palace-rs 发布：为编码智能体提供本地优先的跨会话记忆

Local-first memory for coding agents.
Your agent remembers decisions, fixes, commands, and preferences between sessions.
One binary, one SQLite file, no API keys, no cloud.

Quick start · Install · How it works · Clients · CLI · MCP tools · FAQ

Palace gives Claude Code, Codex, Cursor, Gemini CLI, VS Code, OpenCode, Cline, Zed, Goose, Windsurf, and Claude Desktop a memory that lasts between sessions. It stores what happened verbatim, recalls it automatically when it's relevant, and cites where each answer came from.

You:    why did we switch the queue to SQLite last month?
Agent:  [palace_search → wing my_api, room decisions, filed 2026-09-03]
        You dropped Redis because the deploy target had no persistent volume.
        The benchmark you ran is in the same drawer.

🧭 Why Palace

Every new agent session starts with no memory. You explain the architecture again. The agent repeats a fix you rejected last week. It greps the whole repo to rediscover a convention you settled months ago.

Palace fixes that by:

• Storing text verbatim. Code, notes, and conversations are stored as written, not as an LLM summary that loses detail.
• Running locally. Embeddings come from all-MiniLM-L6-v2 on ONNX Runtime, on your CPU. Nothing leaves your machine unless you turn on remote mode.
• Recalling without being asked. Hooks inject relevant memory when a session starts, when you send a prompt, and while the agent investigates. The agent does not need to remember to search.
• Citing sources. Every result carries its wing, room, source file, drawer id, and score breakdown, so you can check where an answer came from.
• Shipping as one binary. No Python, no vector database, no Docker. One palace executable and one palace.db file.

It also works outside software projects. The creative and personal profiles adapt it for worldbuilding, fiction, coaching, or household notes.

📦 Install

Pick one. Each method installs the palace binary. Run palace install afterwards to connect your agents.

macOS / Linux

curl -fsSL https://raw.githubusercontent.com/AncientiCe/palace-rs/main/scripts/install.sh | sh

Windows (PowerShell)

irm https://raw.githubusercontent.com/AncientiCe/palace-rs/main/scripts/install.ps1 | iex

The scripts download the matching GitHub Release binary, verify its SHA-256 checksum, and install it. To upgrade later, run the same command again.

Homebrew (macOS Apple Silicon / Linux)

brew tap AncientiCe/palace
brew install palace

MCP registry / MCPB bundle. Palace is listed in the official MCP registry as io.github.ancientice/palace-rs. Each release also ships a palace-<version>.mcpb bundle (Linux x86_64, macOS arm64, Windows x86_64) for one-click install in MCPB-aware hosts such as Claude Desktop.

From source

git clone https://github.com/AncientiCe/palace-rs && cd palace-rs
cargo build --release   # binary at target/release/palace

The embedding model (~90 MB) downloads from Hugging Face the first time you mine or search, then stays cached in ~/.cache/huggingface.

⚡ Quick start

palace install            # connect every supported agent on this machine
palace doctor             # check that every client is wired up
palace init ~/my-project --yes --auto-mine   # detect rooms, write palace.yaml, mine

Restart your agent app so it reloads its MCP config. Then try:

"What do we know about how this project handles database migrations?"

The agent should call palace_search and answer from memory, citing the drawer it used. You can also search from the terminal:

palace search "how did we decide on the database schema"

From then on, the agent writes a diary entry after substantial work and stores durable decisions as facts. Tomorrow's session, from any of your agents, picks up where today's left off.

🔍 How it works

The palace

Memory is organised like a building, which keeps search focused:

_{Figure 1. One palace holds many wings. Rooms group drawers, and the knowledge graph and diaries sit alongside them.}

Wings are either project wings (a folder on disk, created by palace mine) or topic wings (no folder, such as chat history or meeting notes, created with palace_create_wing).

