Autonomous financial research agent built on LangGraph — orchestrates multi-step web research, synthesis, and report generation.
Financial research traditionally requires analysts to manually sift through multiple data sources, earnings calls, news feeds, and market reports — a time-intensive process that is prone to information gaps and delayed decision-making.
Decomposing complex research queries into executable sub-tasks required careful graph design to maintain context across steps.
Implemented a hierarchical planning system in LangGraph with deterministic validation at each node, ensuring the agent stays on track and produces coherent intermediate results.
Financial documents are lengthy — earnings transcripts and regulatory filings easily exceed standard context limits.
Designed a chunking and retrieval strategy that preserves semantic coherence across segments while supporting 100K to 2M+ token contexts through dynamic windowing.
Coordinating multiple AI tool calls (market data APIs, web search, document parsing) with real-time feedback required robust error handling.
Built an orchestration layer with retry logic, fallback tool chains, and streaming progress updates so the user sees intermediate results as they are produced.
Multi-step research workflows needed clear visual feedback in a terminal environment — standard logging was insufficient.
Used the Rich library to build a live-updating terminal UI with progress bars, collapsible sections, and color-coded status indicators for each research step.
Jasper transforms hours of manual financial research into seconds of automated execution, delivering structured reports with cited sources directly in the terminal — enabling faster, data-driven investment decisions.