Magpie-search, a locally-run search tool for AI agents, indexes conversation history and files to let agents recover from crashes and search across six sources at once without sending data to servers.

What happened: A developer has released Magpie-search, a federated search engine that runs entirely on a user's machine and searches across five sources—AI conversation transcripts, local files, a knowledge graph, a vector store, and the live web—using keyword, semantic, and exact-match modes. Results are ranked by trust tier (fact, reference, lead, stale) and deduplicated across sources.

Why it matters: For AI agents, losing context after a crash or power outage is costly. Magpie indexes everything an agent has worked through locally, so recovery is possible. The tool also reduces token cost dramatically: a deep research question that would cost a multi-agent swarm ~2,000,000 tokens costs Magpie's deepweb mode ~1,050 tokens—about 1/2000th the cost—because searching and page-reading are pure retrieval with zero model tokens.

What to watch: Magpie is installed via pip install magpie-search and runs on Python 3.10+ on Windows, macOS, and Linux. It connects to any MCP-capable agent (a standard protocol for AI tools) and strips ~30 classes of secrets at ingest before indexing. The optional session summarizer requires Ollama, which is free and runs locally.