slowave 0.5.10

Brain-inspired long-term memory for AI agents — zero LLM during ingest or retrieval

Slowave

One private memory layer across your AI clients.

Slowave gives every MCP-compatible tool a shared, persistent memory. No LLM in the loop, fully local, $0 per query.

Demo

See Slowave in action:

Cold start discovers project facts. Rule stored in Claude. Recalled days later in Cline — same memory, different sessions, different tools.

What makes Slowave different?

👊 Central memory across every AI tool.
Claude Code, Cline, Claude Desktop, Cursor, Windsurf, and any MCP-compatible client read from and write to the same memory store. Fix a bug in Claude Code tonight — Cline recalls the lesson tomorrow. Context follows you across tools instead of dying inside one chat.

🧠 Memory that learns from use.
Slowave runs a 5-verb cognitive cycle: activate → remember → recall → reinforce → commit. Useful memories get stronger, stale ones decay, and outdated facts are superseded automatically. Recall is shaped by salience, time, scope, and feedback — not just raw vector similarity.

🔮 Zero-config cold start.
Drop Slowave into a new project and it auto-discovers key facts from CLAUDE.md, README.md, and other knowledge files. Your agents walk into context without you writing a single prompt.

⚙️ Procedural memory: workflows that stick.
Store reusable procedures — "how we do deploys in this repo", "steps to implement a new feature across projects". Recalled by goal and situation, not keyword search. Your agents learn habits, not just facts.

📐 Smart scoping.
Memory is scoped to exactly what matters: project:my-app, domain:cooking, relationship:alex — or unscoped for universal context. Cross-project bleed is prevented by default.

🔒 Fully local, zero LLM calls.
Ingestion, consolidation, and recall run on your machine using embeddings, FAISS, and SQLite — no API key, no cloud backend. Memory operations cost $0 per query.

💰 Compact context instead of history replay.
Slowave injects a small working-memory brief instead of replaying full chat history. In internal tests, this reduced context size by 86% over 20 sessions while preserving high recall quality. See the test →

What Slowave is — and isn't

Slowave is not a markdown file manager, not a static RAG system, and not an LLM wrapper over a vector database.

It's built on a single idea:

Memory consolidation does not require language.

Under the hood, Slowave has two layers:

• Latent layer — pure geometry over embeddings. Consolidation, reinforcement, decay, supersession, and graph-based connections all run here. Zero LLM calls, ever.
• Symbolic layer — the language interface. Text is stored and retrieved, but only rendered into natural language when an agent asks for it.

The LLM is an output channel — it verbalizes what memory already knows. It never operates on memory itself.

Design rationale → — Architecture →

The big picture

Install

pipx

pipx install slowave

Homebrew

brew tap mrsalty/slowave https://github.com/mrsalty/slowave
brew install slowave

Then run setup:

slowave setup --dry-run
slowave setup
slowave doctor

slowave setup detects your platform, wires every client it finds, injects lifecycle hooks, and starts the background worker. Idempotent and safe to re-run. See what gets modified →

[!NOTE] The default text encoder downloads its model from HuggingFace on first use (~45 MB); subsequent runs work fully offline.

[!IMPORTANT] Claude Desktop: after setup, paste the lifecycle block into Settings → General → Instructions for Claude. Cursor: after setup, paste the lifecycle block into Settings → Rules for AI. slowave setup prints the exact text and location for both. All other clients (Cline, Claude Code, Windsurf) are fully automated.

slowave doctor   # verify installation
slowave stats    # memory snapshot

Memory is stored at ~/.slowave/slowave.db. No Ollama, no vector database, no cloud service required.

Privacy: Slowave stores all memory (facts, episodes, embeddings, logs) locally in a plain SQLite database file. No memory leaves your machine — it's never sent to a cloud service, and the database file is unencrypted (you can inspect it with SQLite tools). If you store sensitive information, protect the database file using OS-level permissions or full-disk encryption.

Full install guide →

Benchmarks

93.4% LongMemEval · 81% LoCoMo · 86–89% stale-memory detection — all with zero LLM calls, fully local. Full benchmarks →

What Slowave remembers

Anything that should survive across sessions and tools: preferences, decisions, constraints, lessons learned, open questions, and reusable workflows — for work, research, or personal use. Each memory carries a timestamp, decays if never recalled, and strengthens when it proves useful. Contradictions are detected geometrically and old facts are superseded automatically — no LLM required.

Dashboard

Keep Slowave always under control through the local dashboard.

Use it, and Slowave starts connecting the dots.

Documentation

docs/design	the brain-inspired rationale behind Slowave
docs/architecture.md	How memory consolidation works
docs/install.md	Install, setup, per-client wiring, troubleshooting
docs/slowave_setup.md	slowave setup command help
docs/manual_setup.md	Step-by-step manual configuration guide
docs/benchmarks.md	Per-category results, strengths, known gaps, reproducibility
docs/token_efficiency.md	Token efficiency vs. history replay and static knowledge files
docs/limitations.md	Capability gaps, design trade-offs, deployment limits
docs/cli.md	CLI reference
docs/dashboard.md	Local web UI (slowave dashboard)

Contributing

Slowave is open source under AGPL-3.0-or-later. Bug reports, install feedback, and focused improvements are welcome — read CONTRIBUTING.md before opening a PR. Commercial licensing terms may be offered in the future.