The open-source core for institutional-grade financial intelligence and AI-driven quantitative research.
The OpenBB SDK (now evolved into the OpenBB Platform) serves as the industry's most comprehensive open-source framework for financial data access. Built for data scientists, quantitative analysts, and AI developers, it provides a unified Python interface to over 100 diverse data providers including Bloomberg, Refinitiv, Tiingo, and Polygon.io. By 2026, the architecture has transitioned into a highly modular 'provider-agnostic' system, enabling seamless switching between data sources without refactoring code. The platform utilizes a standardized data model that normalizes disparate schemas from various vendors into a consistent structure, making it the premier choice for feeding financial context into Large Language Models (LLMs) and autonomous AI agents. Its technical architecture supports local execution, containerized deployment via Docker, and integration into enterprise pipelines through a REST API layer. As financial institutions move toward 'sovereign data' models, OpenBB's open-source nature allows for complete transparency in calculation methodologies and data lineage, bridging the gap between proprietary terminal silos and modern cloud-native quantitative workflows.
A standardized middleware layer that maps various vendor JSON responses to a single, consistent Pydantic model.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Native integration with LangChain and LlamaIndex for function calling within LLM workflows.
Allows developers to build and share their own 'toolkits' (extensions) for specific asset classes like Carbon Credits or niche Crypto protocols.
Built-in wrappers for Riskfolio-Lib and statsmodels for sophisticated portfolio optimization and econometrics.
Supports automatic conversion of results to DataFrames, Lists of Objects, or interactive Plotly charts.
The ability to host a local instance of the OpenBB API to serve data to internal enterprise applications.
Unified access to FRED, OECD, and ECB data for correlated macroeconomic analysis.
LLMs lack access to real-time, structured financial data and hallucinate numbers.
Registry Updated:2/7/2026
Backtesting frameworks often struggle with data ingestion from multiple sources.
Manual tracking of social sentiment and on-chain metrics is fragmented.