masai-framework 0.2.4

Multi-Agent System Framework for AI Agents with Custom LangGraph Implementation

MAS-AI Framework: Complete Implementation Guide

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MAS AI: Multi-Agent System Framework

MAS AI is a powerful framework for building scalable, intelligent multi-agent systems with advanced memory management and flexible collaboration patterns built using langgraph.

• Each agent in mas-ai has different components that work together to achieve the goal.

• Combine such agents in Multi-Agent Systems to achieve more complex goals.

• Combine such Multi-Agent Systems in an Orchestrated Multi-Agent Network (OMAN) to achieve even more complex goals.

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Agent Architecture

MAS AI introduces two agent architectures optimized for different use cases:

Table of Contents

1. Introduction
2. Framework Architecture
3. Custom LangGraph Implementation
4. Agent Components
5. Memory System
6. Multi-Agent Workflows
7. Parameter Reference
8. Use Cases & Best Practices
9. Advanced Features
10. Installation & Setup

---

Introduction

MAS-AI (Multi-Agent System AI) is a modular framework for building intelligent agent systems with a custom LangGraph implementation. It provides:

• Custom LangGraph Engine: Built-in workflow execution engine with zero external dependencies
• Modular Agent Architecture: Router, Evaluator, Reflector, Planner components
• Hierarchical Memory System: Short-term, component-shared, long-term, and vector store memory
• Multiple Collaboration Patterns: Sequential, Hierarchical, Decentralized workflows
• LLM Flexibility: Support for OpenAI, Gemini, Anthropic, Groq, Ollama, HuggingFace
• Tool Integration: LangChain-compatible tools with Redis caching support

Why MAS-AI?

MAS-AI stands apart from conventional multi-agent frameworks by offering:

1. Explicit Node Separation: Unlike monolithic LLM systems, MAS-AI distributes responsibilities across specialized components (Router, Evaluator, Reflector, Planner)
2. State-Machine Orchestration: LangGraph-based state machine allows dynamic transitions between nodes based on satisfaction criteria
3. Granular Memory Integration: Multi-layered memory system (short-term, component-shared, long-term, vector store)
4. Optimized for Complex Workflows: Particularly well-suited for research-intensive tasks, multi-step decision processes, and tool-augmented execution

---

Framework Architecture

Agent Control Flow

The MASAI framework follows a sophisticated control flow pattern where agents process queries through multiple specialized nodes. The detailed workflow diagrams are shown in the architecture sections below.

Key Control Flow Rules:

1. Entry Point: Router (default) or Planner (if planning enabled)
2. Conditional Edges: ALL nodes use checkroutingcondition() to decide next step
3. Routing Logic:
   • "continue" → Execute tool (router/planner) or Evaluate (execute_tool) or Execute again (evaluator/reflector)
   • "reflection" → Reflect and improve
   • "end" → Terminate workflow
4. Return Direct: Tools with return_direct=True cause execute_tool to go directly to END
5. Loop Control: Continues until satisfied=True and current_tool="" or max iterations reached

Core Components

┌─────────────────────────────────────────────────────────────┐
│                    MAS-AI FRAMEWORK                         │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌──────────────────────────────────────────────────────┐  │
│  │           AGENT MANAGER                              │  │
│  │  - Creates and manages agents                        │  │
│  │  - Configures tools and memory                       │  │
│  │  - Handles model configuration                       │  │
│  └──────────────────────────────────────────────────────┘  │
│                          │                                  │
│                          ▼                                  │
│  ┌──────────────────────────────────────────────────────┐  │
│  │           SINGULAR AGENT                             │  │
│  │  ┌────────────┐  ┌────────────┐  ┌────────────┐     │  │
│  │  │   Router   │→ │ Evaluator  │→ │ Reflector  │     │  │
│  │  └────────────┘  └────────────┘  └────────────┘     │  │
│  │         OR                                           │  │
│  │  ┌────────────┐  ┌────────────┐  ┌────────────┐     │  │
│  │  │  Planner   │→ │  Executor  │→ │ Reflector  │     │  │
│  │  └────────────┘  └────────────┘  └────────────┘     │  │
│  └──────────────────────────────────────────────────────┘  │
│                          │                                  │
│                          ▼                                  │
│  ┌──────────────────────────────────────────────────────┐  │
│  │      MULTI-AGENT SYSTEM (MAS)                        │  │
│  │  - Sequential: Fixed pipeline                        │  │
│  │  - Hierarchical: Supervisor-based                    │  │
│  │  - Decentralized: Peer-to-peer                       │  │
│  └──────────────────────────────────────────────────────┘  │
│                          │                                  │
│                          ▼                                  │
│  ┌──────────────────────────────────────────────────────┐  │
│  │  ORCHESTRATED MULTI-AGENT NETWORK (OMAN)             │  │
│  │  - Coordinates multiple MAS instances                │  │
│  │  - Network-level memory and routing                  │  │
│  └──────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

Component Reference Table

Component	Purpose	Import Path
AgentManager	Central registry for creating and managing agents	from masai.AgentManager.AgentManager import AgentManager, AgentDetails
Agent	Singular agent with Router-Evaluator-Reflector architecture	from masai.Agents.singular_agent import Agent
BaseAgent	Base class with common agent functionality	from masai.Agents.base_agent import BaseAgent
MASGenerativeModel	LLM wrapper with memory and context management	from masai.GenerativeModel.generativeModels import MASGenerativeModel
BaseGenerativeModel	Simple LLM wrapper without agent architecture	from masai.GenerativeModel.baseGenerativeModel.basegenerativeModel import BaseGenerativeModel
MultiAgentSystem	Coordinates multiple agents in workflows	from masai.MultiAgents.MultiAgent import MultiAgentSystem, SupervisorConfig
TaskManager	Manages concurrent tasks in hierarchical MAS	from masai.MultiAgents.TaskManager import TaskManager
OMAN	Orchestrates multiple MAS instances	from masai.OMAN.oman import OrchestratedMultiAgentNetwork
InMemoryDocStore	Vector store for semantic search	from masai.Memory.InMemoryStore import InMemoryDocStore
ToolCache	Redis-based caching for tools	from masai.Tools.utilities.cache import ToolCache
Config	Global configuration parameters	from masai.Config import config

