Create persistent investment-research workspaces where agents process market data, filings, models, charts, and thesis updates over time.
--- name: run-persistent-finance-research-workspaces-with-lan-aa4f121e68ca slug: "run-persistent-finance-research-workspaces-with-langalpha" description: "Create persistent investment-research workspaces where agents process market data, filings, models, charts, and thesis updates over time." github_stars: 1175 verification: "security_reviewed" source: "https://github.com/ginlix-ai/LangAlpha" author: "ginlix-ai" publisher_type: "organization" category: "Data Extraction & Transformation" framework: "Multi-Framework" tool_ecosystem: github_repo: "ginlix-ai/LangAlpha" github_stars: 1175 --- # Run persistent finance research workspaces with LangAlpha Create persistent investment-research workspaces where agents process market data, filings, models, charts, and thesis updates over time. ## Prerequisites Python 3.12+, LangAlpha backend and web or CLI/TUI, configured model credentials, optional financial data provider keys and MCP servers ## Installation Use the upstream install or setup path that matches your environment: - All tiers are enabled by default. To run with **free data only** (Yahoo Finance), run make config with prompted selection. You can also edit agent_config.yaml manually. - git clone https://github.com/ginlix-ai/langalpha.git - make config # interactive wizard — creates .env, configures LLM, data sources, sandbox, and search - make up # starts PostgreSQL, Redis, backend, and frontend Requirements and caveats from upstream: - <img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="Python 3.12+" /> - **Programmatic Tool Calling (PTC)** — The agent writes and executes Python to process financial data from mcp servers instead of pouring raw data into the LLM context window, enabling complex multi-step analysis while... - LLM["LLM"] -- "1 — Writes Python" --> EC["ExecuteCode Tool"] Basic usage or getting-started notes: - <a href="#getting-started">Getting Started</a> • - EC -- "2 — Sends to sandbox" --> Run["Code Runner"] - Run -- "3 — import tools.*" --> Wrappers["Generated Wrappers<br/>One module per MCP server"] - Source: https://github.com/ginlix-ai/LangAlpha - Extracted from upstream docs: https://raw.githubusercontent.com/ginlix-ai/LangAlpha/HEAD/README.md ## Documentation - https://github.com/ginlix-ai/LangAlpha ## Source - [Agent Skill Exchange](https://agentskillexchange.com/skills/run-persistent-finance-research-workspaces-with-langalpha/)--- name: run-persistent-finance-research-workspaces-with-lan-aa4f121e68ca slug: "run-persistent-finance-research-workspaces-with-langalpha" description: "Create persistent investment-research workspaces where agents process market data, filings, models, charts, and thesis updates over time." github_stars: 1175 verification: "security_reviewed" source: "https://github.com/ginlix-ai/LangAlpha" author: "ginlix-ai" publisher_type: "organization" category: "Data Extraction & Transformation" framework: "Multi-Framework" tool_ecosystem: github_repo: "ginlix-ai/LangAlpha" github_stars: 1175 --- # Run persistent finance research workspaces with LangAlpha Create persistent investment-research workspaces where agents process market data, filings, models, charts, and thesis updates over time. ## Prerequisites Python 3.12+, LangAlpha backend and web or CLI/TUI, configured model credentials, optional financial data provider keys and MCP servers ## Installation Use the upstream install or setup path that matches your environment: - All tiers are enabled by default. To run with **free data only** (Yahoo Finance), run make config with prompted selection. You can also edit agent_config.yaml manually. - git clone https://github.com/ginlix-ai/langalpha.git - make config # interactive wizard — creates .env, configures LLM, data sources, sandbox, and search - make up # starts PostgreSQL, Redis, backend, and frontend Requirements and caveats from upstream: - <img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="Python 3.12+" /> - **Programmatic Tool Calling (PTC)** — The agent writes and executes Python to process financial data from mcp servers instead of pouring raw data into the LLM context window, enabling complex multi-step analysis while... - LLM["LLM"] -- "1 — Writes Python" --> EC["ExecuteCode Tool"] Basic usage or getting-started notes: - <a href="#getting-started">Getting Started</a> • - EC -- "2 — Sends to sandbox" --> Run["Code Runner"] - Run -- "3 — import tools.*" --> Wrappers["Generated Wrappers<br/>One module per MCP server"] - Source: https://github.com/ginlix-ai/LangAlpha - Extracted from upstream docs: https://raw.githubusercontent.com/ginlix-ai/LangAlpha/HEAD/README.md ## Documentation - https://github.com/ginlix-ai/LangAlpha ## Source - [Agent Skill Exchange](https://agentskillexchange.com/skills/run-persistent-finance-research-workspaces-with-langalpha/)
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.