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Readme

🧠 Axon

Axon is an emergent cognitive architecture for multi-agent systems based on Global Workspace Theory.

import { Axon } from "axon-ai";

const axon = new Axon({
    llm: {
        baseUrl: "http://localhost:1234/v1",
        model: "qwen/qwen3-4b-2507",
        embeddingModel: "text-embedding-nomic-embed-text-v1.5",
        temperature: 0.7,
        provider: "lmstudio",
    },
});

const result = await axon.process("Brainstorm sustainable energy solutions");

console.log(result.finalBroadcast);

🌟 Features

Self-organizing - No predefined workflows or hardcoded agent interactions
Emergent cognition - Thoughts compete for attention in a shared workspace
Flexible configuration - Use any LLM provider (OpenAI, local, etc.)
Dynamic specialists - Meta-agent creates specialists tailored to each problem
Streaming support - Real-time observation of the cognitive process

🚀 Installation

yarn add axon-ai
# or
pnpm add axon-ai

🧩 Core Concepts

ThoughtChunk: The fundamental data unit with content, embeddings, activation energy, and lineage
Workspace: An active medium managing ThoughtChunks, decay, and cluster detection
Agents: Specialist processing units with specific roles and prompts
Meta-Agent: Analyzes the initial prompt and spawns appropriate specialist agents
Orchestrator: Manages cognitive cycles and the broadcast mechanism

📐 Architecture

The system operates in cognitive cycles:

User provides an initial prompt
Meta-Agent defines specialist agents needed
Each cycle:
- Agents receive the most active ThoughtChunks
- Agents generate new thoughts
- New thoughts are added to the Workspace
- All thoughts decay in energy
- High-energy clusters trigger a broadcast
Process continues for a defined number of cycles

📚 Usage

Basic Example

import { Axon } from "axon-ai";

// Create a new Axon instance with custom config
const axon = new Axon({
    llm: {
        apiKey: "your-api-key", // Not needed for local LLMs
        baseUrl: "https://api.openai.com/v1",
        model: "gpt-4o",
        embeddingModel: "text-embedding-3-large",
    },
});

// Process a prompt
async function main() {
    const result = await axon.process(
        "Design a sustainable transportation system",
        { verbose: true },
    );

    console.log(result.finalBroadcast);
    console.log(`Total thoughts: ${result.thoughts.length}`);
}

main();

Local LLM Example

// Using a local LLM server (LM Studio, Ollama, etc.)
const axon = new Axon({
    llm: {
        baseUrl: "http://localhost:1234/v1",
        model: "mistralai/mixtral-8x7b-instruct",
        provider: "lmstudio",
    },
});

const result = await axon.process("Analyze quantum computing impacts");

⚙️ Configuration

Axon can be fully configured through code:

const axon = new Axon({
    llm: {
        apiKey: "sk-...",
        baseUrl: "https://api.openai.com/v1",
        model: "gpt-4o",
        embeddingModel: "text-embedding-3-large",
        temperature: 0.7,
        provider: "openai",
    },
    context: {
        maxContextTokens: 32000,
        bufferTokens: 2000,
    },
    workspace: {
        decayRate: 0.95,
        activationThreshold: 7.0,
        baseActivationEnergy: 1.0,
        maxResonanceFactor: 2.0,
    },
    orchestrator: {
        maxCycles: 10,
        broadcastThreshold: 10.0,
    },
});

📄 Advanced Features

Custom Event Handlers

// Listen for specific events
axon.on("broadcast", (broadcast) => {
    console.log("New broadcast synthesized:", broadcast);
});

axon.on("agentThought", (agent, thought) => {
    console.log(`Agent ${agent} thinking: ${thought}`);
});

Streaming Results

const result = await axon.processWithStreaming(
    "Analyze the implications of quantum computing",
);

Advanced Workspace Manipulation

// Access the workspace directly
const workspace = axon.getWorkspace();

// Add custom thoughts
workspace.createChunk(
    "Important insight to consider",
    undefined,
    "custom-source",
    [],
);

// Get most active thoughts
const activeThoughts = workspace.getMostActiveChunks(5);

🧪 Examples

The package includes several examples demonstrating different usage scenarios:

# Run the basic example
yarn example:basic

# Run the advanced example with events and streaming
yarn example:advanced

Check the examples directory for more detailed examples and documentation.

🔬 Research & Theory

Axon implements Global Workspace Theory (GWT) as proposed by Bernard Baars and further developed by Stan Franklin in the LIDA cognitive architecture. GWT suggests that consciousness emerges from a competition among specialized cognitive processes, with winners being "broadcast" globally to the entire system.

📜 License

MIT