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Computer or cloud★ 34,245MITUpdated 4 days ago
Install
Slowave
Overview
Description from the maker (original English)
Local adaptive memory for AI coding agents through a five-tool MCP lifecycle.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Developer tools
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"slowave": {
"command": "uvx",
"args": [
"slowave",
"slowave-mcp"
]
}
}
}Run this in your terminal
claude mcp add slowave -- uvx slowave slowave-mcpOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"slowave": {
"command": "uvx",
"args": [
"slowave",
"slowave-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"slowave": {
"type": "stdio",
"command": "uvx",
"args": [
"slowave",
"slowave-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account slowave-ai, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Developer toolsi
- More stars than 0% of the other 4908 servers in this category
- Published to the registry more recently than 46% of the other 4908 servers in this category
- Basic checks equal to or better than 100% of the other 4908 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Slowave safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.20.3) reached the registry 2 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: GitHub, Desktop Commander, Cloudflare MCP
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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Runs on your computer★ 10,225MITUpdated 16 days ago
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Runs in the cloud★ 4,446Apache-2.0Updated 14 months ago
Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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
https://github.com/user-attachments/assets/2d4eeb4e-419f-46fa-b23e-cf501a715d1e
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 LRLLM["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 slowaveslowave 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 stoppipx 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.
sequenceDiagramactor 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 dashboardFrom 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.
| Client | macOS | Linux | Windows | Setup |
|---|---|---|---|---|
| Claude Code | ✅ | ✅ | ✅ | slowave setup --client claude-code |
| Copilot CLI | ✅ | ✅ | ✅ | slowave setup --client copilot |
| Codex | ✅ | ✅ | ✅ | slowave setup --client codex |
| Cursor | ✅ | ✅ | ✅ | slowave setup --client cursor ¹ |
| OpenCode | ✅ | ✅ | ✅ | slowave setup --client opencode |
| Windsurf | ✅ | ✅ | ✅ | slowave setup --client windsurf |
| Cline | ✅ | ✅ | ✅ | slowave setup --client cline |
| Antigravity | ✅ | ✅ | ✅ | slowave setup --client antigravity |
| Claude Desktop | ✅ | ✅ | ✅ | slowave setup --client claude-desktop ¹ |
| All the above | slowave setup |
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 asused,not_used,unassessable,irrelevant,already_known, orstale.Commit: save the task outcome and any reusable procedure.
A background worker consolidates relevant memories and procedures.
Slowave MCP lifecycle
flowchart LRA[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
Only the beginning is shown; read the rest in the repo