How Kensho built a multi-agent framework with LangGraph to solve trusted financial data retrieval
📰 LangChain Blog
Kensho built a multi-agent framework with LangGraph for trusted financial data retrieval
Action Steps
- Leverage LangGraph to create a unified access layer
- Implement a multi-agent framework for data retrieval
- Integrate the framework with existing enterprise systems
- Monitor and refine the framework for optimal performance
Who Needs to Know This
Data scientists and software engineers at enterprises like S&P Global can benefit from this framework to streamline financial data retrieval and integration
Key Insight
💡 A unified agentic access layer can streamline fragmented financial data retrieval
Share This
📈 Kensho's Grounding framework solves financial data retrieval at scale with LangGraph
Key Takeaways
Kensho built a multi-agent framework with LangGraph for trusted financial data retrieval
Full Article
Discover how Kensho, S&P Global’s AI innovation engine, leveraged LangGraph to create its Grounding framework–a unified agentic access layer solving fragmented financial data retrieval at enterprise scale.
DeepCamp AI