agentMemory
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
Best use case
agentMemory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
Teams using agentMemory should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
How agentMemory Compares
| Feature / Agent | agentMemory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# agentMemory Skill
This skill extends your capabilities by providing a persistent, searchable memory bank that automatically syncs with project documentation.
## Prerequisites
- Node.js installed
- Check if `agentMemory` is already installed in the project:
```bash
ls -la .agentMemory
```
## Setup
1. **Install Dependencies**:
```bash
npm install
```
2. **Build the Project**:
```bash
npm run compile
```
3. **Start the Memory Server**:
You need to run the MCP server to interact with the memory bank.
```bash
npm run start-server <project_id> <absolute_path_to_workspace>
```
*Note: This skill typically runs as a background process or via an mcp-server configuration. ensuring it is running is key.*
## Capabilities (MCP Tools)
Once the server is running, you can use these tools:
### `memory_search`
Search for memories by query, type, or tags.
- **Args**: `query` (string), `type?` (string), `tags?` (string[])
- **Usage**: "Find all authentication patterns" -> `memory_search({ query: "authentication", type: "pattern" })`
### `memory_write`
Record new knowledge or decisions.
- **Args**: `key` (string), `type` (string), `content` (string), `tags?` (string[])
- **Usage**: "Save this architecture decision" -> `memory_write({ key: "auth-v1", type: "decision", content: "..." })`
### `memory_read`
Retrieve specific memory content by key.
- **Args**: `key` (string)
- **Usage**: "Get the auth design" -> `memory_read({ key: "auth-v1" })`
### `memory_stats`
View analytics on memory usage.
- **Usage**: "Show memory statistics" -> `memory_stats({})`
## Workflow
1. **Initialization**: The first time you run this in a project, it may attempt to import existing markdown memory banks from `.kilocode/`, `.clinerules/`, or `.roo/`.
2. **Development Loop**:
- **Before Task**: Search memory for relevant context.
- **During Task**: Use read/search to answer questions.
- **After Task**: Write new findings to memory.
3. **Sync**: Your writes are automatically synced to standard markdown files in the project.Related Skills
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