agent-skills/skills/langgraph-csv-workflow/SKILL.md

name, version, description, inputs, steps, outputs, tags, metadata

name	version	description	inputs	steps	outputs	tags	metadata
langgraph-csv-workflow	1.0.0	Transform simple CSV files into powerful AI agent workflows using LangGraph orchestration	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	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	Executed workflow with agent decisions and state transitions Traced execution path through the graph nodes Logged agent interactions and output fields populated		source_repo extracted_at confidence https://github.com/jwwelbor/AgentMap.git 0.95

langgraph-csv-workflow

Transform simple CSV files into powerful AI agent workflows using LangGraph orchestration

Setup

Dependencies:

pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml fastapi uvicorn

Setup steps:

1. Install agentmap: pip install agentmap[all]
2. Initialize configuration: agentmap init-config
3. Configure LLM providers in agentmap_config.yaml
4. Run workflow: agentmap run workflow.csv

Key Files

• agentmap_config.yaml - Main configuration for LLM providers and paths
• agentmap_config_storage.yaml - Storage configuration for CSV/JSON/Vector DBs
• hello_world.csv - Sample workflow definition

Steps

1. Define workflow in CSV format specifying graph nodes, agent types, and data flow between them
2. Configure LLM providers and storage backends in the agentmap configuration files
3. Execute the workflow using the agentmap CLI or Python API

Implementation Details

CSV format: graph_name,node_name,agent_type,next_node,on_failure,prompt,input_fields,output_field

CLI command: agentmap run hello_world.csv --pretty

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

Outputs

• Executed workflow with agent decisions and state transitions
• Traced execution path through the graph nodes
• Logged agent interactions and output fields populated

Failure Modes

• Invalid CSV format causing parse errors during workflow loading
• Missing or misconfigured LLM provider credentials leading to runtime failures
• Incorrect agent configuration (e.g., missing input_fields) causing processing errors

Source

Extracted from: https://github.com/jwwelbor/AgentMap.git Confidence: 0.95