agent-skills/skills/branching-agent-pattern/SKILL.md

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

name	version	description	inputs	steps	outputs	tags	metadata
branching-agent-pattern	1.0.0	Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic	CSV workflow files defining agent graphs 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 backend configuration in agentmap_config_storage.yaml	Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure) Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace	Executed workflow with results stored in the specified output_field Detailed execution trace showing success/failure decisions at each branching point Updated workflow state persisted in the configured storage backend		source_repo extracted_at confidence https://github.com/jwwelbor/AgentMap.git 0.95

branching-agent-pattern

Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic

Setup

Dependencies:

pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml>=6.0.0 fastapi>=0.111.0 uvicorn>=0.34.3

Setup steps:

1. Install AgentMap: pip install agentmap[all]
2. Configure llm providers in agentmap_config.yaml (OpenAI, Anthropic, Google models)
3. Create CSV workflow files with graph definitions
4. Initialize runtime with ensure_initialized()
5. Run workflow with agentmap run <csv_file> --pretty

Key Files

• agentmap_config.yaml - Main configuration with LLM and storage settings
• agentmap_config_storage.yaml - Storage backend configuration
• hello_world.csv - Sample workflow demonstrating basic agent chain
• examples/host_integration/custom_agents.py - Custom agent implementations with host service integration

Steps

1. Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)
2. Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary
3. Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
4. Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace

Implementation Details

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

BranchingAgent context example: {'input_fields': ['success'], 'output_field': 'result', 'success_values': ['PASSED', 'COMPLETED']}

Execution command: agentmap run hello_world.csv --pretty

Inputs

• CSV workflow files defining agent graphs 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 backend configuration in agentmap_config_storage.yaml

Outputs

• Executed workflow with results stored in the specified output_field
• Detailed execution trace showing success/failure decisions at each branching point
• Updated workflow state persisted in the configured storage backend

Failure Modes

• Invalid CSV format causing parsing errors during workflow loading
• Missing or misconfigured LLM provider settings leading to execution failures
• Storage backend unavailable or misconfigured preventing workflow persistence
• Agent execution timeout due to long-running operations or infinite loops

Source

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