Metadata-Version: 2.4
Name: refinire
Version: 0.1.1
Summary: Model adapters for OpenAI Agents SDK
Project-URL: Homepage, https://github.com/kitfactory/refinire
Project-URL: Documentation, https://kitfactory.github.io/refinire/
Project-URL: Repository, https://github.com/kitfactory/refinire
Project-URL: Bug-Tracker, https://github.com/kitfactory/refinire/issues
License-File: LICENSE
Requires-Python: >=3.9
Requires-Dist: httpx>=0.28.1
Requires-Dist: openai-agents>=0.0.6
Requires-Dist: openai>=1.68.0
Requires-Dist: pydantic<3,>=2.10
Description-Content-Type: text/markdown

# Refinire ✨ - The Art of AI Agent Development

[![PyPI Downloads](https://static.pepy.tech/badge/refinire)](https://pepy.tech/projects/refinire)
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[![OpenAI Agents 0.0.9](https://img.shields.io/badge/OpenAI-Agents_0.0.9-green.svg)](https://github.com/openai/openai-agents-python)
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**Elegant AI agent development platform that transforms complexity into simplicity.**

---

## The Philosophy of Refinement

In the world of AI development, complexity has become the norm. Countless lines of configuration, provider-specific implementations, manual quality management—all standing between you and your vision.

**Refinire changes that.**

We believe in the power of simplicity. Not the simplicity that sacrifices capability, but the kind that emerges when complexity is distilled to its essence.

## What You'll Experience

### Development Time: From Days to Minutes

```python
# Traditional approach: 50-100 lines of configuration
# Refinire approach: The essence
from refinire import create_simple_gen_agent, Context
import asyncio

agent = create_simple_gen_agent(
    name="assistant",
    instructions="You are a helpful assistant.",
    model="gpt-4o-mini"
)

result = asyncio.run(agent.run("Hello, world!", Context()))
print(result.shared_state["assistant_result"])
```

**Two lines. One complete AI agent.**

### Provider Boundaries: Dissolved

```python
# The same elegant interface, regardless of provider
llm = get_llm("gpt-4o-mini")      # OpenAI
llm = get_llm("claude-3-sonnet")  # Anthropic  
llm = get_llm("gemini-pro")       # Google
llm = get_llm("llama3.1:8b")      # Ollama
```

**Switch providers with a single line change. The abstraction remains beautiful.**

### Quality Management: Autonomous

```python
# Quality emerges naturally
agent = create_evaluated_gen_agent(
    name="quality_assistant",
    generation_instructions="Generate helpful responses",
    evaluation_instructions="Evaluate for accuracy and helpfulness",
    threshold=85.0,
    model="gpt-4o-mini"
)

# Automatic evaluation, improvement, and refinement
result = asyncio.run(agent.run("Explain quantum computing", Context()))
```

**Set the standard once. Quality maintains itself.**

---

## The Art of Simplicity

| Aspect | Traditional Approach | Refinire |
|--------|---------------------|----------|
| **Setup Time** | Hours to days | Minutes |
| **Configuration Lines** | 50-100+ | 2-3 |
| **Provider Migration** | Complete rewrite | Single line change |
| **Quality Management** | Manual, ongoing | Autonomous |
| **Parallel Processing** | Complex async code | Simple DAG definition |
| **Debugging Complexity** | Opaque, difficult | Transparent, intuitive |

### Real-World Impact

**Time to First AI Agent**: 5 minutes instead of 5 hours  
**Provider Migration Effort**: 99% reduction  
**Quality Assurance Overhead**: Eliminated through automation  
**Parallel Processing Performance**: 3.9x speedup with zero complexity  
**Learning Curve**: Gentle slope instead of steep cliff  

---

## Elegant Architecture

### Unified LLM Interface

```python
from refinire import get_llm

# One interface, infinite possibilities
llm = get_llm("gpt-4o-mini")
response = llm.complete("Explain the concept of refinement")
```

### Intelligent Tool Integration

```python
from refinire import create_tool_enabled_llm_pipeline

def get_weather(city: str) -> str:
    """Get current weather for a city"""
    return f"Weather in {city}: Sunny, 22°C"

def calculate(expression: str) -> float:
    """Perform mathematical calculations"""
    return eval(expression)

# Tools integrate seamlessly
pipeline = create_tool_enabled_llm_pipeline(
    name="smart_assistant",
    instructions="You are a helpful assistant with access to tools.",
    tools=[get_weather, calculate],
    model="gpt-4o-mini"
)

# The LLM decides when and how to use tools
result = pipeline.run("What's the weather in Tokyo and what's 15 * 23?")
```

