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Agents & Nevron

Behind Neurobro’s intelligence lies a network of specialized “Nevrons” — modular AI agents working in harmony. Each Nevron handles specific tasks, from news analysis to technical evaluations, forming the building blocks of our ecosystem. Nevrons serve as the foundational units of our architecture, enabling specialized task execution, modular scalability, and efficient resource management. The Nevron framework is open source and available on GitHub, with comprehensive technical documentation to help you get started.
Since Neurobro is being constantly updated, the core technologies may differ from the ones described in this section.

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Key Benefits of Modular Nevrons

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Shared Resources

Neurobro maintains a unified state across platforms through shared resources:

Dynamic Communication

Real-time agent collaboration

Knowledge Sharing

Centralized information pool

Cross-Platform Sync

Consistent state management

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Open Source Framework

To increase transparency, we’ve fully open-sourced Nevron - our core framework written 100% in Python.

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Core Features of Nevron

Modular Design

Easily extend functionality with plug-and-play modules

Advanced Memory

Context retention and intelligent recall capabilities

Self-Learning

Continuous improvement through real-world feedback

Third-Party Integration

Seamless connection with external platforms

Easy Deployment

Flexible deployment options for any environment

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Platform Integrations of Nevron

Telegram

Messaging

Twitter

Social media

Discord

Broadcasting

More

Expandable
Ready to build your own AI agent? Follow our beginner-friendly guide to create your first agent in 5 simple steps. Join our developer community to share experiences and get support from other builders.

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Agent Components

Neurobro is present as a single agent, but from the technical perspective he is more complex. Neurobro includes multiple components that work together to provide both intelligence and functionality. Here is the overview and architecture note:
Each platform-specific Neurobro instance maintains its own:
  • Functionality scope
  • Data sources
  • Environmental interaction points

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Knowledge Base

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Embeddings

All embeddings are generated using OpenAI’s state-of-the-art text-embedding-3-large model, ensuring:

Precision

High-accuracy matching

Relevance

Context-aware results

Performance

Optimized processing

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API Integration

For the most part, Neurobro uses proprietary APIs to get the most accurate and up-to-date information.

Proprietary APIs

~90% of visible value

Public APIs

~10% of functionalityExamples: Coingecko, DexScreener, Base BlockScout

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Large Language Models

LLMs form the backbone of the AI, powering Neurobro with advanced intelligence capabilities. Different LLMs are used for different purposes:

R1

Specialized long-running reasoning tasks (e.g. internal evaluation of the found alpha)

V3

Fast direct communication with users

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Tools & Workflows

Each Nevron uses specialized tools and workflows to deliver optimal performance:
1

Tool Identification

Agent analyzes context and determines required functionality
2

Tool Execution

Selected tools represented by Nevrons process data and perform specific operations
3

Response Generation

Results are synthesized into coherent, contextually appropriate output
4

Feedback Integration

System learns from interaction outcomes to improve future performance
Neurobro itself uses a set of nevrons to perform complex tasks: chatting, analysis, research, etc.

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Platform-Specific Examples

While Neurobro remains a single agent, his presece on different platforms significantly varies. Here are some examples of how Neurobro works on different platforms:
Want to learn more about our tool development? Check out the Nevron documentation.