Hermes Pipeline
101 lines
3.6 KiB
Markdown
101 lines
3.6 KiB
Markdown
---
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name: graph-based-node-workflow
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version: 1.0.0
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description: Create and execute typed node graphs for AI/robotics workflows by defining
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nodes with inputs/outputs and connecting them with edges, then cooking the graph
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to run the workflow.
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inputs:
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- bn.Graph() - the graph container for the workflow
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- Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified
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inputs and outputs
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- Edge connections mapping from_port to to_port between nodes
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steps:
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- 'Step 1: Initialize a bn.Graph() instance to serve as the workflow container'
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- 'Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal
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for data, LLMAgent for inference, Concat for combining, Output for final results)'
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- 'Step 3: Create edges connecting nodes by specifying source from_port and destination
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to_port for each data flow'
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- 'Step 4: Execute the graph by calling g.cook() to process the defined workflow and
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produce results'
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outputs:
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- Executed workflow results stored in the graph's output nodes
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- Cooked graph ready for inspection, replay, or deployment
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- Potential error states if node dependencies are missing or ports don't match
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tags: []
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metadata:
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source_repo: https://github.com/temiroff/Blacknode.git
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extracted_at: ''
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confidence: 0.95
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---
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# graph-based-node-workflow
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Create and execute typed node graphs for AI/robotics workflows by defining nodes with inputs/outputs and connecting them with edges, then cooking the graph to run the workflow.
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## Setup
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**Dependencies:**
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```text
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pip install blacknode (core Python package) anthropic, openai, docker, petgraph (dependencies) Rust extensions in blacknode-core, blacknode-runtime (optional)
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```
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**Setup steps:**
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1. Install blacknode with Python 3.11+ and required dependencies
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1. Clone repository and navigate to project directory
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1. Run examples/converted_text_pipeline.py to see basic graph execution
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1. Modify node definitions and edges to create custom workflows
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## Key Files
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- `examples/converted_text_pipeline.py - basic pipeline pattern`
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- `examples/hello_agent.py - LLM agent workflow pattern`
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- `examples/research_pipeline.py - multi-node research workflow`
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- `blacknode.py - main CLI entry point`
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## Steps
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1. Step 1: Initialize a bn.Graph() instance to serve as the workflow container
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2. Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results)
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3. Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow
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4. Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results
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## Implementation Details
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```python
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g = bn.Graph()
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```
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```python
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g._edges = [{"from": "model", "from_port": "value", "to": "agent", "to_port": "model"}]
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```
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```python
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result = g.cook(output, "value")
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```
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## Inputs
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- bn.Graph() - the graph container for the workflow
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- Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs
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- Edge connections mapping from_port to to_port between nodes
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## Outputs
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- Executed workflow results stored in the graph's output nodes
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- Cooked graph ready for inspection, replay, or deployment
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- Potential error states if node dependencies are missing or ports don't match
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## Failure Modes
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- Missing node dependencies causing undefined variable errors
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- Port mismatch in edge connections leading to no data flow
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- Incomplete graph definition causing cook() to fail
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- Model API key missing or invalid for LLMAgent nodes
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## Source
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Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
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Confidence: 0.95
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