DeepAgents employs a fully local deployment scheme, running the big language model in the user's local environment via the Ollama tool. This architectural design provides three key advantages: data privacy, runnability and model optionality.
Technology Implementation Program:Users need to install Python 3.8+ environment and Ollama tools locally first, and then download open source language models such as gpt-oss as the AI engine. The whole analysis process is done completely locally, stock data is acquired in real-time through the yfinance library, but all data processing and model inference is done on the user terminal.
Key features include:
- Private operation: all sensitive data and intermediate analysis results do not leave the user's device
- Controllable computational resources: the right size language model can be selected according to the local GPU/CPU configuration
- Open source freedom: the project code is completely open source, allowing users to modify the data source or add analysis modules
Compared to cloud-based financial analysis tools, this localized solution requires users to prepare their own computing resources, but completely solves the privacy concerns of uploading investment strategies and position data to third-party services. For institutional investors and data security-conscious professional traders, this architecture provides the necessary security.
This answer comes from the articleDeepAgents: an AI Intelligence for Professional-Grade Equity ResearchThe
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