MCP Connector

Build a Local Knowledge Graph from Markdown

Gives an AI persistent memory as local Markdown files, with a knowledge graph and semantic search.

Works with markdownsqlitepostgresobsidianvscode

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91
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out of 100
Updated 9 days ago
Version 0.22.1
Models
universal

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Why it matters

Establish persistent memory for LLM interactions by building a semantic knowledge graph from your Markdown files. This system enables natural language querying and bidirectional reading/writing between humans and AI.

Outcomes

What it gets done

01

Ingest and index Markdown files into a semantic graph.

02

Enable natural language search and retrieval of information.

03

Facilitate bidirectional knowledge exchange with LLMs.

04

Manage multiple knowledge projects and synchronize across devices.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-basic-memory | bash

Capabilities

Tools your agent gets

write_note

Create or update notes in the knowledge base

read_note

Read notes by name or permalink

read_content

Read raw file content including text, images, and binary files

view_note

View notes as formatted artifacts

edit_note

Edit notes step by step

move_note

Move notes while preserving database integrity

delete_note

Delete notes from the knowledge base

build_context

Navigate the knowledge graph through memory:// URLs

+7 tools

Overview

Basic Memory MCP Server

A local-first MCP server that gives an AI persistent memory as Markdown files with Observations, Relations, semantic search, and progressive tool discovery. Use when an AI assistant needs to remember project context and decisions across sessions, stored as portable Markdown rather than locked chat history.

What it does

A local-first MCP server that gives an AI persistent memory as plain Markdown files instead of ephemeral chat history: both the human and the AI read and write the same files, and sync keeps them in step. Each file is an Entity with Observations (bracketed-category facts, e.g. [method], [tip], optional #tags and parenthetical context) and Relations (wiki-style links using single-token or quoted multi-word relation types, e.g. requires [[Burr Grinder]]) that together form a traversable knowledge graph the LLM can follow link by link across sessions - a bare - [[Target]] link or a prose mention indexes as links_to. On top of that graph, semantic search finds notes by meaning rather than keyword, and every one of its MCP tools carries behavior-hint annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) so an agent can pick the right tool without burning context on trial and error. Tools cover content (write_note, read_note, edit_note, move_note, delete_note, read_content, view_note), search and discovery (search, search_notes, recent_activity, list_directory), the knowledge graph itself (build_context for navigating memory:// URLs, canvas for Obsidian canvas generation), project management, and a schema system (schema_infer, schema_validate, schema_diff) that can infer, validate, and diff the structure of a knowledge base. Recent additions include hybrid full-text-plus-vector semantic search (FastEmbed embeddings, on SQLite or Postgres), per-project cloud routing so individual projects can route through the cloud while others stay local, and edit_note auto-creating a missing note on append/prepend while write_note guards against accidental overwrites.

When to use - and when NOT to

Use it when an AI assistant needs to remember project context, decisions, or running notes across sessions instead of starting from zero each time - it works with Claude Desktop, Claude Code, Codex, Cursor, VS Code, ChatGPT (via OpenAI-compatible search/fetch Custom GPT actions), Obsidian (reads/writes the same Markdown directly, no separate setup), or any other MCP-speaking client. The local install is free forever, licensed under AGPL-3.0, keeps every file on disk, and works air-gapped; an optional hosted Cloud tier (from $15/mo, locked for the subscription's life, with a 7-day free trial) adds cross-device sync, mobile/web access, and automatic snapshots/backups for teams or multi-device use, but is never required - both paths run the identical open-source engine on the identical Markdown files, with no lock-in either way. Beyond the core MCP server, the same repository ships official host-native agent packages: a Claude Code plugin (session-start briefings, pre-compaction checkpoints, /basic-memory:remember/:share/:status commands) that requires the MCP server connected first as a hard prerequisite, shared framework-agnostic SKILL.md files, a Hermes plugin, and an OpenClaw plugin.

Inputs and outputs

{
  "mcpServers": {
    "basic-memory": {
      "command": "uvx",
      "args": ["basic-memory", "mcp"]
    }
  }
}

Input is natural-language requests like "make a note about X" or "what have I been working on this week"; output is a Markdown file with frontmatter (title, type, permalink, tags), an Observations section, and a Relations section, written directly into the configured notes directory (~/basic-memory by default for local installs). All MCP tools default to text output, with output_format="json" available for structured responses. The CLI covers project management (project list/add/set-cloud/set-local), health checks (status, doctor for file-to-database consistency), and imports from Claude conversations, ChatGPT, or a memory JSON export.