Framework Levels Explained

Level 1: Singular Agent

• Single agent with internal components (Router, Evaluator, Reflector, optional Planner)
• Handles queries independently using tools
• Can delegate to other agents in decentralized mode
• Use when: Single domain, tool-heavy tasks, simple queries

Level 2: Multi-Agent System (MAS)

• Multiple agents working together in coordinated workflows
• Three workflow types: Sequential, Hierarchical, Decentralized
• Shared memory and context across agents
• Use when: Multi-domain tasks, complex workflows, quality control needed

Level 3: Orchestrated Multi-Agent Network (OMAN)

• Multiple MAS instances coordinated by OMAN supervisor
• Each MAS specializes in different domains
• Network-level routing and memory
• Use when: Enterprise-scale, multiple specialized systems, cross-domain coordination

---

Custom LangGraph Implementation

MAS-AI now includes a custom LangGraph implementation that provides all the functionality of the original LangGraph library while being fully integrated into the MASAI framework. This eliminates external dependencies and gives you complete control over the workflow execution engine.

Key Features

• 🔧 Zero External Dependencies: No need to install the external langgraph package
• ⚡ Optimized Performance: Custom implementation tailored for MASAI's specific needs
• 🎯 Full Compatibility: Drop-in replacement for existing LangGraph functionality
• 🔍 Enhanced Debugging: Better error messages and debugging capabilities
• 🚀 Async-First Design: Built from the ground up for async/await patterns

Core Components

StateGraph

The main graph building class that allows you to create complex workflows:

from masai.langgraph.graph import StateGraph, END, START

# Create a new state graph
graph = StateGraph(YourStateType)

# Add nodes (functions that process state)
graph.add_node("router", router_function)
graph.add_node("evaluator", evaluator_function)

# Add edges between nodes
graph.add_edge("router", "evaluator")

# Add conditional edges based on state
graph.add_conditional_edges("evaluator", condition_function, {
    "continue": "router",
    "end": END
})

# Set entry point and compile
graph.set_entry_point("router")
compiled_graph = graph.compile()

CompiledStateGraph

The executable graph that runs your workflow:

# Execute the entire workflow
final_state = await compiled_graph.ainvoke(initial_state)

# Stream state updates in real-time
async for update in compiled_graph.astream(initial_state):
    print(f"Node update: {update}")

# Visualize the graph structure
graph_viz = compiled_graph.get_graph()
mermaid_diagram = graph_viz.get_mermaid_text()
print(mermaid_diagram)

# Generate PNG diagram (for display method compatibility)
png_data = graph_viz.draw_mermaid_png()

Migration from External LangGraph

If you're migrating from the external LangGraph library, simply update your imports:

# Old imports
from langgraph.graph import StateGraph, END, START
from langgraph.graph.state import CompiledStateGraph

# New imports (MASAI custom implementation)
from masai.langgraph.graph import StateGraph, END, START
from masai.langgraph.graph.state import CompiledStateGraph

No other code changes required! The API is 100% compatible.

Graph Visualization

The custom LangGraph implementation includes powerful visualization capabilities:

# Create and compile your graph
compiled_graph = graph.compile()

# Get the visualization object
graph_viz = compiled_graph.get_graph()

# Generate Mermaid diagram text
mermaid_text = graph_viz.get_mermaid_text()
print(mermaid_text)

# Generate PNG data (for compatibility with MASAI display method)
png_data = graph_viz.draw_mermaid_png()

# Save Mermaid diagram to file
graph_viz.save_mermaid_text("my_workflow.mmd")

MASAI Agent Display Method:

# Works exactly as before with custom implementation
agent = Agent(...)
agent.display()  # Saves PNG diagram to MAS/Database/mermaid/

Advanced Features

• Graph Visualization: Built-in Mermaid diagram generation with get_graph() and draw_mermaid_png()
• Async Condition Functions: Support for both sync and async condition functions
• Flexible State Management: Works with any TypedDict state structure
• Error Handling: Comprehensive error messages for debugging
• Execution Statistics: Built-in metrics for performance monitoring
• Recursion Protection: Configurable limits to prevent infinite loops
• Display Method Compatibility: Full compatibility with MASAI's display() method for agent visualization

---

Agent Components

MAS AI introduces two agent architectures optimized for different use cases:

1. Router, Reflector, Evaluator

A reactive architecture for dynamic task routing and output validation:

graph TD
    START --> router
    router -->|continue| execute_tool
    router -->|reflection| reflection
    router -->|end| END
    execute_tool -->|continue| evaluator
    execute_tool -->|reflection| reflection
    execute_tool -->|end| END
    evaluator -->|continue| execute_tool
    evaluator -->|reflection| reflection
    evaluator -->|end| END
    reflection -->|continue| execute_tool
    reflection -->|reflection| reflection
    reflection -->|end| END
    evaluator[evaluator]
    reflection[reflection]
    execute_tool[execute_tool]
    router[router]
    START([START])
    END([END])

• Router: Analyzes queries and directs them to appropriate processing components
• Evaluator: Reviews outputs to ensure quality and relevance
• Reflector: Updates memory and improves routing strategies based on outcomes

2. Planner, Executor, Reflector

graph TD
    START --> planner
    planner -->|continue| execute_tool
    planner -->|reflection| reflection
    planner -->|end| END
    execute_tool -->|continue| evaluator
    execute_tool -->|reflection| reflection
    execute_tool -->|end| END
    evaluator -->|continue| execute_tool
    evaluator -->|reflection| reflection
    evaluator -->|end| END
    reflection -->|continue| execute_tool
    reflection -->|reflection| reflection
    reflection -->|end| END
    evaluator[evaluator]
    reflection[reflection]
    execute_tool[execute_tool]
    planner[planner]
    START([START])
    END([END])

A proactive architecture for task planning and dependency management:

• Planner: Breaks queries into structured task plans
• Executor: Assigns tasks to appropriate components or agents
• Reflector: Assesses results and adjusts plans as needed

1. Router-Evaluator-Reflector Architecture

Purpose: Reactive architecture for dynamic task routing and validation

Router

• Function: Analyzes queries and routes to appropriate tools/agents
• Input: User query + chat history + context
• Output: Tool selection OR agent delegation OR direct answer
• Model Config: model_config.json → router

Evaluator

• Function: Validates tool outputs and agent responses
• Input: Tool output + original query + context
• Output: Satisfaction status (satisfied/not_satisfied) + reasoning
• Model Config: model_config.json → evaluator

Reflector

• Function: Updates memory and refines strategies
• Input: Conversation history + outcomes
• Output: Updated memory + insights
• Model Config: model_config.json → reflector

Workflow:

Query → Router → Tool/Agent → Evaluator → [Satisfied?]
                                              ↓ No
                                          Reflector → Router (retry)
                                              ↓ Yes
                                          Final Answer

2. Planner-Executor-Reflector Architecture

Purpose: Proactive architecture for complex task decomposition

Planner

• Function: Decomposes queries into structured task plans
• Input: User query + context
• Output: Task list with dependencies
• Model Config: model_config.json → planner

Executor

• Function: Executes tasks using tools/agents
• Input: Task plan + tools
• Output: Task results
• Model Config: Uses router model

Reflector

• Function: Evaluates results and adjusts plans
• Input: Task results + original plan
• Output: Re-planning decisions
• Model Config: model_config.json → reflector

Workflow:

Query → Planner → Task List → Executor → Results → Reflector
                                                      ↓
                                              [Complete?]
                                                ↓ No
                                            Planner (re-plan)
                                                ↓ Yes
                                            Final Answer

3. Agent State Machine

MAS-AI agents use a LangGraph state machine to manage workflow execution. Understanding the state is crucial for debugging and optimization.

State Structure

class State(TypedDict):
    messages: List[Dict[str, str]]           # Chat history
    current_tool: str                        # Currently selected tool
    tool_input: Any                          # Input for the tool
    tool_output: Any                         # Output from the tool
    answer: str                              # Current answer
    satisfied: bool                          # Satisfaction flag
    reasoning: str                           # LLM reasoning
    delegate_to_agent: Optional[str]         # Agent to delegate to
    current_node: str                        # Current node in workflow
    previous_node: Optional[str]             # Previous node
    plan: Optional[dict]                     # Task plan (if using planner)
    passed_from: Optional[str]               # Source of delegation
    reflection_counter: int                  # Number of reflections
    tool_loop_counter: int                   # Number of tool loops

State Transitions

┌─────────────────────────────────────────────────────────────┐
│                  AGENT STATE MACHINE                        │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  START                                                      │
│    ↓                                                        │
│  [plan=True?] ──Yes──→ PLANNER ──→ EXECUTOR                │
│    ↓ No                              ↓                      │
│  ROUTER ─────────────────────────────┘                      │
│    ↓                                                        │
│  [tool selected?]                                           │
│    ↓ Yes                                                    │
│  EXECUTE_TOOL                                               │
│    ↓                                                        │
│  EVALUATOR                                                  │
│    ↓                                                        │
│  [satisfied=True?]                                          │
│    ↓ No                                                     │
│  [tool_loop_counter > MAX?] ──Yes──→ REFLECTOR             │
│    ↓ No                                ↓                    │
│  ROUTER (retry)                        ↓                    │
│    ↓ Yes                               ↓                    │
│  [delegate_to_agent?] ──Yes──→ DELEGATE_TO_AGENT           │
│    ↓ No                                ↓                    │
│  END (Final Answer)                    END                  │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Loop Prevention Mechanisms

1. Tool Loop Counter

• Tracks consecutive tool uses
• Max limit: config.max_tool_loops (default: 3)
• When exceeded: Triggers warning prompt and forces reflection

2. Reflection Counter

• Tracks number of reflection cycles
• Max limit: config.MAX_REFLECTION_COUNT (default: 3)
• When exceeded: Forces final answer or delegation

3. Recursion Limit

• Overall workflow recursion limit
• Max limit: config.MAX_RECURSION_LIMIT (default: 100)
• Prevents infinite loops in complex workflows

Configuration Parameters

from masai.Config import config

# Modify global config
config.max_tool_loops = 5              # Default: 3
config.MAX_REFLECTION_COUNT = 5        # Default: 3
config.MAX_RECURSION_LIMIT = 150       # Default: 100
config.stream_mode = "updates"         # LangGraph stream mode
config.truncated_response_length = 500 # Logging truncation

