# LangGraph: Workflows vs Agents - Complete Guide

## Overview

LangGraph offers two fundamental approaches to building AI systems: **Workflows** and **Agents**. Understanding when to use each is critical for building efficient, maintainable AI applications.

### The Fundamental Difference

| Aspect | Workflows | Agents |
|--------|-----------|--------|
| **Control Flow** | Developer-defined | LLM-determined |
| **Predictability** | High - predetermined paths | Lower - dynamic decisions |
| **Complexity** | Simpler to debug | More complex reasoning |
| **Use Case** | Known process steps | Open-ended problems |
| **LLM Role** | Embedded in predefined steps | Orchestrates its own actions |

---

## Core Concepts

### What is a Workflow?

A **workflow** is a graph where:
- The control flow is **predetermined** by the developer
- LLMs operate **within** the defined structure
- Paths are **known at design time**
- The system follows **explicit routing logic**

```
Input → LLM Call →  Gate →        LLM Call → Output
        (fixed)     (conditional) (fixed)
```

### What is an Agent?

An **agent** is a system where:
- The LLM **decides** the control flow
- Tools are available but **usage is dynamic**
- The system **adapts** to environmental feedback
- Paths emerge from **agent decisions**


```
Input → Agent decides → Tool/Action → Feedback → Agent decides → ... 
        (dynamic)       (variable)    (loop)     (dynamic)
```

---

## Workflows Explained

### Key Characteristics

1. **Predetermined Paths**: You define all possible routes
2. **Embedded Intelligence**: LLMs enhance specific steps
3. **Explicit Control**: Clear start, end, and decision points
4. **Verifiable Stages**: Each step can be tested independently

### Workflow Patterns

#### 1. Prompt Chaining
Sequential LLM calls where each processes the previous output.

**When to Use:**
- Translation with quality checks
- Content generation with refinement
- Multi-step verification processes


**Example Structure:**
```python
def workflow():
    draft = llm_generate(input)
    if quality_check(draft) == "fail":
        draft = llm_improve(draft, feedback)
    final = llm_polish(draft)
    return final
```

#### 2. Parallelization
Multiple independent LLM calls executing simultaneously.

**When to Use:**
- Processing multiple data sources
- Generating varied content types
- Independent analysis tasks

**Example Structure:**
```python
def workflow(topic):
    # Parallel execution
    summary_future = llm_summarize(topic)
    analysis_future = llm_analyze(topic)
    keywords_future = llm_extract_keywords(topic)
    
    # Aggregate results
    return combine(summary_future.result(), 
                   analysis_future.result(),
                   keywords_future.result())
```

#### 3. Routing
Directing inputs to specialized handlers based on classification.

**When to Use:**
- Customer service categorization
- Document type processing
- Multi-intent handling

**Example Structure:**
```python
def workflow(query):
    category = llm_classify(query)
    
    if category == "technical":
        return technical_handler(query)
    elif category == "billing":
        return billing_handler(query)
    else:
        return general_handler(query)
```

#### 4. Orchestrator-Worker
Orchestrator breaks down tasks and delegates to workers.

**When to Use:**
- Report generation with sections
- Complex document processing
- Multi-component analysis

**Example Structure:**
```python
def workflow(task):
    # Orchestrator plans
    subtasks = orchestrator_plan(task)
    
    # Workers execute in parallel
    results = [worker_execute(st) for st in subtasks]
    
    # Orchestrator synthesizes
    return orchestrator_synthesize(results)
```

#### 5. Evaluator-Optimizer
Generate-evaluate-refine loop until quality criteria met.

**When to Use:**
- Content meeting specific criteria
- Iterative refinement tasks
- Quality-gated outputs

**Example Structure:**
```python
def workflow(requirements):
    output = generator(requirements)
    
    while True:
        evaluation = evaluator(output, requirements)
        if evaluation.passed:
            break
        output = optimizer(output, evaluation.feedback)
    
    return output
```

---


## Agents Explained

### Key Characteristics

1. **Autonomous Decision-Making**: Agent chooses tools and actions
2. **Dynamic Tool Usage**: Tools selected based on context
3. **Feedback Loops**: Continuous observation-action cycles
4. **Adaptive Behavior**: Responds to environmental changes

