Palace

[palace banner: decisions, commands, and preferences from today's session go into a local palace, so the agent's next session remembers them]

[CI status] [Latest release] [License: MIT] [Platforms: Linux, macOS, Windows] [Rust 1.82+] [Listed in the MCP registry] [Listed on mcpservers.org] [Works with Claude Code, Codex, Cursor, Gemini CLI, VS Code, OpenCode, Cline, Zed, Goose, Windsurf and Claude Desktop]

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 (or another supported model) 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/palacebrew install palace

To upgrade, refresh the tap first. Without brew update, Homebrew can keep reporting the old version as already installed:

brew update && brew upgrade palace

When a release changes the memory protocol rule (the CHANGELOG says so), run palace install again afterwards so your agents get the new rule text.

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-rscargo build --release   # binary at target/release/palace

The embedding model (~23 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 machinepalace doctor             # check that every client is wired uppalace 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:

[Inside a palace: a project wing my_api with rooms backend, decisions, and testing full of drawers; a topic wing sales_notes; a knowledge graph fact my_api uses_queue SQLite; and agent diaries from claude and codex]

_{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

_{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).

Embedding models

Palace ships with all-MiniLM-L6-v2 and can switch a palace to another local model. Every vector is rebuilt from the stored text, so switching never loses memory.

Experimental models scored below MiniLM on the coding-agent eval and about level on probes from a real palace (see the bake-off). You can try them on your own palace: verification decides whether it switches.

palace embeddings list                       # models, which is active, which are downloadedpalace embeddings estimate minilm-l6-v2-q8   # time, disk and latency, measured on this machinepalace embeddings migrate minilm-l6-v2-q8    # build, verify, switch (e.g. an older fp32 palace)palace embeddings rollback                   # back to the previous model, instantlypalace embeddings cleanup                    # drop the previous model's vectors

migrate builds the new vectors in batches while search keeps using the current model, so it is safe to interrupt and rerun. When the vectors are built, it checks the new model against the current one on 200 known-item probes taken from your palace (hybrid and vector-only recall@1/@5, plus query latency). It switches only if recall@5 drops by at most 0.01 and p95 query latency stays within budget. The budget is twice the current model's p95, and at least 25 ms. Run palace embeddings cutover --force to switch anyway, or palace embeddings cancel to keep the current model.

Setting embedding_model in config.json (or PALACE_EMBEDDING_MODEL) picks the model for a new, empty palace. An existing palace never switches on its own. palace status, palace_status, and palace_session_context report the pending migration until you run migrate. Nothing migrates on its own: you run migrate, or ask your agent to, which uses the palace_embeddings MCP tool (it runs the same steps in the background of the server). In remote mode the server owns the model, so these commands run on the server.

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:

[One agent session, four hooks: session start, prompt submit, and post tool use recall from palace.db; stop asks the agent to save a diary entry and decisions]

_{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 onlypalace install --scope project --path ./my-project   # project-level rule filespalace install --no-rule                             # MCP wiring only, no rulepalace install --dry-run                             # preview the changespalace 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).

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

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

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 keypalace remote onpalace remote test      # runs the MCP handshake and reports the tool countpalace remote status    # mode, endpoint, masked keypalace 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")]

_{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 dayspalace gain --project my_project --since 7dpalace gain --history             # recent tool callspalace gain --jsonpalace 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

🔧 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, embedding_model, embedding_latency_budget_ms. 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_projectrooms:  - 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 51 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?

This eval is partly tuned, on purpose. The ranker's coding-agent boosts include a handful of phrases taken from the eval's own questions (such as "explain themselves", "proof priority" and "release theme"), so the tuned scores here and in the bake-off below are higher than general ranking alone would give. We keep them until general improvements reach the same numbers, then remove them. Measured on the same code with those phrases removed:

The phrases have no effect outside this eval: real-palace probes and every per-session LongMemEval ranking are identical with and without them.

Embedding model bake-off

tests/embedding_bakeoff.rs scores every embedding model on this eval with embedded drawers, and on 200 known-item probes from a real palace. It also measures indexing throughput and query latency. Each probe is an inner sentence of a drawer with about 30% of its words dropped.

PALACE_RUN_BAKEOFF=1 PALACE_BAKEOFF_DB=~/.palace/palace/palace.db \  cargo test --release --test embedding_bakeoff -- --nocapture

Hybrid recall, 400 drawers sampled from a 10,800-drawer palace, Windows, 16 threads. int8 models embed one text per call: their activation scales cover the whole batch, so batching would make a text's vector depend on its neighbours. On longer drawers (the synthetic speed corpus) q8 indexes about 1.7x faster than fp32. BGE ranks best with a 0.5 cosine floor and an even cosine/BM25 split, which its registry entry carries. A model ships as non-experimental only if it matches MiniLM on the coding-agent eval, so the BGE models stay experimental for now; on real-palace probes they are about level. verify measures the same thing on your own palace before it switches.

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.

The exchange-pair row is how Palace actually stores conversations: each session goes through the same normaliser and chunker as palace mine-convos (one chunk per user turn and its reply), and a session ranks by its best chunk. The per-session rows are the usual LongMemEval granularity, for comparison with other systems.

🔒 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

📚 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 fmtcargo clippy --all-targets --all-features -- -D warningscargo test --all-featurescargo 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

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

📜 License

MIT