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+---
+layout: integration
+name: Fidelis Memory
+description: Local-first, zero-LLM agent memory for Haystack agents, with dense-vector and optional BM25 plus reciprocal-rank-fusion retrieval through a Model Context Protocol server.
+authors:
+    - name: Hermes Labs
+      socials:
+        github: hermes-labs-ai
+pypi: https://pypi.org/project/fidelis-memory/
+repo: https://github.com/hermes-labs-ai/fidelis
+type: Custom Component
+report_issue: https://github.com/hermes-labs-ai/fidelis/issues
+version: Haystack 2.0
+toc: true
+mcp: true
+---
+### **Table of Contents**
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+
+[Fidelis](https://github.com/hermes-labs-ai/fidelis) is a local-first memory and retrieval service for AI agents. It stores notes and session context locally (`~/.cogito/`) and supports dense-vector retrieval plus optional BM25 and reciprocal-rank-fusion (RRF) scoring. The default retrieval path makes no LLM call. Its MCP recall and query tools return evidence excerpts from the stored passages rather than generated summaries.
+
+Fidelis ships as an MCP server (`fidelis mcp serve`, stdio transport) and is already published on the [official MCP Registry](https://registry.modelcontextprotocol.io/v0.1/servers/io.github.hermes-labs-ai%2Ffidelis-memory/versions/0.1.0) as `io.github.hermes-labs-ai/fidelis-memory`. A Haystack agent can connect to it the same way it connects to any other MCP server, using Haystack's own `mcp-haystack` integration (`MCPTool`/`MCPToolset` with `StdioServerInfo`), to give the agent a private, locally stored memory backend instead of a hosted memory platform.
+
+On a checked-in 470-question LongMemEval-S retrieval run, Fidelis measured 83.2% R@1.
+
+## Installation
+
+```bash
+pip install "fidelis-memory[hybrid]==0.1.0" mcp-haystack
+fidelis init
+fidelis health
+```
+
+`fidelis init` configures and starts the local `fidelis-server` used by the MCP bridge. Follow the [Fidelis quickstart](https://github.com/hermes-labs-ai/fidelis#quickstart) first to install its local Ollama embedding prerequisite.
+
+## Usage
+
+```python
+from haystack_integrations.tools.mcp import MCPTool, StdioServerInfo
+
+server_info = StdioServerInfo(
+    command="fidelis",
+    args=["mcp", "serve"],
+)
+tool = MCPTool(name="fidelis_recall", server_info=server_info)
+
+# Use directly, or add `tool` to a Haystack Agent's tools list
+result = tool.invoke(query="what did we decide about the retrieval backend?")
+```
+
+See the [Fidelis README](https://github.com/hermes-labs-ai/fidelis#readme) for the full MCP tool surface and the [user-fit matrix](https://github.com/hermes-labs-ai/fidelis/blob/main/docs/user-fit.md) for supported workflows and prerequisites.
+
+## License
+
+MIT — see the [Fidelis repository](https://github.com/hermes-labs-ai/fidelis/blob/main/LICENSE).