How do I install the Chromadb Memory skill?

Run npx skillmds@latest add kbarbel640-del/chromadb-memory in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Chromadb Memory safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: CAUTION, Skill Scanner: WARNING. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Chromadb Memory?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Chromadb Memory free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Chromadb Memory?

kbarbel640-del (@kbarbel640-del) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Chromadb Memory by kbarbel640-del — Agent Skill

Name: Chromadb Memory
Rating: 4.2 (5 reviews)
Author: kbarbel640-del

File contents

ChromaDB Memory

Long-term semantic memory backed by ChromaDB and local Ollama embeddings. Zero cloud dependencies.

What It Does

Auto-recall: Before every agent turn, queries ChromaDB with the user's message and injects relevant context automatically
chromadb_search tool: Manual semantic search over your ChromaDB collection
100% local: Ollama (nomic-embed-text) for embeddings, ChromaDB for vector storage

Prerequisites

ChromaDB running (Docker recommended):

docker run -d --name chromadb -p 8100:8000 chromadb/chroma:latest

Ollama with an embedding model:
```
ollama pull nomic-embed-text
```
Indexed documents in ChromaDB. Use any ChromaDB-compatible indexer to populate your collection.

Install

# 1. Copy the plugin extension
mkdir -p ~/.openclaw/extensions/chromadb-memory
cp {baseDir}/scripts/index.ts ~/.openclaw/extensions/chromadb-memory/
cp {baseDir}/scripts/openclaw.plugin.json ~/.openclaw/extensions/chromadb-memory/

# 2. Get your collection ID
curl -s http://localhost:8100/api/v2/tenants/default_tenant/databases/default_database/collections | python3 -c "import json,sys; [print(f'{c[\"id\"]}  {c[\"name\"]}') for c in json.load(sys.stdin)]"

# 3. Add to your OpenClaw config (~/.openclaw/openclaw.json):

{
  "plugins": {
    "entries": {
      "chromadb-memory": {
        "enabled": true,
        "config": {
          "chromaUrl": "http://localhost:8100",
          "collectionId": "YOUR_COLLECTION_ID",
          "ollamaUrl": "http://localhost:11434",
          "embeddingModel": "nomic-embed-text",
          "autoRecall": true,
          "autoRecallResults": 3,
          "minScore": 0.5
        }
      }
    }
  }
}

# 4. Restart the gateway
openclaw gateway restart

Config Options

Option	Default	Description
`chromaUrl`	`http://localhost:8100`	ChromaDB server URL
`collectionId`	required	ChromaDB collection UUID
`ollamaUrl`	`http://localhost:11434`	Ollama API URL
`embeddingModel`	`nomic-embed-text`	Ollama embedding model
`autoRecall`	`true`	Auto-inject relevant memories each turn
`autoRecallResults`	`3`	Max auto-recall results per turn
`minScore`	`0.5`	Minimum similarity score (0-1)

How It Works

You send a message
Plugin embeds your message via Ollama (nomic-embed-text, 768 dimensions)
Queries ChromaDB for nearest neighbors
Results above minScore are injected into the agent's context as <chromadb-memories>
Agent responds with relevant long-term context available

Token Cost

Auto-recall adds ~275 tokens per turn worst case (3 results × ~300 chars + wrapper). Against a 200K+ context window, this is negligible.

Tuning

Too noisy? Raise minScore to 0.6 or 0.7
Missing context? Lower minScore to 0.4, increase autoRecallResults to 5
Want manual only? Set autoRecall: false, use chromadb_search tool

Architecture

User Message → Ollama (embed) → ChromaDB (query) → Context Injection
                                                  ↓
                                          Agent Response

No OpenAI. No cloud. Your memories stay on your hardware.

---name: chromadb-memorydescription: Long-term memory via ChromaDB with local Ollama embeddings. Auto-recall injects relevant context every turn. No cloud APIs required — fully self-hosted.---# ChromaDB MemoryLong-term semantic memory backed by ChromaDB and local Ollama embeddings. Zero cloud dependencies.## What It Does- **Auto-recall**: Before every agent turn, queries ChromaDB with the user's message and injects relevant context automatically- **`chromadb_search` tool**: Manual semantic search over your ChromaDB collection- **100% local**: Ollama (nomic-embed-text) for embeddings, ChromaDB for vector storage## Prerequisites1. **ChromaDB** running (Docker recommended): ```bash docker run -d --name chromadb -p 8100:8000 chromadb/chroma:latest ```2. **Ollama** with an embedding model: ```bash ollama pull nomic-embed-text ```3. **Indexed documents** in ChromaDB. Use any ChromaDB-compatible indexer to populate your collection.## Install```bash# 1. Copy the plugin extensionmkdir -p ~/.openclaw/extensions/chromadb-memorycp {baseDir}/scripts/index.ts ~/.openclaw/extensions/chromadb-memory/cp {baseDir}/scripts/openclaw.plugin.json ~/.openclaw/extensions/chromadb-memory/# 2. Get your collection IDcurl -s http://localhost:8100/api/v2/tenants/default_tenant/databases/default_database/collections | python3 -c "import json,sys; [print(f'{c[\"id\"]} {c[\"name\"]}') for c in json.load(sys.stdin)]"# 3. Add to your OpenClaw config (~/.openclaw/openclaw.json):``````json{ "plugins": { "entries": { "chromadb-memory": { "enabled": true, "config": { "chromaUrl": "http://localhost:8100", "collectionId": "YOUR_COLLECTION_ID", "ollamaUrl": "http://localhost:11434", "embeddingModel": "nomic-embed-text", "autoRecall": true, "autoRecallResults": 3, "minScore": 0.5 } } } }}``````bash# 4. Restart the gatewayopenclaw gateway restart```## Config Options| Option | Default | Description ||--------|---------|-------------|| `chromaUrl` | `http://localhost:8100` | ChromaDB server URL || `collectionId` | *required* | ChromaDB collection UUID || `ollamaUrl` | `http://localhost:11434` | Ollama API URL || `embeddingModel` | `nomic-embed-text` | Ollama embedding model || `autoRecall` | `true` | Auto-inject relevant memories each turn || `autoRecallResults` | `3` | Max auto-recall results per turn || `minScore` | `0.5` | Minimum similarity score (0-1) |## How It Works1. You send a message2. Plugin embeds your message via Ollama (nomic-embed-text, 768 dimensions)3. Queries ChromaDB for nearest neighbors4. Results above `minScore` are injected into the agent's context as `<chromadb-memories>`5. Agent responds with relevant long-term context available## Token CostAuto-recall adds ~275 tokens per turn worst case (3 results × ~300 chars + wrapper). Against a 200K+ context window, this is negligible.## Tuning- **Too noisy?** Raise `minScore` to 0.6 or 0.7- **Missing context?** Lower `minScore` to 0.4, increase `autoRecallResults` to 5- **Want manual only?** Set `autoRecall: false`, use `chromadb_search` tool## Architecture```User Message → Ollama (embed) → ChromaDB (query) → Context Injection ↓ Agent Response```No OpenAI. No cloud. Your memories stay on your hardware.

Frequently asked questions

Chromadb Memory