Natural memory with super-natural powers.

Human-like memory with in-human skills.

Continuous context summaries as you work.

Forgets gracefully. Recalls perfectly.

Compaction that doesn't forget.

Ragger is local-first semantic memory for AI agents — and for the humans working with them. Think of it as running compaction with tiered recall: every turn your agent has gets captured and summarized in the background while it stays warm, then served back as a layered "what just happened?" payload that fades the way a person's memory fades — while keeping the verbatim transcript a single query away. All embeddings are local. The whole database is one SQLite file you own.

C++ port of the original Python Ragger Memory, diverged at v0.9.4 — now the sole focus.

• Captured turns, summarized live. Turns land in the DB as they happen; an in-daemon worker writes per-turn summaries immediately and closes session summaries on idle, all without blocking the agent. If the summarizer model is unreachable, a draft is stored and rewritten later — the agent never waits on inference it didn't ask for.
• Recipes for the recall payload. Instead of dumping a raw transcript, build_context walks back from the latest prompt and assembles a token-budgeted mix of raw turns, turn-summaries, session summaries, project summaries, and decisions. Five recipes ship; users can drop more JSON into ~/.ragger/recipes/.
• Hybrid search. BM25/FTS5 keyword search blended with dense vector cosine via Eigen3. Configurable weights; both signals normalized before blending.
• Local embeddings. all-MiniLM-L6-v2 via ONNX Runtime (384-dim). Stored as IEEE half (f16) by default to halve disk; in-memory math stays f32. Re-encode any time with ragger rebuild-embeddings.
• HTTP and MCP, same data. Daemon serves a REST API; ragger mcp speaks JSON-RPC over stdio. Bearer-token auth on remote requests, unix-socket and loopback are pre-authenticated.
• Markdown-aware import. Heading-aware chunking; Claude Code and claude.ai conversation archives import with original timestamps.

Single binary. Single-user out of the box. No external services, no network calls once the embedding model is on disk.

Per-user, no sudo (binary → ~/.local/bin, data → ~/.ragger/):

./scripts/build.sh        # check deps, build
./scripts/install.sh      # install binary + user service unit + recipes
ragger start              # start the daemon

install.sh is idempotent — re-run after a rebuild to refresh the binary, the service unit, and the shipped recipes. Your config, database, and any custom recipe files are preserved.

ragger store "The deploy script needs Node 18+"
ragger search "deployment requirements"
ragger import notes.md

ragger recipe                # interactive picker over available recipes
ragger recipe reconnect      # set the active recipe (persisted in the DB)

ragger start | stop | restart | status

Dev build (manual cmake):

cmake -B build -DBOOST_ROOT=/opt/local/libexec/boost/1.88 \
  && cmake --build build -j8
./build/ragger version

• System: SQLite3, Eigen3, Boost (program_options), OpenSSL, libcurl, Rust (for tokenizers-cpp; rustup stable is fine).
• Auto-fetched at configure time: ONNX Runtime (downloaded into the build dir for your platform). Drop a matching vendor/onnxruntime/ in place if you want an offline build.
• Vendored (already in the repo): cpp-httplib, nlohmann/json, tokenizers-cpp source.

# macOS — MacPorts
sudo port install boost eigen3 sqlite3 rust openssl curl cmake

# macOS — Homebrew
brew install boost eigen sqlite openssl@3 curl cmake rustup-init && rustup-init -y

# Debian / Ubuntu
sudo apt install build-essential cmake pkg-config \
                 libboost-program-options-dev libeigen3-dev libsqlite3-dev \
                 libssl-dev libcurl4-openssl-dev
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y

# Fedora / RHEL
sudo dnf install gcc-c++ cmake pkgconf-pkg-config \
                 boost-devel eigen3-devel sqlite-devel \
                 openssl-devel libcurl-devel
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y

Then:

cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j
./scripts/install.sh    # places binary + downloads ~90 MB embedding model

macOS (arm64 and x86_64) and Linux (x86_64 and aarch64) are supported. Windows needs porting (fork(), the bash/launchctl/systemd install scripts).

cd build && ctest --output-on-failure   # 12 suites

GPL v3. Commercial (non-GPL) licenses available — contact reid@diskerror.com.