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Chromadb Memory Pub

Long-term memory via ChromaDB with local Ollama embeddings. Auto-recall injects relevant context every turn. No cloud APIs required — fully self-hosted.
使用ChromaDB和本地Ollama嵌入实现长期记忆。自动召回每轮注入相关上下文。无需云端API,完全本地部署。
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概述

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

```

  1. Ollama with an embedding model:

```bash

ollama pull nomic-embed-text

```

  1. 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. Add to your OpenClaw config (~/.openclaw/openclaw.json):
{
  "plugins": {
    "entries": {
      "chromadb-memory": {
        "enabled": true,
        "config": {
          "chromaUrl": "http://localhost:8100",
          "collectionName": "longterm_memory",
          "ollamaUrl": "http://localhost:11434",
          "embeddingModel": "nomic-embed-text",
          "autoRecall": true,
          "autoRecallResults": 3,
          "minScore": 0.5
        }
      }
    }
  }
}
# 4. Restart the gateway
openclaw gateway restart

Config Options

OptionDefaultDescription
------------------------------
chromaUrlhttp://localhost:8100ChromaDB server URL
collectionNamelongterm_memoryCollection name (auto-resolves UUID, survives reindexing)
collectionIdCollection UUID (optional fallback)
ollamaUrlhttp://localhost:11434Ollama API URL
embeddingModelnomic-embed-textOllama embedding model
autoRecalltrueAuto-inject relevant memories each turn
autoRecallResults3Max auto-recall results per turn
minScore0.5Minimum 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
  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.

版本历史

共 1 个版本

  • v1.2.1 当前
    2026-03-28 09:51 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
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腾讯云安全 (Sanbu)

安全,无风险
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