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✨ What is this?

SelfExtendingAgent is a full-stack agentic application built on Google's Agent Development Kit (ADK) that demonstrates a groundbreaking concept: an AI agent that can dynamically create and reuse its own skills at runtime.

🧠 Dual-Model "Smart Routing" Architecture

This project implements an efficient, cost-effective dual-model architecture:

• 🚀 Qwen 2.5 (3B Local) acts as the fast, low-latency conversational interface, formatting responses for the user without incurring API costs for simple or known queries.
• 💡 Gemini 2.5 Flash acts as the heavy-duty Orchestrator and Researcher. It evaluates intent, searches the web, and generates new robust technical definitions when a knowledge gap is detected.

Instead of being limited to a fixed set of capabilities, this agent:

• 🧠 Understands what it needs to do
• 🔍 Checks whether it already has a skill for it
• 🛠️ Creates a new skill on the fly if one doesn't exist
• ♻️ Reuses previously created skills for future tasks using Qwen

This is self-extending intelligence — the agent's capability surface grows automatically as it encounters new tasks.

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🎬 See It In Action

🔄 Skill Reuse — Already Knows What to Do

When the agent encounters a task it has handled before, it intelligently detects the existing skill and applies it directly — zero redundancy, maximum efficiency delivered via Qwen.

⚡ Skill Creation — Learning on the Fly

When a new task is encountered, the agent's Skill Creator module kicks in — Gemini researches, designs, writes, and stores a brand-new skill, then Qwen executes it immediately.

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🏗️ Architecture

SelfExtendingAgent_ADKGoogle/
│
├── 🐍 backend/                  # ADK-powered Python agent core
│   └── agent logic, skill registry, tool definitions
│
├── 🎨 frontend/                 # Conversational UI
│   └── HTML + CSS + JS chat interface
│
├── 🤖 dev_assistant_app/        # ADK Dev Assistant application
│   └── skill-aware orchestration layer
│
├── 🔁 repro_answer.py           # Reproducible answer generation
├── 📦 requirements.txt          # Python dependencies
└── 🚀 run.sh                    # One-command launcher

How the Dual-Model Loop Works

User Request
     │
     ▼
┌─────────────────────┐
│   Intent Matcher    │  ◄── Gemini 2.5 Flash
│   (ADK LlmAgent)    │
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐     YES ──► Route to Presenter
│  Skill Registry     │◄── Does a skill exist for this task?
│  (Persistent Store) │
└────────┬────────────┘
         │ NO
         ▼
┌─────────────────────┐
│   Skill Creator     │  ◄── Gemini 2.5 Flash (Web Research + Tool Use)
│   (Learn Pass)      │
└────────┬────────────┘
         │ Generates SKILL.md
         ▼
┌─────────────────────┐
│ Answer Presenter    │  ◄── Qwen 2.5 3B Local (Fast, cheap UI inference)
│   (Tool-Free)       │
└────────┬────────────┘
         │
         ▼
  Save to Registry ──► Execute New Skill ──► Return Result

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🚀 Getting Started

Prerequisites

• Python 3.10+
• A Google AI / Gemini API key
• Ollama (installed and running locally)
• Node.js (for frontend)

Installation

# 1. Clone the repository
git clone https://github.com/simranjeet97/SelfExtendingAgent_ADKGoogle.git
cd SelfExtendingAgent_ADKGoogle

# 2. Install Python dependencies
pip install -r requirements.txt

# 3. Pull the local Qwen model for UI formatting
ollama pull qwen2.5:3b

# 4. Set your API keys (create dev_assistant_app/.env)
# export GOOGLE_API_KEY="your_gemini_api_key_here"

# 5. Launch everything
bash run.sh

Running with ADK CLI

# Start the ADK dev UI for the agent
adk web dev_assistant_app

# Or run in terminal mode
adk run dev_assistant_app

The app will be available at http://localhost:8000.

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🧩 Core Concepts

Agent Skills

Skills are self-contained knowledge modules (structured as SKILL.md files) that tell the agent how to perform a specific task. The agent discovers, activates, and creates these skills dynamically — keeping context lean while expanding capability.

Multi-Model Execution

By routing heavy logic (planning, API tools, coding, routing) to Gemini, and conversational formatting and generation to local Qwen models, the app minimizes latency and cost without sacrificing intelligence.

Google ADK Integration

Built on Google's Agent Development Kit, the agent leverages:

• LlmAgent — the reasoning core powered by LLMs
• Tool Use — custom tools for skill read/write operations

Persistent Skill Registry

Created skills are persisted to disk and indexed — so the agent's knowledge compounds over time. Every new skill makes the agent permanently smarter for future sessions.

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🛠️ Tech Stack

Layer	Technology
Agent Framework	Google ADK (Agent Development Kit)
Reasoning Core	Gemini 2.5 Flash
UI/Inference Core	Ollama (Qwen 2.5 3B)
Backend	Python 3.10+
Frontend	HTML, CSS, JavaScript
Skill Format	Markdown (SKILL.md)

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💡 Why Self-Extending?

Traditional AI agents have a fixed set of tools. When they encounter something new, they either fail or hallucinate. SelfExtendingAgent takes a different approach:

Traditional Agent	Self-Extending Agent
Fixed capabilities	Grows with every task
Fails on unknown tasks	Creates skills for new tasks
Repeats work inefficiently	Reuses previously learned skills efficiently
Manual tool engineering	Autonomous skill generation

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🤝 Contributing

Contributions are welcome! Feel free to:

• 🐛 Open an issue for bugs
• 💬 Start a discussion for new ideas
• 🔀 Submit a pull request with improvements

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📄 License

This project is open source. See LICENSE for details.

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