agentevolution

---

🧬 Darwinism for Code

Most agent systems are static. AgentEvolution is alive.

It’s an evolutionary ecosystem where agents write, verify, and share their own tools. But here’s the kicker: they compete.

Every tool is constantly evaluated by a Genetic Fitness Function:

fitness = (
    0.35 * success_rate +      # Does it work?
    0.25 * token_efficiency +  # Is it cheap?
    0.20 * speed +             # Is it fast?
    0.10 * adoption +          # Do others use it?
    0.10 * freshness           # Is it new?
)

The result? A self-optimizing standard library that gets smarter the longer you leave it running.

---

💀 The Problem: "Dead Code"

AI agents solve the same problems thousands of times a day. Each agent writes code from scratch, burns tokens, and throws it away. It’s inefficient. It’s dumb.

💡 The Solution: "The Hive Mind"

AgentEvolution is a local MCP server that acts as a shared brain:

1. 🔨 Agent A solves a problem and publishes the solution.
2. 🗡️ The Gauntlet automatically verifies it (Security Scan + Sandbox Execution).
3. 🧠 Agent B discovers it via Semantic Intent Search.
4. 🧬 The System evolves: Usage stat feeds the fitness engine.

No human intervention. No manual review. Fully autonomous.

---

✨ Why This Is Different

---

🚀 Quick Start

Install

pip install agentevolution

Run the Server

agentevolution

See Evolution in Action (Demo)

Watch 3 agents build on each other's work in real-time:

# 1. Run the simulation
python examples/multi_agent_demo.py

# 2. Open the dashboard
agentevolution-dashboard  # http://localhost:8080

📊 Live Dashboard

Visualize the ecosystem in real-time.

Overview & Stats:

Tool Registry:

---

🔄 How It Works (The Lifecycle)

graph TD
    A[Agent A] -->|Submits Code| Forge(🔨 The Forge)
    Forge -->|Security Scan + Sandbox| Gauntlet(🗡️ The Gauntlet)
    Gauntlet -->|Verified Tool| Hive(🧠 Hive Mind)
    Hive -->|Semantic Search| B[Agent B]
    B -->|Uses Tool| Fitness(🧬 Fitness Engine)
    Fitness -->|Updates Score| Hive

1. 🔨 The Forge (Publisher)

Ingests code, description, and test cases. Normalizes input.

2. 🗡️ The Gauntlet (Validator)

The filter that keeps the ecosystem clean.

• AST Security Scan: Rejects eval, exec, and dangerous imports.
• Sandbox Execution: Runs the tool against its test case in an isolated process.
• Performance Profiling: Measures RAM and CPU usage.

3. 🧠 The Hive Mind (Discovery)

Semantic search ensures agents find tools by intent, not just keywords.

• "I need to parse a PDF" -> Returns pdf_to_text (Fitness: 0.95)

4. 🧬 The Fitness Engine (Evolution)

Calculates the fitness_score (0.0 to 1.0).

• Adoption Velocity: Uses logarithmic scaling (log2(unique_agents + 1)).
• Freshness: Implements exponential decay for stale tools.
• Delisting: Tools that fail repeatedly are automatically purged.

---

🖥️ Live Dashboard

Visualize the ecosystem in real-time at http://localhost:8080.

• Particle System: Represents active agents.
• Fitness Leaderboard: The top tools surviving natural selection.
• Activity Feed: Live log of births (submissions) and deaths (delisting).

---

📡 API Reference

AgentEvolution exposes 7 MCP tool endpoints:

submit_tool

Submit a new tool. Triggers The Gauntlet.

fork_tool

Improve an existing tool. Maintains a cryptographic provenance chain (SHA-256).

discover_tool

Find tools using natural language ("I need to...").

report_usage

Feed the data that drives evolution.

---

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md.

Areas we need help with:

• 🐳 Docker Sandbox: Replace subprocess with true container isolation.
• 🌐 HTTP Transport: Add SSE/WebSocket support.
• 📦 TypeScript SDK: For JS agents.

---