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QuantaLogic is a ReAct framework for building advanced AI agents that seamlessly integrates large language models with a robust tool system. It aims to bridge the gap between advanced AI models and practical implementation in business processes by enabling agents to understand, reason about, and execute complex tasks through natural language interaction. The framework includes features such as ReAct Framework, Universal LLM Support, Secure Tool System, Real-time Monitoring, Memory Management, and Enterprise Ready components.

README:

Hey there, welcome to QuantaLogic—your cosmic toolkit for crafting AI agents and workflows that shine! Whether you’re coding up a storm, automating a business process, chatting with a clever assistant, or dreaming up something wild, QuantaLogic is here to make it happen. We’re talking large language models (LLMs) fused with a stellar toolset, featuring three powerhouse approaches: the ReAct framework for dynamic problem-solving, the dazzling new Flow module for structured brilliance, and a shiny Chat mode for conversational magic with tool-calling capabilities.

Picture this: a CLI that’s as easy as a snap, a Python API that’s pure magic, and a framework that scales from quick hacks to galactic enterprises. Ready to launch? Let’s blast off!

Full Docs | How-To Guide

Chinese Version French Version German Version

Why QuantaLogic?

At QuantaLogic, we spotted a black hole: amazing AI models from OpenAI, Anthropic, and DeepSeek weren’t fully lighting up real-world tasks. Our mission? Ignite that spark! We’re here to make generative AI a breeze for developers, businesses, and dreamers alike—turning ideas into action, one brilliant solution at a time, whether through task-solving, structured workflows, or natural conversation.

"AI should be your co-pilot, not a puzzle. QuantaLogic makes it happen—fast, fun, and fearless!"

Key Features

ReAct Framework: Reasoning + action = unstoppable agents!
Flow Module: Structured workflows that flow like a river.
Chat Mode: Conversational brilliance with tool-calling powers.
LLM Galaxy: Tap into OpenAI, DeepSeek, and more via LiteLLM.
Secure Tools: Docker-powered safety for code and files.
Live Monitoring: Watch it unfold with a web interface and SSE.
Memory Magic: Smart context keeps things snappy.
Enterprise-Ready: Logs, error handling, and validation—rock solid.

Why QuantaLogic?
Key Features
Installation
Quick Start
ReAct Framework: Dynamic Agents
Flow Module: Structured Workflows
- 📘 Workflow YAML DSL Specification: Comprehensive guide to defining powerful, structured workflows using our Domain-Specific Language
- 📚 Flow YAML Documentation: Dive into the official documentation for a deeper understanding of Flow YAML and its applications
Chat Mode: Conversational Power
ReAct vs. Flow vs. Chat: Pick Your Power
Using the CLI
Examples That Spark Joy
Core Components
Developing with QuantaLogic
Contributing
License
Project Growth
API Keys and Environment Configuration

Installation

Let’s get QuantaLogic orbiting your system—it’s as easy as 1-2-3!

What You’ll Need

Python 3.12+: The fuel for our rocket.
Docker (optional): Locks down code execution in a safe pod.

Option 1: pip—Fast and Simple

pip install quantalogic

Option 2: pipx—Isolated Stardust

pipx install quantalogic

Option 3: Source—For Space Explorers

git clone https://github.com/quantalogic/quantalogic.git
cd quantalogic
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
poetry install

Tip: No Poetry? Grab it with pip install poetry and join the crew!

Quick Start

Ready to see the magic? Here’s your launchpad:

CLI: Instant Action

quantalogic task "Write a Python function for Fibonacci numbers"

Boom! ReAct whips up a solution in seconds.

CLI: Chat Mode

quantalogic chat --persona "You are a witty space explorer" "Tell me about Mars with a search"

Chat mode engages, uses tools if needed, and delivers a conversational response!