---

Memory System

Memory Hierarchy

┌─────────────────────────────────────────────────────────────┐
│                    MEMORY HIERARCHY                         │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Level 1: AGENT SHORT-TERM MEMORY                          │
│  ├─ Chat history (last N messages)                         │
│  ├─ Current context                                        │
│  └─ Parameter: memory_order (default: 5)                   │
│                                                             │
│  Level 2: COMPONENT MEMORY                                 │
│  ├─ Component short-term (per Router/Evaluator/etc.)       │
│  ├─ Component shared (between components)                  │
│  └─ Component long-term (summarized history)               │
│      └─ Parameter: long_context_order (default: 10)        │
│                                                             │
│  Level 3: MULTI-AGENT SYSTEM MEMORY                        │
│  ├─ Shared across all agents in MAS                        │
│  └─ Parameter: shared_memory_order (default: 3)            │
│                                                             │
│  Level 4: NETWORK MEMORY                                   │
│  ├─ Spans all MAS instances in OMAN                        │
│  └─ Parameter: network_memory_order                        │
│                                                             │
│  Level 5: EXTENDED MEMORY STORE                            │
│  ├─ Vector store for semantic search                       │
│  ├─ InMemoryDocStore (sentence-transformers)               │
│  └─ Parameter: in_memory_store, top_k                      │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Memory Parameters

Parameter	Scope	Default	Purpose
memory	Agent	True	Enable/disable memory
memory_order	Agent	5	Number of recent messages to keep
long_context	Agent	False	Enable long-term memory summarization
long_context_order	Agent	10	Number of old messages to summarize
shared_memory_order	MAS	3	Shared memory size across agents
network_memory_order	OMAN	N/A	Network-level memory size
in_memory_store	Agent	None	Vector store instance
top_k	Agent	3	Number of vector search results
chat_log	Agent	None	File path to save chat history

Memory Flow Detailed

1. Chat History Management

┌─────────────────────────────────────────────────────────────┐
│              CHAT HISTORY LIFECYCLE                         │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  New Message                                                │
│    ↓                                                        │
│  Append to chat_history[]                                   │
│    ↓                                                        │
│  [len(chat_history) > memory_order?]                        │
│    ↓ Yes                                                    │
│  [long_context=True?]                                       │
│    ↓ Yes                          ↓ No                      │
│  Summarize old messages      Truncate to memory_order/2     │
│    ↓                              ↓                         │
│  Add to context_summaries[]  [chat_log set?]                │
│    ↓                              ↓ Yes                     │
│  [len(summaries) > long_context_order?]  Save to file      │
│    ↓ Yes                          ↓                         │
│  [LTIMStore set?]            Keep recent messages           │
│    ↓ Yes                                                    │
│  Move old summaries to vector store                         │
│    ↓                                                        │
│  Keep recent summaries                                      │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Key Points:

• chat_history stores raw messages as {'role': 'user/assistant', 'content': '...'}
• When len(chat_history) > memory_order, old messages are processed
• If long_context=True, old messages are summarized using a separate LLM
• Summaries are stored in context_summaries[] as LangChain Document objects
• When summaries exceed long_context_order, oldest are moved to LTIMStore (if configured)
• If chat_log is set, truncated messages are saved to file before removal

2. Context Summaries

Purpose: Compress old conversations while retaining key information

Process:

1. When chat_history exceeds memory_order, oldest messages are selected
2. A separate LLM (GenerativeModel with temperature=0.5) summarizes them
3. Summary prompt focuses on: main topics, key information, specific keywords, conclusions
4. Summary is stored as a Document with page_content field
5. Summaries are included in prompts under <EXTENDED CONTEXT> section

Example Summary:

"The user asked about implementing a caching system for API calls.
The assistant recommended Redis with a TTL of 30 minutes.
Key points: Use pickle for serialization, handle connection errors,
implement cache invalidation strategy. User confirmed implementation."

3. InMemoryDocStore (LTIMStore)

Purpose: Semantic search over very old conversation history

How It Works:

┌─────────────────────────────────────────────────────────────┐
│           LTIMSTORE WORKFLOW                                │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Old Summaries (beyond long_context_order)                  │
│    ↓                                                        │
│  Convert to Document objects                                │
│    ↓                                                        │
│  Embed using SentenceTransformer                            │
│    ↓                                                        │
│  Store in InMemoryDocStore                                  │
│    ↓                                                        │
│  On New Query:                                              │
│    ↓                                                        │
│  Embed query                                                │
│    ↓                                                        │
│  Cosine similarity search                                   │
│    ↓                                                        │
│  Return top_k most relevant summaries                       │
│    ↓                                                        │
│  Include in prompt under <EXTENDED CONTEXT>                 │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Setup:

from masai.Memory.InMemoryStore import InMemoryDocStore

# Create vector store
memory_store = InMemoryDocStore(
    embedding_model="all-MiniLM-L6-v2"  # SentenceTransformer model
)

# Or use custom embedding function
def custom_embedder(texts: List[str]) -> np.ndarray:
    # Your embedding logic
    return embeddings

memory_store = InMemoryDocStore(embedding_model=custom_embedder)

# Or use LangChain embeddings
from langchain.embeddings import OpenAIEmbeddings
memory_store = InMemoryDocStore(embedding_model=OpenAIEmbeddings())

# Pass to agent
manager.create_agent(
    agent_name="research_agent",
    tools=tools,
    agent_details=details,
    long_context=True,
    long_context_order=20,
    in_memory_store=memory_store,
    top_k=3  # Return top 3 relevant memories
)

4. Component Context

Purpose: Share information between agent components (Router → Evaluator → Reflector)

Mechanism:

• Each component can add messages to component_context[]
• These messages are passed to the next component
• Controlled by shared_memory_order parameter
• Allows components to communicate reasoning and intermediate results

Example:

# Router adds context
component_context = [
    {'role': 'router', 'content': 'Selected database_tool because query mentions "users"'}
]

# Evaluator receives this context and adds its own
component_context.append(
    {'role': 'evaluator', 'content': 'Tool returned 150 users, satisfies query'}
)

# Reflector receives both contexts

5. Chat Log Persistence

Purpose: Save conversation history to file for later analysis or resumption

Setup:

manager = AgentManager(
    context={},
    logging=True,
    model_config_path="model_config.json",
    chat_log="./logs/agent_chat.json"  # File path
)

Behavior:

• When chat_history is truncated, removed messages are saved to file
• File format: JSON array of message objects
• Useful for debugging, analysis, or resuming conversations
• Automatically creates directory if it doesn't exist

---

Multi-Agent Workflows

1. Sequential Workflow

Use Case: Fixed pipeline processing (ETL, document processing)

Architecture:

Agent 1 → Agent 2 → Agent 3 → Final Output

Implementation:

from masai.MultiAgents.MultiAgent import MultiAgentSystem

mas = MultiAgentSystem(agentManager=manager)

result = mas.initiate_sequential_mas(
    query="Process this document",
    agent_sequence=["research_agent", "analysis_agent", "summary_agent"],
    memory_order=3  # Shared memory across agents
)

Parameters:

• query (str): Input query
• agent_sequence (List[str]): Ordered list of agent names
• memory_order (int): Shared memory size

Data Flow:

Query → Agent 1 (output_1) → Agent 2 (output_1 + output_2) → Agent 3 (final)
         ↓                      ↓                              ↓
      Memory[0]              Memory[1]                     Memory[2]

2. Hierarchical Workflow

Use Case: Complex tasks requiring supervision and quality control

Architecture:

                    Supervisor LLM
                         │
        ┌────────────────┼────────────────┐
        ▼                ▼                ▼
    Agent 1          Agent 2          Agent 3
        │                │                │
        └────────────────┴────────────────┘
                         │
                    Task Manager
                         │
                  Result Callback

Implementation:

from masai.MultiAgents.MultiAgent import MultiAgentSystem, SupervisorConfig

def handle_task_result(task):
    print(f"Task completed: {task['answer']}")

supervisor_config = SupervisorConfig(
    model_name="gpt-4",
    temperature=0.2,
    model_category="openai",
    memory_order=20,
    memory=True,
    extra_context={"organization": "Benosphere"}
)

mas = MultiAgentSystem(
    agentManager=manager,
    supervisor_config=supervisor_config,
    heirarchical_mas_result_callback=handle_task_result,
    agent_return_direct=True
)

result = await mas.initiate_hierarchical_mas("Complex research task")

Parameters:

• supervisor_config (SupervisorConfig): Supervisor LLM configuration
• heirarchical_mas_result_callback (Callable): Callback for task completion
• agent_return_direct (bool): Return agent output directly without supervisor review

Data Flow:

Query → Supervisor → Task Queue → Agent → Result → Supervisor Review
                                                         ↓
                                                  [Satisfied?]
                                                    ↓ No
                                            Revision Request → Agent
                                                    ↓ Yes
                                                Callback → Final Output

3. Decentralized Workflow

Use Case: Peer-to-peer collaboration, adaptive workflows

Architecture:

Entry Agent ⇄ Agent 2 ⇄ Agent 3
     ↕           ↕         ↕
  Agent 4 ⇄  Agent 5 ⇄ Agent 6

Implementation:

mas = MultiAgentSystem(agentManager=manager)

result = await mas.initiate_decentralized_mas(
    query="Research AI trends and schedule meeting",
    set_entry_agent=manager.get_agent("personal_assistant")
)

Parameters:

• query (str): Input query
• set_entry_agent (Agent): Initial agent to handle query

Data Flow:

Query → Entry Agent → [Delegate?] → Agent 2 → [Delegate?] → Agent 3
                         ↓ No                    ↓ No
                      Answer                  Answer

---

Parameter Reference

1. AgentManager.init()

Parameter	Type	Required	Default	Description
logging	bool	No	True	Enable/disable logging of agent activities
context	dict	No	None	Static context shared with all agents (e.g., {"org": "MyCompany"})
model_config_path	str	Yes	N/A	Path to model configuration JSON file
chat_log	str	No	None	File path to save chat history when truncated
streaming	bool	No	False	Enable streaming responses from LLMs
streaming_callback	Callable	No	None	Async callback function for streaming chunks (required if streaming=True)

Example:

from masai.AgentManager.AgentManager import AgentManager

manager = AgentManager(
    context={"organization": "MyCompany", "environment": "production"},
    logging=True,
    model_config_path="./config/model_config.json",
    chat_log="./logs/chat_history.json",
    streaming=True,
    streaming_callback=async_streaming_handler
)

