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 Hermes Pipeline
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---
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name: branching-agent-pattern
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version: 1.0.0
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description: Define and execute AI agent workflows using CSV-based declarative definitions
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with configurable branching logic
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inputs:
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- 'CSV workflow files defining agent graphs with columns: graph_name, node_name, agent_type,
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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 backend configuration in agentmap_config_storage.yaml
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steps:
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- Define workflow graph in CSV with nodes representing agent steps and their connections
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(next_node, on_failure)
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- Configure BranchingAgent with customizable success/failure values and fallback fields
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in the context dictionary
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- Initialize the agent runtime with ensure_initialized() and configure execution tracking
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and state adapter services
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- Execute the workflow using agentmap run with appropriate inputs and monitor the
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execution trace
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outputs:
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- Executed workflow with results stored in the specified output_field
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- Detailed execution trace showing success/failure decisions at each branching point
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- Updated workflow state persisted in the configured storage backend
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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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# branching-agent-pattern
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Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic
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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>=6.0.0 fastapi>=0.111.0 uvicorn>=0.34.3
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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. Configure llm providers in agentmap_config.yaml (OpenAI, Anthropic, Google models)
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1. Create CSV workflow files with graph definitions
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1. Initialize runtime with ensure_initialized()
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1. Run workflow with agentmap run <csv_file> --pretty
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## Key Files
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- `agentmap_config.yaml - Main configuration with LLM and storage settings`
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- `agentmap_config_storage.yaml - Storage backend configuration`
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- `hello_world.csv - Sample workflow demonstrating basic agent chain`
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- `examples/host_integration/custom_agents.py - Custom agent implementations with host service integration`
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## Steps
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1. Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)
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2. Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary
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3. Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services
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4. Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace
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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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BranchingAgent context example: {'input_fields': ['success'], 'output_field': 'result', 'success_values': ['PASSED', 'COMPLETED']}
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```
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```python
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Execution command: agentmap run hello_world.csv --pretty
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```
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## Inputs
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- CSV workflow files defining agent graphs 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 backend configuration in agentmap_config_storage.yaml
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## Outputs
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- Executed workflow with results stored in the specified output_field
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- Detailed execution trace showing success/failure decisions at each branching point
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- Updated workflow state persisted in the configured storage backend
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## Failure Modes
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- Invalid CSV format causing parsing errors during workflow loading
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- Missing or misconfigured LLM provider settings leading to execution failures
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- Storage backend unavailable or misconfigured preventing workflow persistence
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- Agent execution timeout due to long-running operations or infinite loops
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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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# Commands: branching-agent-pattern
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## Available Commands
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- `/skill branching-agent-pattern` — Load this skill
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- `/run branching-agent-pattern` — Execute workflow
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# Examples: branching-agent-pattern
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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 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
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# Process: 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
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# 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
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```
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{
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"name": "branching-agent-pattern",
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"version": "1.0.0",
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"goal": "Define and execute AI agent workflows using CSV-based declarative definitions with configurable branching logic",
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"inputs": [
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"CSV workflow files defining agent graphs 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 backend configuration in agentmap_config_storage.yaml"
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],
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"steps": [
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"Define workflow graph in CSV with nodes representing agent steps and their connections (next_node, on_failure)",
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"Configure BranchingAgent with customizable success/failure values and fallback fields in the context dictionary",
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"Initialize the agent runtime with ensure_initialized() and configure execution tracking and state adapter services",
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"Execute the workflow using agentmap run with appropriate inputs and monitor the execution trace"
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],
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"outputs": [
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"Executed workflow with results stored in the specified output_field",
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"Detailed execution trace showing success/failure decisions at each branching point",
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"Updated workflow state persisted in the configured storage backend"
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],
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"failure_modes": [
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"Invalid CSV format causing parsing errors during workflow loading",
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"Missing or misconfigured LLM provider settings leading to execution failures",
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"Storage backend unavailable or misconfigured preventing workflow persistence",
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"Agent execution timeout due to long-running operations or infinite loops"
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],
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"confidence": 0.95,
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"explanation": "The BranchingAgent pattern provides a reusable framework for creating conditional AI workflows. The CSV-based workflow definition allows defining complex agent graphs declaratively, while the BranchingAgent handles dynamic branching based on success/failure conditions with customizable value sets. This pattern can be adapted to various use cases including task routing, error handling, and conditional execution paths across different domains.",
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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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+1
-1
@@ -1,4 +1,4 @@
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# Tests: literature-review-with-traceable-ai-evidence
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# Tests: branching-agent-pattern
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## Test Checklist
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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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@@ -1,6 +0,0 @@
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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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@@ -1,37 +0,0 @@
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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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Reference in New Issue
Block a user