Practical overview
When it fits
Use Langalpha when financial research needs to persist across sessions and feed into a thesis, dashboard or permission-bounded trading workflow rather than end with a one-off answer.
Who it is for
- Investors who want agent-assisted research and continuing thesis tracking.
- Developers exploring open-source agent workflows for financial research and permission-bounded brokerage trading.
Documented uses
- Split a market-research question among parallel analysts examining filings, prices, options and macro data.
- Keep conversations, research files and agent notes together so future sessions can build on earlier work.
- Turn collected research into an HTML primer or interactive dashboard.
- Connect a thesis and its research to sizing, an order and subsequent monitoring within configured permissions.
Requirements & limits
The project repository is public and licensed under Apache-2.0. Its README identifies Python 3.13+ and desktop releases for macOS, Windows and Linux, and links to a hosted experience.
- The repository identifies Python 3.13+ for the project.
- Workspace persistence is central: files, conversations and agent notes support work across sessions.
- The README demonstrates parallel analysts and reports or dashboards, including an example using filings, prices, options and macro data.
- Trading is described as permission-bounded, but specific brokerage integrations and API interfaces are not identified in the supplied sources.
- The repository is open source under Apache-2.0; desktop release badges list macOS, Windows and Linux.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Start a financial research question in LangAlpha
Submit a research question and choose whether it belongs to a workspace or should be handled as a quick question.
Before you start
- A LangAlpha account; the support page directs users to sign up at langalpha.ai.
Steps
Sign up at langalpha.ai.
Choose All workspaces for a quick question, or select a workspace to have its Analyst work there.
Ask the agent a research question.
Expected outcome
The Chief of Staff handles quick questions or routes deeper work to an Analyst; a selected workspace's Analyst does the work in that workspace.
Guide limitations
- AI outputs may be incorrect or out of date; verify cited sources before acting.
- LangAlpha is a research tool, not an investment adviser, and its outputs are not financial, legal, tax, or investment advice.
Research a market thesis in a persistent workspace
Use an existing workspace to build on prior research and files when developing a market thesis.
Before you start
- Access to LangAlpha and a workspace for the thesis, sector, or portfolio.
Steps
Select a workspace for the thesis, sector, or portfolio.
Ask a market-research question; the repository describes analysts that can work in parallel on filings, prices, options, macro data, and calculations in code.
Continue the work in the same workspace so its files, chats, and agent notes are available to later sessions.
Expected outcome
Research can accumulate across sessions in a workspace, supporting an investment thesis that is revisited as new evidence arrives.
Guide limitations
- The repository describes agentic trading within permissions set by the user but does not document a specific brokerage integration or execution procedure in the available material.
- Verify cited sources before acting; LangAlpha is a research tool, not financial advice.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 11 October 2026
- Original website title
- ginlix-ai/LangAlpha · Official homepage title · retained in its source language