How to install

Local install requires Python via uv: uv tool install basic-memory, then add the server to your client's MCP config as shown above (or claude mcp add basic-memory -- uvx basic-memory mcp for Claude Code). CLI installs (via uv tool or Homebrew) auto-update every 24 hours by default; bm update triggers a manual check, and auto-update can be disabled in ~/.basic-memory/config.json. Logging defaults to a rotating local file (~/.basic-memory/basic-memory.log, 10MB/10-day retention) rather than stdout, since the MCP server's stdout is reserved for JSON-RPC. Telemetry is minimal and anonymous (cloud-promo impressions and login outcomes only - never file contents, note titles, or PII) and can be fully disabled with export BASIC_MEMORY_NO_PROMOS=1.

Integrations

Works as an MCP server for Claude Desktop, Claude Code, Codex CLI, Cursor, VS Code, and any other MCP-speaking client (stdio or HTTPS transport), plus a ChatGPT Custom GPT action and direct, setup-free interoperability with Obsidian over the same Markdown files. The optional Cloud tier is built on WorkOS AuthKit, Neon Postgres, and Tigris S3, and syncs to local installs via rclone with conflict resolution.

Who it's for

Developers and teams who want their AI assistant to retain project context, decisions, and running notes across sessions and tools, stored as portable, human-editable Markdown rather than locked in a proprietary chat history or a hosted vector database.

Source README

License: AGPL v3
PyPI version
Python 3.12+
Tests
Ruff


Ask DeepWiki

Skip the install - try Basic Memory in the cloud

Claude, Codex, or Cursor connected in 30 seconds. No Python, no JSON, no
terminal. $15.00/mo locked in for life (12.50/mo yearly pricing). 7-day free
trial - cancel any time before day 7 if it's not for you. Beta pricing -
sign up now and your rate never goes up. OSS users: code BMFOSS takes
another 20% off for 3 months.

Start free trial →

Basic Memory Teams is now available!

Give your team a single, shared cloud workspace. Knowledge isn't confined to one person - anything a teammate writes is immediately available to everyone else and to their AI assistants.
Edit a note together in real time, hand work off between humans and agents, and build one connected knowledge base instead of scattered copies. Same pricing - start with one user and add more as needed.


Basic Memory

Your AI never forgets again.

Pick up right where you left off - in Claude, Codex, Cursor, ChatGPT, or
anything that speaks MCP. Your knowledge
lives as Markdown files that both you and your AI can read, write, and
search.

  • Local-first. Plain text on your disk. Forever.
  • Two-way. AI and humans write to the same files; sync keeps them in step.
  • A real knowledge graph. Observations and wikilinks compound into context.
  • Semantic search. Find notes by meaning, not just keywords.
  • MCP-native. Works with every major AI client and IDE.
  • Progressive tool discovery. Every tool is tagged with behavior hints
    (read-only, destructive, idempotent) so agents pick the right tool on
    demand - no wasted context trying things to see what they do.
  • Cloud, optional. Sync across devices when you want - never required.

Get started

Pick the path that fits you. Both run the same product on the same Markdown.

☁️   Cloud 💻   Local install

30 seconds. Sign up, connect your AI client, done.

  • Works in any browser
  • Mobile, web, desktop
  • Cross-device sync built in
  • We handle hosting, backups, snapshots

$15.00/mo locked for life · 7-day free trial · cancel any time

Start free trial →

2 minutes. Install, configure your AI client, run.

  • Free forever (AGPL-3.0)
  • All data on your disk
  • Air-gapped friendly
  • Requires Python via uv
uv tool install basic-memory

Configure your client ↓

What people are saying

Basic Memory changed my whole relationship with LLMs. I switched from GPT
and Gemini to exclusively Claude and Claude Code because of this
integration and am completely revamping all our company's processes around
a Basic Memory workflow.

  • Alex, TrainerDay

Basic Memory is the missing 'wow' factor in AI chatbots. Now I can't
imagine Claude or Claude Code without it.

  • Caleb, Caleb Picker Consulting

I don't code without Basic Memory anymore. It's such a time saver to be
able to refer to projects I don't currently have active and keep a running
log of all my learnings and ProTips.

  • @groksrc, Developer

More on basicmemory.com.

Basic Memory Cloud

The hosted version of Basic Memory. Same product, same Markdown files, same
MCP tools - we just host the database, run the sync, and put it on your
phone.

