OpenGPTs: Customizable AI Model Framework

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OpenGPTs

OpenGPTs, powered by LangChain's technology stack, offers developers a versatile framework for harnessing AI capabilities. Leveraging over 60 language models, LangSmith's prompt customization, and a suite of 100+ tools, OpenGPTs provides unparalleled control and flexibility in AI model configurations.

General
Author	LangChain
Repository	GitHub - LangChain OpenGPTs
Type	Customizable AI Model Framework

Framework Overview

OpenGPTs serves as a customizable AI framework, allowing users to fine-tune language models, prompts, tools, vector databases, retrieval algorithms, and chat history databases. This level of control surpasses direct usage of OpenAI, enabling developers to interact with APIs directly and craft tailored user interfaces.

Technology Tutorials

Customization

1. Language Models (LLMs): Select from over 60 LLMs integrated with LangChain. Note the varying prompts required for different models.
2. Prompt Customization: Debug and fine-tune prompts with LangSmith for enhanced accuracy.
3. Tool Integration: Access a diverse suite of 100+ tools provided by LangChain or easily create custom tools.
4. Vector Databases: Choose from 60+ vector database integrations within LangChain.
5. Retrieval Algorithms: Optimize retrieval algorithms based on project requirements.
6. Chat History Databases: Tailor chat history databases to suit specific project needs.

Agent Types (Default):

"GPT 3.5 Turbo"
"GPT 4"
"Azure OpenAI"
"Claude 2"

OpenGPTs' appeal lies in its high level of customization compared to direct usage of OpenAI. Users gain control over language model selection, seamless addition of custom tools, and direct API utilization. Furthermore, developers can craft custom UIs as needed.

Utilize OpenGPTs to harness the power of AI tailored precisely to your project requirements.

For a deeper dive into usage and configuration, refer to the OpenGPTs Documentation.

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Langchain OpenGPTs AI technology Hackathon projects

Discover innovative solutions crafted with Langchain OpenGPTs AI technology, developed by our community members during our engaging hackathons.

ABI (Automated Business Intelligence)

Corporations are drowning in data but starving for immediate insight. Traditional Business Intelligence (BI) workflows require data engineering pipelines, complex SQL dashboards, and manual assembly of PowerPoint presentations just to share weekly updates. Business leaders lose critical windows of opportunity waiting for static reports that fail to offer interactive depth. Abi is an automated enterprise BI assistant that bridges the gap between raw corporate data and presentation-ready insights. Users simply connect their data sources and organize them into workspace folders. Abi instantly takes over, allowing users to explore their data through intuitive conversational chat or automatically spin up comprehensive, publication-grade analytical reports. The core architectural engine is built using LangGraph, establishing a modular, robust multi-agent pipeline. It features: Dynamic Intent Classification: An intelligent routing system that determines whether a user needs a casual data answer or a deep-dive report pipeline. Autonomous Sandbox Exploration: A secure Python REPL environment where the agent writes, tests, and executes Pandas code directly against datasets (like our functional supermarketsales.csv deployment) to pull real-time calculations without risking data exposure. What We Built & The Future Roadmap Due to the hackathon timeline constraints, our current submission showcases the critical validation engine: a fully functional conversation analyst node that dynamically ingests, queries, and interprets complex retail transactions. The groundwork is explicitly laid for Abi's definitive killer feature: transforming structured dashboard data into fully automated document reports (.pptx, .pdf, .xlsx) and automatically synthesizing them into a synthetic video presentation. Abi turns static numbers into a shareable, human-like data briefing video with a single click. Abi is the future of hands-free corporate reporting.

AsterRoute: Zero Token Binary LLM Router

AsterRoute is a hybrid, token efficient general purpose agent built for Track 1 of the AMD Developer Hackathon: ACT II. Instead of spending LLM tokens to decide which model should answer, it runs a fine tuned 66M parameter DistilBERT classifier locally. The router predicts whether the inexpensive Fireworks tier can answer reliably and escalates uncertain requests directly to the strongest allowed tier. The system combines learned confidence thresholds with auditable category rules. Factual knowledge, math, logic, sentiment, code debugging, and code generation default to the inexpensive tier, while named-entity recognition and summarization use a P(cheap_ok) confidence gate. Training uses 176 empirically labeled examples across eight categories, negative oversampling, class weighting, and an independent grading pipeline. Code-generation answers are executed against tests, while open ended outputs use structured judging. All answer generation remains on Fireworks. The local model only selects the answer model, so routing consumes zero API tokens. Offline replay achieved 98.9% accuracy, matching the strongest model baseline while routing 173 of 176 tasks to tier0. The Linux/AMD64 Docker image implements the required /input/tasks.json to /output/results.json contract and completed our end to end Fireworks test in approximately nine seconds. The project also includes threshold evaluation, a 72 prompt no overlap holdout suite, reproducible training scripts, automated smoke tests, and an interactive Streamlit demonstration. The training code runs without modification on CPU, CUDA, Apple MPS, or ROCm backed AMD hardware.

CaptionHaki

CaptionHaki is an AI-powered Dockerized agent built for the AMD AI Hackathon Track 2. The system accepts a list of video captioning tasks, downloads each video, extracts representative frames, analyzes the visual content using multimodal AI models, converts the analysis into a structured JSON representation, and generates captions in multiple requested writing styles. The pipeline was designed to generalize across different categories of videos rather than memorizing the provided examples. It processes urban environments, animals, office scenes, nature, and other unseen content by extracting observable facts before generating captions. Separating visual understanding from caption generation improves consistency while allowing multiple caption styles to be created from the same structured description.

Four Takes - The AI Captioning Studio

Every AI captioning tool does video-in, caption-out. FOUR TAKES is the only one that ships captions PRE-JUDGED: an internal LLM judge scores every caption on accuracy and tone before you see it, and anything below 8/8 is automatically retaken with a concrete fix instruction. Brand-safe by construction. HOW IT WORKS 1. SAMPLE — ffmpeg pulls 16–32 frames evenly across the FULL clip (a user-facing Sampler slider trades speed vs accuracy). 2. TRANSCRIBE — Whisper v3 on Fireworks captures speech, in parallel with sampling. 3. DOSSIER — Gemma (multimodal) compiles a strict facts-only scene dossier: subjects, actions in order, on-screen text. Nothing invented. 4. FOUR TAKES — Gemma writes all four styles concurrently from that ONE dossier, so every voice agrees on the facts while the tone transforms. Each style is a calibrated persona (The Anchor, The Cynic, The Debugger, The Standup) with a voice spec, few-shot anchor, and explicit avoid-list. 5. JUDGE — scores each take 0–10 on accuracy and tone; failures get up to two retakes, shown live in the UI. Gemma · Fireworks AI · Whisper · ffmpeg · FastAPI · Docker. Crafted by The Maker (Rahul D).

Video Captioning AI Agent

Video Captioning AI Agent is an end-to-end multimodal pipeline that automatically generates high-quality captions from short videos. Given a video URL and a list of requested caption styles, the system downloads the video, extracts representative frames using FFmpeg, performs scene understanding with a vision-language model, and generates captions tailored to the requested tone using a large language model. The pipeline separates visual understanding from language generation. The vision stage produces a structured description of the scene, including subjects, actions, setting, and key events. This intermediate representation is then passed to a caption generation model, allowing consistent caption quality while supporting multiple writing styles such as formal, sarcastic, humorous (tech), and humorous (non-tech). The project is fully containerized with Docker and follows the required input/output specification for the hackathon evaluation. It supports processing multiple captioning tasks, includes retry logic for external API calls, automatic cleanup of temporary files, and produces structured JSON output. By combining efficient frame extraction, multimodal reasoning, and style-aware language generation, the agent delivers captions that are both contextually accurate and tailored to the desired audience.

CaptionAI Intelligent MultiStyle Video Captioning

## Overview CaptionAI is an intelligent video captioning application developed for the AMD Developer Hackathon. The system analyzes uploaded videos using multimodal AI models and automatically produces captions in four unique styles while preserving the video's original context. The platform demonstrates how modern AI can enhance accessibility, content creation, and social media engagement by generating captions tailored to different audiences. ## Problem Creating engaging captions manually is time-consuming and often requires creativity and consistency. Content creators frequently need multiple caption styles for different platforms and audiences. ## Solution CaptionAI automates the entire caption generation process by combining computer vision, speech understanding, and large language models. Users simply upload a video, and the system returns four different caption styles: • Formal • Sarcastic • Humorous-Tech • Humorous-Non-Tech ## Features * AI-powered video understanding * Automatic caption generation * Four unique caption styles * Modern React user interface * FastAPI backend * Dockerized deployment * AMD Fireworks AI integration * Responsive design ## Technology Stack Frontend: * React.js * HTML5 * CSS3 * JavaScript Backend: * Python * FastAPI AI: * AMD Fireworks AI API * Large Language Models Deployment: * Docker * Docker Compose ## Future Improvements * Real-time caption streaming * Multi-language support * Voice narration * Subtitle (.srt) export * Video editing integration * Mobile application CaptionAI demonstrates how AMD AI infrastructure can simplify intelligent multimedia processing while delivering creative, high-quality outputs for content creators and developers.