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[PyPI](https://pypi.org/project/slowave/)
[Python](https://pypi.org/project/slowave/)
[PyPI Status](https://pypi.org/project/slowave/)
[License: AGPL-3.0-or-later](https://github.com/slowave-ai/slowave/blob/main/LICENSE)
---
[Slowave](https://github.com/slowave-ai/slowave/blob/main/img/slowave-logo-text.jpeg)
**Living memory layer across your AI tools.**
---
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:
> **An effective memory system should help an agent achieve its goals.**
Agent memory is not only a retrieval problem. A useful memory system should retain what helps the agent, 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**Improves with use:** Useful memories strengthen, irrelevant ones lose priority, stale knowledge can be suppressed or superseded.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.
### Supported integrations:
- Claude Code
- Codex
- Cursor
- Cline
- Windsurf / Devin Desktop
- OpenCode
- Claude Desktop
See [platform coverage and manual steps](https://github.com/slowave-ai/slowave#supported-clients).
## 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](https://github.com/slowave-ai/slowave/blob/main/docs/install.md).
Important
**No LLM API key required.**
To remove Slowave, see the [removal guide](https://github.com/slowave-ai/slowave/blob/main/docs/install.md#remove-slowave).
## What changes in your workflow?
Slowave is transparent to your work.
You keep working with your agent as usual.
When your agent encounters a durable fact or decision, the installed lifecycle directs it to preserve that claim.
On a later task, Slowave can return a compact, scoped set of relevant recorded memories 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.
[Slowave local dashboard](https://github.com/slowave-ai/slowave/blob/main/img/overview.jpg)
[Memory detail](https://github.com/slowave-ai/slowave/blob/main/img/schemas.jpg)
[Procedures](https://github.com/slowave-ai/slowave/blob/main/img/procedures.jpg)
[Retrieval](https://github.com/slowave-ai/slowave/blob/main/img/retrieval.jpg)
[Activity](https://github.com/slowave-ai/slowave/blob/main/img/activity.jpg)
[Memory graph](https://github.com/slowave-ai/slowave/blob/main/img/graph.jpg)
## Supported clients
Client coverage is actively expanding. Suggest more integrations or report broken ones with setup details.
✅ = manually verified · ⬜ = pending verification
| Client | macOS | Linux | Windows | Setup |
| --- | --- | --- | --- | --- |
| [Claude Code](https://github.com/slowave-ai/slowave/blob/main/integrations/claude-code/README.md) | ✅ | ✅ | ✅ | `slowave setup --client claude-code` |
| [Cline](https://github.com/slowave-ai/slowave/blob/main/integrations/cline/README.md) | ✅ | ✅ | ✅ | `slowave setup --client cline` |
| [Cursor](https://github.com/slowave-ai/slowave/blob/main/integrations/cursor/README.md) | ✅ | ✅ | ✅ | `slowave setup --client cursor` ¹ |
| [Windsurf](https://github.com/slowave-ai/slowave/blob/main/integrations/windsurf/README.md) | ✅ | ✅ | ✅ | `slowave setup --client windsurf` |
| [Claude Desktop](https://github.com/slowave-ai/slowave/blob/main/integrations/claude-desktop/README.md) | ✅ | ✅ | ✅ | `slowave setup --client claude-desktop` ¹ |
| [OpenCode](https://github.com/slowave-ai/slowave/blob/main/integrations/opencode/README.md) | ✅ | ✅ | ✅ | `slowave setup --client opencode` |
| [Codex](https://github.com/slowave-ai/slowave/blob/main/integrations/codex/README.md) | ✅ | ✅ | ✅ | `slowave setup --client codex` |
| All the above | | | | `slowave setup` |
¹ requires one manual paste after setup
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](https://github.com/slowave-ai/slowave/blob/main/docs/install.md#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`: 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](https://github.com/slowave-ai/slowave/blob/main/docs/architecture.md) and [design.md](https://github.com/slowave-ai/slowave/blob/main/docs/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
```
Loading
See [architecture.md](https://github.com/slowave-ai/slowave/blob/main/docs/architecture.md) and [design.md](https://github.com/slowave-ai/slowave/blob/main/docs/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.
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](https://github.com/slowave-ai/slowave/blob/main/docs/benchmarks.md) before treating any result as a
production-quality claim.
## Documentation
- [Mintlify documentation](https://slowave-ai.mintlify.app/): full auto-generated documentation
- [design.md](https://github.com/slowave-ai/slowave/blob/main/docs/design.md): design rationale, boundaries, and positioning
- [architecture.md](https://github.com/slowave-ai/slowave/blob/main/docs/architecture.md): brain-inspired memory model and lifecycle
- [install.md](https://github.com/slowave-ai/slowave/blob/main/docs/install.md): installation, setup, lifecycle instructions, modified files, and removal
- [benchmarks.md](https://github.com/slowave-ai/slowave/blob/main/docs/benchmarks.md): benchmark results, methodology, and reproduction
- [troubleshooting.md](https://github.com/slowave-ai/slowave/blob/main/docs/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](https://github.com/slowave-ai/slowave/blob/main/CONTRIBUTING.md) before submitting a pull request.
## License
Slowave is open source under the [GNU AGPL-3.0-or-later](https://github.com/slowave-ai/slowave/blob/main/LICENSE) license.