Python: ReAct Agent

from quantalogic import Agent

agent = Agent(model_name="deepseek/deepseek-chat")
result = agent.solve_task("Code a Fibonacci function")
print(result)
# Output: "def fib(n): return [0, 1] if n <= 2 else fib(n-1) + [fib(n-1)[-1] + fib(n-1)[-2]]"

Python: Chat Mode

from quantalogic import Agent

agent = Agent(model_name="gpt-4o", chat_system_prompt="You are a friendly guide")
response = agent.chat("What's the weather like in Tokyo?")
print(response)
# Engages in conversation, potentially calling a weather tool if configured

Synchronous Agent Example

from quantalogic import Agent

# Create a synchronous agent
agent = Agent(model_name="gpt-4o")

# Solve a task synchronously
result = agent.solve_task(
    task="Write a Python function to calculate Fibonacci numbers",
    max_iterations=10  # Optional: limit iterations
)

print(result)

Async Agent Example 🌊

import asyncio
from quantalogic import Agent

async def main():
    # Create an async agent
    agent = Agent(model_name="gpt-4o")
    
    # Solve a task asynchronously with streaming
    result = await agent.async_solve_task(
        task="Write a Python script to scrape top GitHub repositories",
        max_iterations=15,  # Optional: limit iterations
        streaming=True      # Optional: stream the response
    )
    
    print(result)

# Run the async function
asyncio.run(main())

Event Monitoring Examples 🔍

Synchronous Event Monitoring with Finance Tools

from quantalogic import Agent
from quantalogic.console_print_events import console_print_events
from quantalogic.console_print_token import console_print_token
from quantalogic.tools import (
    DuckDuckGoSearchTool, 
    TechnicalAnalysisTool, 
    YFinanceTool
)

# Create an agent with finance-related tools
agent = Agent(
    model_name="gpt-4o",
    tools=[
        DuckDuckGoSearchTool(),  # Web search tool
        TechnicalAnalysisTool(),  # Stock technical analysis
        YFinanceTool()            # Stock data retrieval
    ]
)

# Set up comprehensive event listeners
agent.event_emitter.on(
    event=[
        "task_complete",
        "task_think_start", 
        "task_think_end", 
        "tool_execution_start", 
        "tool_execution_end",
        "error_max_iterations_reached",
        "memory_full",
        "memory_compacted"
    ],
    listener=console_print_events
)

# Optional: Monitor streaming tokens
agent.event_emitter.on(
    event=["stream_chunk"], 
    listener=console_print_token
)

# Execute a multi-step financial analysis task
result = agent.solve_task(
    "1. Find the top 3 tech stocks for Q3 2024 "
    "2. Retrieve historical stock data for each "
    "3. Calculate 50-day and 200-day moving averages "
    "4. Provide a brief investment recommendation",
    streaming=True  # Enable streaming for detailed output
)
print(result)

Async Event Monitoring with Finance Tools

import asyncio
from quantalogic import Agent
from quantalogic.console_print_events import console_print_events
from quantalogic.console_print_token import console_print_token
from quantalogic.tools import (
    DuckDuckGoSearchTool, 
    TechnicalAnalysisTool, 
    YFinanceTool
)

async def main():
    # Create an async agent with finance-related tools
    agent = Agent(
        model_name="gpt-4o",
        tools=[
            DuckDuckGoSearchTool(),  # Web search tool
            TechnicalAnalysisTool(),  # Stock technical analysis
            YFinanceTool()            # Stock data retrieval
        ]
    )

    # Set up comprehensive event listeners
    agent.event_emitter.on(
        event=[
            "task_complete",
            "task_think_start", 
            "task_think_end", 
            "tool_execution_start", 
            "tool_execution_end",
            "error_max_iterations_reached",
            "memory_full",
            "memory_compacted"
        ],
        listener=console_print_events
    )

    # Optional: Monitor streaming tokens
    agent.event_emitter.on(
        event=["stream_chunk"], 
        listener=console_print_token
    )