2. AgentManager.create_agent()

Parameter	Type	Required	Default	Description
agent_name	str	Yes	N/A	Unique identifier for the agent (converted to lowercase)
tools	List[Tool]	Yes	N/A	List of LangChain tools the agent can use
agent_details	AgentDetails	Yes	N/A	Agent configuration (capabilities, description, style)
memory_order	int	No	10	Number of recent messages to keep in short-term memory
long_context	bool	No	True	Enable long-term memory with summarization
long_context_order	int	No	20	Number of old message summaries to keep
shared_memory_order	int	No	10	Shared memory size between components
plan	bool	No	False	Use Planner-Executor-Reflector architecture (vs Router-Evaluator-Reflector)
temperature	float	No	0.2	Default temperature for all LLMs (can be overridden per component)
context_callable	Callable	No	None	Function to fetch dynamic context on each query
in_memory_store	InMemoryDocStore	No	None	Vector store for semantic search over old conversations
top_k	int	No	3	Number of results to retrieve from vector store
config_dict	dict	No	None	Per-component configuration overrides (see below)

config_dict Structure:

config_dict = {
    "router_memory_order": 10,
    "router_long_context_order": 15,
    "router_temperature": 0.3,
    "evaluator_memory_order": 5,
    "evaluator_long_context_order": 10,
    "evaluator_temperature": 0.1,
    "reflector_memory_order": 15,
    "reflector_long_context_order": 20,
    "reflector_temperature": 0.7,
    "planner_memory_order": 10,  # Only if plan=True
    "planner_long_context_order": 15,
    "planner_temperature": 0.2
}

Example:

from masai.AgentManager.AgentManager import AgentDetails
from masai.Memory.InMemoryStore import InMemoryDocStore

# Create vector store
memory_store = InMemoryDocStore(embedding_model="all-MiniLM-L6-v2")

# Define agent details
agent_details = AgentDetails(
    capabilities=["database queries", "data analysis", "report generation"],
    description="Analyzes database data and generates insights",
    style="concise and data-focused"
)

# Create agent with all options
manager.create_agent(
    agent_name="data_analyst",
    tools=[database_tool, chart_tool],
    agent_details=agent_details,
    memory_order=15,
    long_context=True,
    long_context_order=30,
    shared_memory_order=10,
    plan=False,
    temperature=0.3,
    context_callable=fetch_user_permissions,
    in_memory_store=memory_store,
    top_k=5,
    config_dict={
        "router_temperature": 0.2,
        "evaluator_temperature": 0.1,
        "reflector_temperature": 0.5
    }
)

3. AgentDetails

Parameter	Type	Required	Default	Description
capabilities	List[str]	Yes	N/A	List of agent capabilities (e.g., ["reasoning", "coding", "science"])
description	str	No	""	Detailed description of agent's purpose and behavior
style	str	No	"gives very elaborate answers"	Communication style for responses

Example:

from masai.AgentManager.AgentManager import AgentDetails

agent_details = AgentDetails(
    capabilities=["database queries", "data analysis", "visualization"],
    description="Specializes in analyzing database data and creating visual reports",
    style="concise and data-focused, uses bullet points"
)

4. MASGenerativeModel.init()

Parameter	Type	Required	Default	Description
model_name	str	Yes	N/A	LLM model name (e.g., "gpt-4", "gemini-2.0-flash")
temperature	float	Yes	N/A	Temperature for randomness (0.0-1.0)
category	str	Yes	N/A	Model category: "openai", "gemini", "anthropic", "groq", "ollama", "huggingface"
prompt_template	ChatPromptTemplate	No	None	LangChain prompt template
memory_order	int	No	5	Number of recent messages to keep
extra_context	dict	No	None	Static context (e.g., {"user_id": "123"})
long_context	bool	No	False	Enable long-term memory summarization
long_context_order	int	No	10	Number of summaries to keep
chat_log	str	No	None	File path to save chat history
streaming	bool	No	False	Enable streaming responses
streaming_callback	Callable	No	None	Async callback for streaming chunks
context_callable	Callable	No	None	Function to fetch dynamic context per query
memory_store	InMemoryDocStore	No	None	Vector store for semantic search (kwarg)
k	int	No	3	Number of vector search results (kwarg)

Example:

from masai.GenerativeModel.generativeModels import MASGenerativeModel

llm = MASGenerativeModel(
    model_name="gemini-2.0-flash",
    temperature=0.3,
    category="gemini",
    memory_order=10,
    extra_context={"user_role": "admin"},
    long_context=True,
    long_context_order=20,
    context_callable=fetch_dynamic_context
)

5. MultiAgentSystem.init()

Parameter	Type	Required	Default	Description
agentManager	AgentManager	Yes	N/A	AgentManager instance with created agents
supervisor_config	SupervisorConfig	No	None	Supervisor configuration (required for hierarchical MAS)
heirarchical_mas_result_callback	Callable	No	None	Callback function for task completion in hierarchical MAS
agent_return_direct	bool	No	False	If False, supervisor evaluates agent responses before returning

Example:

from masai.MultiAgents.MultiAgent import MultiAgentSystem, SupervisorConfig

supervisor_config = SupervisorConfig(
    model_name="gpt-4",
    temperature=0.2,
    model_category="openai",
    memory_order=20,
    memory=True,
    extra_context={"organization": "MyCompany"},
    supervisor_system_prompt="You are an efficient task coordinator"
)

mas = MultiAgentSystem(
    agentManager=manager,
    supervisor_config=supervisor_config,
    heirarchical_mas_result_callback=handle_result,
    agent_return_direct=False
)

6. SupervisorConfig

Parameter	Type	Required	Default	Description
model_name	str	Yes	N/A	LLM model name for supervisor
temperature	float	Yes	N/A	Temperature (0.0-1.0)
model_category	str	Yes	N/A	Model category
memory_order	int	Yes	N/A	Supervisor memory size
memory	bool	Yes	N/A	Enable supervisor memory
extra_context	dict	Yes	N/A	Additional context for supervisor
supervisor_system_prompt	str	No	None	Custom system prompt (uses default if not provided)

