Top Builders

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

OpenAI Codex

OpenAI Codex is an artificial intelligence model developed by OpenAI. It parses natural language and generates code in response. It is used to power GitHub Copilot, a programming autocompletion tool. Codex is a descendant of OpenAI's GPT-3 model, fine-tuned for use in programming applications. OpenAI has released an API for Codex in closed beta. Based on GPT-3, a neural network trained on text, Codex has additionally been trained on 159 gigabytes of Python code from 54 million GitHub repositories. You can find more information here https://openai.com/blog/openai-codex/

General
Relese dateAugust 31, 2021
AuthorOpenAI
Repository-
TypeAutoregressive, Transformer, Language model

Start building with Codex

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

Boilerplates

Kickstart your development with a Codex based boilerplate. Boilerplates is a great way to headstart when building your next project with Codex.

  • Codex Boilerplate Create a function just by typing what it should do, with help of OpenAI Codex.

Libraries

A curated list of libraries and technologies to help you build great projects with Codex.

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OpenAI Codex AI technology Hackathon projects

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

MidContext Live Translation Agent

MidContext Live Translation Agent

MidContext Live Translation Agent solves a major challenge for companies operating across multilingual markets: customer support becomes slower, more expensive and less personal as customers and agents do not speak the same jargon. Beyond language, each generation have its unique way of talking and AI enables hyper customisation capabilities. We identified low scalable workflows, high wait times, low resolution quality and inconsistent customer experience as key pain points for companies, especially for companies scaling across Europe with different languages, accents and local expectations, and low maturity with their internal knowledge bases. Scalable globally, and also interesting to mayor incumbents that can not afford losses in their reputation. Our solution is a real-time voice translation layer between customer care agents and customers. The system captures voice input, converts speech through ASR, routes the conversation through a customer support layer, and generates natural voice responses using TTS. It does more than translate words: it preserves context, intent, tone and company jargon, while connecting to local knowledge bases and support workflows. It works today, right away in the company as it is, and help build its future enriching their local customer service knowledge base. The target users are multinational companies, customer operations teams, CCaaS providers and enterprises that need scalable multilingual support without losing the human connection. MidContext uses a glocal strategy: one global architecture, adapted to local languages, customer behaviors, policies and knowledge bases. A human-in-the-loop quality model keeps agents responsible for sensitive cases, approvals and escalations, reducing technological complexity while improving trust, resolution quality and customer satisfaction.

InboxFlow Agent

InboxFlow Agent

InboxFlow Agent is an autonomous QA agent for email marketing campaigns, designed to test customer journeys before they go live. Instead of relying on teams to manually refresh inboxes, click links, compare expected branches, and assemble screenshots, InboxFlow uses seed inboxes and test personas to simulate real recipient behavior across the campaign flow. It can validate click and no-click journeys, confirm that the right personas receive the right follow-up emails, detect broken or mismatched links, inspect email content for missing personalization or compliance issues, and collect evidence from every step of the test run. The result is a clear launch-readiness report that tells the team whether the campaign is safe to launch, what failed, where it failed, and what should be fixed before customers see it.

4RealEstate Flow

4RealEstate Flow

Real estate agents are losing listings to better marketers, not better agents. 73% of homeowners now say they're more likely to list with an agent who uses video marketing. But here is the thing, most agents face two bad options: pay €3,000+ a month for a content agency, or use generic AI tools that produce videos their audience scrolls past. Neither works. 4RealEstate Flow is the end-to-end AI content engine built for real estate agents. Four specialized AI agents run the entire content operation. The Content Planner continuously analyzes viral content across social media to build a weekly content calendar tailored to each agent's market. The Video Agent handles creative execution end-to-end script, edit, motion design, captions. The Listing Video Maker turns property photos and details into cinematic listing videos in minutes. And the Ads Manager dynamically allocates paid spend based on which content is actually converting. From the first idea to the last click. Autonomously. Every real estate agent, their own marketing team.

Executor

Executor

Executor is an autonomous meeting-to-execution enforcement system built for enterprise teams that lose accountability after meetings. Most AI meeting tools stop at summaries, but Executor continues into execution by extracting commitments, identifying owners, deadlines, priorities, and source quotes, then turning those decisions into tracked work. Users can upload transcripts or run live meetings with Speechmatics real-time transcription. Gemini analyzes the meeting, extracts structured action items, scores execution risk, and powers follow-up drafting. Each commitment can be synced directly into Jira, Asana, and Notion, connecting meeting decisions to the tools teams already use. Executor also runs a background enforcement agent deployed on Vultr. The monitor checks deadlines every six hours, marks overdue commitments, drafts follow-up emails, retries failed sends, and escalates to managers after repeated missed follow-ups. Human review remains available before sensitive messages are sent, while every autonomous action is stored in an audit log. The application is deployed publicly on Cloudflare Workers, uses Supabase for authentication and database storage, Gemini for reasoning, Speechmatics for voice transcription, and Vultr for the scheduled enforcement worker. Executor closes the gap between “we decided” and “it actually got done.”

AgentPayOps Finance controls for autonomous agents

AgentPayOps Finance controls for autonomous agents

Autonomous agents will buy data, tools, compute, and services. Companies need a control plane that decides when those agents are allowed to spend money, blocks risky or duplicate purchases, escalates high-value actions, and records every decision for finance teams. AgentPayOps demonstrates that control layer through one vertical workflow: An invoice agent reviews a vendor invoice. The agent needs a paid vendor-risk report. The report endpoint returns 402 Payment Required. AgentPayOps evaluates vendor, category, amount, approval threshold, and duplicate-purchase rules. The system approves, blocks, or escalates the payment. Transactions and reasoning are shown in an audit dashboard.

FinAgent V2 — Autonomous Multi-Agent Investment AI

FinAgent V2 — Autonomous Multi-Agent Investment AI

FinAgent V2 is an autonomous multi-agent investment analysis system built for VC/PE firms, fund managers, and investment analysts who need structured, deep analysis without a dedicated research team. Unlike single-model approaches, V2 deploys five specialized AI agents coordinated by an LLM-driven Orchestrator that uses function calling to dynamically plan which agents to activate based on user input and natural language intent. Three Analysis Modes: Mode A — Market Analysis: Enter a stock ticker. The Orchestrator dispatches QuantAgent, PeerAgent, and CIOAgent in parallel to deliver complete market analysis including valuation models (PE/PB/PEG/EV-EBITDA/DCF), peer comparison, technical trends, and investment verdict. Mode B — Report + Market Analysis: Upload a financial report PDF and enter a ticker. FundamentalAgent and QuantAgent run simultaneously — one reads the document using Gemini multimodal AI, one pulls live market data via yfinance. CIOAgent then cross-analyzes both outputs, surfacing divergence signals between reported fundamentals and current market performance. Mode C — AI Dialogue: Talk to the Orchestrator in natural language. It introduces itself, asks clarifying questions, confirms the analysis plan, then dispatches the right combination of agents. True conversational orchestration that adapts to user needs. Key technical features: streaming dashboard where results appear section by section as each agent completes, intelligent peer identification using sector and geography context, reflection loop where the Orchestrator synthesizes from LLM knowledge when agent data is incomplete, and optional Featherless open-source model inference. Built on: Gemini API, OpenAI function calling, React/TypeScript, Express, Vultr (Milan region), yfinance, Featherless.