What you get

  • Every device, same brain. Your knowledge graph on web, mobile, and
    desktop. No copy-paste between machines.
  • Connect any MCP client. Claude Desktop, Claude Code, Codex, Cursor,
    ChatGPT (Custom GPTs), VS Code - one-click connect from the web app.
  • Bidirectional sync to local. Edit on your phone, see it in Obsidian on
    your laptop. rclone-powered with conflict resolution.
  • Snapshots and backups. Point-in-time restore. Browse history. Never
    lose a note.
  • No lock-in. Your notes are plain Markdown. Export to local Markdown any
    time - same files, same format, same wikilinks. Cancel anytime, your data
    stays yours.

Built on WorkOS AuthKit, Neon Postgres, and Tigris S3.

Pricing

$15.00/mo, locked in for the life of your subscription (regular price
$19). Sign up during beta and the rate never goes up - as long as you stay
subscribed, you keep the price. One plan, no tiers, no surprise upgrades.
Unlimited notes, unlimited projects, every feature.

  • 7-day free trial. Cancel any time before day 7 if it's not for you.
  • Cancel anytime after that too - export your notes whenever you want.
  • OSS users: code BMFOSS for another 20% off for 3 months (~$11.40/mo).

Start your 7-day free trial →

Cloud vs. local

Cloud Local
Setup time 30 seconds 2 minutes (requires Python)
Cost $15.00/mo, locked for life (7-day trial) Free
Storage We host (Tigris S3) Your disk
Cross-device sync Built in Manual (Git, Syncthing, etc.)
Mobile access Yes (web + app) No
Air-gapped No Yes
Your data stays yours Yes - export anytime Yes - already there
Source code AGPL-3.0 AGPL-3.0
Snapshots & backups Built in Roll your own

Both paths use the same OSS engine and the same Markdown files. There's no
lock-in either way - flip between them when your needs change.

Works with the tools you already use

Client Transport Notes
Cloud web app https Sign in at basicmemory.com - no install
Claude Desktop stdio/https macOS / Windows / Linux
Claude Code stdio/https claude mcp add
Codex stdio/https OpenAI's coding agent
Cursor stdio/https .cursor/mcp.json
VS Code stdio/https Native MCP support
ChatGPT https Custom GPT actions (search / fetch)
Obsidian - Reads/writes the same Markdown directly
Anything MCP stdio/https If it speaks MCP, it works

Official agent packages

This repository is also the canonical home for Basic Memory's host-native
agent packages. The core Python package, Claude Code plugin, shared skills,
Hermes plugin, and OpenClaw plugin all ship from the same source tree.

Maintainers can verify the whole consolidated surface from the repo root:

just package-check

Package-local justfiles are also available when working inside one host:

just package-check-claude-code
just package-check-skills
just package-check-hermes
just package-check-openclaw

Claude Code plugin

The Claude Code plugin is the bridge between Claude's working memory and Basic
Memory - session-start briefings, pre-compaction checkpoints, an opt-in capture
output style, and /basic-memory:bm-setup · :remember · :share · :status.

Connect the Basic Memory MCP server first - see Connect your AI
client
. The plugin's hooks and skills call it, so it's a
hard prerequisite. Then add the marketplace and install:

claude plugin marketplace add basicmachines-co/basic-memory \
  --sparse .claude-plugin plugins/claude-code
claude plugin install basic-memory@basicmachines-co

Source: plugins/claude-code.

Shared skills

Framework-agnostic SKILL.md files live in skills/. If your
Skills CLI supports repository subdirectory sources:

npx skills add basicmachines-co/basic-memory/skills

If your installed Skills CLI cannot load that source, update the CLI or copy
the memory-* directories from skills/ into your agent's skills directory.

Hermes

Hermes keeps its native plugin shape under integrations/hermes:

hermes plugins install basicmachines-co/basic-memory --path integrations/hermes

If your Hermes build lacks subpath installs, use the final deprecated
basicmachines-co/hermes-basic-memory pointer release until host support
lands.

OpenClaw

OpenClaw stays package-native and publishes from
integrations/openclaw:

openclaw plugins install @basicmemory/openclaw-basic-memory

Pick up where you left off

https://github.com/user-attachments/assets/a55d8238-8dd0-454a-be4c-8860dbbd0ddc

Connect your AI client

If you went the Cloud route, the web app walks you through
client connect. The snippets below are for local installs.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "basic-memory": {
      "command": "uvx",
      "args": ["basic-memory", "mcp"]
    }
  }
}

Restart Claude Desktop. Notes live in ~/basic-memory by default.

Claude Code, Codex CLI, Cursor, VS Code, ChatGPT, Obsidian

Claude Code

claude mcp add basic-memory -- uvx basic-memory mcp

For the full memory bridge - session briefings, pre-compaction checkpoints, and
the /basic-memory:* commands - also install the Claude Code
plugin
on top of this.

Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.basic-memory]
command = "uvx"
args = ["basic-memory", "mcp"]

Cursor

Add to .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):

{
  "mcpServers": {
    "basic-memory": {
      "command": "uvx",
      "args": ["basic-memory", "mcp"]
    }
  }
}

VS Code

Add to your User Settings (JSON):

{
  "mcp": {
    "servers": {
      "basic-memory": {
        "command": "uvx",
        "args": ["basic-memory", "mcp"]
      }
    }
  }
}

ChatGPT

Basic Memory exposes OpenAI-compatible search and fetch tools for Custom
GPT actions. See the ChatGPT integration
guide
.

Obsidian

No setup. Point Obsidian at ~/basic-memory (or your project folder) and the
same wikilinks, frontmatter, and Markdown your AI writes appear in your graph
view. Edit either side - sync handles the rest.

Try a prompt:

"Create a note about our project architecture decisions."
"Find information about JWT auth in my notes."
"What have I been working on this week?"

What's New

  • Automatic updates. Basic Memory keeps itself up to date for uv tool
    and Homebrew installs; bm update triggers a manual check.
  • Semantic vector search. Find notes by meaning, not just keywords.
    Hybrid full-text + vector ranking with FastEmbed embeddings, on SQLite or
    Postgres.
  • Schema system. Infer, validate, and diff the structure of your
    knowledge base with schema_infer, schema_validate, schema_diff.
  • Per-project cloud routing. Route individual projects through the cloud
    while others stay local, via API key (bm project set-cloud).
  • Smarter editing. edit_note append/prepend auto-creates notes when
    missing; write_note guards against accidental overwrites.
  • Richer search results. Matched chunk text is included so the LLM gets
    context, not just hits.
  • FastMCP 3.0 + tool annotations. Every tool ships with MCP behavior
    hints (readOnlyHint, destructiveHint, idempotentHint,
    openWorldHint) so agents can discover capabilities progressively at
    runtime instead of guessing or burning tokens.
  • CLI overhaul. --json output for scripting, workspace-aware commands,
    and an htop-inspired project dashboard.

Full CHANGELOG for v0.18 → v0.20.

Why Basic Memory

Most LLM conversations are ephemeral. You ask a question, get an answer, then
everything is forgotten. Workarounds have limits:

  • Chat history captures conversations but isn't structured knowledge.
  • RAG lets the LLM query your documents but not write back to them.
  • Vector DBs need complex infra and usually live in someone else's cloud.
  • Knowledge graphs need specialized tooling to maintain.

Basic Memory takes a simpler path: structured Markdown files that humans
and LLMs both read and write.

  • All knowledge stays in plain files you control.
  • Both sides read and write to the same files.
  • Familiar Markdown with semantic patterns - no new format to learn.
  • A traversable graph the LLM can follow link by link.
  • Works with the editors you already use (Obsidian, VS Code, anything).
  • Just files plus a local SQLite index. No servers required.

How it works

You're chatting normally about coffee:

I've been experimenting with brewing methods. Pour over gives more clarity
than French press, water at 205°F seems best, and freshly ground beans
make a huge difference.

Ask the LLM to capture it:

"Make a note on coffee brewing methods."

A Markdown file appears in your project directory in real time:

---
title: Coffee Brewing Methods
permalink: coffee-brewing-methods
tags: [coffee, brewing]
---

# Coffee Brewing Methods

## Observations
- [method] Pour over highlights subtle flavors over body
- [technique] Water at 205°F (96°C) extracts optimal compounds
- [principle] Freshly ground beans preserve aromatics

## Relations
- relates_to [[Coffee Bean Origins]]
- requires [[Proper Grinding Technique]]
- affects [[Flavor Extraction]]

Next session, the LLM picks up the thread. It follows the relations to
surface what you already know about Ethiopian beans and burr grinders, and
builds on it instead of starting over. You see the same files in Obsidian or
your editor. Edit them by hand - the AI sees your changes too.

Real two-way flow: humans edit Markdown, LLMs read/write through MCP, sync
keeps everything consistent, and the source of truth is always your files.

The Markdown format

Each file is an Entity. Entities have Observations (facts about them) and
Relations (links to other entities). That's the whole grammar.

Frontmatter

---
title: <Entity title>
type: note
permalink: <uri-slug>
tags: [optional, list]
---

Observations

Facts about the entity. Categories in [brackets], tags with #, optional
context in parens.

- [method] Pour over highlights subtle flavors
- [tip] Grind medium-fine for V60 #brewing
- [fact] Lighter roasts contain more caffeine than dark
- [resource] James Hoffmann's V60 technique on YouTube
- [question] How does temperature affect compound extraction?

