Multi-Source Search

14.2. Multi-Source Search — Searching Across Multiple Sources

Don’t stop at one search engine

Exactly per the toolset described in chapters 8.3/8.4, research collection comes from whatever sources are configured: web_search, web_extract, browser_navigate, or terminal (curl, dedicated CLIs) — a good research process doesn’t limit itself to a single backend; it picks the right tool for the right kind of source.

Combining Extract + Compare — the classic multi-source operation

Fetch these three competitor pricing pages and create a comparison table:
https://example-a.com/pricing
https://example-b.com/pricing
https://example-c.com/pricing

The standard flow: fetch the page → strip navigation/ads/boilerplate → return clean markdown → the agent analyzes and builds the comparison table — exactly the browser/web_extract workflow described in chapter 8.4, applied in parallel across multiple sources at once.

Keeping context across follow-up questions

Because Hermes keeps the conversation context, a follow-up question like “which of these frameworks has the best documentation?” automatically understands it refers to the frameworks from the earlier search — no need to repeat the whole context with every follow-up.

Running in parallel via Parallel Delegation (chapter 13.2)

When you need to cross-check several options or several independent sources at once, the most effective approach is to hand each source to its own dedicated subagent for independent research, avoiding cross-contamination between the evaluations, and then let the parent agent synthesize — exactly the “evaluate multiple options in parallel” pattern described in chapter 13.2.

Notes

• Pick the right tool per source type: web_search for initial exploration, web_extract/browser when you need to read a specific page in depth (especially post-login or dynamically rendered content), terminal when the source has its own dedicated API/CLI.
• When comparing multiple sources of the same kind (pricing pages, changelogs, documentation), always structure the request as a table from the start instead of letting the agent answer in prose — easier to cross-check and easier to reuse.
• For long-running research projects, leverage cross-session memory (chapter 9) so you don’t have to “brief from scratch” every time you come back — this is precisely the core advantage over stateless search tools.