Top Builders

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

Mistral AI

Mistral AI develops a wide spectrum of AI models and services, enabling developers, researchers, and businesses to build, deploy, and fine-tune large language and multimodal models.
The company focuses on open weights, reasoning capability, multimodality, and enterprise-grade features such as long context windows, domain-specific deployments, and fine-tuning options.

General
Founded2023 (Paris, France)
FoundersArthur Mensch, Guillaume Lample, Timothée Lacroix
Valuation~€14 billion (Series C, September 2025)
InvestorsASML (largest shareholder), Microsoft, CMA CGM, others
TypeLarge language and multimodal models

Mistral Models

Mistral divides its lineup into open models (weights freely available) and premier models (API-first, enterprise-grade).
Here are the most important families:

  • Mistral 7B – Compact, open-weight dense model for efficient deployment.
  • Mixtral 8×7B / 8×22B – Sparse mixture-of-experts models balancing performance and cost.
  • Mistral NeMo 12B – Strong open-weight model for multilingual and reasoning tasks.
  • Codestral – Code-oriented models for software engineering and developer tools.
  • Pixtral – Multimodal family supporting text + image inputs (e.g. Pixtral-12B, Pixtral Large).
  • Magistral – Reasoning-focused models; Magistral Small (open-weight) and Magistral Medium (enterprise).
  • Mistral Medium 3 / 3.1 – Premier multimodal models with ~131K context length, enterprise-grade APIs.
  • Mistral Large / Large 2 (123B) – Very large dense models with long context, available via API.
  • Specialized Models – OCR models (e.g. mistral-ocr-2503), embeddings, moderation, and speech (Voxtral).

La Plateforme

Mistral provides its own developer and enterprise platform, called La Plateforme, where you can:


Mistral AI - Boilerplates

Get started quickly with open-weight or API integrations:


Mistral AI - Tutorials

Learn how to build with Mistral’s models:


Mistral AI

Most important links to explore Mistral’s ecosystem:


Mistral AI AI technology page Hackathon projects

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

WASI: Multi-Agent WhatsApp Food Ordering SaaS

WASI: Multi-Agent WhatsApp Food Ordering SaaS

WASI revolutionizes the fast-food ordering experience by transforming WhatsApp into an intelligent, seamless conversational storefront, deeply integrated with and monitored by Band AI Cloud. Traditional chatbots rely on rigid decision trees, but WASI utilizes an advanced LangGraph multi-agent state machine powered by Groq’s lightning-fast Llama-3-70B model. The entire conversational pipeline relies on Band AI Cloud telemetry for enterprise-grade observability. When a customer messages the restaurant in Roman-Urdu, Band AI tracks the Supervisor Agent as it dynamically routes the conversation through specialized sub-agents: Menu, Delivery, Payment, and Profile. These agents natively parse complex natural language into strictly typed JSON objects, dynamically updating the customer's cart, handling size variants, and calculating subtotals—all while streaming execution events back to the Band AI dashboard in real-time. To handle edge cases that AI cannot solve alone, WASI features a "Human-in-the-Loop" Receptionist Dashboard built with React and Vite. If an address is invalid or an item goes out of stock, a human receptionist can reject the order with a note. WASI’s AI instantly interprets this feedback, alters the internal database state, and re-engages the customer to fix the issue. Powered by Band AI's robust agent infrastructure and strict asynchronous locks to prevent race conditions, WASI is a production-ready SaaS template designed to bring powerful AI to local restaurants.

