# braindb

> Use this tool when you need to store and retrieve complex, contextual knowledge for AI agents, enabling them to learn from experiences and make informed decisions. Braindb solves problems of knowledge retention and recall, allowing agents to adapt to changing environments and user needs. It accepts structured and unstructured data as input and provides queryable, semantic outputs, ideal for use cases requiring long-term memory and insight generation.

Canonical page: https://skillsregistry.net/skills/chair4ce-braindb  
JSON: https://api.skillsregistry.net/v1/skills/chair4ce-braindb

## Description

Persistent, semantic memory for AI agents.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/chair4ce-braindb)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "chair4ce-braindb"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/chair4ce-braindb` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/chair4ce-braindb/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt