Generative Agents

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Explore the top contributors showcasing the highest number of app submissions within our community.

Generative Agents

Generative Agents are computer programs designed to replicate human actions and responses within interactive software. To create believable individual and group behavior, they utilize memory, reflection, and planning in combination. These agents have the ability to recall past experiences, make inferences about themselves and others, and devise strategies based on their surroundings. They have a wide range of applications, including creating immersive environments, rehearsing interpersonal communication, and prototyping. In a simulated world resembling The Sims, automated agents can interact, build relationships, and collaborate on group tasks while users watch and intervene as necessary.

General
Relese date	April 7, 2023
Type	Autonomous Agent Simulation

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We have collected the best Generative Agents libraries and resources to help you get started to build with Generative Agents today. To see what others are building with Generative Agents, check out the community built Generative Agents Use Cases and Applications.

Generative Agents Tutorials

Building an Intelligent AI Agent for Content Moderation with Structured Output

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Generative Agents resources

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Generative Agents AI technology page Hackathon projects

Discover innovative solutions crafted with Generative Agents AI technology page, developed by our community members during our engaging hackathons.

VIDEO-AGENT TACK 2

This project is an automated Video Captioning Agent developed for Track 2 of the AMD Developer Hackathon. Packaged as a standalone linux/amd64 Docker container, the Node.js (TypeScript) application acts as a complete end-to-end pipeline for video understanding and stylistic text generation. Key Architecture & Features: - Media Processing: The agent dynamically reads video input URLs and utilizes ffmpeg to extract optimal visual frames (1 frame every 10 seconds). This keeps the payload lightweight and efficient while retaining enough visual context for accurate captioning. - AI Integration: The extracted frames are encoded and sent to a Large Vision-Language Model via the Fireworks AI API. The agent parses the multimodal context to understand the scene, setting, and subjects. Intelligent Fault Tolerance: To ensure strict compliance with the hackathon's automated evaluator rules, the agent includes custom retry and validation logic. If the AI model hallucinates or fails to return the exact requested JSON schema, the agent automatically adjusts parameters and retries the request until all four required caption styles are perfectly generated. - Automated I/O Compliance: The pipeline is architected to run seamlessly in headless environments, automatically pulling job configurations from /input/tasks.json and writing structured outputs to /output/results.json upon completion.

IOLITE: Intelligent workout planner

Iolite is an AI-powered training planner designed to help endurance athletes achieve their goals through personalised, adaptive, and sport-specific workout programmes. Powered by Fireworks AI, Iolite generates and adjusts training plans based on each user’s chosen sport, fitness level, availability, and upcoming events. Supporting running, cycling, swimming, Hyrox, rowing, and athletic events, Iolite uses periodised training principles to structure workouts into progressive phases, ensuring users build fitness effectively while peaking at the right time for competition. Users can select multiple sports and events, with an intelligent priority system balancing training demands and identifying which workouts and exercises provide the greatest impact towards their goals. Iolite adapts continuously by analysing performance, completed sessions, and progress to optimise future workouts. A smart fitness scoring algorithm tracks development by evaluating consistency, performance improvements, and sport-specific progress. Gamification features such as workout streaks, milestones, and achievements keep users motivated and encourage long-term consistency. By combining AI coaching, sports science, and personalised training optimisation, Iolite provides athletes with an intelligent platform to train smarter, improve consistently, and prepare confidently for their next challenge.

