Run multi-agent LLM financial trading analysis
Real-world thread: you install the repo, set an API key for an LLM provider, and pick a ticker and date in the CLI or through the Python TradingAgentsGraph call. Analyst agents cover fundamentals, sentiment, news and technicals. The source reports no benchmark results.
- Agent or tool
- Custom agent
- Topic
- Software Development
- Reported on
- Hacker News
- Added
- 2026-10-15
Why it matters
Coding agents are moving from autocomplete to doing whole tasks: reading a codebase, making a plan and changing many files. This case is a concrete example of where that works in practice. It is one of 106 sightings filed under Software Development on Agent Sightings (number 89 by interest), and one of 182 involving Custom agent.
How you could try it
Here is a reusable prompt, written by us in our own words, that captures the task. Paste it into your own agent or any similar agent and adjust the details to your situation.
In this repo, run multi-agent LLM financial trading analysis. Read relevant code, apply a minimal patch, run available checks, and summarize the diff. Stop before merge or deploy.
The prompt breaks down into 3 steps you can check as the agent works:
- In this repo, run multi-agent LLM financial trading analysis.
- Read relevant code, apply a minimal patch, run available checks, and summarize the diff.
- Stop before merge or deploy.
Tip: Work on a fresh branch, ask for a plan before edits, and review the diff and test results before merging.
Source
Read the original first-hand account on Hacker News: https://news.ycombinator.com/item?id=49605822. Our summary is a paraphrase; details are in the source.
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