7. InMemoryDocStore.init()

Parameter	Type	Required	Default	Description
documents	List[Union[str, Document]]	No	None	Initial documents to store
ids	List[str]	No	None	Document IDs (auto-generated if not provided)
embedding_model	Union[str, Callable, object]	No	"all-MiniLM-L6-v2"	Embedding model: string (SentenceTransformer name), callable, or object with embed_documents method

Example:

from masai.Memory.InMemoryStore import InMemoryDocStore

# Option 1: SentenceTransformer model name
store = InMemoryDocStore(embedding_model="all-MiniLM-L6-v2")

# Option 2: Custom embedding function
def my_embedder(texts: List[str]) -> np.ndarray:
    # Your embedding logic
    return embeddings

store = InMemoryDocStore(embedding_model=my_embedder)

# Option 3: LangChain embeddings
from langchain.embeddings import OpenAIEmbeddings
store = InMemoryDocStore(embedding_model=OpenAIEmbeddings())

8. ToolCache.init()

Parameter	Type	Required	Default	Description
host	str	No	"localhost"	Redis server host
port	int	No	6379	Redis server port
db	int	No	0	Redis database number
password	str	No	None	Redis password (if required)
timeout	int	No	30	Cache timeout in minutes

Example:

from masai.Tools.utilities.cache import ToolCache
from langchain.tools import tool

# Initialize cache
cache = ToolCache(
    host="localhost",
    port=6379,
    db=0,
    timeout=60  # 60 minutes
)

# Use as decorator
@tool
@cache.masai_cache
def expensive_api_call(query: str) -> dict:
    """Makes an expensive API call. Results are cached."""
    return api_response

9. OrchestratedMultiAgentNetwork.init()

Parameter	Type	Required	Default	Description
mas_instances	List[MultiAgentSystem]	Yes	N/A	List of MAS instances to orchestrate
network_memory_order	int	No	3	Network-level memory size
oman_llm_config	dict	No	None	OMAN supervisor LLM configuration
extra_context	dict	No	None	Additional context for OMAN supervisor

oman_llm_config Structure:

oman_llm_config = {
    "model_name": "gemini-2.0-flash-001",
    "category": "gemini",
    "temperature": 0.2,
    "memory_order": 3
}

Example:

from masai.OMAN.oman import OrchestratedMultiAgentNetwork

# Create multiple MAS instances
mas1 = MultiAgentSystem(agentManager=manager1)
mas2 = MultiAgentSystem(agentManager=manager2)

# Create OMAN
oman = OrchestratedMultiAgentNetwork(
    mas_instances=[mas1, mas2],
    network_memory_order=5,
    oman_llm_config={
        "model_name": "gpt-4",
        "category": "openai",
        "temperature": 0.2,
        "memory_order": 5
    },
    extra_context={"environment": "production"}
)

---

Use Cases & Best Practices

When to Use Each Architecture

Architecture	Use Case	Example
Router-Evaluator-Reflector	Dynamic routing, tool-heavy tasks	Database queries, API integrations, data retrieval
Planner-Executor-Reflector	Complex multi-step tasks	Research projects, data analysis, report generation

When to Use Each Workflow

Workflow	Use Case	Example
Sequential	Fixed pipeline, deterministic flow	ETL, document processing, data transformation
Hierarchical	Quality control, supervision needed	Research with review, complex analysis, content creation
Decentralized	Adaptive collaboration, peer tasks	Personal assistant systems, multi-domain problem solving

Memory Configuration Guidelines

Scenario	memory_order	long_context	long_context_order	in_memory_store
Short conversations	5	False	N/A	No
Medium conversations	10	True	10	No
Long conversations	10	True	20	Yes
Research tasks	5	True	30	Yes

Model Selection Guidelines

Component	Recommended Model	Reasoning
Router	Fast model (Gemini Flash, GPT-3.5)	Frequent calls, simple routing
Evaluator	Fast model (Gemini Flash Lite)	Binary decision (satisfied/not)
Reflector	Medium model (Gemini Flash)	Summarization and insights
Planner	Strong model (GPT-4, Gemini Pro)	Complex task decomposition
Supervisor	Strong model (GPT-4, Gemini Pro)	High-level coordination

---

Advanced Features

1. Using BaseGenerativeModel (Without Agent Architecture)

For simple conversational AI without tools or routing, use BaseGenerativeModel:

Use Case: Simple chatbots, Q&A systems, content generation

Configuration:

from masai.GenerativeModel.baseGenerativeModel.basegenerativeModel import BaseGenerativeModel

model = BaseGenerativeModel(
    model_name="gemini-2.0-flash",
    category='gemini',
    temperature=1,
    memory=True,
    memory_order=20,
    info={'USER_NAME': 'John', 'CONTEXT': 'Customer support'},
    system_prompt="You are a helpful assistant"
)

# Streaming response
async for chunk in model.astream_response(prompt):
    print(chunk, end='', flush=True)

# Non-streaming response
response = await model.generate_response(prompt)
print(response)

Key Features:

• No tools, no routing - pure LLM interaction
• Simple memory management
• Streaming and non-streaming support
• Custom system prompts
• Lightweight and fast

When to Use:

• Simple Q&A without external data
• Content generation tasks
• Conversational interfaces without tool needs
• Prototyping before adding agent architecture

2. Redis Caching for Tools

Cache tool outputs to improve performance for frequently used queries:

Setup:

from langchain.tools import tool
from masai.Tools.utilities.cache import ToolCache

# Initialize Redis cache
redis_cache = ToolCache(host='localhost', port=6379, db=0)

@tool
@redis_cache.masai_cache
def expensive_api_call(query: str) -> str:
    """
    Makes an expensive API call. Results are cached.

