Complete Migration

README.md

@@ -9,7 +9,6 @@ A composable DSPy agent that optimizes tweets using a reflective generate-evalua
 - **Customizable Categories**: Define evaluation criteria (engagement, clarity, tone, etc.)
 - **Multiple Usage Modes**: Single generation, full optimization, or standalone evaluation
 - **Structured Evaluation**: 1-9 scoring with detailed reasoning per category
-- **CLI Compatibility**: Same functionality as the original CLI tool
 - **Easy Configuration**: Flexible model, iteration, and patience settings
 
 ## Installation
@@ -48,22 +47,10 @@ from tweet_optimizer_agent import TweetOptimizerAgent, TweetOptimizerConfig
 
 # Create agent with default settings
 config = TweetOptimizerConfig()
-agent = TweetOptimizerAgent(config)
+tweet_optimizer = TweetOptimizerAgent(config)
 
-# Single tweet generation
+# run complete optimization
-tweet = agent(
+results = tweet_optimizer(
-    input_text="Create a tweet about HuggingFace transformers",
-    current_tweet="",
-    previous_evaluation=None
-)
-print(f"Generated: {tweet}")
-```
-
-### Full Optimization Process
-
-```python
-# Run complete optimization (like CLI)
-results = agent.optimize(
     input_text="Create a tweet about HuggingFace transformers",
     iterations=10,
     patience=5
@@ -75,6 +62,12 @@ print(f"Score: {results['best_score']:.2f}")
 print(f"Iterations: {results['iterations_run']}")
 ```
 
+### Full Optimization Process
+
+```python
+
+```
+
 ### Custom Configuration
 
 ```python
@@ -94,20 +87,6 @@ config = TweetOptimizerConfig(
 agent = TweetOptimizerAgent(config)
 ```
 
-### Tweet Evaluation
-
-```python
-# Evaluate a specific tweet
-evaluation = agent.evaluate_tweet(
-    tweet_text="Excited to share our new AI model!",
-    original_text="We released a new AI model",
-    current_best_tweet=""
-)
-
-for eval in evaluation.evaluations:
-    print(f"{eval.category}: {eval.score}/9 - {eval.reasoning}")
-```
-
 ### Deploy to Modaic Hub
 
 ```python
@@ -122,38 +101,27 @@ agent.push_to_hub(
 ### Load from Hub
 
 ```python
-# Load a pre-trained agent from Modaic Hub
+# Load this pre-trained agent from Modaic Hub
-agent = TweetOptimizerAgent.from_precompiled("your-username/tweet-optimizer")
+from modaic import AutoAgent
+
+tweet_optimizer = AutoAgent.from_precompiled("farouk1/tweet-optimizer-v2")
 
 # Use immediately
-optimized = agent("Your tweet content here")
+results = tweet_optimizer(
+    input_text="Your tweet content here",
+    iterations=20,
+    patience=7
+)
+
+print(f"Original: {results['initial_text']}")
+print(f"Optimized: {results['final_tweet']}")
+print(f"Score: {results['best_score']:.2f}")
+print(f"Iterations: {results['iterations_run']}")
 ```
 
-## CLI Tool
+### Configuration Options
 
-The original CLI functionality is still available:
+#### TweetOptimizerConfig
-
-```bash
-# Basic usage
-python cli.py "Create a tweet about AI breakthroughs"
-
-# With custom settings
-python cli.py "Product launch announcement" \
-  --model "Claude Sonnet 4.5" \
-  --iterations 15 \
-  --patience 8 \
-  --categories "Excitement" "Clarity" "Call-to-action"
-
-# List available models
-python cli.py --list-models
-
-# Quiet mode (output only final tweet)
-python cli.py "Content here" --quiet
-```
-
-## Configuration Options
-
-### TweetOptimizerConfig
 
 | Parameter        | Type      | Default                                | Description                                 |
 |------------------|-----------|----------------------------------------|---------------------------------------------|
@@ -202,7 +170,6 @@ This Modaic agent implementation is based on the original DSPy Tweet Optimizer b
 
 - Core DSPy modules (TweetGeneratorModule, TweetEvaluatorModule)
 - Hill-climbing optimization algorithm
-- CLI interface and utilities
 - Comprehensive testing framework
 
 **Original Author**: Tom Doerr ([@tom-doerr](https://github.com/tom-doerr))
@@ -213,5 +180,4 @@ This Modaic agent implementation is based on the original DSPy Tweet Optimizer b
 - Packaged as a Modaic PrecompiledAgent
 - Added hub deployment functionality
 - Enhanced configuration options
-- Maintained CLI compatibility
 - Extended usage examples

agent/agent.py

@@ -37,6 +37,9 @@ class TweetOptimizerAgent(PrecompiledAgent):
 
         self.lm = config.lm
         self.eval_lm = config.eval_lm
+        self.categories = config.categories
+        self.max_iterations = config.max_iterations
+        self.patience = config.patience
 
         # initialize DSPy with the specified model
         self.tweet_generator.set_lm(get_dspy_lm(config.lm))
@@ -49,8 +52,8 @@ class TweetOptimizerAgent(PrecompiledAgent):
         patience: Optional[int] = None
     ) -> str:
         """Run full optimization process."""
-        max_iterations = iterations or self.config.max_iterations
+        max_iterations = iterations or self.max_iterations
-        patience_limit = patience or self.config.patience
+        patience_limit = patience or self.patience
 
         results = {
             'initial_text': input_text,