    # Execute a multi-step financial analysis task asynchronously
    result = await agent.async_solve_task(
        "1. Find emerging AI technology startups "
        "2. Analyze their recent funding rounds "
        "3. Compare market potential and growth indicators "
        "4. Provide an investment trend report",
        streaming=True  # Enable streaming for detailed output
    )
    print(result)

# Run the async function
asyncio.run(main())

Python: Flow Workflow

from quantalogic.flow import Workflow, Nodes

@Nodes.define(output="greeting")
def greet(name: str) -> str:
    return f"Hello, {name}!"

workflow = Workflow("greet").build()
result = await workflow.run({"name": "Luna"})
print(result["greeting"])  # "Hello, Luna!"

ReAct Framework: Dynamic Agents

The ReAct framework is your AI sidekick—think fast, act smart. It pairs LLM reasoning with tool-powered action, perfect for tasks that need a bit of improvisation.

How It Rolls

You Say: "Write me a script."
It Thinks: LLM plots the course.
It Acts: Tools like PythonTool get to work.
It Loops: Keeps going until it’s done.

Check this out:

graph TD
    A[You: 'Write a script'] --> B[ReAct Agent]
    B --> C{Reason with LLM}
    C --> D[Call Tools]
    D --> E[Get Results]
    E --> F{Task Done?}
    F -->|No| C
    F -->|Yes| G[Deliver Answer]
    G --> H[You: Happy!]
    style A fill:#f9f,stroke:#333
    style H fill:#bbf,stroke:#333

Example: Code Generator

quantalogic task "Create a Python script to sort a list"

ReAct figures it out, writes the code, and hands it over—smooth as silk!

Why It’s Cool

Perfect for coding, debugging, or answering wild questions on the fly.

Flow Module: Structured Workflows

The Flow module is your architect—building workflows that hum with precision. It's all about nodes, transitions, and a steady rhythm, ideal for repeatable missions.

🔍 Want to dive deeper? Check out our comprehensive Workflow YAML DSL Specification, a detailed guide that walks you through defining powerful, structured workflows. From basic node configurations to complex transition logic, this documentation is your roadmap to mastering workflow design with QuantaLogic.

📚 For a deeper understanding of Flow YAML and its applications, please refer to the official Flow YAML Documentation.

The Flow YAML documentation provides a comprehensive overview of the Flow YAML language, including its syntax, features, and best practices. It's a valuable resource for anyone looking to create complex workflows with QuantaLogic.

Additionally, the Flow YAML documentation includes a number of examples and tutorials to help you get started with creating your own workflows. These examples cover a range of topics, from simple workflows to more complex scenarios, and are designed to help you understand how to use Flow YAML to create powerful and flexible workflows.

The Building Blocks

Nodes: Tasks like functions or LLM calls.
Transitions: Paths with optional conditions.
Engine: Runs the show with flair.
Observers: Peek at progress with events.

Example: Story Weaver

from quantalogic.flow import Workflow, Nodes

@Nodes.llm_node(model="openai/gpt-4o-mini", output="chapter")
async def write_chapter(ctx: dict) -> str:
    return f"Chapter 1: {ctx['theme']}"

workflow = (
    Workflow("write_chapter")
    .then("end", condition="lambda ctx: True")
    .add_observer(lambda e: print(f" {e.event_type}"))
)
engine = workflow.build()
result = await engine.run({"theme": "Cosmic Quest"})
print(result["chapter"])

Example: Story Generator Agent

from typing import List
import anyio
from loguru import logger
from quantalogic.flow import Nodes, Workflow

# Define node functions with decorators
@Nodes.validate_node(output="validation_result")
async def validate_input(genre: str, num_chapters: int) -> str:
    """Validate input parameters."""
    if not (1 <= num_chapters <= 20 and genre.lower() in ["science fiction", "fantasy", "mystery", "romance"]):
        raise ValueError("Invalid input: genre must be one of science fiction, fantasy, mystery, romance")
    return "Input validated"

