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⚡ Optimas Examples

Working examples and demos for all Optimas integration targets. These examples have been verified to work correctly with the latest SuperOptiX version.

Background reading and references:

🚀 Quick Demo

Pull Demo Playbooks

Bash
# Initialize a new project
super init test_optimas
cd test_optimas

# Pull working demo playbooks for each target
super agent pull optimas_openai      # OpenAI SDK (recommended)
super agent pull optimas_crewai      # CrewAI
super agent pull optimas_autogen     # AutoGen
super agent pull optimas_dspy        # DSPy

Full Flow Example

Bash
# 1. Compile
super agent compile optimas_openai --target optimas-openai

# 2. Evaluate
super agent evaluate optimas_openai --engine optimas --target optimas-openai

# 3. Optimize
SUPEROPTIX_OPRO_MAX_TOKENS=256 \
SUPEROPTIX_OPRO_NUM_CANDIDATES=3 \
SUPEROPTIX_OPRO_MAX_WORKERS=3 \
super agent optimize optimas_openai --engine optimas --target optimas-openai --optimizer opro

# 4. Run
super agent run optimas_openai --engine optimas --target optimas-openai --goal "Write a Python function to add two numbers"

🎯 Target-Specific Examples

Status: ✅ Fully Working - Most reliable target

Quick Demo

Bash
# Pull and test
super agent pull optimas_openai
super agent compile optimas_openai --target optimas-openai
super agent evaluate optimas_openai --engine optimas --target optimas-openai

Full Workflow with Optimization

Bash
# Optimize with environment variables
SUPEROPTIX_OPRO_MAX_TOKENS=256 \
SUPEROPTIX_OPRO_NUM_CANDIDATES=3 \
SUPEROPTIX_OPRO_MAX_WORKERS=3 \
SUPEROPTIX_OPRO_TEMPERATURE=0.8 \
SUPEROPTIX_OPRO_COMPILE_TIMEOUT=120 \
super agent optimize optimas_openai --engine optimas --target optimas-openai

# Run optimized agent
super agent run optimas_openai --engine optimas --target optimas-openai --goal "Write a Python function to calculate prime numbers"

Why It's Great

  • No threading issues
  • Fast optimization and execution
  • Works perfectly with all optimizers
  • Most stable target for production use

2. CrewAI Target

Status: ✅ Fully Working - Excellent for multi-agent workflows

Quick Demo

Bash
# Pull and test
super agent pull optimas_crewai
super agent compile optimas_crewai --target optimas-crewai
super agent evaluate optimas_crewai --engine optimas --target optimas-crewai

Full Workflow with Optimization

Bash
# Optimize with environment variables
SUPEROPTIX_OPRO_MAX_TOKENS=256 \
SUPEROPTIX_OPRO_NUM_CANDIDATES=3 \
SUPEROPTIX_OPRO_MAX_WORKERS=3 \
SUPEROPTIX_OPRO_TEMPERATURE=0.8 \
LITELLM_TIMEOUT=60 \
LITELLM_MAX_RETRIES=3 \
super agent optimize optimas_crewai --engine optimas --target optimas-crewai

# Run optimized agent
super agent run optimas_crewai --engine optimas --target optimas-crewai --goal "Write a Python function to calculate factorial"

Dependencies Required

Bash
# Install manually to avoid conflicts
pip install crewai
pip install json-repair>=0.30.0

Why It's Great

  • Excellent for multi-agent scenarios
  • Fast optimization and execution
  • No threading issues
  • Great for team-based tasks

3. AutoGen Target

Status: ⚠️ Mostly Working - Optimization can be slow

Quick Demo

Bash
# Pull and test
super agent pull optimas_autogen
super agent compile optimas_autogen --target optimas-autogen
super agent evaluate optimas_autogen --engine optimas --target optimas-autogen

Full Workflow with Optimization

Bash
# Optimize with extended timeout
SUPEROPTIX_OPRO_MAX_TOKENS=256 \
SUPEROPTIX_OPRO_NUM_CANDIDATES=3 \
SUPEROPTIX_OPRO_MAX_WORKERS=3 \
SUPEROPTIX_OPRO_COMPILE_TIMEOUT=180 \
LITELLM_TIMEOUT=60 \
LITELLM_MAX_RETRIES=3 \
super agent optimize optimas_autogen --engine optimas --target optimas-autogen

# Run optimized agent
super agent run optimas_autogen --engine optimas --target optimas-autogen --goal "Write a Python function to reverse a string"

Configuration Requirements

YAML
# Requires detailed model_info for non-OpenAI models
language_model:
  provider: ollama
  model: llama3.2:1b
  base_url: http://localhost:11434
  api_key: ""
  model_info:
    model_name: "llama3.2:1b"
    max_tokens: 4096
    temperature: 0.7
    top_p: 0.9

Why It's Great

  • Excellent for complex multi-agent workflows
  • Reliable execution despite slow optimization
  • Great for conversational agents
  • Handles complex interactions well

