An AI agent that automatically improves its own prompt. Give it any task — it runs, evaluates its own output, rewrites its prompt based on the feedback, and tries again. Each iteration gets better.
Built from scratch. No LangChain. No AutoGen. Just Python + GPT-4o-mini.
User gives a task
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Agent runs with current prompt → produces output
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Evaluator scores output (1-10) across 5 criteria
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Improver reads score + weaknesses → rewrites the prompt
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Agent runs again with the improved prompt
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Repeat until target score reached or max iterations hit
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Best output wins
The prompt literally rewrites itself every iteration based on what the evaluator found wrong.
Iteration 1 — Generic prompt → Score 7/10
Iteration 2 — Improver adds analogies, examples, comparisons → Score 8/10
Iteration 3 — Improver refines persona + adds summary instruction → Accuracy 9/10
Score improved from 7 → 8 → 8 across 3 iterations on "Explain transformer architecture to a beginner."
self-improving-agent/
├── app.py # Streamlit UI with iteration viewer
├── agent.py # Runs the task with current prompt
├── evaluator.py # Scores output across 5 criteria
├── improver.py # Rewrites prompt based on feedback
├── prompt_memory.py # Saves all prompt versions + scores
├── prompts.py # Base prompts for all 3 agents
├── config.py # Settings + API keys from .env
└── requirements.txt
| Layer | Tool |
|---|---|
| LLM | GPT-4o-mini (OpenAI) |
| Frontend | Streamlit |
| Memory | JSON persistence |
| Pattern | Meta-agent / self-optimization loop |
| Config | python-dotenv |
1. Clone the repo
git clone https://github.com/ES7/self-improving-agent
cd self-improving-agent2. Install dependencies
pip install -r requirements.txt3. Set up API key
cp .env.example .envFill in .env:
OPENAI_API_KEY=your_openai_key_here
4. Run
streamlit run app.pyEvery output is scored across 5 dimensions:
| Criteria | What it measures |
|---|---|
| Completeness | Does it fully address the task? |
| Accuracy | Is the content correct? |
| Clarity | Is it well-structured and easy to understand? |
| Depth | Does it go beyond surface level? |
| Usefulness | Would this actually help someone? |
The improver uses weak scores to rewrite the prompt specifically for those gaps.
Adjustable from the sidebar:
- Max iterations — how many times the agent improves (1-5)
- Target score — stop early when this score is reached (7-10)
Most AI systems run once and return an output. This one:
- Runs multiple times with an evolving prompt
- Has a separate evaluator agent with strict scoring criteria
- Has a separate improver agent that does prompt engineering automatically
- Remembers high-scoring prompts across sessions and reuses them
This is the foundation of how production AI systems do automated prompt optimization.
Ebad Sayed — Final year, IIT (ISM) Dhanbad, Co-founder of Voke AI
