Slowave

[Slowave]

Living memory layer across your coding agents and AI tools.

[PyPI version] [圖片] [圖片] [圖片]

Supports: Claude Code, Copilot CLI, Codex, Cursor, OpenCode, Windsurf/Devin Desktop, Cline, Claude Desktop

Slowave is a local MCP memory server that carries your decisions, constraints and procedures across your coding sessions and tools.

Slowave adapts through your usage, building over time compounding reusable context for your agent so that it can learn from experience.

It runs on your machine. No separate LLM for memory maintenance. With the local dashboard you can inspect what your agent remembers and reports valuable from memory.

At the core Slowave is built around a simple concept:

Reasoning and memory feed each other in a continuous loop.

graph LR    LLM["Reasoning<br/>(Claude, Codex, Copilot, etc.)"]    MEM["Memory<br/>(Slowave)"]        LLM -->|Feedback| MEM    MEM -->|Retrieve| LLM    MEM -->|Adapt| MEM

Slowave retains what helps agents achieve their goals, weakens what does not, and continuously adapts based on use. It does this through a continuous feedback loop between your agent and its memory. It works on 5 simple MCP endpoints:

activate → remember → recall → feedback → commit

Memory is continuously reshaped by use rather than a static collection of facts waiting to be retrieved.

• Keep context across tasks: Your agents can reuse recorded decisions, preferences, constraints, and lessons.
• Evolves through feedback: Useful memories grow stronger, irrelevant ones lose priority, stale knowledge can be superseded.
• Learns from experience: Decisions, outcomes, and multi-step solutions can become reusable context your agents can act on.
• Runs locally: Slowave stores memory in SQLite and does not send it to a hosted memory service.
• No LLM API key: The memory core performs maintenance and retrieval without LLM calls or an LLM API key.
• Inspectable and measurable: Review memories, retrievals, feedback, procedures, and system performance in the local dashboard.

The first useful payoff is simply not having to repeat the same constraint in the next task.

Over time, the way you work becomes reusable context for your agent.

_{Platform coverage and setup details}

Installation

Quick start

pipx install slowaveslowave setup --dry-runslowave setup

The quick start configures every detected client. To configure just one client at a time, see the installation reference.

[!IMPORTANT] No LLM API key required.

For upgrades and service management, see Install, upgrade, run, and remove Slowave. Use slowave start, slowave stop, and slowave restart for installed services; use slowave status --services and slowave doctor to diagnose problems. See Troubleshooting for recovery and logs, or run slowave docs troubleshooting to open it from your terminal.

Workflow

You keep working with your agent as usual.

You can switch agents or start new sessions.

Slowave enriches your agents context with relevant memories and procedures. Your agent feedbacks Slowave on what was useful. Slowave keeps building relevant context for your agents in the background.

sequenceDiagram    actor User as You    participant Agent as Your Agent<br/>(Claude Code, Codex, etc.)    participant Slowave    participant Worker as Slowave Worker
    User->>Agent: Prompt    Agent->>Slowave: Activate: Start task (set goal)    Slowave-->>Agent: Relevant memories and procedures
    opt         Agent->>Slowave: Remember: Save new knowledge    end
    opt         Agent->>Slowave: Recall: Look up more context        Slowave-->>Agent: Matching memories and procedures    end
    Agent->>Agent: Reason upon enriched context, work on the task    Agent->>Slowave: Feedback: Assess retrieved context    Agent->>Slowave: Commit: Record outcome    Slowave-->>Agent: Task outcome saved, session closed    Agent-->>User: Deliver the result
    Worker->>Slowave: Consolidate, reinforce, decay memories

Dashboard

The local dashboard shows what Slowave is actually doing with your agent's memory over time: what it remembers, what it retrieves, and what your agent reports using.

As your history grows, you can follow a memory from where it came from to the tasks where it was later retrieved and used. You can inspect learned procedures, see when retrieved context was useful or irrelevant, and understand whether Slowave is helping your agent reuse past work rather than simply accumulating memories.

