---
name: chromadb-memory-bb1af0e560e1
description: Long-term memory via ChromaDB with local Ollama embeddings. Auto-recall injects relevant context every turn. No cloud APIs required — fully self-hosted.
version: 1.0.0
author: matts
homepage: https://github.com/openclaw/openclaw
metadata:
  openclaw:
    emoji: "🧠"
    requires:
      bins: ["curl"]
    category: "memory"
tags:
  - memory
  - chromadb
  - ollama
  - vector-search
  - local
  - self-hosted
  - auto-recall
---

# 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

1. **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 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):
```

```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 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

1. You send a message
2. Plugin embeds your message via Ollama (nomic-embed-text, 768 dimensions)
3. Queries ChromaDB for nearest neighbors
4. Results above `minScore` are injected into the agent's context as `<chromadb-memories>`
5. 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.


---
Source attribution: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/msensintaffar/chromadb-memory/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 clawdbot

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/msensintaffar/chromadb-memory/SKILL.md
Attribution basis: Source repository record

Fields of work: Development & Engineering
Roles: Backend Developer, Data Engineer, ML / AI Engineer
Specialties: API, Backend
Tools: Chromadb, Ollama
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 6484d9d3415c7da830841b4b397d638357d8514a1d94632626defa5018befc18