> ## Documentation Index
> Fetch the complete documentation index at: https://mcp-server-langgraph.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 10. LangGraph Functional API over Object-Oriented Approach

> Architecture Decision Record: 10. LangGraph Functional API over Object-Oriented Approach

# 10. LangGraph Functional API over Object-Oriented Approach

Date: 2025-10-13

## Status

Accepted

## Category

Core Architecture

## Context

LangGraph offers two approaches for defining agent workflows:

1. **Functional API**: Define nodes as pure functions, compose with StateGraph
2. **Object-Oriented**: Define agents as classes with methods

Agent systems require:

* Clear data flow and state management
* Conditional routing logic
* Graph visualization for debugging
* Testability (unit test individual nodes)

## Decision

Use **LangGraph's Functional API** with StateGraph for agent implementation.

### Architecture

```python theme={null}
class AgentState(TypedDict):
    messages: Annotated[list[BaseMessage], operator.add]
    next_action: str

def route_input(state: AgentState) -> AgentState:
    # Pure function - easy to test
    ...

def generate_response(state: AgentState) -> AgentState:
    ...

graph = StateGraph(AgentState)
graph.add_node("route", route_input)
graph.add_node("respond", generate_response)
graph.add_edge(START, "route")
graph.add_conditional_edges("route", should_use_tools)
```

## Consequences

### Positive Consequences

* **Declarative**: Graph structure visible, easy to visualize
* **Testable**: Pure functions, easy unit tests
* **Composable**: Nodes reusable across graphs
* **Debuggable**: Clear state transitions

### Negative Consequences

* **Verbose**: More boilerplate than class-based
* **Learning Curve**: Requires understanding StateGraph concepts

## Alternatives Considered

1. **Class-Based Agents**: Less transparent, harder to visualize
2. **Direct LangChain**: No graph structure, harder to debug
3. **Custom State Machine**: Reinventing the wheel

## References

* Implementation: `src/mcp_server_langgraph/core/agent.py:50-200`
* Related ADRs: [ADR-0015](https://github.com/vishnu2kmohan/mcp-server-langgraph/blob/main/adr/adr-0015-memory-checkpointing.md), [ADR-0005](https://github.com/vishnu2kmohan/mcp-server-langgraph/blob/main/adr/adr-0005-pydantic-ai-integration.md)