uAgentIQ

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Inspiration

As AI continues to evolve, managing and deploying intelligent agents for specific tasks remains a technical and fragmented process. We were inspired by the vision of creating a modular, low-code framework where users can design, communicate with, and deploy AI agents easily — much like managing apps on a dashboard. uAgentIQ was born from this vision, combining uAgents with cutting-edge AI models to form a powerful, extensible agent ecosystem.

What it does

uAgentIQ is a multi-agent system that enables seamless execution of end-to-end data tasks through intelligent collaboration. It supports:

📡 Real-time web data scraping

✨ Data cleaning and keyword/entity extraction

📊 Automated EDA (Exploratory Data Analysis)

🧠 Machine Learning model training

📄 Final report generation with AI summaries

Users can input a query, and behind the scenes, specialized agents handle each stage — like a smart team!

How we built it

Framework: We used uAgents (Fetch.ai) for decentralized multi-agent communication.

Agents: Separate agents for prompt processing, scraping, cleaning, EDA, model training, and reporting.

LLM: Google Gemini API for summarization and context-aware responses.

Fallback: SerpAPI integration when scraping fails.

Code: Python-based pipeline, modular architecture using main.py and agents.py.

Challenges we ran into

💥 Handling partial failures (e.g. scraper agent timeout) and enabling fallback logic

🔐 Managing agent identity and registration flow using CLI vs visual tools

🔄 Ensuring consistent communication flow between agents without circular dependencies

🧠 Making LLM summaries meaningful without hallucination or data repetition

🧪 Testing individual agents as microservices during parallel development

Accomplishments that we're proud of

Built a fully functional multi-agent pipeline from scratch using uAgents

Integrated fallback logic for robustness (Gemini + SerpAPI)

Seamless inter-agent communication with message-based triggers

Final report combines analytics + natural language summary — autonomously generated!

Made the system modular enough for easy plug-and-play with new agents

What we learned

How to design protocols and enable message passing in agent frameworks

Leveraging LLMs as both task performers and assistants

The importance of robust error handling in distributed systems

Fetch.ai’s agent registry and identity management

Clean separation between agent logic and orchestration improves maintainability

What's next for uAgentIQ

🌐 Launch a web dashboard for users to create and monitor their own agent workflows

🧩 Add agents for translation, speech-to-text, and image captioning

🔐 OAuth + wallet-based access for secure agent interactions

📦 Publish it as a plug-and-play agent pack for the uAgents community

📊 Improve analytics and logging for agent decisions and performance

🤝 Enable agent collaboration across different users’ machines via P2P networks

Built With

• python
• regexlib
• scikit-learn
• serpapi
• uagents