GridAI - DER Coordination Protocol

GridAI - DER Coordination Protocol

Problem Australia has roughly 15 GWh of home batteries and the number is climbing fast. They mostly see the same thing: the National Electricity Market price signal. When price drops in the evening they all discharge at once. Following one shared signal makes a fleet synchronise, and a synchronised fleet builds a new evening demand peak instead of smoothing the old one, while pushing voltage outside legal limits at the edge of the distribution network. This failure mode gets worse as virtual-power-plant deployment scales, because it appears precisely when fleets start coordinating against shared signals. It is a second-order problem that today's market design walks straight into. GridAI's novelty is the diagnosis: desynchronisation depends on fleet-level value heterogeneity, and each voltage breach can be attributed by cause, separating PV-export conditions from battery-herding events so only protocol-induced failures escalate. Solution GridAI is a multi-agent coordination protocol. Four agents, Forecaster, Coordinator, Compliance, and Operator, collaborate through Band as the actual collaboration layer, not a notification wrapper. The Coordinator runs a priority-based dispatch: each battery's slot is allocated from global fleet state using its state of charge and the owner's willingness-to-discharge. The fleet desynchronises through heterogeneity, the diversity in what each battery wants, not through symmetric negotiation. The Compliance agent reviews every plan against AS IEC 60038:2022 voltage limits, flags battery-herding breaches (kept distinct from midday PV-export breaches), and escalates to a human Operator with a full Band-native audit trail. Result: battery-herding overvoltage breaches cut from 471 to 0, fleet synchrony from 1.000 to 0.167. Convergence takes 1 to 2 rounds, runs on existing inverter hardware, and fits the CSIP-AUS standard already mandated in Australia.

Freight Room: a response room for ship delays

Freight Room: a response room for ship delays

Ocean carriers billed $15.4 billion in demurrage and detention between 2020 and 2025, at $150 to $250 per container per day. When a ship is stuck at anchor, sorting it out means logistics, finance, the carrier, and customer teams chasing each other across separate companies, tools, and inboxes. That coordination gap is where the money and the time leak out. Freight Room pulls that into one room. A Sentinel agent watches a live AIS feed; when a real vessel dwells at anchor, it opens a room in Band and recruits only the agents the incident needs, at runtime. It then names the actual importers whose cargo is on that ship, pulled from public U.S. Customs bills of lading. Seven agents across three frameworks (LangGraph, PydanticAI, CrewAI) work through Band by @mentioning each other: logistics proposes recovery options, finance prices each one against the published Maersk demurrage tariff, the carrier counters on tariff terms, and a dissent agent challenges the leading option before anyone commits. A quorum vote produces a single recommendation, with the dissent recorded right next to it. A human ops manager approves or rejects in the dashboard. The decision flows back into the Band room, and a one-page audit dossier is generated from the full record: every option, cost, vote, the dissent, and the human call, each line tagged with where the number came from. Nothing is mocked. Vessel positions are live AIS, importers come from real customs records (pre-arrival matches are labeled inferred), and costs come from a published tariff. Band is the part that makes it work: the room, the runtime recruitment, the @mention handoffs, and the audit trail all run on Band.

ChainSentinel — Multi-Agent Web3 Security

ChainSentinel — Multi-Agent Web3 Security

ChainSentinel is an AI-powered Web3 security platform where a team of five autonomous agents collaborate through Band to audit smart contracts the way a real security firm would — except in minutes, not weeks. When a contract is submitted, the SecurityOrchestrator opens a Band room and dynamically recruits specialists: the ThreatAnalyst runs deep semantic and economic-attack analysis (flash loans, oracle manipulation, reentrancy), the PocGenerator writes runnable Foundry exploit tests to prove the findings are real, the RemediationAdvisor rewrites the contract to fix every critical issue, and the ReportPublisher synthesizes everything into an executive risk report. The agents message each other in the room, hand off context, and reach a verdict together — Band is the actual coordination layer, not a notification wrapper. Under the hood, each agent wraps a LangGraph pipeline combining rule-based detection (35+ SWC vulnerability signatures), LLM reasoning — Claude Opus and Sonnet for deep analysis, Gemini for fix generation — and a FAISS RAG engine grounded in the SWC Registry and 50+ real DeFi exploits. The stack is model-agnostic (OpenAI also supported for evaluation), with a roadmap toward fine-tuned and self-hosted local models for lower cost and full privacy. A second Sentinel Mode delivers 24/7 on-chain monitoring with a live transaction feed and threat alerting via Telegram and Discord. DeFi lost over $2.2B to smart-contract exploits in 2024. Traditional audits cost $30k–$100k and take weeks, pricing out most teams. ChainSentinel makes attacker-grade security continuous, explainable, and affordable — and shows what a fleet of specialized agents collaborating on Band can deliver for high-stakes, regulated workflows.