mempalace/README.md

# MemPalace — local fork

Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.

This is a personal fork configured for **server-mode deployment** — MemPalace runs as a Docker container (typically on Unraid) and multiple AI tools (Claude Code, Codex, Antigravity) connect to a single shared palace from any machine on the network.

The upstream project lives at <https://github.com/MemPalace/mempalace>; refer there for benchmark methodology, contribution guidelines, project history, and the public docs site at <https://mempalaceofficial.com>.

---

## What it is

MemPalace stores your conversation history as verbatim text and retrieves
it with semantic search. It does not summarize, extract, or paraphrase.
The index is structured — people and projects become *wings*, topics
become *rooms*, and original content lives in *drawers* — so searches
can be scoped rather than run against a flat corpus.

The retrieval layer is pluggable. The current default is ChromaDB; the
interface is defined in [`mempalace/backends/base.py`](mempalace/backends/base.py)
and alternative backends can be dropped in without touching the rest of
the system.

Nothing leaves your machine unless you opt in.

Architecture, concepts, and mining flows:
[mempalaceofficial.com/concepts/the-palace](https://mempalaceofficial.com/concepts/the-palace.html).

---

## Install

We recommend [`uv`](https://docs.astral.sh/uv/) — `uv tool install` puts
the `mempalace` CLI in an isolated environment on your PATH:

```bash
uv tool install mempalace
mempalace init ~/projects/myapp
```

If you prefer pip, `pip install mempalace` still works.

## Quickstart

```bash
# Mine content into the palace
mempalace mine ~/projects/myapp                    # project files
mempalace mine ~/.claude/projects/ --mode convos   # Claude Code sessions (scope with --wing per project)

# Search
mempalace search "why did we switch to GraphQL"

# Load context for a new session
mempalace wake-up
```

For Claude Code, Gemini CLI, MCP-compatible tools, and local models, see
[mempalaceofficial.com/guide/getting-started](https://mempalaceofficial.com/guide/getting-started.html).

Benchmark methodology and per-question result files live in the upstream repository — this fork has had the `benchmarks/` directory removed since it isn't needed for deployment.

---

## Server mode (Unraid / shared across machines)

Most users run MemPalace locally on a single machine. If you work
across multiple machines and want one shared memory, you can deploy it
as a Docker container — typically on a home NAS like Unraid — and
point Claude Code, Codex, Antigravity, or any MCP client on each
machine at the same palace.

The `deploy/unraid/` directory ships a complete two-container stack:

* `mempalace` runs the existing MCP-over-SSE endpoint plus a small
  HTTP transcript-ingest endpoint, both in a single process so there's
  exactly one ChromaDB writer.
* `caddy` sidecar terminates TLS, enforces a bearer-token check on
  every request, and reverse-proxies `/sse` and `/ingest`.

Auto-save hooks have remote-aware variants
(`hooks/mempal_save_hook_remote.sh`,
`hooks/mempal_precompact_hook_remote.sh`) that POST transcripts to the
server instead of running `mempalace mine` locally.

Full install, client config, hook setup, and troubleshooting:
[`deploy/unraid/README.md`](deploy/unraid/README.md).

## Knowledge graph

MemPalace includes a temporal entity-relationship graph with validity
windows — add, query, invalidate, timeline — backed by local SQLite.
Usage and tool reference:
[mempalaceofficial.com/concepts/knowledge-graph](https://mempalaceofficial.com/concepts/knowledge-graph.html).

## MCP server

29 MCP tools cover palace reads/writes, knowledge-graph operations,
cross-wing navigation, drawer management, and agent diaries. Installation
and the full tool list:
[mempalaceofficial.com/reference/mcp-tools](https://mempalaceofficial.com/reference/mcp-tools.html).

## Agents

Each specialist agent gets its own wing and diary in the palace.
Discoverable at runtime via `mempalace_list_agents` — no bloat in your
system prompt:
[mempalaceofficial.com/concepts/agents](https://mempalaceofficial.com/concepts/agents.html).

## Auto-save hooks

Two hooks save periodically and before context compression. In this fork the **remote** variants ship — they POST the active transcript to the server's `/ingest/transcript` endpoint with bearer auth instead of running `mempalace mine` locally. Setup, env-var contract, and troubleshooting: [`hooks/README.md`](hooks/README.md).

For per-message recall on top of the file-level chunks the hooks produce, `mempalace sweep <transcript-dir>` runs inside the container (`docker exec mempalace mempalace sweep ...`) — stores one verbatim drawer per user/assistant message, idempotent and resume-safe.

---

## Requirements

- Python 3.9+ (server image uses 3.13)
- A vector-store backend (ChromaDB by default)
- ~300 MB disk for the default embedding model
- Docker + Compose Manager plugin on Unraid for the server-mode path

No API key is required for any path.

## Docs

- Server-mode deployment → [`deploy/unraid/README.md`](deploy/unraid/README.md)
- Hook setup (remote variants) → [`hooks/README.md`](hooks/README.md)
- Release notes → [`CHANGELOG.md`](CHANGELOG.md)
- Project conventions → [`CLAUDE.md`](CLAUDE.md)
- Upstream CLI / Python API reference → [mempalaceofficial.com](https://mempalaceofficial.com)

## License

MIT — see [LICENSE](LICENSE).