@Nodes.llm_node(
    model="gemini/gemini-2.0-flash",
    system_prompt="You are a creative writer specializing in story titles.",
    prompt_template="Generate a creative title for a {{ genre }} story. Output only the title.",
    output="title",
)
async def generate_title(genre: str) -> str:
    """Generate a title based on the genre (handled by llm_node)."""
    pass  # Logic handled by llm_node decorator

@Nodes.define(output="manuscript")
async def compile_book(title: str, outline: str, chapters: List[str]) -> str:
    """Compile the full manuscript from title, outline, and chapters."""
    return f"Title: {title}\n\nOutline:\n{outline}\n\n" + "\n\n".join(
        f"Chapter {i}:\n{chap}" for i, chap in enumerate(chapters, 1)
    )

# Define the workflow with conditional branching
workflow = (
    Workflow("validate_input")
    .then("generate_title")
    .then("generate_outline")
    .then("generate_chapter")
    .then("update_chapter_progress")
    .then("generate_chapter", condition=lambda ctx: ctx["completed_chapters"] < ctx["num_chapters"])
    .then("compile_book", condition=lambda ctx: ctx["completed_chapters"] >= ctx["num_chapters"])
    .then("quality_check")
    .then("end")
)

# Run the workflow
async def main():
    initial_context = {
        "genre": "science fiction",
        "num_chapters": 3,
        "chapters": [],
        "completed_chapters": 0,
    }
    engine = workflow.build()
    result = await engine.run(initial_context)

This example demonstrates:

Input validation with @Nodes.validate_node
LLM integration with @Nodes.llm_node
Custom processing with @Nodes.define
Conditional branching for iterative chapter generation
Context management for tracking progress

The full example is available at examples/flow/story_generator/story_generator_agent.py.

Flow Visualized

graph LR
    A[Start] --> B[WriteChapter]
    B -->|Condition: True| C[End]
    subgraph WriteChapter
        D[Call LLM] --> E[Save Chapter]
    end
    A -->|Observer| F[Log: NODE_STARTED]
    B -->|Observer| G[Log: NODE_COMPLETED]
    style A fill:#dfd,stroke:#333
    style C fill:#dfd,stroke:#333
    style B fill:#ffb,stroke:#333

Example: Data Pipeline

@Nodes.define(output="processed")
def clean_data(data: str) -> str:
    return data.strip().upper()

workflow = Workflow("clean_data").build()
result = await workflow.run({"data": " hello "})
print(result["processed"])  # "HELLO"

Why It Rocks

Think content pipelines, automation flows, or any multi-step task that needs order.

Chat Mode: Conversational Power

The Chat mode is your conversational companion—engaging, flexible, and tool-savvy. Built on the same robust ReAct foundation, it lets you chat naturally with an AI persona while seamlessly integrating tool calls when needed. Perfect for interactive dialogues or quick queries with a dash of utility.

How It Works

You Chat: "What’s the weather like today?"
It Responds: Engages conversationally, deciding if a tool (like a weather lookup) is needed.
Tool Magic: If required, it calls tools using the same XML-based system as ReAct, then weaves the results into the conversation.
Keeps Going: Maintains context for a smooth, flowing chat.

Example: CLI Chat with Tool Call

quantalogic chat --persona "You are a helpful travel guide" "Find me flights to Paris"

The agent chats back: "Looking up flights to Paris… Here are some options from a search tool: [flight details]. Anything else I can help with?"

Example: Python Chat with Tool Call

from quantalogic import Agent
from quantalogic.tools import DuckDuckGoSearchTool

agent = Agent(
    model_name="gpt-4o",
    chat_system_prompt="You are a curious explorer",
    tools=[DuckDuckGoSearchTool()]
)
response = agent.chat("Tell me about the tallest mountain")
print(response)
# Might output: "I’ll look that up! The tallest mountain is Mount Everest, standing at 8,848 meters, according to a quick search."

