Introducing effGen — The Future of SLM Agents

BuildPowerfulAIAgents
with Small Language Models

Optimized for SLMs. 5-10x faster with complexity routing, automatic task decomposition, multi-agent orchestration, and vLLM.

$
0x
Faster
0
Built-in Tools
0
Presets
0
Protocols
0%
Open Source
effgen-terminal● RUNNING
agent_demo.py
$
AGENT LOOP
ThoughtActionObservationAnswer
SCROLL
Features

Everything You Need to Build
Production-Ready AI Agents

Optimized for Small Language Models with enterprise-grade features

Intelligent Task Decomposition

Automatically breaks down complex tasks with multi-dimensional complexity analysis and spawns specialized sub-agents.

  • Automatic complexity scoring
  • Sub-agent routing
  • Parallel execution
Click for details

Multi-Agent Orchestration

Coordinate multiple specialized agents with lifecycle management and agent-to-agent communication.

  • Task routing
  • Shared memory
  • A2A protocol
Click for details

Ultra-Fast vLLM Integration

Native vLLM support delivers 5-10x faster inference. Auto multi-GPU tensor parallelism and PagedAttention.

  • 5-10x faster inference
  • 60% memory reduction
  • Auto multi-GPU support
Click for details

Universal Tool Integration

14 built-in tools with full MCP, A2A, and ACP protocol support. Create custom tools in minutes.

  • 14 tools (search, code, JSON, weather...)
  • Plugin system for custom tools
  • MCP/A2A/ACP protocols
Click for details

SLM-Optimized Prompts

Advanced prompt engineering designed for smaller models with Jinja2 templates and few-shot learning.

  • Template management
  • Context compression
  • Chain orchestration
Click for details

Agent Presets

One-line agent creation with ready-to-use configurations. Math, research, coding, general, and minimal.

  • create_agent() factory
  • 5 built-in presets
  • CLI --preset flag
Click for details

Integrated Memory System

Short-term, long-term, and vector memory connected to every agent. Persistent multi-turn context.

  • ShortTerm + LongTerm memory
  • Vector store (FAISS/Chroma)
  • Auto-summarization
Click for details

Production Infrastructure

Docker sandboxed execution, comprehensive logging, state persistence, and enterprise security.

  • CI/CD pipelines
  • OpenTelemetry tracing
  • Prometheus metrics
Click for details
Architecture

How effGen Works

The ReAct agent loop — reasoning and acting in perfect harmony

01

User Input

Natural language task or query is received by the agent

agent.run("Calculate 24344 * 334")
Click for details
02

Reasoning

Agent analyzes the task using ReAct-style thinking

Thought: I need to multiply these numbers...
Click for details
03

Tool Selection

Best tool is selected from 14 built-in options

Action: Calculator(24344 * 334)
Click for details
04

Execution

Tool runs in a sandboxed environment with safety controls

Executing Calculator...
Click for details
05

Observation

Agent observes and validates the tool output

Observation: 8130896
Click for details
06

Final Answer

Synthesized response returned to the user

Answer: 8,130,896
Click for details
Steps 2-5 repeat until the task is complete (max_iterations configurable)
Built-in Tools

14 Tools Ready to Use

From web search to code execution — everything your agent needs, built in

🧮
COMPUTATION

Calculator

Perform mathematical calculations, evaluate expressions, and convert units

expressionoperationprecision
CODE

CodeExecutor

Execute code in a secure sandboxed environment (Python, JS, Bash)

codelanguagetimeout
🐍
CODE

PythonREPL

Execute Python code in a persistent REPL session

codesession_idrestricted_mode
🔍
INFO

WebSearch

Search the web using DuckDuckGo, SerpAPI, or Google

querynum_resultsbackend
🌐
INFO

URLFetchTool

Fetch webpage content and extract readable text

urlmax_lengthinclude_links
📋
DATA

JSONTool

Parse, query (JSONPath), validate, and format JSON data

dataoperationquery
📁
SYSTEM

FileOperations

Safe file system operations: read, write, search, convert

operationpathformat
💻
SYSTEM

BashTool

Execute shell commands with security controls

commandtimeoutworking_directory
Agent Presets

One-Line Agent Creation with 5 Presets

Ready-to-use configurations optimized for common use cases

🧮

math

Mathematical computations

temp
0.3
itr: 8
🔢
🧮
📊
CalculatorPythonREPL
create_agent("math", model)
CLICK FOR DETAILS
🔬

research

Web research & information

temp
0.5
itr: 10
🔍
🌐
📚
WebSearchURLFetchWikipedia
create_agent("research", model)
CLICK FOR DETAILS
💻

coding

Code execution & development

temp
0.4
itr: 12
💭
⌨️
🧪
🚀
CodeExecutorPythonREPLFileOpsBash
create_agent("coding", model)
CLICK FOR DETAILS
🚀

general

All 11 tools for any task

temp
0.7
itr: 10
📝
🔧
📊
All 11 tools included
create_agent("general", model)
CLICK FOR DETAILS

minimal

Direct inference, no tools

temp
0.7
itr: 1
💬
No tools — pure LLM
create_agent("minimal", model)
CLICK FOR DETAILS
Quick Start

Up and Running in 60 Seconds

Three simple steps to your first AI agent

01

Install

Get started with pip. Includes vLLM for blazing-fast inference.

bash
pip install effgen[vllm]
02

Create Agent

One-line agent creation with built-in presets and all 11 tools.

python
from effgen import load_model
from effgen.presets import create_agent

model = load_model("Qwen/Qwen2.5-3B-Instruct", quantization="4bit")
agent = create_agent("general", model)  # All 11 tools included
03

Execute Tasks

Run tasks with real-time token streaming and tool execution.

python
result = agent.run(
    "Analyze the latest tech trends and "
    "create a comprehensive report"
)
Start Building Today — It's Free

ReadytoBuildthe
FutureofAI?

Join thousands of developers building next-gen agents with effGen. Open source, production-ready, and blazing fast.

...
GitHub Stars
...
Forks
...
Contributors