### Agent Architecture

```python
# Core Agent Loop
def agent(input):
    state = initialize(input)
    
    while not is_complete(state):
        # Agent decides next action
        action = llm_decide(state, available_tools)
        
        # Execute action
        result = execute_tool(action)
        
        # Update state with feedback
        state = update_state(state, result)
    
    return state.final_answer
```


### Agent Components

#### 1. Tools
Functions the agent can invoke:
```python
@tool
def search_database(query: str) -> str:
    """Search internal database for information."""
    return database.search(query)

@tool
def calculate(expression: str) -> float:
    """Evaluate mathematical expression."""
    return eval(expression)
```

#### 2. Memory
State that persists across agent decisions:
```python
class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], operator.add]
    context: dict
    tool_results: list
    iterations: int
```


#### 3. Decision Logic
The agent's reasoning process:
```python
def agent_node(state):
    # Agent sees state and available tools
    response = llm_with_tools.invoke(state["messages"])
    
    # Agent decides: answer or use tool
    if response.tool_calls:
        return {"action": "use_tool", "tool_calls": response.tool_calls}
    else:
        return {"action": "respond", "response": response}
```


---

## Decision Framework

### Choose Workflows When:

✅ **Process is well-defined**
- Steps are known in advance
- Decision points are clear
- Quality gates are explicit

✅ **Determinism is important**
- Need consistent execution paths
- Debugging requires clarity
- Compliance requires auditability

✅ **Subtasks are independent**
- Can parallelize operations
- Each step is verifiable
- Clear input-output contracts

✅ **Examples:**
- Document translation pipeline
- Report generation with sections
- Multi-step content validation
- Structured data processing

### Choose Agents When:

✅ **Problem is open-ended**
- Solution path unknown upfront
- Requires exploration
- Multiple valid approaches

✅ **Tool selection is dynamic**
- Agent must choose appropriate tools
- Context determines actions
- Adaptive behavior needed

✅ **Iterative refinement required**
- Feedback loops essential
- Self-correction needed
- Learning from attempts

✅ **Examples:**
- Research assistants
- Code debugging
- Complex problem solving
- Interactive troubleshooting

---


## Architecture Patterns

### Hybrid Approach: Workflows with Agent Nodes

Combine both paradigms for optimal results:

```python
def hybrid_system(input):
    # Workflow structure
    classified = routing_llm(input)
    
    if classified == "simple":
        # Use workflow for simple cases
        return simple_workflow(input)
    else:
        # Use agent for complex cases
        return agent_system(input)
```


### Multi-Agent Workflows

Orchestrate multiple specialized agents:

```python
def multi_agent_workflow(task):
    # Orchestrator (workflow) coordinates agents
    plan = orchestrator_plan(task)
    
    results = []
    for subtask in plan:
        # Each subtask handled by specialized agent
        agent = select_agent(subtask.type)
        result = agent.execute(subtask)
        results.append(result)
    
    return synthesize(results)
```

---


## Real-World Use Cases

### Workflow Use Cases

#### 1. Content Localization Pipeline
```
Input (EN) → Translate (LLM) → Cultural Check (LLM) → 
Quality Gate → Refinement (if needed) → Output (ES)
```

**Why Workflow:** Fixed stages, clear quality criteria, verifiable steps

#### 2. Financial Report Generation
```
Data → Section Analysis (Parallel LLMs) → 
Orchestrator Combines → Executive Summary → Final Report
```

**Why Workflow:** Known report structure, parallel processing, deterministic output

#### 3. Resume Screening System
```
Resume → Extract Info (LLM) → Category Router → 
[Technical/Creative/Management] Handler → Ranking
```

**Why Workflow:** Clear categorization, specialized processing, audit trail

### Agent Use Cases

#### 1. Research Assistant
```
Question → Agent decides: [Search/Read/Synthesize] → 
Evaluates completeness → More research or Answer
```

**Why Agent:** Unknown information needs, dynamic tool selection, iterative refinement

#### 2. Code Debugger
```
Bug Report → Agent: [Read code/Run tests/Check logs] → 
Hypothesis → [Test/Verify] → Solution or iterate
```

