context to load project context, query_graph to retrieve relevant history, and observe_conversation to record durable outcomes. build_context assembles a compact context pack for a specific task. Tool availability alone does not make an agent use memory automatically; its hooks, skills, or instructions must call these tools.

See the tool reference and configuration reference for the full API and retrieval settings.

Graph Studio

Graph Studio makes project memory inspectable and editable. Browse nodes and relationships, add or remove graph content, inspect source evidence, and review how a memory changed over time.

The browser workspace provides focused views for project context, memories, proposals, and activity. Graph Studio provides the graph-level view of that server-backed memory; private browser imports remain in the workspace tab.

Open Graph Studio

WebMCP — memory in your browser

The Waggle workspace lets a human and a compatible browser agent work with the same project memory. Its WebMCP adapter registers page-level Site tools through document.modelContext.registerTool. This is separate from installing Waggle's MCP server or configuring a remote MCP connector.

Connect and use the workspace

Open the workspace in ChatGPT's built-in browser using a model and account configuration that supports Site tools.
In the address bar, check Site tools → Available site tools and confirm the five Waggle tools are available. Keep this workspace tab open while working with its memory.
Explore the sample project or select Load private .abhi to work with your own graph.
Ask for a project brief or recall a specific decision. On the hosted workspace, use project_id: waggle-webmcp, including after importing your own graph.
To correct a memory, ask the agent to propose a replacement. Review it in Proposals, edit it if needed, and approve the exact value.
Ask the agent to apply the approved proposal using only its actual proposal ID. Recall the decision again to confirm the result and inspect its history.

For example:

Call Waggle's get_project_brief with project_id "waggle-webmcp".
Use the returned memories to catch me up on this project.

If Site tools are unavailable, check the browser's permissions and configuration. If the browser blocks an apply call, you can use Apply approved change on the approved proposal and confirm the action yourself. This uses the same approval and freshness checks; it does not bypass browser safeguards.

Browser tools

Tool	What it does
`get_project_brief`	Returns the project's goal, current decisions, constraints, state, and open questions.
`recall_memory`	Finds current authoritative memories for a query, with supersession provenance when available.
`propose_memory_change`	Creates a pending correction for human review without changing authoritative memory.
`apply_approved_memory_change`	Applies the exact approved value using only a proposal ID.
`load_abhi_for_session`	Loads a portable graph into this browser tab and returns a brief.

Corrections you can review

flowchart LR
    A[Agent proposes a correction] --> B[Human reviews and approves]
    B --> C[Waggle checks approval and target version]
    C --> D[Approved value becomes authoritative]
    D --> E[Previous memory stays in history]

Approved content cannot be changed by the applying agent. If the target memory has changed since the proposal was created, the proposal is marked stale instead of overwriting newer information. Application preserves the previous memory and links it to the replacement with an updates edge.

These approval rules govern the WebMCP correction workflow. They are not a claim that every direct graph-editing or local MCP operation requires approval.

Bring your own memory

A .abhi file is Waggle's portable memory graph. The browser importer reads existing memories; it does not ingest a source repository to invent a project brief.

Select Load private .abhi in the workspace.
Choose an unencrypted schema 2.x file, up to 700 KiB compressed and 4 MiB expanded.
Confirm the Private session graph indicator appears. Briefs, recall, proposals, and approvals now use that imported copy.

The import replaces this tab's active workspace; it does not merge with the sample graph, modify your original file, or affect another visitor.

You can also attach the file to a compatible chat and ask:

Use Waggle's load_abhi_for_session tool to load this .abhi file into
project "waggle-webmcp" for this session. Then call get_project_brief.

The agent must be able to read the attachment and provide its bytes as base64. The tool does not accept a local file path or download URL. Use the page's file picker if the chat cannot access the attachment.

See the portable memory format for archive structure and supported operations.

Your data and deployment options

Where you use Waggle	Where memory lives
Local MCP server	SQLite on your machine, at `~/.waggle/waggle.db` by default.
Private browser import	This tab's `sessionStorage`; the importer does not upload the graph to Waggle's backend.
Hosted sample workspace	An isolated, temporary server-side sample project; not an account-based cloud backup.
Self-hosted remote MCP server	Your configured Neo4j backend and infrastructure.

Browser lifetime. Private imports survive page reloads, and browser session restoration can preserve them. Use the workspace's Reset Demo control to explicitly clear the private copy and return to the sample project. Closing a chat conversation alone is not a guaranteed deletion signal. Browser storage is not encrypted by the importer.

AI services. Attaching a file in chat shares it with that chat provider. Memory returned through tool calls is also shared with the requesting AI service. Local storage does not mean that model inputs stay on your device.

Hosted preview. The public workspace is for trying the browser experience, not durable production storage. Its free hosting can pause when idle, take time to wake, and lose sample state on restart or redeployment. Opening it does not connect to your local Waggle database. The included SQLite preview configuration stores disposable state under /tmp, caps admission at 128 sessions per database, and rejects new sessions at capacity without evicting existing ones.

Self-hosting. The browser preview's governance backend currently requires SQLite. Remote MCP hosting with Neo4j is a separate configuration, not a drop-in replacement for that browser workflow. For remote deployment, configure HTTPS, authentication, persistent storage, and backups. Read the production deployment guide, security model, and hardening checklist before exposing a server publicly.

Development

Waggle uses Python for the memory engine and MCP server, and React/Vite for Graph Studio and the browser workspace.

git clone https://github.com/Abhigyan-Shekhar/Waggle-mcp.git
cd Waggle-mcp
python -m venv .venv
source .venv/bin/activate
# Windows PowerShell: .venv\Scripts\Activate.ps1
python -m pip install -e ".[dev]"

WAGGLE_MODEL=deterministic pytest -q
ruff check src/ tests/
ruff format --check src/ tests/

On PowerShell, set $env:WAGGLE_MODEL="deterministic" before running pytest -q. Deterministic embeddings keep tests offline; use the normal embedding model when evaluating semantic retrieval.

For frontend changes:

npm ci --prefix apps/mcp/graph-ui
npm run test:unit --prefix apps/mcp/graph-ui
npm run build --prefix apps/mcp/graph-ui

Documentation and contributing

License

Waggle is open source under the Apache License 2.0.