Tool Integration

Chat mode uses the same tool-calling mechanism as ReAct:

<action>
<duckduckgo_tool>
  <query>tallest mountain</query>
  <max_results>5</max_results>
</duckduckgo_tool>
</action>

Tools are auto-executed (configurable with --auto-tool-call) and results are formatted naturally.
Prioritize specific tools with --tool-mode (e.g., search or code).

Why It’s Awesome

Ideal for casual chats, quick info lookups, or interactive assistance with tool-powered precision—without the rigid task-solving structure of ReAct.

ReAct vs. Flow vs. Chat: Pick Your Power

All three modes are stellar, but here’s the scoop:

Feature	ReAct Framework	Flow Module	Chat Mode
Vibe	Free-spirited, adaptive	Organized, predictable	Conversational, flexible
Flow	Loops ‘til it’s solved	Follows a roadmap	Flows with the chat
Sweet Spot	Creative chaos (coding, Q&A)	Steady workflows (pipelines)	Casual chats, quick queries
State	Memory keeps it loose	Nodes lock it down	Context keeps it flowing
Tools	Grabbed as needed	Baked into nodes	Called when relevant
Watch It	Events like `task_complete`	Observers like `NODE_STARTED`	Events like `chat_response`

When to Choose

ReAct: Code on-the-fly, explore answers, debug like a pro.
Flow: Build a pipeline, automate a process, keep it tight.
Chat: Converse naturally, get quick answers, use tools on demand.

Using the CLI

The CLI is your command center—fast, flexible, and fun!

Syntax

quantalogic [OPTIONS] COMMAND [ARGS]...

Description

QuantaLogic AI Assistant - A powerful AI tool for various tasks.

Environment Variables

OpenAI: Set OPENAI_API_KEY to your OpenAI API key
Anthropic: Set ANTHROPIC_API_KEY to your Anthropic API key
DeepSeek: Set DEEPSEEK_API_KEY to your DeepSeek API key

Use a .env file or export these variables in your shell for seamless integration.

Commands

task: Kick off a mission.

quantalogic task "Summarize this file" --file notes.txt

chat: Start a conversation.

quantalogic chat --persona "You are a tech guru" "What’s new in AI?"

list-models: List supported LiteLLM models with optional fuzzy search.
```
quantalogic list-models --search "gpt"
```

Options

--model-name TEXT: Specify the model to use (litellm format). Examples:
- openai/gpt-4o-mini
- openai/gpt-4o
- anthropic/claude-3.5-sonnet
- deepseek/deepseek-chat
- deepseek/deepseek-reasoner
- openrouter/deepseek/deepseek-r1
- openrouter/openai/gpt-4o
--mode [code|basic|interpreter|full|code-basic|search|search-full|chat]: Agent mode
--vision-model-name TEXT: Specify the vision model to use (litellm format)
--log [info|debug|warning]: Set logging level
--verbose: Enable verbose output
--max-iterations INTEGER: Maximum number of iterations (default: 30, task mode only)
--max-tokens-working-memory INTEGER: Set the maximum tokens allowed in working memory
--compact-every-n-iteration INTEGER: Set the frequency of memory compaction
--thinking-model TEXT: The thinking model to use
--persona TEXT: Set the chat persona (chat mode only)
--tool-mode TEXT: Prioritize a tool or toolset (chat mode only)
--auto-tool-call: Enable/disable auto tool execution (chat mode only, default: True)
--version: Show version information

Tip: Run quantalogic --help for the complete command reference!

Examples That Spark Joy

Explore our collection of examples to see QuantaLogic in action:

Flow Examples: Discover practical workflows showcasing Quantalogic Flow capabilities
Agent Examples: See dynamic agents in action with the ReAct framework
Tool Examples: Explore our powerful tool integrations

Each example comes with detailed documentation and ready-to-run code.

