# LionAGI Philosophy: Orchestration, Not Operations

## The Paradigm Shift

LionAGI is evolving from a collection of operations to a **central coordination engine** for arbitrary orchestration patterns.

### Current State (v0.x - Deprecating)
```python
# Branch bloated with every possible operation
branch.chat()
branch.communicate()
branch.reason()
branch.plan()
branch.analyze()
# ... dozens more built-in operations
```

### Future State (v1.0+)
```python
# Branch as minimal interface - only core operations
branch.chat()        # Basic LLM interaction
branch.communicate() # Structured communication
branch.operate()     # Execute arbitrary operations
branch.react()       # ReAct pattern for tool use

# Everything else is user-defined operations
my_custom_op = create_operation(...)
branch.operate(my_custom_op)
```

## Core Philosophy

### Branch as a Space
In category theory terms, a `Branch` is a **space where operations happen**, not a collection of operations itself.

```python
# Branch = Space for computation
# Operation = Instantiation of orchestration pattern
# LionAGI = Coordinator that assembles operations
```

### Operations Are Arbitrary
Operations can be:
- **Agentic** - LLM-based reasoning
- **Computational** - Pure functions
- **External** - API calls, database queries
- **Composite** - Operations built from other operations
- **Cross-Branch** - Operations spanning multiple branches

## Creating Custom Operations

### Simple Operation
```python
from lionagi import Operation

def my_analysis_operation(data):
    """Any arbitrary computation"""
    return {"analyzed": data}

# Use it in a branch
result = await branch.operate(my_analysis_operation, data=my_data)
```

### Composite Operation Across Branches
```python
async def cross_branch_synthesis(branches: list[Branch]):
    """Operation spanning multiple branches"""
    # Gather from all branches
    results = await asyncio.gather(*[
        b.communicate("Your perspective?") 
        for b in branches
    ])
    
    # Synthesize in a new branch
    synthesizer = Branch()
    return await synthesizer.communicate(
        f"Synthesize: {results}"
    )
```

### Orchestration Pattern as Operation
```python
class FanOutFanIn(Operation):
    """Reusable orchestration pattern"""
    
    async def execute(self, question, expert_branches):
        # Fan-out
        analyses = await asyncio.gather(*[
            branch.communicate(question) 
            for branch in expert_branches
        ])
        
        # Fan-in
        return await self.synthesizer.communicate(
            f"Combine analyses: {analyses}"
        )

# Use like any operation
pattern = FanOutFanIn(synthesizer=Branch())
result = await branch.operate(pattern, question="...", expert_branches=[...])
```

## Why This Matters

### 1. **Unbounded Flexibility**
You're not limited to what LionAGI provides. Create ANY operation you need.

### 2. **Clean Composition**
Operations compose naturally - build complex from simple.

### 3. **Framework Agnostic**
Your operations can wrap LangChain, CrewAI, or any other framework.

### 4. **True Orchestration**
LionAGI becomes the conductor, not the orchestra.

## Migration Guide

### Old Way (Deprecated)
```python
# Using built-in operations
result = await branch.analyze(data)
summary = await branch.summarize(result)
plan = await branch.plan(summary)
```

### New Way (v1.0+)
```python
# Define your operations
analyze = create_operation(my_analysis_logic)
summarize = create_operation(my_summary_logic)
plan = create_operation(my_planning_logic)

# Orchestrate them
result = await branch.operate(analyze, data)
summary = await branch.operate(summarize, result)
plan = await branch.operate(plan, summary)

# Or compose them
pipeline = compose(analyze, summarize, plan)
result = await branch.operate(pipeline, data)
```

## Primary Orchestration Patterns

LionAGI will maintain these core patterns as building blocks:

1. **Sequential Pipeline** - Operations in order
2. **Parallel Execution** - Operations simultaneously  
3. **Fan-Out/Fan-In** - Distribute then synthesize
4. **Conditional Branching** - Dynamic paths
5. **Recursive Decomposition** - Break down complex tasks

These are provided not as fixed operations, but as **composable patterns** you can adapt.

## The Future: Complex Composite Operations

```python
# Complex operation spanning multiple branches and external systems
class EnterpriseWorkflow(Operation):
    async def execute(self, request):
        # Spawn specialized branches
        branches = {
            "research": Branch(system="Researcher"),
            "analysis": Branch(system="Analyst"),
            "review": Branch(system="Reviewer")
        }
        
        # External operations
        db_data = await self.database.query(request.context)
        api_data = await self.external_api.fetch(request.params)
        
        # Orchestrate across branches with external data
        research = await branches["research"].communicate(
            instruction=request.question,
            context={"db": db_data, "api": api_data}
        )
        
        analysis = await branches["analysis"].communicate(
            instruction="Analyze findings",
            context=research
        )
        
        review = await branches["review"].communicate(
            instruction="Review and validate",
            context={"research": research, "analysis": analysis}
        )
        
        return {"workflow_result": review}

# Use it like any other operation
workflow = EnterpriseWorkflow(database=db, external_api=api)
result = await branch.operate(workflow, request=my_request)
```

## Summary

**LionAGI is becoming a coordination engine, not an operation library.**

- **Minimal core**: Just `chat`, `communicate`, `operate`, `ReAct`
- **User-defined everything**: Create your own operations
- **Arbitrary orchestration**: Not limited to agentic patterns
- **Composable patterns**: Build complex from simple
- **Cross-branch coordination**: Operations can span multiple spaces

This is the future of LionAGI: **You bring the operations, we provide the orchestration.**
