Shubhamsaboo/awesome-llm-apps PR #953 — Wire up researcher agent and fix instruction list — PR #953
Shubhamsaboo/awesome-llm-apps · pull request #953 ·
Transcript
PlainEnglish
Let's walk through pull request nine fifty three. This one tackles two problems in the travel agent files: the researcher agent is defined but never actually runs, and a missing comma is silently breaking the instruction list.
PlainEnglish
Our main goal is to activate the researcher agent so it actually conducts web searches before the planner builds the itinerary. Along the way, we're also fixing a sneaky Python bug where a missing comma was joining two instruction strings together. And we're keeping all the existing agent setup and session state untouched.
PlainEnglish
The local travel agent file prompts the user for a SerpAPI key and defines a researcher agent, but that researcher never actually runs. Only the planner executes, so itineraries are generated from the local Llama model with no web input. The SerpAPI key input and researcher agent are effectively dead code. On top of that, a missing comma in the instruction list silently joins two strings together, turning a four-item list into three. This PR brings the local implementation in line with the working two-step pattern already used in the remote travel agent file.
Architecture
Two files are touched here. The local travel agent file gets the bulk of the changes: wiring up the researcher agent and fixing the instruction list. The remote travel agent file gets the same comma fix for consistency.
Architecture
Here's the comma fix. Without it, Python's implicit string concatenation joins line eighty seven and eighty eight into a single instruction, silently breaking the four-item list. Adding the comma restores the intended structure. The same fix is applied in travel agent dot py.
Architecture
And here's the core change. The original code skipped straight to the planner with just the destination and number of days. Now we run the researcher first with an explicit research prompt. It performs web searches via SerpAPI and returns curated results. Then the planner receives those results in a detailed prompt and generates the itinerary. The spinner messages are also clearer: researching your destination, then creating your personalized itinerary.
Architecture
Let's trace the new flow. The user's request first goes to the researcher agent. The researcher calls SerpAPI to search Google, gets back search results, and returns curated research. Then the user's request moves to the planner agent, which now receives the research results as part of its prompt and generates the final itinerary. This mirrors the working pattern already in travel agent dot py.
PlainEnglish
After this PR lands, local travel agent dot py performs a true two-stage workflow. The researcher agent runs web searches and returns curated results. The planner receives those research results in an explicit prompt and generates the itinerary. The SerpAPI key input is now functional, and the researcher agent is no longer dead code. Both travel agent files have correctly formed four-item instruction lists, and the Streamlit spinners give users clearer feedback on which stage is executing.
CodeQuality
We took a close look at several areas. Session state management for storing the itinerary is unchanged. The calendar file generation function isn't affected. Agent initialization only changed its invocation pattern, not the definitions. There are no new error paths, and both agent calls now have consistent type annotations. The planner's prompt construction injects content from the internal researcher output, which is safe. And both calls use stream equals false consistently. Everything looks solid.
PlainEnglish
And that's pull request nine fifty three. A clean fix that activates the researcher agent so it actually performs web searches, and repairs the instruction-list bug in both files. The two-stage workflow is now consistent across the local and remote travel agent implementations, and the SerpAPI integration is fully functional.
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