Video Magic

Hands-On Examples

Name	What’s It Do?	File
Simple Agent	Basic ReAct agent demo	01-simple-agent.py
Event Monitoring	Agent with event tracking	02-agent-with-event-monitoring.py
Interpreter Mode	Agent with interpreter	03-agent-with-interpreter.py
Agent Summary	Task summary generation	04-agent-summary-task.py
Code Generation	Basic code generation	05-code.py
Code Screen	Advanced code generation	06-code-screen.py
Tutorial Writer	Write technical tutorials	07-write-tutorial.py
PRD Writer	Product Requirements Document	08-prd-writer.py
Story Generator	Flow-based story creation	story_generator_agent.py
SQL Query	Database query generation	09-sql-query.py
Finance Agent	Financial analysis and tasks	10-finance-agent.py
Textual Interface	Agent with textual UI	11-textual-agent-interface.py
Composio Test	Composio integration demo	12-composio-test.py
Synchronous Agent	Synchronous agent demo	13-synchronous-agent.py
Async Agent	Async agent demo	14-async-agent.py

Bonus: Math Whiz

quantalogic task "Solve 2x + 5 = 15"

Output: "Let’s solve it! 2x + 5 = 15 → 2x = 10 → x = 5. Done!"

Bonus: Chat Query

quantalogic chat "Search for the latest AI breakthroughs"

Output: "I’ll dig into that! Here’s what I found with a search: [latest AI news]. Pretty cool, right?"

Core Components

ReAct Agents

Brain: LLMs power the thinking.
Hands: Tools like PythonTool do the work.
Memory: Ties it all together.

Flow Workflows

Nodes: Your task blocks.
Engine: The maestro of execution.

Chat Mode

Persona: Customizable conversational style.
Tools: Integrated seamlessly via ReAct’s system.
Context: Keeps the conversation flowing.

Tools Arsenal

Code: PythonTool, NodeJsTool.
Files: ReadFileTool, WriteFileTool.
More in REFERENCE_TOOLS.md.

Developing with QuantaLogic

Setup Your Lab

git clone https://github.com/quantalogic/quantalogic.git
cd quantalogic
python -m venv venv
source venv/bin/activate
poetry install

Test the Cosmos

pytest --cov=quantalogic

Polish It Up

ruff format  # Shine that code
mypy quantalogic  # Check types
ruff check quantalogic  # Lint it

Create Custom Tools

The create_tool() function transforms any Python function into a reusable Tool:

from quantalogic.tools import create_tool

def weather_lookup(city: str, country: str = "US") -> dict:
    """Retrieve current weather for a given location.
    
    Args:
        city: Name of the city to look up
        country: Two-letter country code (default: US)
    
    Returns:
        Dictionary with weather information
    """
    # Implement weather lookup logic here
    return {"temperature": 22, "condition": "Sunny"}

# Convert the function to a Tool
weather_tool = create_tool(weather_lookup)

# Now you can use it as a Tool
print(weather_tool.to_markdown())  # Generate tool documentation
result = weather_tool.execute(city="New York")  # Execute as a tool

Using Custom Tools with ReAct Agent

from quantalogic import Agent
from quantalogic.tools import create_tool, PythonTool

# Create a custom stock price lookup tool
def get_stock_price(symbol: str) -> str:
    """Get the current price of a stock by its ticker symbol.
    
    Args:
        symbol: Stock ticker symbol (e.g., AAPL, MSFT)
    
    Returns:
        Current stock price information
    """
    # In a real implementation, you would fetch from an API
    prices = {"AAPL": 185.92, "MSFT": 425.27, "GOOGL": 175.43}
    if symbol in prices:
        return f"{symbol} is currently trading at ${prices[symbol]}"
    return f"Could not find price for {symbol}"

# Create an agent with standard and custom tools
agent = Agent(
    model_name="gpt-4o",
    tools=[
        PythonTool(),  # Standard Python execution tool
        create_tool(get_stock_price)  # Custom stock price tool
    ]
)

