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Show HN: MCP Memory – Fast Agent Memory Using Google's OKF and SQLite FTS5

MCP-Memory, a Model Context Protocol server, provides AI agents with persistent memory using Google's Open Knowledge Format v0.2 and SQLite FTS5, enabling sub-20ms key lookups and full-text search. The server exposes four MCP tools for storing, retrieving, and searching memory records, and supports namespace isolation and automatic setup for tools like Claude Desktop, Cursor, and Codex.

read4 min views1 publishedAug 13, 2026
Show HN: MCP Memory – Fast Agent Memory Using Google's OKF and SQLite FTS5
Image: source

MCP-Memory is a Model Context Protocol (MCP) server that equips AI agents (such as Claude Desktop, Cursor, Antigravity, Windsurf, or Codex) with persistent, long-term memory capabilities.

Memory records are formatted using the Open Knowledge Format (OKF v0.2) standard and indexed with a local

SQLite instance (supporting FTS5 full-text search) for fast key-value lookups, tag filtering, and content search.

Fast Track:[Jump directly to Quick Start]

Persistent State Across Sessions: Enables AI agents to read, store, search, and delete stateful memory snippets that persist across chat turns and sessions.OKF Standard Compliance: Stores every memory item formatted as an OKF v0.2 Markdown document with YAML frontmatter (type

,key

,namespace

,tags

,generated

,sources

,verified

,status

,stale_after

), adhering strictly toandSPEC.md

.OKF_RULES.md

**Dual-Layer Architecture:**Human-Browseable OKF Directory: Automatically dumps and syncs every memory to disk as a raw.md

file inside thememory/

bundle directory with hierarchicalindex.md

progressive disclosure files (rootindex.md

versioned withokf_version: "0.2"

) andlog.md

update history tracking.High-Performance SQLite Indexing: SQLite FTS5 (Full-Text Search) and automatic triggers for sub-20ms key lookups and instant keyword searches.

Namespace Isolation: Supports contextual separation (e.g.user/preferences

,project/architecture

,default

).Zero Boilerplate Setup: Quick setup wizard (python3 setup.py

) auto-configures installed MCP tools (Antigravity, Claude, Cursor, Windsurf, Codex).

The server exposes four primary MCP tools to interacting agents:

Stores or updates a memory record in OKF v0.2 format.

Parameters:key

(string, required): Unique identifier or path for the memory (e.g.user/preferences/coding_style

orproject/architecture

).content

(string or object, required): Core information to store.project_root

(string, required): Absolute path to the active project root directory (e.g./Users/user/Projects/my-app

).tags

(array of strings, optional): Classification tags for filtering.namespace

(string, optional, default:: Scope/namespace."default"

)concept_type

(string, optional, default:: OKF concept type (e.g."Agent Memory"

)Metric

,Playbook

,Attested Computation

).title

(string, optional): Display name.description

(string, optional): One-line summary.resource

(string, optional): Canonical URI of underlying asset.status

(string, optional, default:: Lifecycle state ("stable"

)draft

|stable

|deprecated

).stale_after

(string, optional): ISO date (YYYY-MM-DD

).sources

(array of objects, optional): Provenance sources[{resource, id, title, author, usage_count, last_modified}]

.verified

(array of objects or object, optional): Verification events[{by, at}]

.generated_by

(string, optional): Actor identifier following actor convention (<producer>/<version>

,human:<id>

,process:<id>

).

Retrieves a specific memory by its key and namespace.

Parameters:key

(string, required): The memory key to look up.project_root

(string, required): Absolute path to the active project root directory.namespace

(string, optional, default:: Scope/namespace."default"

)

Finds memories matching keywords, tags, or namespace filters.

Parameters:project_root

(string, required): Absolute path to the active project root directory.query

(string, optional): Keyword search query across keys, frontmatter, and content.tags

(array of strings, optional): Filter by specific tags.namespace

(string, optional): Scope search to a namespace.limit

(integer, optional, default: 10): Maximum number of results.

AGENT DIRECTIVE (Session Start): Retrieves the last recorded session checkpoint (system/last_memory

) so the AI agent immediately knows where work was left off when opening a project or starting a session.

Parameters:project_root

(string, required): Absolute path to active project root directory.namespace

(string, optional, default:: Scope/namespace."default"

)

AGENT DIRECTIVE (Milestones & Progress): Updates the canonical session checkpoint (system/last_memory

) whenever completing a milestone, making key changes, or pausing work.

Parameters:content

(string or object, required): Brief note or structured dictionary summarizing progress and referencing key memory files.project_root

(string, required): Absolute path to active project root directory.namespace

(string, optional, default:: Scope/namespace."default"

)summary

(string, optional): One-sentence description of the milestone achieved.

Every stored memory strictly adheres to the OKF v0.2 specification ( SPEC.md &

):

OKF_RULES.md

---
type: Agent Memory
title: Coding Style
key: user/preferences/coding_style
namespace: default
tags:
- preferences
- style
status: stable
generated:
  by: mcp-memory/0.2.0
  at: '2026-08-12T19:23:35Z'
created_at: '2026-08-12T19:23:35Z'
updated_at: '2026-08-12T19:23:35Z'
---

User prefers functional programming style with explicit type annotations.
git clone https://github.com/fellowgeek/mcp-memory
cd mcp-memory

Run setup.py

to auto-detect and register mcp-memory

with your AI tools:

python3 setup.py

Note:Oncesetup.py

finishes configuring your tools, your AI client will launchmcp-memory

automatically in the background whenever needed. You do not need to manually start or keep a server process running in your terminal.

If you want to manually verify startup, inspect stdio output, or pre-initialize the virtual environment (.venv

), you can run run.sh

directly:

./run.sh

If you prefer to configure your MCP client manually, add the "memory"

server entry pointing to run.sh

:

Add to your client's mcp_config.json

or claude_desktop_config.json

:

{
  "mcpServers": {
    "memory": {
      "command": "/ABSOLUTE/PATH/TO/run.sh"
    }
  }
}

Add to ~/.codex/config.toml

:

[mcp_servers.memory]
command = "/ABSOLUTE/PATH/TO/run.sh"

Claude Code CLI:

claude mcp add --scope user memory -- /ABSOLUTE/PATH/TO/run.sh

Codex CLI:

codex mcp add memory -- /ABSOLUTE/PATH/TO/run.sh

Run the automated test suite to verify OKF serialization, SQLite database operations, and FastMCP tool execution:

python3 test_memory.py

By default, mcp-memory

creates project-isolated memory stores inside each project's root directory:

OKF Markdown Files (Human-readable):memory/

folder in project root.SQLite Database (Hidden index):.mcp_memory/memories.db

in project root.

You can customize this behavior using environment variables:

MCP_MEMORY_PROJECT_ROOT

: Project root directory (default: process current working directorycwd

).MCP_MEMORY_DB_PATH

: SQLite database file path (default:.mcp_memory/memories.db

relative to project root).MCP_MEMORY_DIR

: Directory for Open Knowledge Format (OKF).md

files (default:memory

relative to project root).

Tip:If you prefer a single global memory store shared across all projects, setMCP_MEMORY_DB_PATH=~/.mcp_memory/memories.db

andMCP_MEMORY_DIR=~/.mcp_memory/memory

in your client's MCP configuration.

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