4. DSPy Target

Status: ✅ Fully Working - All optimizers now working properly

Quick Demo

Bash
# Pull and test
super agent pull optimas_dspy
super agent compile optimas_dspy --target optimas-dspy
super agent evaluate optimas_dspy --engine optimas --target optimas-dspy

Full Workflow with Optimization

Bash
# Optimize with environment variables
SUPEROPTIX_OPRO_MAX_TOKENS=256 \
SUPEROPTIX_OPRO_NUM_CANDIDATES=3 \
SUPEROPTIX_OPRO_MAX_WORKERS=3 \
SUPEROPTIX_OPRO_TEMPERATURE=0.8 \
super agent optimize optimas_dspy --engine optimas --target optimas-dspy

# Run optimized agent
super agent run optimas_dspy --engine optimas --target optimas-dspy --goal "Write a Python function to calculate fibonacci numbers"

Root Cause

The issue is with LiteLLM library version compatibility: - DSPy 3.0.0 uses LiteLLM for model communication - LiteLLM has threading issues with concurrent operations - When Optimas tries to run multiple optimization iterations, the thread pool gets corrupted

Workarounds

Bash
# Option 1: Use other targets for optimization
# Option 2: Reduce concurrency (may still fail)
SUPEROPTIX_OPRO_MAX_WORKERS=1
# Option 3: Use for research only (avoid optimization)

🎯 Optimizer Options

The --optimizer flag allows you to specify which optimization method to use:

Available Optimizers

  • --optimizer opro: OPRO (Optimization by PROmpting) - Single-iteration optimization
  • --optimizer mipro: MIPRO (Multi-Iteration PROmpting) - Multi-iteration optimization
  • --optimizer copro: COPRO (Cooperative PROmpting) - Cooperative optimization

Example Usage

Bash
# OPRO optimization (default)
super agent optimize <agent> --engine optimas --target <target> --optimizer opro

# MIPRO optimization (great for DSPy)
super agent optimize <agent> --engine optimas --target <target> --optimizer mipro

# COPRO optimization (cooperative approach)
super agent optimize <agent> --engine optimas --target <target> --optimizer copro

🔧 Environment Variable Examples

Fast Optimization (Development)

Bash
SUPEROPTIX_OPRO_MAX_TOKENS=128 \
SUPEROPTIX_OPRO_NUM_CANDIDATES=2 \
SUPEROPTIX_OPRO_MAX_WORKERS=2 \
SUPEROPTIX_OPRO_TEMPERATURE=0.7 \
SUPEROPTIX_OPRO_COMPILE_TIMEOUT=60 \
super agent optimize <agent> --engine optimas --target <target>

High-Quality Optimization (Production)

Bash
SUPEROPTIX_OPRO_MAX_TOKENS=512 \
SUPEROPTIX_OPRO_NUM_CANDIDATES=5 \
SUPEROPTIX_OPRO_MAX_WORKERS=4 \
SUPEROPTIX_OPRO_TEMPERATURE=0.9 \
SUPEROPTIX_OPRO_COMPILE_TIMEOUT=300 \
super agent optimize <agent> --engine optimas --target <target>

LiteLLM Configuration

Bash
LITELLM_TIMEOUT=60 \
LITELLM_MAX_RETRIES=3 \
LITELLM_MAX_RESPONSE=4000 \
LITELLM_CACHE_ENABLED=false \
LITELLM_LOG_LEVEL=ERROR \
super agent optimize <agent> --engine optimas --target <target>

🚨 Known Limitations

1. DSPy Optimization Issues

  • Problem: LiteLLM threading conflicts during optimization
  • Impact: Cannot use DSPy target for production optimization
  • Workaround: Use OpenAI SDK or CrewAI targets instead

2. AutoGen Optimization Speed

  • Problem: Optimization can be slow (120s+ timeout)
  • Impact: Slower development iteration
  • Workaround: Increase timeout or use faster models

3. CrewAI Dependencies

  • Problem: Manual installation required due to conflicts
  • Impact: Additional setup steps
  • Workaround: Follow manual installation instructions

4. LiteLLM Version Compatibility

  • Problem: DSPy 3.0.0 + LiteLLM threading issues
  • Impact: DSPy target optimization fails
  • Workaround: Use other targets or wait for LiteLLM fixes

🎯 Recommendations

For Production Use

  1. Primary: OpenAI SDK target (most reliable)
  2. Secondary: CrewAI target (excellent for multi-agent)
  3. Avoid: DSPy target (optimization issues)

For Development

  1. Quick Testing: OpenAI SDK target
  2. Multi-agent: CrewAI target
  3. Research: DSPy target (compile/evaluate only)

For Optimization

  1. Fast: OpenAI SDK or CrewAI targets
  2. Quality: Increase timeout and parameters
  3. Avoid: DSPy target optimization

🔗 Next Steps