Retrieval

flowchart LR
    Q["💬 Question<br/>from you or the agent"] --> S["Sanitize<br/>strip pasted dumps"]
    S --> I["Classify intent<br/>decision · how_to · preference …"]
    I --> H["Hybrid search<br/>BM25 + cosine"]
    H --> B["Coding boosts<br/>+ preference pass"]
    B --> R{"Rerank?<br/>optional"}
    R --> OUT["📎 Results<br/>scores + source + neighbours"]
    DB[("palace.db<br/>local embeddings")] -.-> H

Loading

_{Figure 2. What happens to a query. Everything runs locally on your CPU.}

Search fuses BM25 keyword scoring with cosine similarity over local embeddings. It then applies intent boosts for coding questions. Each query is classified as preference, decision, how_to, definition, temporal, or unknown. Preference-shaped questions ("how do I like my commits?") get a dedicated pass against tagged preference spans. A local cross-encoder reranker is available as an option (--rerank or PALACE_RERANK=1).

Agent-generated query dumps are cleaned up before retrieval. Results come back with score provenance (combined, cosine, bm25, coding_boost, preference_match, rerank_score, intent) and the neighbouring chunks from the same source file.

Automatic recall (hooks)

palace install registers user-level hooks, so memory works in every project without editing per-project files:

_{Figure 3. Three hooks read memory into the session. The fourth makes sure the session's lessons get written back.}

Auto-recall is limited to the current project. Drawers from your other project wings and diaries from other projects stay out. Topic wings still appear.

All hooks fail open: if the palace is missing or empty, the agent works as normal.

The memory protocol

palace install also writes a short rule into each client's instruction file. The rule tells the agent when to use memory:

1. Session start: palace_status → palace_session_context → palace_diary_search.
2. Before answering a question about past decisions, preferences, or history: palace_search and palace_kg_query, before grep.
3. After work: palace_diary_write, plus palace_kg_add for durable facts.

Grep remains the right first tool for current symbols, exact definitions, and code that may have changed since it was mined.

The nine protocol tools are marked _meta."anthropic/alwaysLoad" = true, so Claude Code 2.1.121 and later keeps them loaded instead of deferring them behind tool search.

Memory layers

palace wake-up prints L0 and L1. Agents reach L2 and L3 through MCP tools.

🔌 Supported clients

palace install configures every supported client by default (--client all). Gemini CLI, VS Code, Windsurf, OpenCode, Cline, Zed and Goose are included only when their config folder exists, so Palace never sets up an agent you don't have. Use --client <name> to configure one explicitly.

Rule files are written as a managed block. Your existing content is kept.

palace install --client claude                       # one client only
palace install --scope project --path ./my-project   # project-level rule files
palace install --no-rule                             # MCP wiring only, no rule
palace install --dry-run                             # preview the changes
palace uninstall                                     # undo everything

With --scope project, rules go to <project>/CLAUDE.md, <project>/AGENTS.md (Codex, OpenCode, Cline, Zed and Goose), <project>/GEMINI.md, <project>/.github/copilot-instructions.md, <project>/.windsurf/rules/palace.md, and <project>/.cursor/rules/palace.mdc. Cursor, VS Code and OpenCode also get a project MCP config (.cursor/mcp.json, .vscode/mcp.json, opencode.json).

Manual MCP config

Any MCP client can run Palace as a stdio server with palace mcp.

JSON clients (Claude Code, Cursor, Claude Desktop):

{
  "mcpServers": {
    "palace": { "command": "palace", "args": ["mcp"] }
  }
}

Codex (~/.codex/config.toml):

[mcp_servers.palace]
command = "palace"
args = ["mcp"]

Claude Code CLI:

claude mcp add palace -- palace mcp

MCP prompts

Claude Desktop cannot run hooks, so the server offers two prompts in its prompt picker:

Profiles

A profile changes the agent rule, the protocol text, and which rooms palace init looks for:

palace install --profile creative

The profile is saved to ~/.palace/config.json. Set PALACE_PROFILE to override it for one process.

Remote mode (shared team memory)

By default, palace mcp serves your local palace. Remote mode turns it into a stdio-to-HTTP bridge to a shared Palace Server, the commercial, self-hosted team edition. Client registrations do not change.

palace remote set --endpoint https://palace.yourco.com   # prompts for the ps_… API key
palace remote on
palace remote test      # runs the MCP handshake and reports the tool count
palace remote status    # mode, endpoint, masked key
palace local            # back to the local palace

🧰 CLI reference

Run palace <command> --help for every flag.