### Transparent Operations

```python
from refinire import get_global_registry

# See into the mind of your AI
registry = get_global_registry()
traces = registry.search_by_flow_name("customer_support")

for trace in traces:
    print(f"Agent: {trace.agent_name}")
    print(f"Duration: {trace.duration}ms") 
    print(f"Quality: {trace.quality_score}")
```

**Your AI agents become transparent, understandable, improvable.**

---

## Flow Architecture: Composable Intelligence

### Simple Sequential Flow

```python
from refinire import Flow, create_simple_flow

# Define your process
def analyze_request(user_input, ctx):
    ctx.shared_state["analysis"] = f"Analyzed: {user_input}"
    return ctx

def generate_response(user_input, ctx):
    analysis = ctx.shared_state["analysis"]
    ctx.shared_state["response"] = f"Response based on {analysis}"
    ctx.finish()
    return ctx

# Create elegant workflow
flow = create_simple_flow([
    ("analyze", FunctionStep("analyze", analyze_request)),
    ("respond", FunctionStep("respond", generate_response))
])

result = asyncio.run(flow.run(input_data="User request"))
```

### Conditional Intelligence

```python
# AI that makes decisions
def route_by_complexity(ctx):
    return "simple" if len(ctx.user_input) < 50 else "complex"

flow = Flow({
    "router": ConditionStep("router", route_by_complexity, "simple", "complex"),
    "simple": SimpleAgent(),
    "complex": ExpertAgent()
})
```

### Parallel Processing: 3.9x Performance Boost

```python
from refinire import Flow, FunctionStep

# Process multiple analysis tasks simultaneously
flow = Flow(start="preprocess", steps={
    "preprocess": FunctionStep("preprocess", preprocess_text),
    "parallel_analysis": {
        "parallel": [
            FunctionStep("sentiment", analyze_sentiment),
            FunctionStep("keywords", extract_keywords),
            FunctionStep("topic", classify_topic),
            FunctionStep("readability", calculate_readability)
        ],
        "next_step": "aggregate",
        "max_workers": 4
    },
    "aggregate": FunctionStep("aggregate", combine_results)
})

# Sequential execution: 2.0s → Parallel execution: 0.5s (3.9x speedup)
result = await flow.run("Analyze this comprehensive text...")
```

**Intelligence flows naturally through your logic, now with lightning speed.**

---

## Interactive Conversations

```python
from refinire import create_simple_interactive_pipeline

def completion_check(result):
    return "finished" in str(result).lower()

# Multi-turn conversation agent
pipeline = create_simple_interactive_pipeline(
    name="conversation_agent",
    instructions="Have a natural conversation with the user.",
    completion_check=completion_check,
    max_turns=10,
    model="gpt-4o-mini"
)

# Natural conversation flow
result = pipeline.run_interactive("Hello, I need help with my project")
while not result.is_complete:
    user_input = input(f"Turn {result.turn}: ")
    result = pipeline.continue_interaction(user_input)

print("Conversation complete:", result.content)
```

**Conversations that remember, understand, and evolve.**

---

## Monitoring and Insights

### Real-time Agent Analytics

```python
# Search and analyze your AI agents
registry = get_global_registry()

# Find specific patterns
customer_flows = registry.search_by_agent_name("customer_support")
performance_data = registry.complex_search(
    flow_name_pattern="support",
    status="completed",
    min_duration=100
)

# Understand performance patterns
for flow in performance_data:
    print(f"Flow: {flow.flow_name}")
    print(f"Average response time: {flow.avg_duration}ms")
    print(f"Success rate: {flow.success_rate}%")
```

### Quality Monitoring

```python
# Automatic quality tracking
quality_flows = registry.search_by_quality_threshold(min_score=80.0)
improvement_candidates = registry.search_by_quality_threshold(max_score=70.0)

# Continuous improvement insights
print(f"High-quality flows: {len(quality_flows)}")
print(f"Improvement opportunities: {len(improvement_candidates)}")
```

