What I missed from the writeup were some specific cases and how did you test that all this orchestration delivers worthwhile data (actionable and full/correct).
E.g. you have a screenshot of the AI supply chain - more of these would be useful, and also some info about how you tested that this supply chain agrees with reality.
Unless the goal of the project was to just play with agent architecture - then congrats :)
zc2610 2 hours ago [-]
Hi HN. We built LangAlpha because we wanted something like Claude Code but for investment research.
It's a full stack open-source agent harness (Apache 2.0). Persistent sandboxed workspaces, code execution against financial data, and a complete UI with TradingView charts, live market data, and agent management. Works with any LLM provider, React 19 + FastAPI + Postgres + Redis.
zc2610 2 hours ago [-]
Some technical context on what we ran into building this.
MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80 tools across our servers and the prompt cost is the same whether a server has 3 tools or 30. This part isn't finance-specific, it works with any MCP server.
The other big thing was making research actually persist across sessions. Most agents treat a single deliverable (a PDF, a spreadsheet) as the end goal. In investing that's day one. You update the model when earnings drop, re-run comps when a competitor reports, keep layering new analysis on old. But try doing that across agent sessions, files don't carry over, you re-paste context every time. So we built everything around workspaces. Each one maps to a persistent sandbox, one per research goal. The agent maintains its own memory file with findings and a file index that gets re-read before every LLM call. Come back a week later, start a new thread, it picks up where it left off.
We also wanted the agent to have real domain context the way Claude Code has codebase context. Portfolio, watchlist, risk tolerance, financial data sources, all injected into every call. Existing AI investing platforms have some of that but nothing close to what a proper agent harness can do. We wanted both and couldn't find it, so we built it and open-sourced the whole thing.
esafak 42 minutes ago [-]
You shouldn't dump data in the context, only the result of the query.
zc2610 35 minutes ago [-]
Yes, thats is the idea and exactly what we did
erdaniels 2 hours ago [-]
Then people would lose a lot of money
locusofself 12 minutes ago [-]
Agreed. Unless this really helps people somehow make better trading decisions than existing tools, the vast majority of them are probably still better off index investing.
2 hours ago [-]
3 hours ago [-]
zz07 20 minutes ago [-]
One thing I really like about this project is how it fully takes the advantage of LLM throughout the workflow to fill out the last-one-mile gap. Currently most agent products in the market suffer from imcomplete workflows. Imagine a powerful agent that handles 95% of the work perfectly but still requires the user to drag the files or open some links for it to finish the last few steps. The user experience is greatly undermined. However, LangAlpha iterates a lot to solve all those pain points. This is especially important considering the target users, financial workers, may not be export in CS and may not understand the mechanism behind LLM well. For example, LangAlpha uses smart skill injection to provide the agent with skills to help the user update user preferences: everything can literally be done in a casual chat.
tornikeo 14 minutes ago [-]
Forgive my senses, but this writing feels like a low effort Claude response. What's the point adding responses like this to a Show HN post? I don't think you are fooling anyone.
cbg0 1 minutes ago [-]
They're trying to build up new accounts with karma to astroturf products/services.
Rendered at 17:27:06 GMT+0000 (Coordinated Universal Time) with Vercel.
What I missed from the writeup were some specific cases and how did you test that all this orchestration delivers worthwhile data (actionable and full/correct).
E.g. you have a screenshot of the AI supply chain - more of these would be useful, and also some info about how you tested that this supply chain agrees with reality.
Unless the goal of the project was to just play with agent architecture - then congrats :)
It's a full stack open-source agent harness (Apache 2.0). Persistent sandboxed workspaces, code execution against financial data, and a complete UI with TradingView charts, live market data, and agent management. Works with any LLM provider, React 19 + FastAPI + Postgres + Redis.
MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80 tools across our servers and the prompt cost is the same whether a server has 3 tools or 30. This part isn't finance-specific, it works with any MCP server.
The other big thing was making research actually persist across sessions. Most agents treat a single deliverable (a PDF, a spreadsheet) as the end goal. In investing that's day one. You update the model when earnings drop, re-run comps when a competitor reports, keep layering new analysis on old. But try doing that across agent sessions, files don't carry over, you re-paste context every time. So we built everything around workspaces. Each one maps to a persistent sandbox, one per research goal. The agent maintains its own memory file with findings and a file index that gets re-read before every LLM call. Come back a week later, start a new thread, it picks up where it left off.
We also wanted the agent to have real domain context the way Claude Code has codebase context. Portfolio, watchlist, risk tolerance, financial data sources, all injected into every call. Existing AI investing platforms have some of that but nothing close to what a proper agent harness can do. We wanted both and couldn't find it, so we built it and open-sourced the whole thing.