Slowave

Living memory layer across your coding agents and AI tools.

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

AI agents have large context windows, but that context ends with your current session. Open a new session, switch from Claude Code to Codex, and you have to restate the same decisions, constraints, and failed attempts. Slowave gives your agents one local, shared memory, without requiring a separate LLM for memory maintenance.

Slowave is designed as an adaptive memory layer rather than a static retrieval or summarisation system. It approaches agent memory from a different angle:

Memory and reasoning feed each other in a continuous loop.

graph LR
    LLM["Reasoning<br/>(Claude, Codex, etc.)"]
    MEM["Memory<br/>(Slowave)"]

    MEM -->|Retrieve| LLM
    LLM -->|Feedback| MEM
    MEM -->|Adapt| MEM

Agent memory is not only a retrieval problem. A useful memory system should retain what helps the agent achieving its goal, weaken what does not, and continuously adapt based on use. Slowave addresses this with a continuous feedback loop between your agent and its memory:

remember → recall → use → feedback → reinforce / weaken → decay

Slowave adapts the salience of stored memories based on your agent's feedback.

Over time, your agent’s feedback shapes what Slowave returns without needing a separate LLM judge inside the memory layer.

Memory becomes something continuously shaped 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 instead of making you repeat them.
• Improves with use: Useful memories strengthen, irrelevant ones lose priority, stale knowledge can be suppressed or superseded.
• Learns from experience: Decisions, outcomes, and multi-step solutions can become reusable memories and procedures.
• 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: Review memories, retrievals, feedback, procedures, and system activity 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.

See platform coverage and manual steps.

Installation

Quick start

pipx install slowave
slowave setup --dry-run
slowave setup

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

To remove Slowave, see the removal guide.

What changes in your workflow?

Slowave is transparent to your work. You keep working with your agent as usual.

Slowave is strictly connected to your agent in both directions:

• Agent → Slowave: When your agent encounters a durable fact, decision, or procedure the installed lifecycle directs it to preserve that claim into Slowave.
• Slowave → Agent: At the beginning of each task Slowave may return a compact, scoped set of relevant recorded memories or procedures to your agent, so that it can act upon its own memories.

What you will see while working with your agent:

• your agent activating Slowave for the current task and goal,
• Slowave retrieving relevant context to your agent,
• your agent sending feedback to Slowave on what was retrieved.
• your agent committing a Slowave session.

Optionally you will see:

• your agent invoking Slowave to remember durable facts.
• your agent invoking Slowave to recall something critical for the current task or goal.

Slowave does not decide whether a claim is true or important. Your agent makes that judgment and reports whether retrieved memory helped, was irrelevant, or became stale. Slowave maintains the resulting local memory.

Dashboard

Start the local dashboard with:

slowave dashboard

Open the dashboard in your browser, where you can inspect:

• Memories: browse saved decisions, constraints, and lessons.
• Procedures: review reusable step-by-step methods from past work.
• Retrievals: see what memory Slowave returned for each task.
• Activity: follow recent sessions, memory updates, and feedback.
• Memory graph: explore connections between related memories.
• System health: check the database, worker, backups, and local services.

Supported clients

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

✅ = manually verified · ⬜ = pending verification

¹ requires one manual paste after setup

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: mark retrieved memory as useful, irrelevant, or stale.
• Commit: save the task outcome and any reusable procedure.

A background worker consolidates relevant memories and procedures.

See architecture.md and design.md for more details.

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.
• It supplies relevant context, but the connected agent decides how to interpret and use it.
• Memory quality depends on the client agent and the feedback it provides.
• Scopes reduce accidental context leakage; use separate stores when hard isolation is required.
• 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.

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.