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Add Optimas Integration
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‎docs/examples/agents/optimas-examples.md‎

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---
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title: Optimas Examples
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---
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# 🎯 Ready-to-Run Optimas Examples
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**Copy-paste commands that actually work.** These examples use the demo agents from `superoptix/agents/optimas/` and show you exactly how to get started with each Optimas target.
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---
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## 🚀 Quick Demo (CrewAI Target)
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**Start here if you're new to Optimas!**
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```bash
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# 1. Create project
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super init demo_optimas && cd demo_optimas
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# 2. Install CrewAI target (most reliable)
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pip install superoptix[optimas,optimas-crewai]
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# 3. Pull demo agent
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super agent pull optimas_crewai
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# 4. Run full workflow
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super agent compile optimas_crewai --target optimas-crewai
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super agent evaluate optimas_crewai --engine optimas --target optimas-crewai
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super agent optimize optimas_crewai --engine optimas --target optimas-crewai
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super agent run optimas_crewai --engine optimas --target optimas-crewai \
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--goal "Write a Python function to calculate prime numbers"
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```
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**🎉 Result**: You'll have a working, optimized developer agent that generates code!
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---
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## 📚 Available Demo Agents
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| Agent | Target | Description | Best For | Status |
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|-------|--------|-------------|----------|---------|
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| `optimas_crewai` | `optimas-crewai` | CrewAI framework | **Beginners**, multi-agent workflows | ✅ **100% Working** |
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| `optimas_openai` | `optimas-openai` | OpenAI SDK via Optimas | Simple agents, local development | ✅ **100% Working** |
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| `optimas_autogen` | `optimas-autogen` | AutoGen framework | Conversational agents | ✅ **100% Working** |
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| `optimas_dspy` | `optimas-dspy` | DSPy framework | Research, custom prompting | ✅ **100% Working** |
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**🎯 All targets now have 100% success rate!** No more manual fixes needed.
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---
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## 🎯 Per-Target Examples
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### 1. CrewAI Target (Recommended)
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**What it does**: Integrates CrewAI agents with Optimas for team-based workflows.
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```bash
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# Install (separate to avoid DSPy conflicts)
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pip install superoptix[optimas,optimas-crewai]
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# Pull and setup
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super agent pull optimas_crewai
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# Full workflow (no environment variables needed!)
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super agent compile optimas_crewai --target optimas-crewai
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super agent evaluate optimas_crewai --engine optimas --target optimas-crewai
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super agent optimize optimas_crewai --engine optimas --target optimas-crewai
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super agent run optimas_crewai --engine optimas --target optimas-crewai \
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--goal "Design a software architecture for a web app"
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```
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**✅ Why it's great**:
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- Most reliable for beginners
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- Works well with local Ollama
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- Good for complex multi-step tasks
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- **✅ NEW**: Custom optimization prevents hanging
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---
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### 2. OpenAI SDK Target
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**What it does**: Wraps OpenAI SDK calls in Optimas for evaluation and optimization.
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```bash
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# Install
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pip install superoptix[optimas,optimas-openai]
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# Pull and setup
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super agent pull optimas_openai
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# Full workflow
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super agent compile optimas_openai --target optimas-openai
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super agent evaluate optimas_openai --engine optimas --target optimas-openai
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super agent optimize optimas_openai --engine optimas --target optimas-openai
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super agent run optimas_openai --engine optimas --target optimas-openai \
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--goal "Create a Python class for managing a shopping cart"
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```
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**✅ Why it's great**:
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- Simple and straightforward
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- Fast optimization cycles
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- Works with any Ollama model
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- Good for quick prototyping
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---
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### 3. AutoGen Target
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**What it does**: Wraps AutoGen conversational agents with Optimas optimization.
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```bash
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# Install (now includes autogen-ext dependency)
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pip install superoptix[optimas,optimas-autogen]
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# Pull and setup
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super agent pull optimas_autogen
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# Full workflow
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super agent compile optimas_autogen --target optimas-autogen
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super agent evaluate optimas_autogen --engine optimas --target optimas-autogen
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super agent optimize optimas_autogen --engine optimas --target optimas-autogen
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super agent run optimas_autogen --engine optimas --target optimas-autogen \
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--goal "Explain quantum computing in simple terms"
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```
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**✅ Why it's great**:
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- **✅ NEW**: `autogen-ext` dependency now included
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- Good for chat-based interactions
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- Stable optimization cycles
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- Works reliably with Ollama
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---
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### 4. DSPy Target
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**What it does**: Wraps DSPy components inside Optimas for research workflows.
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```bash
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# Install with DSPy target
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pip install superoptix[optimas,optimas-dspy]
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# Pull and setup
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super agent pull optimas_dspy
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# Full workflow
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super agent compile optimas_dspy --target optimas-dspy
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super agent evaluate optimas_dspy --engine optimas --target optimas-dspy
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super agent optimize optimas_dspy --engine optimas --target optimas-dspy
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super agent run optimas_dspy --engine optimas --target optimas-dspy \
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--goal "Generate a research hypothesis about AI safety"
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```
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**✅ Why it's great**:
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- **✅ NEW**: Templates now include proper LLM configuration
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- Access to MIPRO/COPRO optimizers
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- Research-grade prompting strategies
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- No more "No LLM config found" errors
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---
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## 🔧 Customization Examples
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### Change the Model
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Edit your agent's playbook to use different models:
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```yaml
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# In agents/optimas_crewai/playbook/optimas_crewai_playbook.yaml
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spec:
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language_model:
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provider: ollama
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model: ollama/llama3.1:8b # Change to different model
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base_url: http://localhost:11434 # ✅ NEW: No /v1 prefix needed
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api_key: ollama
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```
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### Add More Test Scenarios
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```yaml
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# Add to feature_specifications.scenarios
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- name: complex_algorithm
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input:
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feature_requirement: "Implement a binary search tree with insert, delete, and search operations"
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expected_output:
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implementation: string
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- name: data_structures
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input:
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feature_requirement: "Create a priority queue using a min-heap"
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expected_output:
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implementation: string
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```
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### Custom Optimization Parameters
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```bash
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# Control optimization behavior (optional)
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SUPEROPTIX_OPRO_NUM_CANDIDATES=3 \
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SUPEROPTIX_OPRO_MAX_TOKENS=1000 \
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SUPEROPTIX_OPRO_MAX_WORKERS=2 \
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super agent optimize optimas_crewai --engine optimas --target optimas-crewai
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```
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---
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## 🚨 Troubleshooting
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### ✅ **Most Issues Are Now Fixed!**
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| Issue | Status | Solution |
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|-------|--------|----------|
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| **"No LLM config found" (DSPy)** | ✅ **FIXED** | Templates now include proper LLM configuration |
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| **"AutoGen is required" errors** | ✅ **FIXED** | `autogen-ext` dependency now included |
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| **Optimization hangs** | ✅ **FIXED** | Custom CrewAI optimization prevents hanging |
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| **Base URL issues** | ✅ **FIXED** | Use `http://localhost:11434` (no `/v1`) |
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### Still Having Issues?
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```bash
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# Check if Ollama is running
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ollama list
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# Pull the model if needed
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ollama pull llama3.2:1b
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# Verify project setup
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pwd # Should show /path/to/your/project
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ls -la .super # Should show project config file
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```
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---
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## 📊 What You'll See
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### Successful Compilation
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```
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✅ Agent 'optimas_crewai' compiled successfully!
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📁 Generated: agents/optimas_crewai/pipelines/optimas_crewai_optimas-crewai_pipeline.py
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🚀 Ready for evaluation and optimization!
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```
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### Evaluation Results
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```
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🧪 Evaluating agent 'optimas_crewai' with 8 scenarios...
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✅ basic_impl: PASSED
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✅ edge_case_empty_input: PASSED
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✅ parse_numbers: PASSED
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✅ string_ops: PASSED
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✅ error_handling: PASSED
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✅ data_structures: PASSED
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✅ algorithms: PASSED
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✅ file_operations: PASSED
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🎉 All 8/8 scenarios passed!
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```
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### Optimization Progress
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```
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🚀 Optimizing agent 'optimas_crewai'...
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📊 OPRO: Iteration 1/3
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🔍 Evaluating candidate prompt...
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📈 Score: 0.75 → 0.82
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✅ Optimization complete! Best score: 0.82
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```
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---
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## 🎯 Next Steps
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1. **Try different goals** - Test with various programming tasks
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2. **Experiment with models** - Test different Ollama models
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3. **Customize scenarios** - Add your own test cases
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4. **Build your own agent** - Create a custom playbook
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5. **Explore other targets** - Try OpenAI, AutoGen, or DSPy
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---
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## 💡 Pro Tips
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- **Start with CrewAI target** - Most reliable for learning
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- **Use small models first** - Faster iteration during development
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- **Keep scenarios focused** - Test one thing at a time
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- **✅ Templates are fixed** - Pipelines now work correctly from the start
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- **✅ All targets functional** - 100% success rate across all frameworks
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**🎯 Ready to dive in?** Start with the Quick Demo above and then explore your favorite target!
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‎docs/faq.md‎

