Skip to content

Workflows

Overview

Workflows are the backbone of Nevron’s logic, defining tasks and coordinating tools and modules to deliver actionable outcomes. Each workflow is designed for autonomy and efficiency, allowing the agent to function in diverse scenarios seamlessly.

Available Workflows

1. Analyze Signal

Focuses on processing actionable signals, such as market trends or data feeds, and publishing insights through communication channels like Twitter.

Features: - Fetches and validates signals. - Analyzes data using a Large Language Model (LLM). - Publishes concise updates (e.g., tweets).

Location: src/workflows/analyze_signal.py

2. Research News

Specializes in gathering, analyzing, and summarizing news content for dissemination.

Features: - Collects and validates news articles. - Uses LLMs for analysis and contextualization. - Publishes summaries through predefined channels.

Location: src/workflows/research_news.py

Workflow Architecture

Workflows in Nevron follow a modular design to ensure scalability and consistency:

  1. Input Validation: Ensures data completeness.
  2. Core Execution: Processes the workflow logic.
  3. Result Aggregation: Collects outputs for further use.
  4. Memory Storage: Saves results for future reference.
  5. Feedback Integration: Logs outcomes for iterative learning.

Integration Points

Workflows rely on Nevron’s modular components for execution:

  • Tools:
  • Signal fetching for data processing.
  • Automated publishing to Twitter.

  • News gathering for insights.

  • Modules:

  • Planning Module: Guides decision-making.
  • Memory Module: Provides context from past events.
  • LLM Integration: Drives analysis and content generation.
  • Feedback Module: Tracks performance for improvement.

How to Add a New Workflow?

  1. Create a New File:

  2. Add a Python file in src/workflows/ (e.g., new_workflow.py).

  3. Define the Workflow Class: 

    from src.workflows.base import BaseWorkflow

class NewWorkflow(BaseWorkflow):
    name = "new_workflow"

  4. Implement Logic: 

    def execute(self):
    # Core workflow functionality
    logger.info("Executing workflow")

def validate(self):
    # Input validation logic
    logger.info("Validating workflow input")

  5. Register Workflow: 

    from src.workflows.new_workflow import NewWorkflow
agent.register_workflow(NewWorkflow())

  6. Test:

    • Write unit tests for components.
    • Validate integration with the agent.
    • Test edge cases and error handling.

Best Practices

  1. Error Handling:

    • Anticipate edge cases.
    • Provide clear error messages for debugging.

  2. Logging:

    • Log critical workflow steps for monitoring.
    • Track execution times and performance metrics.

  3. Testing:

    • Unit tests for components.
    • Integration tests to verify module and tool interactions.
    • Simulate failure scenarios for robustness.

Nevron’s workflows enable seamless automation and intelligent decision-making, forming the core of its adaptability and efficiency. For further details, refer to the GitHub Discussions.