Manas - Multi-Agent System Framework for LLM Applications

A robust, modular, and extensible framework for building LLM-powered applications with intelligent agents, tool integration, task decomposition, and dynamic workflows.

Features

• 🤖 Intelligent Agents - Create autonomous agents with think-act-observe cycle and built-in state management
• 🛠️ Tool System - Flexible registry for tools with automatic parameter discovery and documentation
• 🧩 Task Decomposition - Break down complex tasks into manageable subtasks
• 📚 Retrieval Augmented Generation (RAG) - Enhance LLM responses with relevant context and document chunking
• 🔄 Dynamic Flows - Create and modify workflows with visualization and dependency management
• 🔌 Provider Architecture - Modular support for OpenAI, Anthropic, HuggingFace, Ollama, and custom providers
• 💾 Vector Store Integration - FAISS, Chroma, Pinecone support with consistent interfaces
• 🧠 Memory and Middleware - Built-in memory middleware and extensible middleware architecture
• ⚡ Async First - Fully asynchronous architecture with proper error handling
• ✅ Formally Verified - Core flow execution logic validated through formal verification

Installation

Basic Installation

# Install using pip
pip install manas-ai

# Install using poetry
poetry add manas-ai

Installing with Specific Features

# Install with OpenAI support
pip install "manas-ai[openai]"

# Install with Anthropic support
pip install "manas-ai[anthropic]"

# Install with HuggingFace support
pip install "manas-ai[huggingface]"

# Install with Vector Store support
pip install "manas-ai[vector-stores]"

# Install all features with CPU support
pip install "manas-ai[all-cpu]"

# Install all features with GPU support
pip install "manas-ai[all-gpu]"

Quick Start

Here's a simple example of a question-answering agent:

import os
from core import LLM, Agent

# Initialize a model with your API key
model = LLM.from_provider(
    "openai",
    model_name="gpt-4",
    api_key=os.environ.get("OPENAI_API_KEY")
)

# Create an agent
agent = Agent(llm=model, system_prompt="You are a helpful assistant.")

# Generate a response
response = agent.generate("What is the capital of France?")
print(response)

Creating a Multi-Agent Flow

Here's how to create a flow with multiple specialized agents:

from core import Flow
from manas_ai.nodes import QANode

# Initialize a model
model = LLM.from_provider("openai", model_name="gpt-4")

# Create specialized nodes
researcher = QANode(
    name="researcher",
    llm=model,
    system_prompt="You are an expert researcher who provides factual information."
)

writer = QANode(
    name="writer",
    llm=model,
    system_prompt="You are a skilled writer who creates engaging content."
)

# Create a flow
flow = Flow()
flow.add_node(researcher)
flow.add_node(writer)
flow.add_edge(researcher, writer)

# Process a query
result = flow.process("Explain quantum computing.")
print(result)

Documentation

For complete documentation, visit https://arkokoley.github.io/manas/.

• Getting Started
• Core Concepts
• API Reference
• Examples
• FAQ

Core Components

Agent System

Agents in Manas follow a think-act-observe cycle with improved state management:

from core import Agent, LLM

# Create an agent with tools
agent = Agent(
    llm=model,
    system_prompt="You are an agent with access to tools.",
    tools=[calculator_tool, search_tool]
)

# Process a query
response = agent.generate("Calculate 125 * 37 and find information about Mars.")

RAG Integration

Manas provides robust support for Retrieval-Augmented Generation:

from core import RAG
from manas_ai.vectorstores import FaissVectorStore

# Create a vector store
vector_store = FaissVectorStore(dimension=1536)

# Initialize RAG
rag_system = RAG(
    llm=model,
    vector_store=vector_store
)

# Add documents
rag_system.add_file("knowledge_base.pdf")

# Query the RAG system
response = rag_system.query("What are the key findings in the document?")

Flow Orchestration

Create complex workflows with multiple specialized nodes:

from core import Flow
from manas_ai.nodes import QANode, ToolNode, DocumentNode

# Create nodes
nodes = [
    QANode(name="planner", llm=model),
    DocumentNode(name="document_processor", llm=model),
    ToolNode(name="calculator", tool=calculator_tool)
]

# Create flow
flow = Flow()
for node in nodes:
    flow.add_node(node)

# Connect nodes
flow.add_edge(nodes[0], nodes[1])
flow.add_edge(nodes[0], nodes[2])
flow.add_edge(nodes[1], nodes[0])
flow.add_edge(nodes[2], nodes[0])

# Process flow
result = flow.process("Analyze this document and calculate the totals.")

Contributing

Contributions are welcome! Please read our Contributing Guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.