wiki-agent

To search conversation history across multiple AI agents, you can invoke the wiki-claude command to ingest past sessions, query by topic, and extract relevant content into a synthesized answer. It supports cross-agent context awareness by defaulting to recent sessions.

AI agent knowledge synthesis is the process of ingesting specific sessions from conversation histories, extracting relevant information, and summarizing it into a coherent answer. This mechanism enables cross-agent context awareness and knowledge sharing.

Yes, you can retrieve past work from one AI agent while working in another by querying specific session histories. This allows you to ask questions like what was solved last week and synthesize that knowledge into your current context.

Yes, you need Python, grep, and json dependencies installed in your environment to extract knowledge from AI sessions. These tools are required to parse the conversation histories and synthesize the extracted content into a usable format.

The best way to query past Claude agent sessions by topic is to invoke the wiki-claude command followed by your specific query. This extracts relevant knowledge from targeted histories and synthesizes it into a coherent summary or answer.