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persistent-agent-memory

Add persistent memory to any agent so it can remember prior work, maintain context across sessions, and continue long-running workflows. Use when: (1) remembering facts or preferences for later, (2) recalling stored memories by topic or intent, (3) forgetting/removing memories matching a query. NOT for: web search, file system search, or code search — use other tools for those.

divyvasal0 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install divyvasal/persistent-agent-memory

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

Persistent Agent Memory

Memory storage and retrieval powered by Coral Bricks. Store facts, preferences, and context; retrieve them later by meaning. All memories are stored in the default collection.

Setup

Set your API key (get one at https://coralbricks.ai):

export CORAL_API_KEY="ak_..."

Optionally override the API URL (defaults to https://search-api.coralbricks.ai):

export CORAL_API_URL="https://search-api.coralbricks.ai"

Tools

coral_store — Store a memory

Store text with optional metadata for later retrieval by meaning.

scripts/coral_store "text to store" [metadata_json]
  • text (required): Content to remember
  • metadata_json (optional): JSON string of metadata, e.g. '{"source":"chat","topic":"fitness"}'

Output: JSON with status (e.g. {"status": "success"}).

Example:

scripts/coral_store "User prefers over-ear headphones with noise cancellation"
scripts/coral_store "Q3 revenue was $2.1M" '{"source":"report"}'
coral_retrieve — Retrieve memories by meaning

Retrieve stored memories by semantic similarity. Returns matching content ranked by relevance.

scripts/coral_retrieve "query" [k]
  • query (required): Natural language query describing what to recall
  • k (optional, default 10): Number of results to return

Output: JSON with results array, each containing text and score.

Example:

scripts/coral_retrieve "wireless headphones preference" 5
scripts/coral_retrieve "quarterly revenue" 10
coral_delete_matching — Forget memories by query

Remove memories that match a semantic query. Specify what to forget by meaning.

scripts/coral_delete_matching "query"
  • query (required): Natural language query describing memories to remove

Output: JSON confirming the operation completed.

Example:

scripts/coral_delete_matching "dark mode preference"
scripts/coral_delete_matching "forget my workout notes"

Privacy

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Source information

Source
github
Repository
openclaw/skills
Path
skills/divyvasal/persistent-agent-memory
Version
—
Commit
5928346fa928480682d876f4949cf225092e066c
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# Persistent Agent Memory

Memory storage and retrieval powered by Coral Bricks. Store facts, preferences, and context; retrieve them later by meaning. All memories are stored in the default collection.

## Setup

Set your API key (get one at https://coralbricks.ai):

```bash
export CORAL_API_KEY="ak_..."
```

Optionally override the API URL (defaults to `https://search-api.coralbricks.ai`):

```bash
export CORAL_API_URL="https://search-api.coralbricks.ai"
```

## Tools

### coral_store — Store a memory

Store text with optional metadata for later retrieval by meaning.

```bash
scripts/coral_store "text to store" [metadata_json]
```

- `text` (required): Content to remember
- `metadata_json` (optional): JSON string of metadata, e.g. `'{"source":"chat","topic":"fitness"}'`

Output: JSON with `status` (e.g. `{"status": "success"}`).

Example:

```bash
scripts/coral_store "User prefers over-ear headphones with noise cancellation"
scripts/coral_store "Q3 revenue was $2.1M" '{"source":"report"}'
```

### coral_retrieve — Retrieve memories by meaning

Retrieve stored memories by semantic similarity. Returns matching content ranked by relevance.

```bash
scripts/coral_retrieve "query" [k]
```

- `query` (required): Natural language query describing what to recall
- `k` (optional, default 10): Number of results to return

Output: JSON with `results` array, each containing `text` and `score`.

Example:

```bash
scripts/coral_retrieve "wireless headphones preference" 5
scripts/coral_retrieve "quarterly revenue" 10
```

### coral_delete_matching — Forget memories by query

Remove memories that match a semantic query. Specify what to forget by meaning.

```bash
scripts/coral_delete_matching "query"
```

- `query` (required): Natural language query describing memories to remove

Output: JSON confirming the operation completed.

Example:

```bash
scripts/coral_delete_matching "dark mode preference"
scripts/coral_delete_matching "forget my workout notes"
```

## Privacy

[Privacy...

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