Top Builders

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

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
AuthorLangChain
RepositoryGitHub - LangChain OpenGPTs
TypeCustomizable 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):

  1. "GPT 3.5 Turbo"
  2. "GPT 4"
  3. "Azure OpenAI"
  4. "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.

Edit on GitHub

Langchain OpenGPTs AI technology Hackathon projects

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

Hands for AI

Hands for AI

Chrome offers an unusually deep programmatic surface to AI agents — the DevTools Protocol, the accessibility tree, JavaScript evaluation, network interception, multi-tab and multi-context isolation. For most of the web, that surface is enough to skip visual reasoning entirely from the first action, replacing screenshot-and-guess with structured targeting that survives DOM changes. This project drives all of it through a single MCP server, with accessibility references and a six-rung escalation ladder that auto-selects targeting strategy. Vision and OCR remain available as fallbacks for canvas apps, custom-drawn UIs, and anything without useful structure. What makes the architecture more than a Chrome controller is the workflow layer alongside it. While the agent works, network traffic is captured and the underlying API patterns extracted into replayable flows. On subsequent runs the agent skips the browser entirely and executes direct HTTP calls — millisecond execution at a fraction of the inference cost. Credentials and TOTP seeds live in an OS-level vault, so replay works across sessions and machines without secrets ever touching chat context. Optional personal-assistant mode extends the same engine beyond Chrome to native Windows applications via UI Automation and OCR-based control of anything else, for workflows where browser scope isn't enough. Most agentic browser tooling today is either tied to a vendor's cloud or wraps a single automation library. This stack stays fully open, MCP-native, and self-hostable — running over local STDIO, which removes the network hop between agent and tool at every step and meaningfully cuts step latency on multi-action tasks. Composable with any compatible host and any other MCP tool. The reasoning layer runs on AMD Developer Cloud with ROCm-hosted open vision and language models. No proprietary inference dependencies anywhere in the stack.

SimAgent — AI Engineering Simulation Bot

SimAgent — AI Engineering Simulation Bot

SimAgent is an intelligent AI agent that runs real engineering simulations using live data and generates ready-to-paste scripts for professional CAD/CAE software — without generating AI images or hallucinated geometry. What it does NOT do: Generate fake images of designs. What it DOES: Compute real numbers, run real solvers, output real scripts. Traditional simulation tools (ANSYS, COMSOL, SPICE) are manual, slow, and disconnected from modern Al reasoning workflows, creating huge bottlenecks in R&D cycles.SimAgent Core bridges the gap between high-level reasoning and low-level numerical solvers, executing real physics on local AMD hardware.SimAgent Core leverages the ROCm™ 7.0 software stack to achieve massive throughput gains for local Llama 3.1 and DeepSeek models, enabling millisecondlatency agent decision making without the cloud.usage examples Circuit Simulation → Cadence Export You: I have a 5V source with a 10kΩ and 4.7kΩ resistor voltage divider. Run DC analysis and give me the SPICE netlist. SimAgent: [pulls sensor data] [runs DC analysis] V_node1 = 3.402V I_V1 = 0.681mA SPICE netlist ready — paste into Cadence Virtuoso: V1 1 0 DC 5 R10k 1 2 10000 R4k7 2 0 4700 .DC V1 0 5 0.1 .END

Agentic Economy Arc

Agentic Economy Arc

Agentic Economy Arc is a hackathon-ready demonstration of how autonomous agents can transact seamlessly using sub-cent USDC nanopayments. Built on Arc, the project highlights the future of machine-to-machine commerce by enabling agent-to-agent payment loops with transparent settlement feeds, velocity metrics, and margin comparisons. The demo includes a live dashboard where users can trigger workloads, view transaction streams, and analyze settlement data. It integrates proof APIs, JSON export buttons, and x402-compatible metadata for every transaction, ensuring judge-ready validation. A new autonomous agent loop generates bursts of paid interactions, updating feeds with velocity, net margin, and total volume. By addressing the inefficiencies of traditional gas costs, the project demonstrates why micropayments are critical for scalable digital economies. While deterministic demo data is used in this workspace, the architecture is designed for seamless integration with Circle/Arc credentials. Agentic Economy Arc not only proves the viability of nanopayments but also strengthens the hackathon’s agent-to-agent commerce track by showing how autonomous systems can sustain real-time, low-cost economic interactions. This project represents a step toward a fully agentic economy where digital agents transact, negotiate, and collaborate without human intervention.

