Anthropic

Top Builders

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

Anthropic

Anthropic’s Constitutional AI training approach research focuses on developing AI systems safe by design and aligned with human values. By prioritizing safety, we can create strong and corrigible AI systems that are safe for humans to use.

General
Company	Anthropic
Founded	2021
Discord	https://discord.gg/lablab-ai-877056448956346408

Anthropic Claude

Claude is your friendly and versatile AI language model that can assist you as a company representative, research assistant, creative partner, or task automator.

Claude is Safe, Clever, and Yours. Built with safety at its core and with industry leading security practices, Claude can be customized to handle complex multi-step instructions and help you achieve your tasks.

You can easily use Claude for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our Claude tech page.

Claude Code

Claude Code is a command-line tool from Anthropic for agentic coding. It enables Claude to refactor, debug, and manage code directly in the terminal. You can find more information on our Claude Code tech page.

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Anthropic AI Technologies Hackathon projects

Discover innovative solutions crafted with Anthropic AI Technologies, developed by our community members during our engaging hackathons.

RouteZero — Token-Efficient Task Agent

RouteZero is a general-purpose agent built for the Hybrid Token-Efficient Routing Agent track. It reads the task batch from /input/tasks.json, classifies each prompt into one of the eight capability categories (factual knowledge, mathematical reasoning, sentiment, summarization, NER, code debugging, logical reasoning, code generation), and applies category-specific answering strategies designed to satisfy the accuracy judge with the minimum output necessary, no preamble, no filler, format constraints obeyed exactly. The agent follows the official runtime contract strictly: FIREWORKS_API_KEY, FIREWORKS_BASE_URL, and ALLOWED_MODELS are read from the environment at runtime with nothing hardcoded, all remote calls route through the provided base URL, and model selection (including routing code tasks to a code-specialized model when the allowed list offers one) happens dynamically per task. Results are written atomically to /output/results.json with a watchdog that guarantees a valid, complete output file well inside the runtime limit, with parallel dispatch and per-task time budgets. The design philosophy: pass the accuracy gate first, then drive tokens down relentlessly, every answer should cost exactly what it needs and nothing more. Built and verified end-to-end in Docker (linux/amd64) with the container tested against the full I/O contract before submission.

Agentic EWS Advisory system

Smallholder farmers in North Rift and similar rain-fed agricultural zones rely on generic, county- or region-level seasonal forecasts that frequently diverge from what actually happens on their specific plot. This mismatch — worsened by the lack of insurance cover — leads to planting-window errors, crop failure, and unmanaged risk. The farmers who need the most precise information have the least access to it, and existing digital advisory tools are rarely both hyper-local (tied to exact coordinates) and low-bandwidth accessible (WhatsApp/SMS rather than apps or web dashboards). The design goals are> 1. Hyper-local accuracy – advisories tied to the farmer's registered GPS point, not administrative boundaries. 2. Zero-app-install accessibility – WhatsApp Business API as primary channel, plain SMS as fallback for feature phones/no data. 3. Agentic, not just automated – a coordinated set of AI agents that fetch, reconcile, forecast, reason, and communicate, rather than a single rigid pipeline. 4. Ground-truthed – EO and reanalysis data reconciled against in-situ automatic weather stations (AWS) and farmer-reported observations, so the system self-corrects when satellite/model data drifts from reality

ChiefFlow AI — AI Chief of Staff

ChiefFlow AI is an AI Chief of Staff that automates the operational grind every small business faces: reading invoices, reviewing contracts, triaging support tickets, scheduling meetings, and replying to email, all without losing human oversight where it matters most. A Manager Agent classifies every incoming item, whether an email, PDF, pasted text, or API call, by intent: invoice, contract, complaint, tender, meeting request, or support ticket. It then routes the task to one of six specialist agents: Email, Finance, Legal, Research, Calendar, and Support. Each agent extracts structured data and drafts a recommended action. High-risk actions like payments, contracts, and external communication always pause for one-click human approval before executing. Every step is logged to a full audit trail. Rather than sending every task to the biggest model available, ChiefFlow AI routes by task complexity: simple tasks to Gemma, moderate tasks to an AMD GPU-hosted open model, and complex reasoning like contracts and tenders to Fireworks AI, all accessed through Fireworks' AMD-hosted infrastructure. If a tier is unavailable, the system gracefully degrades to a deterministic local reasoning engine, so it is never broken by a missing key or network issue. Built as a full-stack, containerized application: FastAPI backend with real agent orchestration, a Next.js frontend styled as genuine enterprise SaaS, SQLite persistence, live analytics, and a complete audit log. Ships as a single Docker image, one command to run anywhere.

ArchMind: AI Software Architect

ArchMind is an AI-powered software architecture intelligence platform that helps developers understand, analyze, and evolve complex codebases. Instead of treating source code as plain text, ArchMind parses repositories into Abstract Syntax Trees (ASTs), extracts structural relationships, and constructs an architecture knowledge graph representing modules, classes, functions, dependencies, and execution flow. An AI agent reasons over this graph to answer architectural questions that traditional code search cannot. Developers can ask natural language questions such as "How does authentication work?", "What will break if I modify this service?", "Which components depend on this module?", or "Where are the architectural bottlenecks?". The platform performs dependency analysis, impact prediction, dead-code detection, architecture visualization, and automatic documentation generation. ArchMind leverages AMD Developer Cloud for scalable graph processing and Fireworks AI models running on AMD hardware for intelligent reasoning. The system combines symbolic program analysis with large language models, enabling more accurate software understanding while reducing hallucinations common in text-only approaches. The project is designed for software engineers, architects, and engineering teams working with large and evolving repositories. By combining program analysis, graph intelligence, and AI agents into a single platform, ArchMind significantly reduces onboarding time, improves maintainability, accelerates refactoring, and enables architecture-aware software development.

ArchMind: AI Software Architect

Agent Rerouter

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