# Examples: langgraph-csv-workflow

## Usage Example

```python
# How to use this skill
# Inputs: CSV workflow files with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field, LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml, Storage configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
# Process: Define workflow in CSV format specifying graph nodes, agent types, and data flow between them → Configure LLM providers and storage backends in the agentmap configuration files → Execute the workflow using the agentmap CLI or Python API
# Outputs: Executed workflow with agent decisions and state transitions, Traced execution path through the graph nodes, Logged agent interactions and output fields populated
```