Slowave

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English | 中文

Living memory layer across your coding agents and AI tools.

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

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https://github.com/user-attachments/assets/2d4eeb4e-419f-46fa-b23e-cf501a715d1e

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

Slowave adapts as you use it, building reusable context over time so your agent can learn from past experience.

It runs on your machine and requires no LLM API key for memory maintenance. The local dashboard lets you inspect what your agent remembers and which memories it finds valuable.

At the core Slowave is built around a simple concept:

Reasoning and memory feed each other in a continuous loop.

graph LR

LLM["Reasoning (Claude, Codex, Copilot, etc.)"] MEM["Memory (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• Evolves through feedback: Useful memories grow stronger, irrelevant ones lose priority, stale knowledge can be superseded.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

To install and configure all clients in one go:

pipx install slowave

slowave setup

To configure just one client at a time, see the installation reference.

[!IMPORTANT]

No LLM API key required.

Upgrading Slowave

When upgrading to a new version:

slowave stop

pipx upgrade slowave slowave setup

If you are in the middle of a coding client session while upgrading you might have to close the session and re-open it (via /resume command or similar, depending on client).

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 (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 sessions: open the session behind any memory, procedure, or retrieval and step through its ordered Slowave calls, feedback, and linked memories.
• 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.

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