Hermes Pipeline
29 lines
1.7 KiB
JSON
29 lines
1.7 KiB
JSON
{
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"name": "langgraph-csv-workflow",
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"version": "1.0.0",
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"goal": "Transform simple CSV files into powerful AI agent workflows using LangGraph orchestration",
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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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],
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"steps": [
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"Define workflow in CSV format specifying graph nodes, agent types, and data flow 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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],
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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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],
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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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],
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"confidence": 0.95,
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"explanation": "AgentMap provides a declarative pattern where workflows are defined in CSV files with specific columns describing graph nodes, agent types, and data flow. This pattern can be adapted to create multi-agent systems with LangGraph, supporting various LLM providers and storage backends. The workflow is reusable across different use cases by simply modifying the CSV definition.",
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"source_repo": "https://github.com/jwwelbor/AgentMap.git",
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"score": 1.0
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} |