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1 Commits
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 Hermes Pipeline
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---
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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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@@ -1,6 +0,0 @@
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# Commands: langgraph-csv-workflow
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## Available Commands
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- `/skill langgraph-csv-workflow` — Load this skill
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- `/run langgraph-csv-workflow` — Execute workflow
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# Examples: langgraph-csv-workflow
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## Usage Example
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```python
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# How to use this skill
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# 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
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# 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
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# Outputs: Executed workflow with agent decisions and state transitions, Traced execution path through the graph nodes, Logged agent interactions and output fields populated
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```
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@@ -1,29 +0,0 @@
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{
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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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}
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---
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name: literature-review-with-traceable-ai-evidence
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version: 1.0.0
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description: Enable researchers to ingest documents, asynchronously index them, and
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interact with multi-agent AI to answer questions with verifiable citations to original
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text.
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inputs:
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- Documents in PDF, Office, image, or text formats
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- Research questions or topics of interest
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- 'Optional: user model configuration via .env or settings'
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steps:
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- Upload documents to a project (via desktop app or web UI)
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- System asynchronously converts Office docs to PDF if needed, runs OCR to extract
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text with coordinates, chunks and embeds into vector store
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- User starts a main research session or creates exploration branches without waiting
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for indexing to finish
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- Leader agent receives query and delegates subtasks to researcher, reviewer, writer
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subagents
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- Subagents perform hybrid retrieval and rerank to find relevant chunks with source
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coordinates
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- Agents synthesize answers and return evidence with clickable citations that highlight
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original pages
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- User verifies conclusions by navigating to cited source locations and can save notes
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to research memory or mind map
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outputs:
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- AI-generated answers with traceable evidence (coordinates, page highlights)
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- Research session history with branches
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- Indexed document library for future queries
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- Mind maps or structured notes
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- Persistent run events for resuming sessions
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tags: []
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metadata:
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source_repo: https://github.com/0verL1nk/PaperSage.git
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extracted_at: ''
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confidence: 0.85
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---
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# literature-review-with-traceable-ai-evidence
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Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.
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## Steps
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1. Upload documents to a project (via desktop app or web UI)
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2. System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store
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3. User starts a main research session or creates exploration branches without waiting for indexing to finish
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4. Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents
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5. Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates
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6. Agents synthesize answers and return evidence with clickable citations that highlight original pages
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7. User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map
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## Inputs
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- Documents in PDF, Office, image, or text formats
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- Research questions or topics of interest
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- Optional: user model configuration via .env or settings
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## Outputs
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- AI-generated answers with traceable evidence (coordinates, page highlights)
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- Research session history with branches
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- Indexed document library for future queries
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- Mind maps or structured notes
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- Persistent run events for resuming sessions
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## Failure Modes
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- Missing local Office/LibreOffice converter causes document conversion failure
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- First-time model download may be slow or require network
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- OCR may have low confidence on poor quality scans
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- Retrieval might miss context if chunking splits semantics
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- Multi-agent coordination could produce conflicting intermediate results
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## Source
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Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
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Confidence: 0.85
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@@ -0,0 +1,6 @@
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# Commands: literature-review-with-traceable-ai-evidence
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## Available Commands
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- `/skill literature-review-with-traceable-ai-evidence` — Load this skill
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- `/run literature-review-with-traceable-ai-evidence` — Execute workflow
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# Examples: literature-review-with-traceable-ai-evidence
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## Usage Example
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```python
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# How to use this skill
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# Inputs: Documents in PDF, Office, image, or text formats, Research questions or topics of interest, Optional: user model configuration via .env or settings
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# Process: Upload documents to a project (via desktop app or web UI) → System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store → User starts a main research session or creates exploration branches without waiting for indexing to finish
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# Outputs: AI-generated answers with traceable evidence (coordinates, page highlights), Research session history with branches, Indexed document library for future queries, Mind maps or structured notes, Persistent run events for resuming sessions
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```
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@@ -0,0 +1,37 @@
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{
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"name": "literature-review-with-traceable-ai-evidence",
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"version": "1.0.0",
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"goal": "Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.",
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"inputs": [
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"Documents in PDF, Office, image, or text formats",
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"Research questions or topics of interest",
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"Optional: user model configuration via .env or settings"
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],
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"steps": [
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"Upload documents to a project (via desktop app or web UI)",
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"System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store",
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"User starts a main research session or creates exploration branches without waiting for indexing to finish",
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"Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents",
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"Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates",
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"Agents synthesize answers and return evidence with clickable citations that highlight original pages",
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"User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map"
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],
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"outputs": [
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"AI-generated answers with traceable evidence (coordinates, page highlights)",
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"Research session history with branches",
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"Indexed document library for future queries",
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"Mind maps or structured notes",
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"Persistent run events for resuming sessions"
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],
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"failure_modes": [
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"Missing local Office/LibreOffice converter causes document conversion failure",
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"First-time model download may be slow or require network",
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"OCR may have low confidence on poor quality scans",
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"Retrieval might miss context if chunking splits semantics",
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"Multi-agent coordination could produce conflicting intermediate results"
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],
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"confidence": 0.85,
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"explanation": "The README describes PaperSage's core workflow: asynchronous document ingestion with OCR/indexing, followed by multi-agent question answering with cited evidence. This process is not tied to the specific codebase and can be reused as a general literature review methodology for any document-centric research using RAG and agent collaboration.",
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"source_repo": "https://github.com/0verL1nk/PaperSage.git",
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"score": 1.0
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}
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+1
-1
@@ -1,4 +1,4 @@
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# Tests: langgraph-csv-workflow
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# Tests: literature-review-with-traceable-ai-evidence
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## Test Checklist
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Reference in New Issue
Block a user