NeuralTrade v1.01

NeuralTrade v1.01

NeuralTrade v1.01** is a next-generation AI trading agent engineered to merge deep learning, adaptive intelligence, and modular workflow design into a single cohesive system for financial automation. Unlike traditional algorithmic trading bots, NeuralTrade is built to continuously evolve, learning from market conditions, sentiment signals, and historical data to refine its strategies in real time. Its architecture integrates predictive modeling, reinforcement learning, and advanced risk management, enabling it to operate across multiple asset classes including equities, forex, and digital currencies. The project emphasizes transparency, scalability, and accessibility. Traders and researchers can experiment with customizable modules, tailoring workflows to suit their unique strategies while maintaining executive-level autonomy. NeuralTrade’s modular design ensures seamless integration with diverse platforms, APIs, and data sources, making it a versatile tool for professionals and independent experimenters alike. Beyond execution, NeuralTrade v1.01 is designed to serve as a research companion. It provides structured insights, comparative analyses, and scenario forecasting, empowering users to test hypotheses and validate strategies before committing capital. Its adaptive intelligence reduces manual overhead, enhances profitability, and opens pathways for creative experimentation in finance. The system also supports community-driven engagement, encouraging collaboration, feedback, and modular asset sharing. By combining scientific rigor with creative flexibility, NeuralTrade v1.01 positions itself as more than just a trading agent—it is a platform for innovation, discovery, and independence in the evolving landscape of financial technology. With its focus on modular branding, workflow optimization, and cross-platform deployment, NeuralTrade is not only a technical solution but also a foundation for building sustainable, community-supported trading ecosystem

DeFi Guardian: Decentralized Risk Sentry

DeFi Guardian: Decentralized Risk Sentry

**The Problem:** Current generative Web3 trading bots act as highly volatile black-boxes trying to predict alpha. Institutions don't just need alpha; they need strict mathematical guarantees against catastrophic loss. There is currently no enterprise-grade architecture that bridges advanced AI market sentiment directly into trustless, non-custodial portfolio liquidation logic. **The Solution:** DeFi Guardian acts as an absolute institutional backstop. Operating entirely off-chain, the agent continually monitors market conditions via the PRISM AI Oracle. If structural failure triggers are hit, it bypasses centralized exchanges and generates a deterministic EIP-712 cryptographically signed intent. This intent mathematically proves its ERC-8004 identity on-chain and routes a gasless execution through the Surge Risk Router, ensuring smart-contract wallet compatibility (EIP-1271). **Target Audience:** Institutional asset managers, decentralized hedge funds, and High-Net-Worth liquidity providers. **Unique Benefits:** Enforced cross-chain replay protection (EIP-155), non-custodial cryptographic security, developer-native Python Rich CLI presentation, and flawless ERC-8004 identity verification locking portfolio security to on-chain execution. **Technology & Category Tags:** Web3, Blockchain, AI Agents, Risk Management, DeFi, Python, ERC-8004, EIP-712, EIP-1271, Smart Contracts

Kraken AI Agent

Kraken AI Agent

I built this sophisticated autonomous trading agent for the Kraken Challenge. It combines real-time crypto market data with GPT-5.2 level AI intelligence. It tracks live prices for BTC, ETH, SOL, AVAX, and MATIC, runs smart sentiment analysis, generates high-quality trading signals, and executes trades with proper risk management. It also keeps detailed P&L tracking and handles full position management. Built using React for the frontend, FastAPI on the backend, MongoDB for data, and powered by Emergent’s LLM integrations for next-level market analysis. Pretty proud of how it turned out! Thanks for giving this opportunity