# The agent can now use both tools to solve tasks
result = agent.solve_task(
    "Write a Python function to calculate investment growth, "  
    "then analyze Apple stock's current price"
)

print(result)

Using Custom Tools in Chat Mode

from quantalogic import Agent
from quantalogic.tools import create_tool

# Reuse the stock price tool
stock_tool = create_tool(get_stock_price)

# Create a chat agent
agent = Agent(
    model_name="gpt-4o",
    chat_system_prompt="You are a financial advisor",
    tools=[stock_tool]
)

# Chat with tool usage
response = agent.chat("What’s the price of Microsoft stock?")
print(response)
# Might output: "Let me check that for you! MSFT is currently trading at $425.27."

Key features of create_tool():

🔧 Automatically converts functions to Tools
📝 Extracts metadata from function signature and docstring
🔍 Supports both synchronous and asynchronous functions
🛠️ Generates tool documentation and validation

Contributing

Join the QuantaLogic galaxy!

Fork it.
Branch: git checkout -b feature/epic-thing.
Code + test.
PR it!

See CONTRIBUTING.md for the full scoop.

License

2024 QuantaLogic Contributors. Apache 2.0—free and open. Check LICENSE.

Dreamed up by Raphaël MANSUY, founder of QuantaLogic.

Project Growth

API Keys and Environment Configuration

QuantaLogic links to LLMs via API keys—here’s your guide to unlocking the universe!

Setting Up Keys

Store keys in a .env file or export them:

echo "OPENAI_API_KEY=sk-your-openai-key" > .env
echo "DEEPSEEK_API_KEY=ds-your-deepseek-key" >> .env
source .env

The Key Vault: Supported Models

Model Name	Key Variable	What’s It Good For?
`openai/gpt-4o-mini`	`OPENAI_API_KEY`	Speedy, budget-friendly tasks
`openai/gpt-4o`	`OPENAI_API_KEY`	Heavy-duty reasoning
`anthropic/claude-3.5-sonnet`	`ANTHROPIC_API_KEY`	Balanced brilliance
`deepseek/deepseek-chat`	`DEEPSEEK_API_KEY`	Chatty and versatile
`deepseek/deepseek-reasoner`	`DEEPSEEK_API_KEY`	Deep problem-solving
`openrouter/deepseek/deepseek-r1`	`OPENROUTER_API_KEY`	Research-grade via OpenRouter
`mistral/mistral-large-2407`	`MISTRAL_API_KEY`	Multilingual mastery
`dashscope/qwen-max`	`DASHSCOPE_API_KEY`	Alibaba’s power player
`lm_studio/mistral-small-24b-instruct-2501`	`LM_STUDIO_API_KEY`	Local LLM action

Local Setup (e.g., LM Studio)

export LM_STUDIO_API_BASE="http://localhost:1234/v1"
export LM_STUDIO_API_KEY="lm-your-key"

Pro Tips

Security: Keep keys in .env, not code!
Extras: Add OPENROUTER_REFERRER for OpenRouter flair.
More: Dig into LiteLLM Docs.

Final Boost

QuantaLogic is your ticket to AI awesomeness. Install it, play with it—whether solving tasks, crafting workflows, or chatting up a storm—and let’s build something unforgettable together!

For Tasks:

Click tags to check more tools for each tasks

execute task generate tutorial code execution file manipulation real-time monitoring

For Jobs:

ai engineer data scientist machine learning engineer natural language processing engineer software developer

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VimLM is an AI-powered coding assistant for Vim that integrates AI for code generation, refactoring, and documentation directly into your Vim workflow. It offers native Vim integration with split-window responses and intuitive keybindings, offline first execution with MLX-compatible models, contextual awareness with seamless integration with codebase and external resources, conversational workflow for iterating on responses, project scaffolding for generating and deploying code blocks, and extensibility for creating custom LLM workflows with command chains.