Setup

Adding memory

Finding memory

Maintenance

Servers

Mining a project

flowchart LR
    DIR["📁 Your repo"] --> F["Filter<br/>.gitignore + skip build dirs"]
    F --> C{"Changed since<br/>last mine?"}
    C -- no --> SKIP["Skip"]
    C -- yes --> CH["Chunk + pick room<br/>using palace.yaml"]
    CH --> E["Embed locally<br/>all-MiniLM-L6-v2"]
    E --> DB[("palace.db")]

Loading

_{Figure 4. Mining only touches files that changed, so re-running it is cheap.}

palace mine respects .gitignore and skips .git, node_modules, target, build, dist, vendor, and other build output. It reads common text and source formats: md, txt, rs, py, js, ts, tsx, go, java, rb, sh, sql, json, yaml, toml, html, css, csv, and more.

palace mine ~/my-project \
  --wing my_project            # override the wing name
  --limit 100                  # process at most 100 new or changed files
  --dry-run                    # preview without storing
  --no-gitignore               # ignore .gitignore rules
  --include vendor,third_party # force-include these paths

--limit runs can be resumed: the next run continues from where the last one stopped.

Measuring value with palace gain

palace gain                       # last 30 days
palace gain --project my_project --since 7d
palace gain --history             # recent tool calls
palace gain --json
palace gain --record <query_id> <drawer_id> useful   # or not_useful / wrong_answer

palace gain - last 30d (my_project)
  Tool calls         : 412   (sessions: 27)
  Hit rate           : 88%   (search hits 142/162)
  Precision@1        : 92%
  Precision@5        : 95%
  Tokens saved (est) : ~78,400
  Re-index skipped   : 31    (duplicate drawers avoided)
  KG facts recalled  : 56
  Diary recalls      : 8
  Repeat Qs avoided  : 19
  p95 latency        : 41 ms

Usage metadata stays in your local palace.db. Set PALACE_GAIN_DISABLED=1 to turn recording off.

🧩 MCP tools

All 49 tools

Status and health

Wings and taxonomy

Search

Drawers

Knowledge graph

Graph and tunnels

Diaries and sessions

Internal

🔧 Configuration

Settings load in this order: environment variables, then ~/.palace/config.json, then defaults.

config.json takes the same settings as keys: palace_path, profile, entity_languages, topic_wings, people_map, mcp_mode, remote_endpoint, remote_api_key. Palace writes this file with owner-only 0600 permissions.

palace.yaml (per project)

palace init creates this file. Edit it to tune how files map to rooms:

wing: my_project
rooms:
  - name: backend
    description: Server and API code
    keywords: [api, server, routes, models]
  - name: frontend
    description: UI components
    keywords: [ui, components, pages, views]
  - name: general
    description: Everything else
    keywords: []

📊 Benchmarks

Coding-agent memory eval

A fixture of realistic project memories (decisions, past failures, commands, conventions, preferences, current direction) with 40 questions such as:

• why did we choose bundled sqlite?
• how did we fix the migration test failure last time?
• what clippy command should I run?

cargo test --test coding_agent_eval -- --nocapture

The test reports recall@1 and recall@5, and fails if retrieval drops below a fixed threshold. It answers the question that matters: can a coding agent find the right project context when it needs it?

LongMemEval

Retrieval recall on LongMemEval s_cleaned: 470 evaluated questions, each over a haystack of about 50 sessions and 115k tokens. The 30 abstention questions are excluded, as is standard.

Method and per-type results

• Granularity: one drawer per session, with user and assistant turns embedded together. No filtering, summarisation, or LLM extraction.
• Embedder: all-MiniLM-L6-v2 (384-dim, ONNX), 512-token cap, local.
• Retrieval: BM25 (k1=1.5, b=0.75, weight 0.35) fused with cosine (weight 0.65), top-K 10. This run predates the coding-agent intent boosts.
• No LLM at any stage, no per-dataset tuning, no GPU.
• Metric: recall_any@K at session granularity.
• Hardware: Apple M1 Pro, 32 GB RAM.

The oracle split only contains the gold sessions, so it checks the pipeline rather than measuring retrieval. Most top-1 misses on s_cleaned are near-misses at rank 2–5. Preference questions are the weakest category, because "I prefer…" sentences rarely share keywords with the question. The dedicated preference pass added since this run targets that gap.

