Skip to main content
Glama

memshelf-mcp

Put your agent's memory on a shelf, hand it the index.

License: MIT Status MCP Sibling: docshelf

                              _          _  __
 _ __ ___   ___ _ __ ___  ___| |__   ___| |/ _|
| '_ ` _ \ / _ \ '_ ` _ \/ __| '_ \ / _ \ | |_
| | | | | |  __/ | | | | \__ \ | | |  __/ |  _|
|_| |_| |_|\___|_| |_| |_|___/_| |_|\___|_|_|
  ____________________________________________
 | INDEX >> | E-01 | E-02 | E-03 | E-04 | ... |
 |__________|______|______|______|______|_____|
        memory shelves for AI agents

Status: M0 complete (Cases A + B), M1 tool surface shipped. The pattern was validated with zero code on a live shelf — measured numbers in docs/demo.md — and the M1 server/CLI now enforces it: memshelf_shelve / recall / index / search / stats / doctor, plus a Claude Code plugin (adapters/claude-code/). Sibling project of docshelf-mcp, which provides the storage/index layer.

What this is

Long-running agent sessions burn tokens re-sending history and lose detail to lossy auto-compaction. memshelf applies the docshelf pattern — tiny index in context, bodies fetched on demand — to the agent's own working memory:

  1. Closed conversation topics, research dumps, and bulky tool output are offloaded to a local shelf as Markdown episodes.

  2. Each episode carries an LLM-written, contract-validated digest that preserves decisions, rejected alternatives, artifacts, and open threads.

  3. The agent keeps only INDEX.md (kilobytes) + digests in context and recalls exact sections via INDEX → SUBINDEX navigation over MCP.

Positioning in one sentence: claude-mem's loop, git's substrate, docshelf's navigation — episodic memory you can grep, diff, review, and carry between hosts. Private and local by default: the standard storage mode is a local git repo with no remote configured.

Related MCP server: Kirok

Quick start

As an MCP server (tools memshelf_init / shelve / recall / index / search / stats / resolve / doctor):

# Claude Code
claude mcp add memshelf -- uvx memshelf-mcp
// Claude Desktop (claude_desktop_config.json)
{
  "mcpServers": {
    "memshelf": { "command": "uvx", "args": ["memshelf-mcp"] }
  }
}

Or from the shell (pip install memshelf-mcp) — the same loop, no MCP:

memshelf init   --shelf ~/my-shelf --name "My working memory"
memshelf shelve --shelf ~/my-shelf --slug 2026-07-23-topic --kind topic \
  --digest "What was decided, what was rejected and why, what stays open." \
  --section "Decisions=..."
memshelf recall --shelf ~/my-shelf --id 2026-07-23-topic --section Decisions --log
memshelf stats  --shelf ~/my-shelf   # claimed + realized savings
memshelf doctor --shelf ~/my-shelf   # exit 1 on integrity errors

Two sessions shelved on parallel branches and the merge collides in INDEX.md / ledger.tsv / .meta.json / stats.svg? That is the multi-writer conflict class (#58) and it resolves mechanically:

memshelf resolve --shelf ~/my-shelf            # union appends, rebuild derived, doctor
memshelf resolve --shelf ~/my-shelf --commit   # same + complete the merge commit

Conflicting episodes are content, not mechanics — resolve reports them and steps aside.

A rejected digest is a feature: the tool prints exactly what to fix and writes nothing. Measured results from a week of dogfooding are in docs/demo.md.

The memory is vendor-portable, and that is now a measured fact, not a design intention: the same live shelf has been read and cross-written by Claude Code (Anthropic) and Gemini CLI (Google) through one shelf-spec server — protocol and field notes in docs/portability.md.

Documents

Doc

What it covers

docs/MANIFEST.md

Problem, the bet, hero scenarios, principles, non-goals

docs/ARCHITECTURE.md

Episode format, digest contract, storage modes, triggers, MCP tool surface, portability model, privacy, failure modes

docs/LANDSCAPE.md

Prior-art survey (2026-07), platform built-ins, positioning, risks

docs/ROADMAP.md

Milestones M0–M3 with exit criteria

docs/DECISIONS.md

Decision log

docs/M0.md

M0 experiment protocol and results (complete): cases, token ledger, recall test

docs/demo.md

Measured numbers from the dogfood shelf: compression, recall test, doctor findings

docs/portability.md

One memory, multiple AIs — the 2026-07-27 experiment: the dogfood shelf read and written by Claude Code (Anthropic) and Gemini CLI (Google) through the same shelf-spec server

docs/examples/

A worked episode file and a memory-shelf INDEX

adapters/claude-code/

Claude Code plugin: /shelve skill + SessionStart/SessionEnd/PreCompact hooks

Origin

Designed as RFC-0001 in the docshelf-mcp repo (#42, #43, #44); this repo is the project's home from 2026-07-13 on. The docshelf copy is frozen as a historical snapshot.

  • docshelf-mcp — the sibling project and storage layer: PDFs/Markdown → chat-project-friendly document shelves with the same index-and-fetch economics (measured: ~3.7K tokens vs 1.2M per question). memshelf was born as RFC-0001 in its repo and reuses its splitter/indexer/read/search verbatim.

  • The dogfood memory shelf is a private repo — by design (MANIFEST principle 5): the tool is public, the memory never is.

License

MIT — see LICENSE.


mcp-name: io.github.ignatenkofi/memshelf-mcp

Install Server
A
license - permissive license
A
quality
B
maintenance

Maintenance

Maintainers
9dResponse time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

View all related MCP servers

Related MCP Connectors

  • Persistent memory for AI agents. Search, store, and recall across sessions.

  • Persistent memory for AI agents — verbatim conversations, searchable by meaning.

  • Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ignatenkofi/memshelf-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server