slowave 0.12.1

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

Slowave

A living local memory layer across your AI tools.

Install once. Your AI tools share a persistent local memory across sessions and clients.

Slowave continuously adapts to your work — capturing decisions, preferences, and context over time.

• No additional LLM calls for memory operations
• No data leaves your machine

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How it feels like

You work daily with your AI tools:

• Day 1 — cold start: Slowave bootstraps memory from existing markdown knowledge, initializing the embedding-based memory state.
• Week 1 — emerging patterns: new interactions begin reinforcing relevant signals, forming stable associations.
• Month 1 — context consolidates: frequently reinforced information becomes consistently retrievable, low-signal data fades.

Multiple AI clients continuously build and reuse the same evolving memory over time:

• no markdown management
• no static RAG
• no LLM extra calls

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What you gain over time

Slowave becomes more useful the more you use it.

• Less repetition — you stop re-explaining the same context across sessions
• Faster continuity — returning to a project feels like resuming, not restarting
• Cross-tool consistency — your working context follows you across AI clients
• Persistent working memory — decisions, patterns, and preferences are retained beyond individual chats
• Lower cognitive overhead — no need to maintain external memory files or prompt scaffolding

Slowave does not just store information — it compounds it into usable context.

The result is a continuous working context that follows you across tools and time.

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Why Slowave is different

Slowave is a brain-inspired architecture.

Principle

The human brain does not need language to remember: experiences are encoded, replayed during sleep, abstracted into patterns, and strengthened or forgotten.

Language acts as the interface, not the storage medium.

Slowave mirrors this separation: the language model is a client of memory, not the memory system itself. Memory activity — consolidation, ranking, decay, retrieval — operates over embeddings rather than rewritten text.

Biological mapping

Neuroscience describes human memory as two complementary learning systems:

• the hippocampus learns fast — it captures individual experiences in one shot, keeping them distinct;
• the neocortex learns slowly — it extracts the statistical regularities across many experiences into stable, general knowledge.

Neither system alone is enough. Fast learning without abstraction produces a pile of anecdotes; slow learning without an episodic buffer forgets everything that happened only once.

Slowave mirrors this division:

Human brain	Slowave	What it does
Hippocampus	Episodic layer	Captures individual experiences as they happen
Slow-wave sleep	Offline consolidation	Replays and groups episodes into prototypes, building a graph of associations — without the LLM
Neocortex	Schema layer	Holds stable, abstracted knowledge as typed claims: decisions, preferences, constraints, conventions

Three additional mechanisms emerge from this architecture:

• Spreading activation. Recall propagates across the prototype association graph — a partial cue can recover a whole memory, the way a fragment of a song brings back the entire experience.
• Hebbian reinforcement. Memories that prove useful strengthen and become easier to retrieve; unused memories gradually fade. Forgetting is a feature that keeps the signal-to-noise ratio high.
• Reconsolidation. Recalling a memory reopens it. Feedback after retrieval — useful, stale, wrong — modifies the memory itself. Memory is a living state, not an append-only log.

Design rationale

Architecture

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Installation

Global setup

Install Slowave and configure every detected client in one go:

pipx install slowave

# or

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

Then wire everything up:

slowave setup --dry-run   # preview what will change
slowave setup             # apply: MCP configs, lifecycle instructions, hooks, services
slowave doctor            # verify: daemon health, client detection

slowave setup is idempotent and safe to run multiple times. The HTTP MCP daemon and background consolidation worker start automatically as system services.

Claude Desktop and Cursor require one manual paste after setup because their instruction surfaces cannot be modified programmatically. slowave setup prints the exact text and path.

Per-client setup

To configure a single client, or to find client-specific details:

Client	Integration doc
Claude Code	integrations/claude-code/README.md
Claude Desktop ¹	integrations/claude-desktop/README.md
Cline	integrations/cline/README.md
Cursor ¹	integrations/cursor/README.md
OpenCode	integrations/opencode/README.md
Windsurf	integrations/windsurf/README.md

¹ requires one manual paste after setup

See the complete setup reference: docs/setup.md

Storage

The default embedding model downloads from Hugging Face on first use (~45 MB, cached locally). Subsequent runs work offline.

Memory is stored in a local SQLite database at ~/.slowave/slowave.db — fully inspectable, never leaves your machine. Not encrypted by default; protect sensitive data with OS permissions or full-disk encryption.

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Dashboard

Monitor Slowave’s health, incoming events, and memory consolidation in real time.

Drill down from consolidated schemas to the underlying episodes, sessions, and raw events.

Explore the evolving memory graph as Slowave forms connections across experiences.

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Supported clients

Work in progress — suggest more integrations or report broken ones with setup details.

✅ = manually verified · ⬜ = pending verification

Client	macOS	Linux	Windows	Setup
Claude Code	✅	✅	✅	slowave setup --client claude-code
Cline	✅	✅	✅	slowave setup --client cline
Cursor	✅	✅	⬜	slowave setup --client cursor ¹
Windsurf (Devin)	✅	✅	⬜	slowave setup --client windsurf
Claude Desktop	✅	✅	✅	slowave setup --client claude-desktop ¹
OpenCode	✅	⬜	⬜	slowave setup --client opencode
All the above				slowave setup

¹ requires one manual paste after setup

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Benchmarks

Benchmarks were run internally during development to evaluate recall quality, stability, and context efficiency. Results have not yet been independently reproduced.

Slowave does not use an LLM for memory operations; all evaluation is based on embedding retrieval and local consolidation.

Benchmark	What it evaluates	Scorer	Result
LongMemEval	Multi-session factual recall with noise and distractors	keyword-overlap / local	87.8%
LoCoMo	Cross-session conversational recall across categories	keyword-overlap / local	74.6%
StaleMemory	Detection of outdated or superseded preferences	keyword-overlap / local	45% overall; 86–89% for concrete preferences

These results are not directly comparable with systems that use LLM-as-a-judge scoring, since Slowave relies on embedding-based matching metrics.

Full benchmark methodology and reproducibility details:

• docs/benchmarks.md

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Honest limits

Slowave is useful in practice but intentionally constrained by its design.

• It recalls stored information; it does not infer missing preferences.
• It retrieves relevant memories; it does not perform reasoning over memory graphs.
• Contradiction handling is heuristic and may not always resolve conflicts correctly.
• It is not designed for safety-critical or compliance-critical memory use cases.
• Memory quality depends on the quality and consistency of prior interactions.

These limitations are a direct consequence of the zero-LLM memory design rather than implementation gaps.

See: docs/limitations.md

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What it is not

Slowave is not:

• a language model
• an agent framework
• a reasoning system
• a prompt manager
• a markdown-based memory store
• a vector database wrapper

The AI client remains responsible for planning, reasoning, and execution.

Slowave only provides persistent, evolving context injection based on prior interactions.

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Documentation

• design.md — design rationale, boundaries, and positioning
• architecture.md — brain-inspired memory model and lifecycle
• setup.md — setup reference, lifecycle block, files modified
• benchmarks.md — evaluation methodology and results
• limitations.md — known constraints and trade-offs
• token_efficiency.md — context efficiency analysis

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Contributing

Slowave is open source under the AGPL-3.0-or-later license.

Contributions are welcome, especially in:

• client integrations
• recall quality improvements
• evaluation datasets
• performance optimization

See CONTRIBUTING.md before submitting a pull request.