Fin-Sight Agents Suite orchestrates a collection of specialized AI agents tailored to the finance domain. Each agent handles discrete tasks: data ingestion from multiple sources, time-series analysis, sentiment extraction from news, and predictive modeling. A coordinating agent manages workflow, chaining tasks and ensuring data consistency. Through simple configuration files, users define agent roles, input parameters, and output formats. The system supports customization of analysis pipelines, from automated earnings summaries to risk exposure dashboards. By combining LLM-based natural language queries with quantitative modules, Fin-Sight Agents Suite accelerates research, reduces manual effort, and enhances decision accuracy across trading, portfolio management, and market intelligence applications.

sma-begin sets up a streamlined codebase to create AI-driven agents by abstracting common components like input processing, decision logic, and output generation. At its core, it implements an agent loop that queries an LLM, interprets the response, and optionally executes integrated tools, such as HTTP clients, file handlers, or custom scripts. Memory modules allow the agent to recall previous interactions or context, while prompt chaining supports multi-step workflows. Error handling catches API failures or invalid tool outputs. Developers only need to define the prompts, tools, and desired behaviors. With minimal boilerplate, sma-begin accelerates prototyping of chatbots, automation scripts, or domain-specific assistants on any Python-supported platform.