Start the dashboard with:

slowave dashboard

From the dashboard you can:

• Inspect memories: see what was saved, where it came from, its current state, and when it was later retrieved or used.
• Inspect procedures: review reusable methods captured from previous work and see when they are retrieved and reused.
• Trace retrievals: see the request, the context Slowave actually returned, and the feedback reported by the agent.
• Follow activity: inspect tasks, retrievals, feedback, memory updates, and their links to each other.
• Explore memory connections: browse relationships and associations between memories.
• Check system health: inspect the database, background maintenance, backups, and local services.

The overview also helps answer whether the memory loop is working: how many memories and procedures exist, how often retrieved context is reported as used, how much is marked irrelevant, whether feedback is being recorded, and how creation, retrieval, and use change over time.

Slowave also shows a rough estimate of the context returned through all MCP endpoints, so you can keep an eye on memory overhead.

[Slowave local dashboard]

[Memory detail] [Procedures] [Retrieval] [Memory graph] [Activity]

Supported clients

Client coverage is actively expanding. Suggest more integrations or report broken ones with setup details.

✅ = supported. Copilot validation limits are listed below.

¹ requires one manual paste after setup

Copilot support targets local CLI execution. Automated integration tests pass, and native configuration discovery is verified on macOS. A user also confirmed memory reads and writes through Copilot CLI on macOS. Full model-driven lifecycle and native Windows/Linux qualification remain pending. IDE, desktop, and hosted/cloud Copilot support is deferred.

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

Memory is stored in plaintext in the current OS user's application-data directory. Slowave does not send it to a hosted memory service. See runtime data location.

How Slowave memory works

Slowave works through 5 simple MCP tools:

• Activate: start a task and load relevant memory.
• Remember: save a fact, decision, preference, or instruction.
• Recall: search memory during a task.
• Feedback: assess retrieved memories as used, not_used, unassessable, irrelevant, already_known, or stale.
• Commit: save the task outcome and any reusable procedure.

A background worker consolidates relevant memories and procedures.

Slowave MCP lifecycle

flowchart LR    A[Agent task] --> B[1. <i>activate</i><br/>start session]    B --> C[Scoped retrieval<br/>and session]    C --> D[Agent reasoning]    D --> E[2. <i>remember</i><br/>durable claims]    D --> F[3. <i>recall</i><br/>mid-task lookup]    C --> G[4. <i>feedback</i><br/>target assessments]    F --> G    E --> H[5. <i>commit</i><br/>outcome and verification]    G --> H    H --> I[(Local SQLite<br/>raw events and evidence)]    I --> J[Offline consolidation]    J --> K[(Episodes, prototypes,<br/>schemas, relations)]    K --> C

See architecture.md and design.md for details.

Boundaries

• Slowave is a memory layer, not a reasoning engine.
• It cannot recall information that was never recorded.
• Memory quality depends on the client agent and the feedback it provides.
• Slowave adds token overhead from tool calls and retrieved context.
• The local SQLite database is plaintext by default; protect it with OS permissions or full-disk encryption.

[!IMPORTANT] Slowave is public beta software. APIs, configuration, and storage schema may change, and migrations are not guaranteed before stable release.

Evaluation

The current evaluation notes report preliminary retrieval-evidence results, methodology, limitations, and commands for running new evaluations. They do not claim end-to-end agent accuracy or a comparison against other memory systems. See benchmarks.md before treating any result as a production-quality claim.

Documentation

• Mintlify documentation: full auto-generated documentation
• design.md: design rationale, boundaries, and positioning
• architecture.md: brain-inspired memory model and lifecycle
• install.md: installation, setup, lifecycle instructions, modified files, and removal
• benchmarks.md: benchmark results, methodology, and reproduction
• troubleshooting.md: daemon, worker, dashboard, client integration, database, backup/restore

Contributing

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

Contributions are welcome, especially in:

• installation and setup quality
• client integrations
• performance optimization

See CONTRIBUTING.md before submitting a pull request.

License

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