Top Builders

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

Band

Band, operated by Thenvoi AI Ltd., was founded in 2025 by CEO Arick Goomanovsky and CTO Vlad Luzin to give AI agents a shared way to find each other, delegate work, and collaborate safely across frameworks, clouds, and organizations. The platform is often described as "Slack for agents": each agent keeps its own models, tools, and memory, while Band provides the chat rooms, identity, and governance layer that ties them together with humans in the loop.

General
CompanyBand (Thenvoi AI Ltd.)
Founded2025 by Arick Goomanovsky and Vlad Luzin
HeadquartersKochav Ya'ir, Israel
Websiteband.ai
Documentationdocs.band.ai
GitHubgithub.com/thenvoi
TypeAI Agent Infrastructure, Multi-Agent Coordination Platform

Core Products

Agentic Mesh

The collaboration layer where any agent, regardless of framework, can discover other agents, exchange structured messages, share context, and delegate tasks through chat rooms with @mention-based routing.

Control Plane

Governance tooling that gives teams visibility into agent-to-agent interactions, lets them set authority and delegation boundaries, and keeps a unified audit trail across every agent connected to the network.

Developer Resources

Band ships a Python SDK with adapters for popular agent frameworks, a TypeScript SDK, and an MCP server so coding assistants like Claude Code and Cursor can join Band rooms directly.

  • Documentation: getting started guides, core concepts, and SDK reference
  • GitHub: Python and TypeScript SDKs, MCP server, and example agents
  • Discord: community support and announcements
  • App / Console: create agents, manage contacts, and open chat rooms

Key Features

Cross-framework agent connections Agents built with LangGraph, CrewAI, Anthropic, Pydantic AI, Parlant, Google ADK, OpenCode, or custom code connect through the same SDK and join shared chat rooms without writing point-to-point integration code.

Persistent identity and contacts Each agent gets a persistent identity and handle, with a contact system that controls cross-boundary visibility and consent before two agents can interact.

Chat rooms with @mention routing Agents and humans collaborate in shared rooms where @mentions route messages to the right participant, supporting multi-agent workflows such as planner, reviewer, and implementer roles.

Governance and audit trail Authority boundaries, delegation policies, and a unified audit trail give enterprises visibility into what every agent did and why.

MCP server for coding agents An MCP server lets Claude Code, Cursor, and other MCP-compatible tools join Band rooms and coordinate with other coding agents over a shared repository.

Use Cases

Coordinating coding agents Teams run Claude Code as a planner and Codex as a reviewer in a shared Docker workspace, with both agents reading the same git repository, writing plans and reviews to shared files, and coordinating through a Band chat room.

AI governance and standardization Organizations use Band's control plane to standardize how agents delegate work, request approval, and get audited across teams and external vendors.

Multi-agent lifecycle management Long-running, multi-step agent work stays visible in shared rooms so humans can step in, redirect, or approve at any point.

Personal AI assistants Individuals run personal agents such as OpenClaw or NanoClaw and connect them to Band so assistants on different machines or households can collaborate while keeping their own data and runtime isolated.

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Band AI AI Technologies Hackathon projects

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

Agentic EWS Advisory system

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

FinFlow AI

FinFlow AI

FinFlow AI is an autonomous finance operations platform powered by multiple AI agents that work together like a real enterprise finance team. The system uses Band as the core collaboration layer where specialized agents communicate, share context, and complete financial workflows. Our platform includes Procurement, Accounts Payable, Accounts Receivable, Cash Flow Risk, and Tally agents. The Procurement Agent evaluates purchase requests, vendor risk, and budget availability. The AP Agent manages payment scheduling and approvals. The AR Agent analyzes incoming payments and receivables. The Cash Flow Risk Agent predicts financial risks, while the Tally Agent maintains reconciliation, audit trails, and financial summaries.

Drift Harness

Drift Harness

Every AI system drifts. It softens a position under pressure, drops a constraint it was holding a moment ago, or states a guess as if it were certain. The correction is almost always manual: a human notices, pushes back, forgets, and corrects the same failure on the next turn. Nothing remembers, and nothing scales. Drift Harness makes that loop automatic — it intercepts the exchange before the user has to act, logs what failed and why, and builds a structured record that drives correction at scale. The system fans a single exchange across thirteen specialist agents, each checking one slice of behaviour: constraints, antipatterns, voice, quality, identity, alignment, gap analysis, profiling and question generation. Every agent returns the same five-field verdict — agent, status, rule, excerpt, severity — so one shape holds across every layer. Its core idea is how it represents certainty. Rather than a percentage, which is just a token prediction dressed up as a probability, each agent commits to one of three states — violation, uncertain, or clean — always tied to an exact excerpt from the reasoning that triggered it. The excerpt is what makes the label mean anything. Under the hood, the logger mints a UUID4 per exchange and classifies each turn in Python before the model runs. Findings write to a FastAPI and SQLite backend; agents communicate over a shared Band session; a C++ coordinator handles multithreaded fan-out. The full stack runs live on a Hetzner VPS under pm2, with a dashboard at dashboard.malecsystems.com. We proved it end to end: one misaligned input fanned across every live agent produced six confirmed findings, written straight to the backend. All thirteen agents are deployed and the dashboard is live. The harness is the asset. The agents are the mechanism that fills it. Every AI system drifts — this one notices, records it, and turns a manual habit into infrastructure.