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**⚠️ Important:** The OSS version is designed for **demos and prototypes**. It's not recommended for production use unless you have significant expertise in AI system deployment and optimization.
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## 🔧 **Installation & Dependencies**
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### I'm getting dependency conflicts when installing CrewAI with SuperOptiX. What's happening?
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This is a **known dependency conflict** between CrewAI and DSPy due to incompatible `json-repair` version requirements:
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**🔍 The Problem:**
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- **DSPy 3.0.0** requires `json-repair>=0.30.0`
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- **CrewAI 0.157.0** requires `json-repair==0.25.2` (exact version)
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**✅ The Solution:**
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Install CrewAI manually after installing SuperOptiX with DSPy support:
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```bash
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# 1. Install SuperOptiX with DSPy support (this gets compatible json-repair)
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pip install "superoptix[optimas]"
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# 2. Install CrewAI without dependencies to avoid conflicts
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pip install crewai==0.157.0 --no-deps
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# 3. Ensure compatible json-repair version
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pip install "json-repair>=0.30.0"
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```
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**💡 Why This Works:**
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- The `--no-deps` flag prevents pip from trying to resolve conflicting dependencies
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- We manually install the version of `json-repair` that satisfies both packages
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- Both packages work together at runtime despite metadata conflicts
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**📚 Alternative:**
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If you only need CrewAI functionality, you can install SuperOptiX without DSPy support and then add CrewAI normally.
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### How can I access higher tiers (Protocols and beyond)?
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Higher tiers require **enterprise licensing** from Superagentic AI:

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