Can I run this locally without paying for API access?

Yes. Use Ollama with a local model. You won't get GPT-4 quality, but you'll get working results in 45 seconds with zero cloud costs. The code checks for local Ollama first before falling back to OpenAI.

What happens if one source times out or fails?

The try-except block in parallel_search_node catches exceptions per source. If GitHub fails, the other nine sources still return results. The agent logs the failure and continues. One source doesn't tank the whole research.

Does the re-planning loop actually improve results?

On niche topics, yes—noticeably. The evaluation node detects gaps and the LLM picks additional sources on the second pass. For broad topics, it usually finds everything on the first pass and skips re-planning entirely.

🛠️ Developer Tools

I Built a Research Agent That Queries 10 Sources in 45 Seconds—Here's Why Your Sequential Approach Is Dead

One developer got tired of waiting 5 minutes for a research tool to query one source at a time. So they parallelized it. Here's what actually changed—and what didn't work before they got it right.

Open Source Beat Apr 03, 2026 6 min read 15 views

Flowchart showing parallel research agent architecture with 10 simultaneous source queries flowing into consolidation and evaluation nodes

⚡ Key Takeaways

Parallelization cuts research time from 5 minutes to 45 seconds by querying 10 sources simultaneously instead of sequentially 𝕏
Smart planning uses LLM-guided personas to select only relevant sources upfront, avoiding wasted API calls on irrelevant data 𝕏
Self-correction loops allow the agent to detect knowledge gaps and re-plan automatically—agentic reasoning that actually delivers results 𝕏
Local Ollama support means building powerful research tools without cloud bills or vendor lock-in 𝕏

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#langgraph #multi-source-search #ollama #parallel-execution #research-agent #threadpoolexecutor

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Originally reported by Dev.to

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