Hermes Pipeline
90 lines
2.9 KiB
Markdown
90 lines
2.9 KiB
Markdown
---
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name: langgraph-csv-workflow
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version: 1.0.0
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description: Transform simple CSV files into powerful AI agent workflows using LangGraph
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orchestration
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inputs:
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- 'CSV workflow files with columns: graph_name, node_name, agent_type, next_node,
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on_failure, prompt, input_fields, output_field'
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- LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml
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- Storage configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
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steps:
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- Define workflow in CSV format specifying graph nodes, agent types, and data flow
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between them
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- Configure LLM providers and storage backends in the agentmap configuration files
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- Execute the workflow using the agentmap CLI or Python API
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outputs:
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- Executed workflow with agent decisions and state transitions
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- Traced execution path through the graph nodes
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- Logged agent interactions and output fields populated
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tags: []
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metadata:
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source_repo: https://github.com/jwwelbor/AgentMap.git
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extracted_at: ''
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confidence: 0.95
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---
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# langgraph-csv-workflow
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Transform simple CSV files into powerful AI agent workflows using LangGraph orchestration
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## Setup
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**Dependencies:**
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```text
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pip install langgraph>=1.0.0 langchain-core>=0.3.0 pyyaml fastapi uvicorn
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```
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**Setup steps:**
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1. Install agentmap: pip install agentmap[all]
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1. Initialize configuration: agentmap init-config
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1. Configure LLM providers in agentmap_config.yaml
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1. Run workflow: agentmap run workflow.csv
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## Key Files
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- `agentmap_config.yaml - Main configuration for LLM providers and paths`
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- `agentmap_config_storage.yaml - Storage configuration for CSV/JSON/Vector DBs`
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- `hello_world.csv - Sample workflow definition`
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## Steps
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1. Define workflow in CSV format specifying graph nodes, agent types, and data flow between them
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2. Configure LLM providers and storage backends in the agentmap configuration files
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3. Execute the workflow using the agentmap CLI or Python API
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## Implementation Details
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```python
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CSV format: graph_name,node_name,agent_type,next_node,on_failure,prompt,input_fields,output_field
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```
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```python
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CLI command: agentmap run hello_world.csv --pretty
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```
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## Inputs
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- CSV workflow files with columns: graph_name, node_name, agent_type, next_node, on_failure, prompt, input_fields, output_field
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- LLM provider configuration (OpenAI, Anthropic, Google) in agentmap_config.yaml
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- Storage configuration for CSV/JSON/Vector DBs in agentmap_config_storage.yaml
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## Outputs
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- Executed workflow with agent decisions and state transitions
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- Traced execution path through the graph nodes
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- Logged agent interactions and output fields populated
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## Failure Modes
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- Invalid CSV format causing parse errors during workflow loading
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- Missing or misconfigured LLM provider credentials leading to runtime failures
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- Incorrect agent configuration (e.g., missing input_fields) causing processing errors
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## Source
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Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
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Confidence: 0.95
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