**Your AI's performance becomes visible, measurable, improvable.**

---

## Installation & Quick Start

### Install

```bash
pip install refinire
```

### Your First Agent (30 seconds)

```python
from refinire import create_simple_gen_agent, Context
import asyncio

# Create
agent = create_simple_gen_agent(
    name="hello_world",
    instructions="You are a friendly assistant.",
    model="gpt-4o-mini"
)

# Run
result = asyncio.run(agent.run("Hello!", Context()))
print(result.shared_state["hello_world_result"])
```

### Provider Flexibility

```python
from refinire import get_llm

# Test multiple providers
providers = [
    ("openai", "gpt-4o-mini"),
    ("anthropic", "claude-3-haiku-20240307"),
    ("google", "gemini-1.5-flash"),
    ("ollama", "llama3.1:8b")
]

for provider, model in providers:
    try:
        llm = get_llm(provider=provider, model=model)
        print(f"✓ {provider}: {model} - Ready")
    except Exception as e:
        print(f"✗ {provider}: {model} - {str(e)}")
```

---

## Advanced Features

### Structured Output

```python
from pydantic import BaseModel
from refinire import LLMPipeline

class WeatherReport(BaseModel):
    location: str
    temperature: float
    condition: str

pipeline = LLMPipeline(
    name="weather_reporter",
    generation_instructions="Generate weather reports",
    output_model=WeatherReport,
    model="gpt-4o-mini"
)

result = pipeline.run("Weather in Tokyo")
weather = result.content  # Typed WeatherReport object
```

### Guardrails and Safety

```python
from refinire import LLMPipeline

def content_filter(content: str) -> bool:
    """Filter inappropriate content"""
    return "inappropriate" not in content.lower()

pipeline = LLMPipeline(
    name="safe_assistant",
    generation_instructions="Be helpful and appropriate",
    output_guardrails=[content_filter],
    model="gpt-4o-mini"
)
```

### Custom Tool Integration

```python
def web_search(query: str) -> str:
    """Search the web for information"""
    # Your search implementation
    return f"Search results for: {query}"

pipeline = LLMPipeline(
    name="research_assistant",
    generation_instructions="Help with research using web search",
    model="gpt-4o-mini"
)

pipeline.add_function_tool(web_search)
```

---

## Why Refinire?

### For Developers
- **Immediate productivity**: Build AI agents in minutes, not days
- **Provider freedom**: Switch between OpenAI, Anthropic, Google, Ollama seamlessly  
- **Quality assurance**: Automatic evaluation and improvement
- **Transparent operations**: Understand exactly what your AI is doing

### For Teams
- **Consistent architecture**: Unified patterns across all AI implementations
- **Reduced maintenance**: Automatic quality management and error handling
- **Performance visibility**: Real-time monitoring and analytics
- **Future-proof**: Provider-agnostic design protects your investment

### For Organizations
- **Faster time-to-market**: Dramatically reduced development cycles
- **Lower operational costs**: Automatic optimization and provider flexibility
- **Quality compliance**: Built-in evaluation and monitoring
- **Scalable architecture**: From prototype to production seamlessly

---

## Examples

Explore comprehensive examples in the `examples/` directory:

### Core Features
- `standalone_agent_demo.py` - Independent agent execution
- `trace_search_demo.py` - Monitoring and analytics
- `llm_pipeline_example.py` - Tool-enabled pipelines
- `interactive_pipeline_example.py` - Multi-turn conversations

### Flow Architecture  
- `flow_show_example.py` - Workflow visualization
- `simple_flow_test.py` - Basic flow construction
- `router_agent_example.py` - Conditional routing
- `dag_parallel_example.py` - High-performance parallel processing

### Specialized Agents
- `clarify_agent_example.py` - Requirement clarification
- `notification_agent_example.py` - Event notifications
- `extractor_agent_example.py` - Data extraction
- `validator_agent_example.py` - Content validation

---

## Supported Environments

- **Python**: 3.9+
- **Platforms**: Windows, Linux, macOS  
- **Dependencies**: OpenAI Agents SDK 0.0.9+

---

## Developer Experience

> *"The complexity that once consumed days now resolves in minutes. Refinire doesn't just simplify—it elevates."*

> *"Switching between AI providers feels like changing a variable. The abstraction is invisible yet powerful."*

> *"Watching AI agents improve themselves through automatic evaluation—it's like witnessing the future of software."*

---

## License & Credits

MIT License. Built with gratitude on the [OpenAI Agents SDK](https://github.com/openai/openai-agents-python).

**Refinire**: Where complexity becomes clarity, and development becomes art.