chromadb-memory

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Atualizado5 de fevereiro de 2026 às 00:34

Long-term memory via ChromaDB with local Ollama embeddings. Auto-recall injects relevant context every turn. No cloud APIs required — fully self-hosted.

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Fonte

tclawde

tclawde/coding-agent

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Outras ocupações de informáticaInformática e Matemática·SOC 15-1299

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SKILL.md

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name	chromadb-memory
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

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.

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name	chromadb-memory
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

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.