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docs: improve README with usage instructions

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  1. README.md +9 -7
  2. requirements.txt +1 -1
README.md CHANGED
@@ -13,16 +13,18 @@ short_description: Gradient-based agent coordination without central control
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  # 🧪 Morphogen Gradients
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- Explore **gradient-based coordination** where agents adapt behavior based on local chemical signals no central controller needed.
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- ## Features
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- - **Tab 1 Manual Gradient**: Set 6 morphogen values and see strategy hints, context injection, and phenotype adaptation
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- - **Tab 2 Orchestrator Simulation**: Watch gradients evolve step-by-step as the orchestrator reacts to successes and failures
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- - **7 presets**: Easy task, crisis mode, exploration, budget crunch, smooth sailing, cascading failures, recovery arc
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  ## How It Works
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- The `MorphogenGradient` holds 6 signal types (complexity, confidence, budget, error_rate, urgency, risk). The `GradientOrchestrator` adjusts these signals after each step result, producing strategy hints and phenotype parameters that shape agent behavior.
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- [GitHub](https://github.com/coredipper/operon) | [PyPI](https://pypi.org/project/operon-ai/)
 
 
 
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  # 🧪 Morphogen Gradients
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+ Explore gradient-based agent coordination where six chemical signals guide behavior without a central controller -- like morphogen gradients directing cell differentiation in developing embryos.
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+ ## What to Try
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+ 1. Open the **Manual Gradient** tab, adjust the six morphogen sliders (complexity, confidence, budget, error_rate, urgency, risk), and click **Analyze** to see strategy hints and phenotype adaptation.
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+ 2. Switch to the **Orchestrator Simulation** tab, pick a preset (e.g. "Crisis mode" or "Cascading failures"), and click **Run Simulation** to watch gradients evolve step-by-step as the orchestrator reacts.
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+ 3. Try "Budget crunch" to see how low budget signals change the agent's strategy, then compare with "Easy task" where all signals are favorable.
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  ## How It Works
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+ The MorphogenGradient holds six signal types that the GradientOrchestrator adjusts after each step based on outcomes. These signals produce strategy hints and phenotype parameters that shape agent behavior -- enabling decentralized coordination without explicit orchestration rules.
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+ ## Learn More
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+
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+ [GitHub](https://github.com/coredipper/operon) | [PyPI](https://pypi.org/project/operon-ai/) | [Paper](https://github.com/coredipper/operon/tree/main/article)
requirements.txt CHANGED
@@ -1,2 +1,2 @@
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  gradio>=4.0
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- operon-ai>=0.14.0
 
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  gradio>=4.0
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+ operon-ai>=0.15.0