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pilottai

PilottAI is a Python framework for building autonomous multi-agent systems with advanced orchestration capabilities. It provides enterprise-ready features for building scalable AI applications. The framework includes hierarchical agent systems, production-ready features like asynchronous processing and fault tolerance, advanced memory management with semantic storage, and integrations with multiple LLM providers and custom tools. PilottAI offers specialized agents for various tasks such as customer service, document processing, email handling, knowledge acquisition, marketing, research analysis, sales, social media, and web search. The framework also provides documentation, example use cases, and advanced features like memory management, load balancing, and fault tolerance.

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LLMVoX

LLMVoX is a lightweight 30M-parameter, LLM-agnostic, autoregressive streaming Text-to-Speech (TTS) system designed to convert text outputs from Large Language Models into high-fidelity streaming speech with low latency. It achieves significantly lower Word Error Rate compared to speech-enabled LLMs while operating at comparable latency and speech quality. Key features include being lightweight & fast with only 30M parameters, LLM-agnostic for easy integration with existing models, multi-queue streaming for continuous speech generation, and multilingual support for easy adaptation to new languages.

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LightRAG

LightRAG is a repository hosting the code for LightRAG, a system that supports seamless integration of custom knowledge graphs, Oracle Database 23ai, Neo4J for storage, and multiple file types. It includes features like entity deletion, batch insert, incremental insert, and graph visualization. LightRAG provides an API server implementation for RESTful API access to RAG operations, allowing users to interact with it through HTTP requests. The repository also includes evaluation scripts, code for reproducing results, and a comprehensive code structure.

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supergateway

Supergateway is a tool that allows running MCP stdio-based servers over SSE (Server-Sent Events) with one command. It is useful for remote access, debugging, or connecting to SSE-based clients when your MCP server only speaks stdio. The tool supports running in SSE to Stdio mode as well, where it connects to a remote SSE server and exposes a local stdio interface for downstream clients. Supergateway can be used with ngrok to share local MCP servers with remote clients and can also be run in a Docker containerized deployment. It is designed with modularity in mind, ensuring compatibility and ease of use for AI tools exchanging data.

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polyfire-js

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agenticSeek

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invariant

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ai-guide

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quantalogic

README:

Why QuantaLogic?

Key Features

Table of Contents

Installation

What You’ll Need

Option 1: pip—Fast and Simple

Option 2: pipx—Isolated Stardust

Option 3: Source—For Space Explorers

Quick Start

CLI: Instant Action

CLI: Chat Mode

Python: ReAct Agent

Python: Chat Mode

Synchronous Agent Example

Async Agent Example 🌊

Event Monitoring Examples 🔍

Synchronous Event Monitoring with Finance Tools

Async Event Monitoring with Finance Tools

Python: Flow Workflow

ReAct Framework: Dynamic Agents

How It Rolls

Example: Code Generator

Why It’s Cool

Flow Module: Structured Workflows

The Building Blocks

Example: Story Weaver

Example: Story Generator Agent

Flow Visualized

Example: Data Pipeline

Why It Rocks

Chat Mode: Conversational Power

How It Works

Example: CLI Chat with Tool Call

Example: Python Chat with Tool Call

Tool Integration

Why It’s Awesome

ReAct vs. Flow vs. Chat: Pick Your Power

When to Choose

Using the CLI

Syntax

Description

Environment Variables

Commands

Options

Examples That Spark Joy

Video Magic

Hands-On Examples

Bonus: Math Whiz

Bonus: Chat Query

Core Components

ReAct Agents

Flow Workflows

Chat Mode

Tools Arsenal

Developing with QuantaLogic

Setup Your Lab

Test the Cosmos

Polish It Up

Create Custom Tools

Using Custom Tools with ReAct Agent

Using Custom Tools in Chat Mode

Contributing

License

Project Growth

API Keys and Environment Configuration

Setting Up Keys

The Key Vault: Supported Models

Local Setup (e.g., LM Studio)

Pro Tips

Final Boost

For Tasks:

For Jobs:

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