| 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) |
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}")pip install effgenpip install effgen[vllm]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 .# Run a task
effgen run "What is the capital of France?"
# Interactive chat
effgen chat
# Start API server
effgen serve --port 8000
# Interactive wizard
effgenfrom 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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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
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")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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📋 For security policies and vulnerability reporting, see SECURITY.md
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},
}
MIT License — see LICENSE for details.
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