Search

More Filters

Show More
Add Listing
Go Back

LLMs in Finance

0
New
Multi-Agent Platforms & Frameworks
Report

Report Abuse

Basic Information

This repository collects hands-on Jupyter notebooks and examples that demonstrate how to apply generative AI to real-world financial workflows. It focuses on building and evaluating AI agents, retrieval augmented generation pipelines, and multimodal language models for finance. The code and notebooks illustrate integrations with multiple provider SDKs and frameworks such as OpenAI Agents SDK, Anthropic, AutoGen, LlamaIndex, CrewAI and LangGraph. Use cases include collaborative multi-agent systems for market analysis, momentum trading strategy development, parsing and analyzing earnings reports, evaluating RAG systems, and testing model understanding of financial charts. The materials also include a curated list of research papers and examples for synthetic data. The content is explicitly intended for educational and research purposes and is not presented as investment advice or production-ready trading software.

Links

Stars
721
App URL
https://github.com/hananedupouy/LLMs-in-Finance
Github Repository
https://github.com/hananedupouy/LLMs-in-Finance/blob/main/README.md

App Details

Features
The repo is organized into folders for Agents, RAG, Multimodals, Papers and Others and contains many runnable Jupyter notebooks. Agent examples cover OpenAI Agents SDK, AutoGen, LlamaIndex, CrewAI, LangGraph and Anthropic and demonstrate collaboration patterns such as evaluator-optimizer workflows, LLM-as-a-judge code review, sentiment extraction, and trading strategy proposals. RAG examples show document parsing techniques including LlamaParse AutoMode, prompt caching with Anthropic, and evaluation workflows using DeepEval and GiskarAI. Multimodal notebooks test models like Claude Sonnet 3.5, GPT-4o and o1 Reasoning on financial chart interpretation. The repo also bundles a curated set of papers in finance and synthetic data examples to support experimentation and learning across multiple providers and toolchains.
Use Cases
This collection helps developers, data scientists and researchers prototype and experiment with generative AI applied to finance by providing concrete, runnable examples and integration patterns. Users can learn to build multi-agent workflows, set up RAG pipelines to parse complex financial reports, and evaluate retrieval and generation quality with included evaluation notebooks. Multimodal examples enable testing of model capabilities on visual artifacts like earnings charts. The curated papers and examples give research context and references for further study. By aggregating cross-provider demos and notebooks, the repository accelerates learning and experimentation while making clear the materials are for educational and research use only, not for live trading.

Categories

Content Creation (5042)
Monetization & Business (2246)
Development Tools (1407)
Marketing & Social Media (1403)
Education & Career (1380)
Multi-Agent Platforms & Frameworks (1009)
Health & Life (957)
AI Resources (920)
Agents & Automation (778)
Data & Analytics (764)
Productivity Tools (679)
Writing & Editing (666)

Similar Listings

listing image

virtual lab

0 (0 review)
listing image

mnemo

0 (0 review)
listing image

EdgeChains

0 (0 review)
listing image

RAG Agents Accelerator

0 (0 review)
listing image

LLM Zero to Hundred

0 (0 review)

Featured Listings

Tags

Please fill the required fields*