gentis-ai 0.1.131

A lightweight library for multi-persona AI agent routing and state management.

The lightweight framework for real-time AI agents.

Build multi-expert systems with precise routing, zero overhead, and 6× less token usage than major competitors.

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Why GentisAI?

Traditional agent frameworks (LangChain, CrewAI, AutoGen…) are created for complex, autonomous, long-running tasks. They introduce:

• Heavy orchestration layers
• Hidden “manager” reasoning loops
• Massive prompts
• Slow response times
• High token usage

GentisAI is built for a different purpose:

👉 Create fast, deterministic, conversational AI agents that feel real-time.

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🔥 The Gentis Advantage

⚡ 6× More Efficient

Our architecture uses ~83% fewer tokens per turn than CrewAI in benchmarks.

🎯 Precise Expert Routing

A tiny, dedicated router sends each message to the correct expert — fast, deterministic, and with no “agent arguing with itself.”

🪶 Minimalist API

Experts are simple Python classes. Router is a single object. Flow is one line.

🔍 Fully Transparent

No black-box loops. You control the routing logic, prompts, and state.

🛠️ Production-Ready Structure

Clean separation of concerns:

• Experts
• Router
• Flow
• Memory
• LLM Adapters

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📊 Benchmark Highlights

Multi-Context Vs Multi-Agents

GentisAI was compared against a leading framework in a real 3-turn conversational scenario requiring expert handoffs.

Model: Gemini 2.5 Flash (Dec 2025)

Metric	GentisAI	CrewAI	Difference
Total Latency	12.6 s	30.0 s	2.4× Faster
Total Tokens	3,077	18,521	~6× Fewer Tokens
Avg Latency/Turn	4.2 s	10.0 s	—
Avg Tokens/Turn	1,025	6,173	~83% Cheaper

Why the difference? CrewAI generates ~15,000 extra "manager" tokens per session to decide which expert should answer. GentisAI routes instantly—no wasted reasoning loops.

** You can find the outputs and code of each framework in comparison/ folder.

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📈 Benchmark Visualization (Bar Plot)

The image will display latency and token usage differences between GentisAI and CrewAI.

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💡 Philosophy: Pragmatic Routing & Naming

The name GentisAI is inspired by the Latin root $\mathbf{agentis}$ (the genitive form of $\mathbf{agens}$), which means “of the agent” or “the one doing.”

Simple things should be simple. Complex things should stay possible.

We achieve this latency and efficiency trade-off by focusing on three core principles:

✔ Context-Aware Routing

A tiny LLM classification call over a recent sliding window, not the entire history.

✔ No Hidden Reasoning Loops

Experts respond immediately. An Orchestrator expert is used only as a fallback.

✔ Sliding-Window Optimization

Keeps token cost low while preserving high conversational accuracy.

The result:

This foundation reflects our core belief: that high-performance AI systems must be built by focusing on the precise actions of the agent—optimizing every step to ensure speed, efficiency (low cost), and deterministic routing.

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🛠️ How It Works

graph TD
    User([User]) -->|Message| Flow[Flow Engine]
    
    subgraph "GentisAI Core"
        Flow -->|1. Classify Intent| Router{Router}
        
        Router -->|Sales| ExpertA[Expert: Sales]
        Router -->|Support| ExpertB[Expert: Support]
        Router -->|Fallback| Orch[Expert: Orchestrator]
        
        ExpertA --> LLM[LLM Adapter]
        ExpertB --> LLM
        Orch --> LLM
    end
    
    LLM -->|Generated Reply| Flow
    Flow -->|Update| Memory[(Session State)]
    Flow -->|Return| User

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📦 Installation

pip install gentis-ai

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⚡ Quick Start (No API Key Required)

The included MockLLM lets you prototype routing instantly, offline.

from gentis_ai import Expert, Router, Flow
from gentis_ai.llm import MockLLM

# 1. Define a Mock LLM for testing
llm = MockLLM(
    routing_rules={
        "help": "support_agent",
        "buy": "sales_agent"
    },
    responses={
        "help": "I can certainly help you with your technical issue.",
        "buy": "Great! Let's get you set up with a new plan."
    },
    default_response="I'm not sure how to route that request."
)

# 2. Define your Experts
support = Expert(name="support_agent", description="Handles technical support queries.")
sales   = Expert(name="sales_agent",   description="Handles sales and upgrades.")

# 3. Initialize Router and Flow
router = Router(experts=[support, sales], llm=llm)
flow   = Flow(router=router, llm=llm)

# 4. Run a turn
response = flow.process_turn("I need help with an error.")

print(response.agent_name)  # support_agent
print(response.content)      # I can certainly help you...

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🌐 Real LLM Usage (Gemini, etc.)

Switching from mock mode to a real model is just one line:

import os
from gentis_ai.llm import GeminiLLM

llm = GeminiLLM(
    model_name="gemini-2.5-flash-lite",
    api_key=os.getenv("GOOGLE_API_KEY")
)

# Everything else stays the same

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📘 Documentation & Examples

• examples/simple_bot.py — A basic two-expert system
• examples/advanced_flow.py — Multi-turn flows and expert handoffs
• comparison/README_comparison.md — Full benchmark logs

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🤝 Contributing

We welcome contributions! Pull requests, issues, and feature ideas are all appreciated. See CONTRIBUTING.md.

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📜 License

MIT License — see LICENSE.

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