AI Agent History RAG FAQs

Question 1

What is AI Agent History RAG?

Accepted Answer

AI Agent History RAG is an MCP server that provides Retrieval-Augmented Generation (RAG) capabilities over your AI coding agent history. It creates a persistent, searchable memory across all your agent sessions to prevent context loss in long interactions.

Question 2

Which AI coding agents does AI Agent History RAG support?

Accepted Answer

It supports ingesting history from popular AI coding agents like Claude Code, Codex, Gemini CLI, and Google Antigravity. It fully ingests user, assistant, tool calls, and tool outputs for comprehensive context.

Question 3

How does it solve the problem of AI agents losing context?

Accepted Answer

By aggregating all your agent session histories into a single vector database, AI Agent History RAG enables hybrid semantic search, allowing you to retrieve relevant context from past conversations and code changes to maintain agent awareness across sessions.

Question 4

Can I use AI Agent History RAG across multiple machines?

Accepted Answer

Yes, it supports a multi-machine mode where you can centralize conversation history from multiple client machines to a single server. This allows for unified search, centralized embeddings, and offline resilience for clients.

Question 5

What kind of search functionality does it offer?

Accepted Answer

It features advanced hybrid search, combining vector similarity with BM25 full-text search and RRF reranking for highly relevant results. Additionally, it tracks and allows searching for specific file modifications and diffs across all sessions.

AI Agent History RAG

AI Agent History RAG

主な機能

ユースケース

主な機能

ユースケース