effgen 0.0.2

A comprehensive framework for building agents with Small Language Models

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📰 News & Updates

	Date	Update
🔧	3 Feb 2026	v0.0.2 Released: vLLM backend fixes with automatic chat template support, GPU memory control, improved OOM error handling, and multi-model family compatibility
📄	2 Feb 2026	Preprint available: EffGen: Enabling Small Language Models as Capable Autonomous Agents
🚀	31 Jan 2026	Initial release of effGen framework (v0.0.1)

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🤔 What is effGen?

effGen transforms Small Language Models into powerful AI agents. While most frameworks require massive LLMs, effGen is optimized from the ground up for efficient, smaller models — delivering fast, capable agents without the compute overhead.

from effgen import Agent, load_model
from effgen.core.agent import AgentConfig
from effgen.tools.builtin import Calculator, PythonREPL

# Load a small but mighty model
model = load_model("Qwen/Qwen2.5-1.5B-Instruct", quantization="4bit")

# Create agent with tools
config = AgentConfig(
    name="math_agent",
    model=model,
    tools=[Calculator(), PythonREPL()]
)
agent = Agent(config=config)

# Run computation
result = agent.run("What is 24344 * 334?")
print(f"Answer: {result.output}")

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⚡ Installation

📦 From PyPI (Recommended)

pip install effgen

🚀 With vLLM for Faster Inference

pip install effgen[vllm]

🔧 From Source

git clone https://github.com/ctrl-gaurav/effGen.git
cd effGen

# Quick install
./install.sh

# Full install (includes vLLM + dev tools)
./install.sh --full

# Manual install
pip install -e .

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🚀 Quick Start

💻 CLI Usage

# Run a task
effgen run "What is the capital of France?"

# Interactive chat
effgen chat

# Start API server
effgen serve --port 8000

# Interactive wizard
effgen

🐍 Python API

from effgen import Agent, load_model
from effgen.core.agent import AgentConfig
from effgen.tools.builtin import Calculator

# Load model
model = load_model("Qwen/Qwen2.5-1.5B-Instruct", quantization="4bit")

# Configure agent
config = AgentConfig(
    name="calculator_agent",
    model=model,
    tools=[Calculator()],
    system_prompt="You are a helpful math assistant."
)

# Create and run
agent = Agent(config=config)
result = agent.run("Calculate 15% tip on $85.50")
print(result.output)

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✨ Features

🧠 SLM Optimized For smaller models

🔄 Multi-Model HF, OpenAI, etc.

🔧 Tool Integration MCP, A2A, ACP

🧩 Task Decomp Auto breakdown

👥 Multi-Agent Coordination

💾 Memory Short & Long

🔒 Sandboxed Docker exec

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🛠️ Built-in Tools

🔢 Calculator Math & Units

🌐 WebSearch DuckDuckGo

💻 CodeExecutor Sandboxed

🐍 PythonREPL Interactive

📁 FileOps Read/Write

🔍 Retrieval RAG Search

🎯 AgenticSearch Exact Match

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📚 Examples

python examples/basic_agent.py      # Basic agent (Transformers backend)

python examples/basic_agent_vllm.py # Basic agent (vLLM backend - 5-10x faster)

python examples/web_agent.py        # Web search agent

python examples/retrieval_agent.py  # RAG-based retrieval

python examples/agentic_search_agent.py # Grep-based agentic search

📖 More Examples

Multi-Tool Agent

from effgen import Agent, load_model
from effgen.core.agent import AgentConfig
from effgen.tools.builtin import Calculator, WebSearch, PythonREPL

model = load_model("Qwen/Qwen2.5-3B-Instruct")

config = AgentConfig(
    name="research_agent",
    model=model,
    tools=[Calculator(), WebSearch(), PythonREPL()],
    system_prompt="You are a research assistant."
)

agent = Agent(config=config)
result = agent.run("Search for the population of Tokyo and calculate what percentage it is of Japan's total population")

Retrieval Agent

from effgen import Agent, load_model
from effgen.core.agent import AgentConfig
from effgen.tools.builtin import Retrieval

model = load_model("Qwen/Qwen2.5-1.5B-Instruct")
retrieval_tool = Retrieval(knowledge_base_path="./docs")

config = AgentConfig(
    name="qa_agent",
    model=model,
    tools=[retrieval_tool]
)

agent = Agent(config=config)
result = agent.run("What does the documentation say about configuration?")

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🔒 Security

🐳 Docker Sandbox Isolated execution

🛡️ Input Validation Auto sanitization

⚡ Rate Limiting Configurable limits

📋 For security policies and vulnerability reporting, see SECURITY.md

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📖 Citation

If you use effGen in your research, please cite our paper:

@software{srivastava2026effgen,
      title={effGen: Enabling Small Language Models as Capable Autonomous Agents},
      author={Gaurav Srivastava and Aafiya Hussain and Chi Wang and Yingyan Celine Lin and Xuan Wang},
      year={2026},
      eprint={2602.00887},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.00887},
}

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🔗 Links

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📄 License

MIT License — see LICENSE for details.

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