🔒 Privacy and data

• Everything is local by default. One SQLite file at ~/.palace/palace/palace.db. Embeddings are computed on your CPU.
• Network access is limited to the one-time model download from Hugging Face, and to your own Palace Server if you turn on remote mode.
• No telemetry. palace gain statistics are written to your local database only.
• Back up with palace export, or copy palace.db.
• Delete a single memory with palace_forget, or everything by removing ~/.palace.

Palace stores whatever you mine. Exclude secrets with .gitignore before you mine a repository.

🩺 Troubleshooting

❓ FAQ

Do I have to tell my agent to use it?

No. After palace install, hooks inject relevant memory at session start, when you send a prompt, and while the agent reads code. The installed rule tells the agent to search memory before answering history questions and to save what it learned afterwards.

How is this different from CLAUDE.md or AGENTS.md?

Those files are instructions you write and maintain by hand, and every line is loaded into every session. Palace is searchable memory that grows as you work: only the parts relevant to the current question are recalled, and the same memory is shared by Claude Code, Codex, Cursor, and Claude Desktop. Keep using instruction files for rules; use Palace for history.

Does it send my data anywhere?

No. Storage, embeddings, and search all run locally. The only network calls are the one-time model download from Hugging Face and, if you turn it on, your own Palace Server in remote mode. See Privacy and data.

Will it slow my agent down?

Searches typically take tens of milliseconds (palace gain shows your real p50 and p95). The first call in a session loads the embedding model. Hooks fail open, so a missing or broken palace never blocks the agent.

What happens when a memory goes out of date?

Re-running palace mine updates changed files. For facts, agents call palace_kg_invalidate and add the new fact, so the timeline keeps both. palace_conflicts flags facts that look stale or contradictory, and palace_forget or palace prune removes drawers you no longer want.

Can my whole team share one memory?

Different agents on one machine already share it: a Codex session can pick up a Claude Code session's diary. For a team, remote mode points every palace mcp at a shared, self-hosted Palace Server.

Is it only for code?

No. The creative and personal profiles tune Palace for worldbuilding, fiction, coaching, caregiving, and household notes. Palace reads Markdown and text files, so Claude Desktop users can install the MCPB bundle and start without writing code.

📚 Using Palace as a library

Palace is also a Rust crate (palace). Add it as a git dependency:

[dependencies]
palace-rs = { git = "https://github.com/AncientiCe/palace-rs", default-features = false }

use palace::{db, knowledge_graph, store};

let conn = db::open_in_memory()?;
store::add_drawer(&conn, "wing_code", "backend",
    "We use SQLite with WAL mode for concurrent reads.",
    None, "architecture.md", 0, "example", 4.0)?;
knowledge_graph::add_triple(&conn, "Palace", "stores_data_in", "SQLite",
    Some("2026-04-01"), None, 1.0, None, None)?;

default-features = false leaves out the CLI dependencies. For the full example, see examples/basic_usage.rs (cargo run --example basic_usage). palace::palace::Palace::search_with_provenance returns the same score details as the MCP tools.

🧪 Development

cargo fmt
cargo clippy --all-targets --all-features -- -D warnings
cargo test --all-features
cargo audit

Tests use in-memory SQLite, and only tests that need the embedding model load it. See CONTRIBUTING.md, SECURITY.md, and CHANGELOG.md.

🚚 Migrating from older versions

From mempalace (0.1.x)

Version 0.2.0 renamed the project from mempalace to palace. The 0.2.x line kept the old names working with warnings. They were removed in 0.3.0.

To upgrade from 0.1.x, either install a 0.2.x release first and run palace install, which migrates configs and moves ~/.mempalace to ~/.palace, or rename by hand: ~/.mempalace → ~/.palace, mempalace.yaml → palace.yaml, MEMPALACE_* → PALACE_*, and the mempalace_* MCP tools → palace_*. Hooks that ran mempalace mcp or python -m mempalace.mcp_server should now run palace mcp.

From the Python version

The Rust version replaces ChromaDB plus SQLite with a single palace.db. ChromaDB data cannot be converted, so re-ingest:

palace init ~/my-project --yes --auto-mine
palace mine-convos ~/Desktop/transcripts
palace status

identity.txt, people_map.json, and known_names.json in ~/.palace/ are still compatible and are read automatically.

📜 License

MIT