Drift Harness

Drift Harness

Every AI system drifts. It softens a position under pressure, drops a constraint it was holding a moment ago, or states a guess as if it were certain. The correction is almost always manual: a human notices, pushes back, forgets, and corrects the same failure on the next turn. Nothing remembers, and nothing scales. Drift Harness makes that loop automatic — it intercepts the exchange before the user has to act, logs what failed and why, and builds a structured record that drives correction at scale. The system fans a single exchange across thirteen specialist agents, each checking one slice of behaviour: constraints, antipatterns, voice, quality, identity, alignment, gap analysis, profiling and question generation. Every agent returns the same five-field verdict — agent, status, rule, excerpt, severity — so one shape holds across every layer. Its core idea is how it represents certainty. Rather than a percentage, which is just a token prediction dressed up as a probability, each agent commits to one of three states — violation, uncertain, or clean — always tied to an exact excerpt from the reasoning that triggered it. The excerpt is what makes the label mean anything. Under the hood, the logger mints a UUID4 per exchange and classifies each turn in Python before the model runs. Findings write to a FastAPI and SQLite backend; agents communicate over a shared Band session; a C++ coordinator handles multithreaded fan-out. The full stack runs live on a Hetzner VPS under pm2, with a dashboard at dashboard.malecsystems.com. We proved it end to end: one misaligned input fanned across every live agent produced six confirmed findings, written straight to the backend. All thirteen agents are deployed and the dashboard is live. The harness is the asset. The agents are the mechanism that fills it. Every AI system drifts — this one notices, records it, and turns a manual habit into infrastructure.

THE COUNCIL: Expert Advisors Who Fight For You

THE COUNCIL: Expert Advisors Who Fight For You

Everyone faces life-changing decisions alone. THE COUNCIL changes that by giving you your own personal advisory board. Submit your query—whether it's a high-stakes startup offer, career transition, or relocation—and watch five autonomous AI agents with distinct, persistent personalities deliberate in a live-streamed debate. Under the hood, THE COUNCIL is a multi-agent system built on Band.ai. Instead of a single LLM wearing five prompts, we deploy genuine model diversity: Qwen 2.5-32B (The Skeptic) analyzes risks, Llama-3.1-70B (The Strategist) identifies long-term growth, DeepSeek-R1-70B (The Numbers) quantifies quantitative metrics, Llama-3.1-8B (The Devil's Advocate) stress-tests consensus, and GPT-4o-mini (The Chair) synthesizes the room. The debate orchestration runs sequentially via FastAPI WebSockets to stream arguments as they are generated. Unlike typical chatbot wrappers, THE COUNCIL features heavy engineering depth: 1. Deterministic Stakes Classifier: Scores severity (1-10) and risk factors (cliff, vesting, relocation) deterministically without AI. 2. Live Market Grounding: Career queries trigger Brightdata web scraping to search salary listings, providing real-world benchmarks to 'The Numbers'. 3. Convergence Calculator: A custom algorithm that measures consensus (0.0-1.0) using position agreement and semantic text similarity. 4. Cryptographic 'Stare Decisis': Inspired by judicial tradition, it extracts minority dissents and SHA-256 hashes them directly into an immutable verdict chain. 5. 6-Table Database Persistence: Complete deliberation history mapping decisions, arguments, verdicts, evidence, and dissents in SQLite. Presented in an Apple-inspired editorial Next.js UI using stark light-mode whitespace, Playfair Display serif typography, and elegant card components with smooth micro-animations, the system feels alive, premium, and authoritative. Deliberation, not just generation.

THE COUNCIL: Expert Advisors Who Fight For You

THE COUNCIL: Expert Advisors Who Fight For You

Everyone faces life-changing decisions alone. THE COUNCIL changes that by giving you your own personal advisory board. Submit your query—whether it's a high-stakes startup offer, career transition, or relocation—and watch five autonomous AI agents with distinct, persistent personalities deliberate in a live-streamed debate. Under the hood, THE COUNCIL is a multi-agent system built on Band.ai. Instead of a single LLM wearing five prompts, we deploy genuine model diversity: Qwen 2.5-32B (The Skeptic) analyzes risks, Llama-3.1-70B (The Strategist) identifies long-term growth, DeepSeek-R1-70B (The Numbers) quantifies quantitative metrics, Llama-3.1-8B (The Devil's Advocate) stress-tests consensus, and GPT-4o-mini (The Chair) synthesizes the room. The debate orchestration runs sequentially via FastAPI WebSockets to stream arguments as they are generated. Unlike typical chatbot wrappers, THE COUNCIL features heavy engineering depth: 1. Deterministic Stakes Classifier: Scores severity (1-10) and risk factors (cliff, vesting, relocation) deterministically without AI. 2. Live Market Grounding: Career queries trigger Brightdata web scraping to search salary listings, providing real-world benchmarks to 'The Numbers'. 3. Convergence Calculator: A custom algorithm that measures consensus (0.0-1.0) using position agreement and semantic text similarity. 4. Cryptographic 'Stare Decisis': Inspired by judicial tradition, it extracts minority dissents and SHA-256 hashes them directly into an immutable verdict chain. 5. 6-Table Database Persistence: Complete deliberation history mapping decisions, arguments, verdicts, evidence, and dissents in SQLite. Presented in an Apple-inspired editorial Next.js UI using stark light-mode whitespace, Playfair Display serif typography, and elegant card components with smooth micro-animations, the system feels alive, premium, and authoritative. Deliberation, not just generation.