Launching today
Hushh Agents
770+ finance, RIA & insurance AI agents with MCP and chat
117 followers
770+ finance, RIA & insurance AI agents with MCP and chat
117 followers
770+ agentic AI agents — financial advisors, RIA, insurance agents, real estate, coding, and more. Each agent has its own chat interface powered by Hushh models with MCP endpoints any AI can connect to. Browse agents, chat instantly, get ratings and directions. Built-in tools: Hushh Code for code generation with extended thinking, Hushh Pro for multilingual chat in English, Hindi, Tamil, and Tamil Live for real-time voice. Free tier with no limits. Privacy-first. Built on Google Cloud.
I used Hushh Agents to build my multi-agent placement assistant and it genuinely accelerated development.
I built a product called “Placement Buddy,” a system that generates personalized prep plans and adaptive mock interviews for students. Instead of manually orchestrating multiple LLM flows, I used Hushh Agents to structure resume analysis, skill-gap detection, roadmap generation, and interview simulation as coordinated agents.
What stood out while building with it:
Clean abstraction for multi-agent orchestration
Easier separation of responsibilities between planning, evaluation, and feedback agents
Faster experimentation with different reasoning chains
Reduced complexity compared to wiring everything manually
It allowed me to focus more on product logic and less on glue code.
That said, as a builder, I’d love to see:
More visibility into agent decision traces for deeper debugging
Stronger evaluation tooling for benchmarking agent outputs
Better observability when multiple agents interact in complex flows
Overall, Hushh Agents feels like a serious infrastructure layer for building structured AI systems not just another wrapper over LLM APIs.
If you’re building multi-agent products, it’s worth exploring.
I used Hussh Agents to build my multi-agent placement system — and it significantly accelerated development.
I built Placement Buddy, a structured AI system that generates personalized preparation roadmaps and adaptive mock interviews for students. Instead of manually chaining prompts and coordinating multiple LLM workflows, I used Hussh Agents to architect resume analysis, skill-gap detection, roadmap planning, and interview simulation as independent but coordinated agents.
What worked particularly well from a builder’s perspective:
Clear abstraction for multi-agent orchestration
Strong separation between planning, evaluator, and feedback agents
Faster iteration on reasoning flows
Reduced glue code compared to manual LLM chaining
Cleaner mental model for scaling agent complexity
It helped me shift focus from wiring infrastructure to refining product logic.
As a builder, I’d love to see further improvements in:
Transparent agent reasoning traces for deeper debugging
Built-in evaluation frameworks for benchmarking agent outputs
Better observability tools when agents interact in layered workflows
Structured logging for production-scale multi-agent systems
Overall, Hussh Agents feels less like a wrapper and more like foundational infrastructure for structured AI applications.
If you're building serious multi-agent products — not just prompt demos — it’s definitely worth exploring.
Hushh tools helped me clearly structure Kai Placement Copilot as a trust-first, anxiety-aware platform rather than a generic AI chat app.
They guided me to use AI only for assistance, while keeping all scoring, flow, and decisions in the application logic.
The focus on user-owned data and transparency shaped features like explainable readiness scores and opt-in leaderboards.
Hushh’s approach encouraged clean onboarding, predictable UX, and ethical data handling, reducing student anxiety.
Overall, Hushh tools strengthened both the product architecture and user trust in the system.
The Hush Agent was very helpful in structuring our Supabase schema and suggesting scalable backend improvements. It provided clear guidance on RLS policies, indexing, and performance optimization, which strengthened our application architecture. The responses were practical and aligned well with real-world deployment needs. Overall, it accelerated our development process while helping us think more critically about security and scalability.
Hey hii !! Iam Naresh from CSE department .
i have used the Hushh.ai and it was really helpful to write code ,debug and also to explain the doubts of us.
What i have done using Hushh.ai is to create a complete frontend with the languages typescript and tailwind JS.
The webpage was really superb to use and very interactive .
Hey Product hunt, I am Monesh R,
I recently got the opportunity to explore Hushh.ai during a hackathon conducted by the Hushh team, and it was a really insightful experience. I first learned about the platform through Ankit Kumar Singh, and using Hushh.ai helped me understand how AI agents can be designed to work meaningfully with user data.
The platform encouraged us to think beyond traditional applications and build more intelligent, goal-driven solutions. It was especially helpful in shaping the architecture and ideas behind my hackathon project. Excited to see how Hushh.ai evolves and empowers more builders in the AI space