Permissioned AutonomyModular Intelligence
A secure, plugin-based execution framework for autonomous agents on Web3. Designed for composability, governed by manifest.
using System;
using System.Threading.Tasks;
public class JuliaAgent
{
private readonly IPluginManager _pluginManager;
private readonly IManifestValidator _manifestValidator;
public JuliaAgent(IPluginManager pluginManager, IManifestValidator manifestValidator)
{
_pluginManager = pluginManager;
_manifestValidator = manifestValidator;
}
public async Task<bool> ExecutePlugin(string pluginId, object parameters)
{
try
{
var plugin = await _pluginManager.LoadPlugin(pluginId);
if (!_manifestValidator.ValidateManifest(plugin.Manifest))
{
throw new InvalidOperationException("Invalid plugin manifest");
}
return await plugin.Execute(parameters);
}
catch (Exception ex)
{
Console.WriteLine($"Error executing plugin: {ex.Message}");
return false;
}
}
}FEATURES
Secure WASM Execution
Agents run in isolated WebAssembly sandboxes with no global I/O or network access.
Modular Plugin System
Agents extend their abilities using permissioned plugins — everything from tx to zk.
Manifest-First Design
Every agent declares its permissions and limits in a human-readable YAML manifest.
Multi-Chain Ready
Supports Ethereum, Base, Optimism — with Solana and Cosmos coming soon.
CLI & WebAPI Interfaces
Manage, debug, and deploy agents with a command-line tool or Web dashboard (planned).
Julia - Unlock the feature
Let's Try Now - Get Started.
Plugin System
Julia plugins extend agent capabilities — from sending transactions and querying chain state, to validating zero-knowledge proofs and solving strategic games through embedded game theory engines.
Nash Equilibrium
public class GameTheorySolver
{
private int[,] payoffMatrixA;
private int[,] payoffMatrixB;
public GameTheorySolver(int[,] a, int[,] b)
{
payoffMatrixA = a;
payoffMatrixB = b;
}
public List<(int, int)> FindPureStrategyNashEquilibria()
{
var equilibria = new List<(int, int)>();
int rows = payoffMatrixA.GetLength(0);
int cols = payoffMatrixA.GetLength(1);
for (int i = 0; i < rows; i++) // Strategy A
{
for (int j = 0; j < cols; j++) // Strategy B
{
bool isBestForA = true;
bool isBestForB = true;
// Check if player A has a better response in row
for (int k = 0; k < rows; k++)
{
if (payoffMatrixA[k, j] > payoffMatrixA[i, j])
{
isBestForA = false;
break;
}
}
// Check if player B has a better response in column
for (int l = 0; l < cols; l++)
{
if (payoffMatrixB[i, l] > payoffMatrixB[i, j])
{
isBestForB = false;
break;
}
}
if (isBestForA && isBestForB)
{
equilibria.Add((i, j));
}
}
}
return equilibria;
}
}This algorithm searches for pure strategy Nash equilibria in a 2-player normal-form game.
Each player selects a strategy, and the algorithm evaluates whether that choice is mutually optimal given the opponent’s action.
Logic:
- For each cell
(i, j)in the payoff matrices: - Check if player A has no better response than strategy
igiven that B playsj - Check if player B has no better response than strategy
jgiven that A playsi - If both conditions hold,
(i, j)is a Nash equilibrium
In other words: neither player has any incentive to deviate unilaterally.
This approach is brute-force and best suited for small strategy spaces (e.g., 2x2, 3x3).
For mixed strategies or large games, numerical or linear programming methods are preferred.
Scale your agent operations.
Unlock production features and extended control for teams, protocols, and DAOs.
What is included in the Advanced Runtime
- Multi-agent orchestration
- Custom plugin registry
- Enforced manifest policies
- Agent metrics + observability
- Web Dashboard (Beta)
- Dedicated Discord Support
Julia is free and open-source. No fees. No lock-in. Run anywhere you want.