**Why Agent:** Unpredictable debugging path, adaptive strategy, tool choice depends on findings

#### 3. Customer Support Bot
```
Query → Agent: [Search KB/Check account/Escalate] → 
Response → Verify satisfaction → Follow-up or close
```

**Why Agent:** Varied customer needs, context-dependent actions, learning from interactions

---


## Implementation Examples

### Workflow Implementation (Evaluator-Optimizer)

```python
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict

class State(TypedDict):
    task: str
    output: str
    feedback: str
    iterations: int

def generator(state: State):
    result = llm.invoke(f"Generate: {state['task']}")
    return {"output": result.content, "iterations": state.get("iterations", 0) + 1}

def evaluator(state: State):
    evaluation = llm.invoke(f"Evaluate: {state['output']} for task: {state['task']}")
    return {"feedback": evaluation.content}

def should_continue(state: State):
    if "approved" in state["feedback"].lower() or state["iterations"] >= 3:
        return END
    return "generator"

# Build workflow
workflow = StateGraph(State)
workflow.add_node("generator", generator)
workflow.add_node("evaluator", evaluator)
workflow.add_edge(START, "generator")
workflow.add_edge("generator", "evaluator")
workflow.add_conditional_edges("evaluator", should_continue, ["generator", END])

app = workflow.compile()
```

### Agent Implementation

```python
from langgraph.graph import StateGraph, MessagesState
from langchain_core.tools import tool

@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Results for: {query}"

@tool
def calculate(expr: str) -> str:
    """Calculate mathematical expression."""
    return str(eval(expr))

tools = [search, calculate]
llm_with_tools = llm.bind_tools(tools)

def agent_node(state: MessagesState):
    response = llm_with_tools.invoke(state["messages"])
    return {"messages": [response]}

def tool_node(state: MessagesState):
    results = []
    for tool_call in state["messages"][-1].tool_calls:
        tool = {t.name: t for t in tools}[tool_call["name"]]
        result = tool.invoke(tool_call["args"])
        results.append(ToolMessage(content=result, tool_call_id=tool_call["id"]))
    return {"messages": results}

def should_continue(state: MessagesState):
    if state["messages"][-1].tool_calls:
        return "tools"
    return END

# Build agent
agent_graph = StateGraph(MessagesState)
agent_graph.add_node("agent", agent_node)
agent_graph.add_node("tools", tool_node)
agent_graph.add_edge(START, "agent")
agent_graph.add_conditional_edges("agent", should_continue, ["tools", END])
agent_graph.add_edge("tools", "agent")

agent_app = agent_graph.compile()
```

---


## Best Practices

### Workflow Best Practices

1. **Keep Nodes Focused**
   - Each node does one thing well
   - Clear input-output contracts
   - Easy to test independently

2. **Use Type Hints**
   ```python
   class State(TypedDict):
       input: str
       result: str
       metadata: dict
   ```

3. **Implement Quality Gates**
   - Validate outputs at each stage
   - Explicit pass/fail criteria
   - Feedback loops for failures

4. **Parallelize When Possible**
   - Identify independent operations
   - Use Send API for dynamic parallelization
   - Aggregate results efficiently

5. **Make Routing Explicit**
   - Clear decision logic
   - Enum types for routes
   - Comprehensive edge cases

### Agent Best Practices

1. **Design Clear Tools**
   ```python
   @tool
   def well_designed_tool(param: str) -> str:
       """
       Clear description of what the tool does.
       
       Args:
           param: Specific parameter description
           
       Returns:
           Specific return value description
       """
       return result
   ```

2. **Implement Safety Limits**
   ```python
   class AgentState(TypedDict):
       messages: list
       iterations: int
       max_iterations: int  # Prevent infinite loops
   ```

3. **Add Human-in-the-Loop**
   ```python
   def should_continue(state):
       if state["iterations"] > 5:
           return "human_review"  # Escalate complex cases
       if state["messages"][-1].tool_calls:
           return "tools"
       return END
   ```

4. **Log Agent Decisions**
   - Track tool selections
   - Record reasoning
   - Monitor performance

