Overview
LocalRecall is a 100% local memory layer and knowledge base service designed for AI agents, providing persistent short-term and long-term memory through a simple RESTful API. It handles file uploads, indexing, semantic retrieval, and collection management entirely on-premises, making it ideal for privacy-first agent architectures that cannot rely on external cloud services.
Key Features
- RESTful API for managing knowledge collections, uploading documents, and performing semantic search and retrieval operations.
- Local vector storage with pluggable backend support, enabling agents to store and query embeddings without cloud dependencies.
- Native integration with LocalAGI and LocalAI for a seamless self-hosted agent stack with built-in memory capabilities.
- Support for multiple document formats including Markdown, PDF, and plain text with automatic chunking and indexing.
Use Cases
- Providing persistent memory for autonomous agents that need to recall past interactions, decisions, and learned facts across sessions.
- Building private RAG pipelines over internal documents, wikis, and knowledge bases in air-gapped or compliance-sensitive environments.
- Equipping chatbots and assistants with long-term contextual awareness without sending conversation history to third-party services.
Technical Details
- Lightweight service with Docker and Docker Compose deployment for rapid setup in any environment.
- Pluggable vector backend architecture allowing users to choose the embedding and storage engine that fits their infrastructure.
- Designed as a standalone memory microservice that any agent framework can consume via its REST API.