CredIQ — Smart Credit Card Spending Awareness App

CredIQ is a real-time credit card spending awareness app for India's 100 million credit card users who overspend without realizing it until the bill arrives. When you pay using GPay or tap your card, there is no live feedback on how much budget you have consumed. You find out when the bill comes. By then the damage is done. CRED focuses on bill payments. Jupiter shows patterns after the fact. Neither gives a live budget bar that updates the moment a transaction happens. That is the gap CredIQ fills. The core feature is a live budget bar powered by Firebase Realtime Database showing status in real time — Very Good in green, Good in light green, OK in yellow, Bad in orange, Very Bad in red. No mental calculation needed. See green, feel safe. See red, stop spending. The status also shows as a persistent notification and home screen widget so spending consciousness is always visible. AI features run on Groq's Llama 3.3 70B model for fast, low latency responses. The chatbot receives the user's live financial context on every turn — budget, spent amount, savings, credit card due, card offers, and recent transactions — giving specific personalized answers, not generic advice. The photo analyzer lets users photograph any product and get an instant recommendation on which card gives the best cashback or reward points for that purchase. For the demo we built a mock bank simulator alongside the main app. Every transaction in the mock bank writes to Firebase and CredIQ updates in under three seconds. In production this is replaced by the RBI regulated Account Aggregator framework through TSPs like Setu. Revenue comes from a freemium subscription at ₹99 per month, card application commissions from banking partners, and merchant affiliate deals. Half a percent of India's credit card market at that price is fifty crore annual recurring revenue. CredIQ is the spending companion the mass market credit card user has never had.

CashFlow AI

CashFlow AI is an agentic AI platform designed to automate and modernize the debt collection workflow for businesses. It combines autonomous AI agents with human-in-the-loop oversight to streamline customer communication, payment recovery, and financial operations while maintaining compliance and transparency. The platform intelligently analyzes outstanding invoices, prioritizes accounts based on payment behavior, and executes personalized outreach across multiple channels such as email, WhatsApp, SMS, and voice. It integrates with accounting platforms like Xero and QuickBooks to synchronize customer records, invoices, payment status, and financial data in real time. Payment gateways such as Stripe enable automatic reconciliation, ensuring that successful transactions immediately update the organization's financial systems. CashFlow AI provides a unified dashboard for tracking collection performance, monitoring agent activities, and reviewing high-impact decisions before execution. Businesses can define custom collection strategies, escalation policies, and approval workflows while AI agents handle repetitive operational tasks. By combining intelligent automation, seamless enterprise integrations, real-time analytics, and secure human oversight, CashFlow AI helps organizations improve cash flow, reduce operational costs, increase recovery rates, and deliver a more professional and customer-friendly collections experience.

Universal Repository Strategy Engine

Universal Repository Strategy Engine is an AI-driven platform that helps founders, developers, and startup teams quickly understand any GitHub repository and turn it into actionable growth assets. Users enter a repository URL, a short project description, and their Fireworks API key, and the system fetches the repository README, analyzes the project, and generates three outputs: a clear project summary, ready-to-publish marketing posts for LinkedIn, X, Reddit, and Hacker News, and a launch and growth strategy. The app is built with Python, Streamlit, CrewAI, and Fireworks AI, making it simple to use locally and easy to deploy. It is designed to accelerate product positioning, storytelling, and go-to-market planning for open-source and early-stage projects.

Express Caption

Express Caption is a video-captioning agent built for AMD Express Track 2. Give it a short clip (30 seconds to 2 minutes) and it writes a caption in four requested styles: formal (precise and objective), sarcastic (dry and deadpan), humorous_tech (developer in-jokes), and humorous_non_tech (warm, jargon-free wit). Rather than describe the video and then restyle text, it captions in a single grounded pass: it samples frames densely across the clip (~1 per second) and, in one multimodal call, the model writes all four styles directly from what it sees, so nothing is lost in a handoff, and temporal coverage drives accuracy. A distinctness check retries if the four voices come back too similar, and every requested style is always returned. The scored deliverable is a headless Docker agent: it reads tasks from /input/tasks.json, streams frames straight from each video URL with ffmpeg (downloading only as a fallback), and writes /output/results.json with per-style fallbacks, an atomic write, and an adaptive time budget so the output is always complete and valid. Built with Fireworks AI (Kimi K2.6, a vision-language model) and ffmpeg for frame sampling.