5. **Handle Failures Gracefully**
   ```python
   def tool_node(state):
       try:
           result = execute_tool(state)
           return {"messages": [result]}
       except Exception as e:
           return {"messages": [ToolMessage(
               content=f"Error: {str(e)}",
               tool_call_id=state["messages"][-1].tool_calls[0]["id"]
           )]}
   ```

---

## Common Pitfalls

### Workflow Pitfalls

❌ **Over-complicating Simple Tasks**
```python
# Don't do this for simple tasks
def overly_complex_workflow(text):
    analyzed = llm_analyze(text)
    categorized = llm_categorize(analyzed)
    processed = llm_process(categorized)
    return llm_format(processed)

# Just do this
def simple_approach(text):
    return llm.invoke(f"Process this: {text}")
```

❌ **Ignoring Error States**
- Always handle LLM failures
- Provide fallback paths
- Don't assume perfect execution

❌ **Tight Coupling**
- Keep nodes independent
- Avoid hidden dependencies
- Make state explicit

### Agent Pitfalls

❌ **Infinite Loops**
```python
# Always add iteration limits
class State(TypedDict):
    iterations: int
    max_iterations: int  # Required!

def should_continue(state):
    if state["iterations"] >= state["max_iterations"]:
        return END  # Safety exit
    # ... rest of logic
```

❌ **Too Many Tools to start with**
- Limit to 5-10 tools per agent (Although langgraph can handle way too many but go iteratively)
- Group related functions
- Consider tool hierarchies

❌ **Unclear Tool Descriptions**
```python
# Bad
@tool
def process(data):
    """Process data."""  # Too vague!
    
# Good
@tool
def extract_email_addresses(text: str) -> list[str]:
    """Extract all email addresses from the given text.
    
    Args:
        text: Input text to search for email addresses
        
    Returns:
        List of email addresses found in the text
    """
```

❌ **No Observability**
- Implement logging
- Track decision paths
- Monitor tool usage
- Measure performance

---

## Performance Considerations

### Workflow Optimization

1. **Minimize Sequential LLM Calls**
   - Combine prompts when possible
   - Use structured outputs to reduce steps

2. **Cache Intermediate Results**
   ```python
   @lru_cache(maxsize=100)
   def expensive_llm_call(input: str):
       return llm.invoke(input)
   ```

3. **Batch When Possible**
   - Process multiple inputs together
   - Use LLM batch APIs

### Agent Optimization

1. **Limit Tool Calls**
   - Set max iterations
   - Encourage efficient tool use in prompts

2. **Use Streaming**
   ```python
   for chunk in agent.stream(input, stream_mode="updates"):
       process_chunk(chunk)  # Handle results as they arrive
   ```

3. **Implement Caching**
   - Cache tool results
   - Store common patterns

---

## Conclusion

### Decision Checklist

**Use Workflows if:**
- [ ] Process steps are known
- [ ] Path is mostly deterministic
- [ ] Need strong auditability
- [ ] Independent subtasks exist
- [ ] Quality gates are clear

**Use Agents if:**
- [ ] Problem is exploratory
- [ ] Tool choice is context-dependent
- [ ] Need adaptive behavior
- [ ] Feedback loops are essential
- [ ] Path emerges from context

**Use Hybrid if:**
- [ ] Some parts are structured, others exploratory
- [ ] Want workflow reliability with agent flexibility
- [ ] Different user types need different approaches

### Next Steps

1. **Experiment**: Start with simple workflows, evolve to agents
2. **Measure**: Track performance, costs, and user satisfaction
3. **Iterate**: Refine based on real-world usage
4. **Scale**: Build on patterns that work

---

## Attribution & License

This guide adapts content from the official LangGraph documentation and tutorials.

**Source**: LangGraph Official Documentation  
**Copyright**: © 2024 LangChain, Inc.  
**License**: MIT License  
**Original Materials**: [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)

The original materials are licensed under the MIT License:
- Full license text: [MIT License](https://github.com/langchain-ai/langgraph/blob/main/LICENSE)
- Permission granted to use, copy, modify, and distribute with attribution

**Aparsoft's Adaptations**: This guide has been enhanced by Aparsoft Private Limited with additional examples, explanations, decision frameworks, and production insights specific to our developer community.

---

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---

*Last Updated: October 2025*  
*Version: 1.0*