Relations

Wiki-style links that form the graph. Single-token relation types, or quote
multi-word ones.

- pairs_well_with [[Chocolate Desserts]]
- grown_in [[Ethiopia]]
- requires [[Burr Grinder]]
- "pairs well with" [[Dark Chocolate]]

Bare - [[Target]] and prose - Worth checking out [[Target]] index as
links_to. Full reference in the
docs.

MCP tools

Basic Memory exposes these tools to any MCP client. Every tool is annotated
with MCP behavior hints (read-only, destructive, idempotent, open-world) so
agents can pick the right one without trial-and-error:

  • Content: write_note, read_note, edit_note, move_note,
    delete_note, read_content, view_note
  • Search & discovery: search, search_notes, recent_activity,
    list_directory
  • Knowledge graph: build_context (navigates memory:// URLs),
    canvas (Obsidian canvas generation)
  • Projects: list_memory_projects, create_memory_project,
    get_current_project, sync_status
  • Schema: schema_infer, schema_validate, schema_diff
  • Cloud: cloud_info, release_notes

All MCP tools default to text output; pass output_format="json" for
structured responses. Full tool reference in the
docs.

CLI essentials

# Projects
basic-memory project list
basic-memory project add research ~/research
basic-memory project set-cloud research   # route through cloud
basic-memory project set-local research   # revert

# Health & maintenance
basic-memory status
basic-memory doctor              # file <-> DB consistency check
basic-memory tool edit-note ...  # CLI access to MCP tools
basic-memory update              # check for and install updates

# Imports
basic-memory import claude conversations
basic-memory import chatgpt
basic-memory import memory-json

Routing flags (--local / --cloud) force a target when you're in mixed
mode. Full CLI reference in the
docs.

Auto-updates

CLI installs check for updates every 24 hours by default and apply them
silently (so the MCP server keeps responding).

  • Supported install sources: uv tool, Homebrew
  • Skipped for uvx (ephemeral runtime managed by uv)
  • Manual: bm update (check + apply) or bm update --check (check only)

Disable in ~/.basic-memory/config.json:

{ "auto_update": false }

Telemetry

Minimal, anonymous events to understand the CLI-to-cloud conversion funnel.

What we collect: cloud promo impressions, cloud login attempts and
outcomes, promo opt-out events.

What we don't: file contents, note titles, knowledge base data, PII, IP
addresses, per-command or per-tool tracking.

Events go to our Umami Cloud instance (open-source,
privacy-focused) on a background thread - never blocks the CLI.

Opt out:

export BASIC_MEMORY_NO_PROMOS=1

This disables promos and all telemetry.

Logging

Basic Memory uses Loguru. Defaults vary
by entry point:

Entry point Default Why
CLI commands File only Doesn't interfere with command output
MCP server File only Stdout would corrupt JSON-RPC
API server File (local) or stdout (cloud) Docker/cloud uses stdout

Log file: ~/.basic-memory/basic-memory.log (10MB rotation, 10 days
retention).

Environment variables

Variable Default Description
BASIC_MEMORY_LOG_LEVEL INFO DEBUG / INFO / WARNING / ERROR
BASIC_MEMORY_CLOUD_MODE false API logs to stdout with structured context
BASIC_MEMORY_FORCE_LOCAL false Force local API routing
BASIC_MEMORY_FORCE_CLOUD false Force cloud API routing
BASIC_MEMORY_EXPLICIT_ROUTING false Mark route selection as explicit
BASIC_MEMORY_ENV dev Set to test for test mode (stderr only)
BASIC_MEMORY_NO_PROMOS false Disable cloud promos and telemetry
BASIC_MEMORY_IMPORT_UPLOAD_MAX_BYTES 104857600 Max uploaded import size
BASIC_MEMORY_LOG_LEVEL=DEBUG basic-memory reindex
tail -f ~/.basic-memory/basic-memory.log

Development

Basic Memory supports SQLite (default, fast, no Docker) and Postgres
(via testcontainers - Docker required).

just install          # Install with dev dependencies
just test-sqlite      # All tests, SQLite
just test-postgres    # All tests, Postgres (testcontainers)
just test             # Both backends
just fast-check       # fix/format/typecheck + impacted tests
just doctor           # File <-> DB consistency check (temp config)
just package-check    # Claude Code, skills, Hermes, OpenClaw package checks
just lint
just typecheck        # Pyright (primary)
just typecheck-ty     # ty (supplemental)
just format
just check            # All quality checks
just migration "msg"  # New Alembic migration

Tests use pytest markers: windows, benchmark, smoke. See
justfile for the full list.

Contributions welcome - see CONTRIBUTING.md.

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