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Giskard Memory

Semantic memory for AI agents with meaning-based recall, ChromaDB storage, and Lightning payments.

Install Giskard Memory

Pick your client, then add it in one click or paste the config.

SSE (Remote)
Server URL
https://your-tunnel.trycloudflare.com/sse

Remote servers are added in the app, not the config file: Customize → Connectors → Add custom connector, then paste this URL.

Overview

Giskard Memory MCP server gives AI agents persistent semantic memory across sessions. It stores text in ChromaDB, uses Sentence Transformers for meaning-based retrieval, and requires Lightning payments through phoenixd before storing or recalling. Agents request an invoice, pay it, then call the relevant memory tool with an agent identity and payment hash. Reach for it when an agent needs cross-session context rather than keyword-only lookup.

Use cases

  • Store agent context between sessions
  • Recall project information by meaning
  • Charge Lightning payments for memory operations
  • Check service health and dependencies

Key features

  • Persistent cross-session agent memory
  • Semantic text retrieval
  • ChromaDB vector storage
  • Lightning invoice flow
  • Agent-identity-based memories
  • Health status reporting

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

4/4 checks healthy over the last 20d

Supply-chain signal

6 high-severity advisories on record for this package. Most advisories affect transitive dependencies and may not be exploitable in this server's actual usage — this is a directional signal, not a security audit.

Critical 0High 6Medium 0Low 6

Scanned 9/10/2026 via OSV.dev · https://your-tunnel.trycloudflare.com/sse (PyPI)

How Giskard Memory works

What Giskard Memory does

The Giskard Memory MCP server provides persistent memory for agents that would otherwise lose their context when a session ends. It accepts text for storage and lets an agent search previously stored information by semantic meaning. Memories are associated with an agent identity, allowing the caller to provide the same identifier when storing and recalling information.

The service uses ChromaDB as its vector database and Sentence Transformers to create semantic embeddings. This supports queries such as asking what an agent knows about a previously discussed project without requiring the query to repeat the original wording.

How it works

Payment is part of each memory operation. Before saving information, the agent requests an invoice for the store action, pays the resulting 5-sat Lightning invoice, and then submits the content, agent ID, and payment hash to store_memory. Recalling follows the same pattern: the agent requests a recall invoice, pays 3 sats, and calls recall_memory with a query, agent ID, and payment hash.

The Giskard Memory MCP server communicates with MCP clients through an HTTP SSE endpoint. The repository example uses a public Cloudflare Tunnel URL that forwards to the locally running service.

Setup and configuration

Run the service locally by cloning the repository, installing its Python dependencies, and starting server.py:

bash
git clone https://github.com/giskard09/giskard-memory
cd giskard-memory
pip install mcp httpx chromadb sentence-transformers python-dotenv
python3 server.py

Create a .env file containing the Phoenix daemon password:

text
PHOENIXD_PASSWORD=your_phoenixd_password

The README starts the service on port 8001 and uses Cloudflare Tunnel to expose it publicly:

bash
cloudflared tunnel --url http://localhost:8001

Add the resulting /sse URL to the MCP client's server configuration.

Tools and capabilities

The Giskard Memory MCP server exposes these documented tools:

  • store_memory: Saves text as a memory for an agent identity.
  • recall_memory: Retrieves stored memories using a meaning-based query.
  • get_invoice: Creates a Lightning invoice for either storing or recalling.
  • get_status: Reports the service name, version, port, uptime, health status, and dependencies.

The documented prices are 5 sats for storing and 3 sats for recalling. The stack combines MCP, ChromaDB, Sentence Transformers, phoenixd, and Cloudflare Tunnel.

Limitations and notes

Public access also depends on separately running Cloudflare Tunnel. Memory retrieval is semantic rather than exact keyword matching, and every documented store or recall operation requires payment first.

Reviews

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Frequently Asked Questions about Giskard Memory

Giskard Memory is an MCP server that connects AI agents to ChromaDB-based semantic storage and phoenixd for Lightning payments. Its main tools are store_memory for saving text, recall_memory for meaning-based retrieval, get_invoice for payment invoices, and get_status for service health information across agent sessions.

Install it by cloning the repository, installing the listed Python packages, creating a .env file with PHOENIXD_PASSWORD, and running python3 server.py. The documented setup then exposes the local port 8001 through Cloudflare Tunnel and places the resulting /sse URL in the MCP client configuration.

Storing a memory costs 5 sats and recalling one costs 3 sats. The agent first calls get_invoice for the desired action, pays the returned Lightning invoice, and then submits the payment hash to store_memory or recall_memory.

Semantic recall searches by meaning instead of requiring exact keywords. Sentence Transformers generate embeddings for the stored text and query, while ChromaDB provides the vector storage used to retrieve relevant memories.

It documents a generic MCP SSE configuration using a URL ending in /sse, so client compatibility depends on whether the client supports that MCP connection format.

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