0c33

0c33 / Agentic-Ai

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An agent that builds agents — closed-loop AI system that interviews, designs, tests, and ships standalone Python agents.

agent-builder agentic-ai ai-agent human-in-the-loop llama-cpp
10
2
75% credibility
Found May 19, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This is a tool that builds AI agents for you through conversation. You describe what you want an AI helper to do, the system interviews you to understand your needs, designs and tests the agent through multiple rounds of improvement, generates real working Python code, and delivers a finished file you can run anytime. It's like having a developer who creates custom AI helpers based on your descriptions - you get to approve each step along the way.

How It Works

1
💡 You have an idea for an AI helper

You think of a task you'd like an AI to handle - maybe extracting action items from meeting notes or organizing data.

2
🤖 You describe your idea to the system

You type your rough idea into the command line. The system doesn't just accept it - it asks you questions to really understand what you're looking for.

3
🔄 The system designs and tests your agent in a loop

It creates a plan for your agent, tries it out with example data, and keeps improving until the results look good. You can say 'yes' or 'make it better' at each step.

4
Something doesn't work as expected
👍
Results look good

You approve the output and the agent gets saved as a file you can run anytime

🔧
Something needs fixing

You tell the system what to improve and it tries again

5
📁 Your agent arrives as a Python file

Once approved, the system writes out a complete, runnable Python file with your new AI helper inside it - ready to use whenever you want.

🎉 You run your agent whenever you need it

Your AI helper is now a standalone file you can execute any time. You simply run it and it does exactly what you described - no setup needed again.

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AI-Generated Review

What is Agentic-Ai?

Agentic-Ai is a Python-based system that builds AI agents through a structured, iterative workflow. You describe what you want, it asks clarifying questions until it fully understands your intent, then designs and tests the agent prompt before generating standalone Python code. The generated code is executed in a real subprocess to verify it works, rated by an LLM, and only saved after human approval. It uses LangChain with OpenAI-compatible endpoints and requires a local LLM running at localhost:8080.

Why is it gaining traction?

The closed-loop design is the hook. Unlike one-shot code generators, this system forces clarification before building, tests prompts via LLM invocation before touching code, then runs actual Python subprocesses to validate output. The human approval gates at each phase mean you never get stuck with broken code. The self-referential generation pattern (injecting its own source as a reference) ensures consistency across generated agents. For developers frustrated with AI tools that skip testing and validation, this iterative approach feels more trustworthy.

Who should use this?

Python developers who want to prototype LLM-powered agents without building the scaffolding from scratch. Researchers exploring agentic AI frameworks will appreciate the structured design loop. Teams evaluating agentic AI architectures for course projects or internal tools might use this as a rapid prototyping baseline. Not ready for production workflows -- the hardcoded paths, manual setup, and alpha status make it a research tool, not a deployment solution.

Verdict

With only 10 stars and a 0.75% credibility score, this is an early-stage experiment worth watching, not adopting. The architecture is thoughtful and the testing philosophy is sound, but the hardcoded model names, venv paths, and lack of configuration management make it brittle for anything beyond personal experimentation. If you're evaluating agentic AI frameworks for serious work, explore more mature options first. If you're curious about meta-programming patterns where an agent builds agents, this provides a concrete playground to explore.

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