Can I use this with local LLMs?

Yes, it fully supports Ollama for both LLM reasoning and embedding generation, allowing for a completely offline and private memory system.

Does this require a separate database server?

No, it uses an embedded LadybugDB, which means the database lives locally on your machine without requiring Docker or external server installation.

What embedding models are recommended for best performance?

For production-grade quality, Voyage or OpenAI (text-embedding-3-small) are recommended. For local-only use, Ollama with nomic-embed-text is a high-performance alternative.

How does the memory help my development workflow?

It automatically extracts insights from your coding sessions and stores them. In future sessions, Claude can query this memory to recall how specific features were implemented or why certain patterns were used.

Auto-Claude Memory System

Name: Auto-Claude Memory System
Author: adaptationio

byadaptationio

Database Management

Enables persistent, graph-based memory for Claude Code to retain context and learn from previous sessions.

About

The Auto-Claude Memory system utilizes Graphiti and an embedded LadybugDB to provide long-term context retention for AI-driven development. It enables Claude to build a project-specific knowledge graph, storing insights as 'episodes' that can be retrieved via semantic search in future sessions. This eliminates the 'forgetting' problem in complex projects, allowing the AI to remember specific patterns, architectural decisions, and previous bug fixes without requiring a Docker setup. It supports a wide range of providers including OpenAI, Anthropic, and local Ollama instances for full privacy and offline use.

Key Features

MCP tools for manual memory management and automated insight extraction
Embedded LadybugDB storage with no Docker dependency
Multi-provider support for LLMs and embeddings (OpenAI, Anthropic, Ollama, etc.)
Persistent cross-session knowledge graph using Graphiti
Semantic search for retrieving relevant entities and relationships
0 GitHub stars

Use Cases

Building a searchable knowledge base of project-specific 'gotchas' and solutions
Implementing offline AI memory using local Ollama models and embeddings
Retaining architectural context and patterns across long-term coding projects

About

Key Features

MCP tools for manual memory management and automated insight extraction
Embedded LadybugDB storage with no Docker dependency
Multi-provider support for LLMs and embeddings (OpenAI, Anthropic, Ollama, etc.)
Persistent cross-session knowledge graph using Graphiti
Semantic search for retrieving relevant entities and relationships
0 GitHub stars

Use Cases

Building a searchable knowledge base of project-specific 'gotchas' and solutions
Implementing offline AI memory using local Ollama models and embeddings
Retaining architectural context and patterns across long-term coding projects