    Args:
        query: Search query

    Returns:
        API response
    """
    # Expensive operation here
    return api_response

Benefits:

• Faster response times for repeated queries
• Reduced API costs
• Automatic cache invalidation
• Monitor cache with Redis CLI

Requirements:

• Redis server running
• redis Python package installed

3. In-Memory Vector Store

For long conversations with semantic search capabilities:

Setup:

from masai.Memory.InMemoryStore import InMemoryDocStore

# Create vector store with embedding model
memory_store = InMemoryDocStore(embedding_model="all-MiniLM-L6-v2")

# Create agent with vector store
manager.create_agent(
    agent_name="research_agent",
    tools=tools,
    agent_details=details,
    long_context=True,
    long_context_order=20,
    in_memory_store=memory_store,
    top_k=3  # Retrieve top 3 relevant memories
)

How It Works:

1. Old messages (beyond memory_order) are summarized
2. Summaries are embedded and stored in vector store
3. On new queries, semantic search retrieves relevant past context
4. Retrieved context is added to prompt

Benefits:

• Semantic search over conversation history
• Better context retention for long conversations
• Efficient memory usage

Supported Embedding Models:

• Sentence Transformers (default: all-MiniLM-L6-v2)
• LangChain embedding models (OpenAI, Cohere, etc.)

4. Streaming Callbacks

For real-time response streaming:

Setup:

async def streaming_callback(chunk):
    """Handle streaming chunks"""
    if isinstance(chunk, dict):
        if "answer" in chunk:
            print(f"Answer: {chunk['answer']}")
    else:
        print(chunk, end="", flush=True)

manager = AgentManager(
    context={},
    logging=True,
    model_config_path="model_config.json",
    streaming=True,
    streaming_callback=streaming_callback
)

Use Cases:

• Real-time UI updates
• Progress indicators
• Debugging and monitoring

5. Per-Component Configuration

Override model settings for specific components:

Setup:

config_dict = {
    "router": {
        "memory_order": 10,
        "temperature": 0.3
    },
    "evaluator": {
        "memory_order": 5,
        "temperature": 0.1
    },
    "reflector": {
        "memory_order": 15,
        "temperature": 0.7
    }
}

manager.create_agent(
    agent_name="custom_agent",
    tools=tools,
    agent_details=details,
    config_dict=config_dict
)

Benefits:

• Fine-tune each component independently
• Optimize for specific use cases
• Balance cost vs. performance

6. Context Callable (Dynamic Context)

Fetch dynamic context on each query:

Setup:

async def get_user_context(query: str) -> dict:
    """Fetch user-specific context dynamically"""
    # Fetch from database, API, etc.
    user_data = await fetch_user_data()
    return {
        "user_name": user_data["name"],
        "user_role": user_data["role"],
        "permissions": user_data["permissions"]
    }

manager.create_agent(
    agent_name="personalized_agent",
    tools=tools,
    agent_details=details,
    context_callable=get_user_context
)

Use Cases:

• User-specific personalization
• Real-time data fetching
• Dynamic permission checks

Note: Currently only called for user queries, not agent-to-agent delegation.

Context Callable Flow:

┌─────────────────────────────────────────────────────────────┐
│           CONTEXT CALLABLE WORKFLOW                         │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  User Query Received                                        │
│    ↓                                                        │
│  [context_callable set?]                                    │
│    ↓ Yes                                                    │
│  [role == "user"?]                                          │
│    ↓ Yes                                                    │
│  Call context_callable(query)                               │
│    ↓                                                        │
│  [Is coroutine?]                                            │
│    ↓ Yes              ↓ No                                  │
│  await callable()   callable()                              │
│    ↓                  ↓                                     │
│  [Result is dict?]                                          │
│    ↓ Yes              ↓ No                                  │
│  Update info dict   Add as 'USEFUL DATA' key                │
│    ↓                                                        │
│  Include in prompt under <INFO>                             │
│    ↓                                                        │
│  After LLM response:                                        │
│    ↓                                                        │
│  Remove 'USEFUL DATA' from info                             │
│                                                             │
└─────────────────────────────────────────────────────────────┘

7. TaskManager (Hierarchical MAS Internals)

The TaskManager handles concurrent task execution in hierarchical MAS:

Key Features:

• Concurrent Execution: Uses ThreadPoolExecutor for parallel task processing
• Task Queue: Manages pending tasks with unique IDs
• Result Callbacks: Async callbacks for task completion
• Completed Task Context: Maintains history of completed tasks for supervisor context

Configuration:

# TaskManager is automatically created by MultiAgentSystem
# You can configure it through SupervisorConfig

supervisor_config = SupervisorConfig(
    model_name="gpt-4",
    temperature=0.2,
    model_category="openai",
    memory_order=20,  # Supervisor memory
    memory=True,
    extra_context={"organization": "MyCompany"},
    supervisor_system_prompt="Custom supervisor prompt"
)

mas = MultiAgentSystem(
    agentManager=manager,
    supervisor_config=supervisor_config,
    heirarchical_mas_result_callback=my_callback,
    agent_return_direct=False  # Supervisor reviews agent output
)

TaskManager Workflow:

┌─────────────────────────────────────────────────────────────┐
│              TASKMANAGER WORKFLOW                           │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Query → Supervisor                                         │
│    ↓                                                        │
│  Supervisor Decision:                                       │
│    ├─ Direct Answer → Return                                │
│    └─ Delegate to Agent                                     │
│         ↓                                                   │
│  Create Task (unique ID)                                    │
│    ↓                                                        │
│  Add to pending_tasks{}                                     │
│    ↓                                                        │
│  Submit to ThreadPoolExecutor                               │
│    ↓                                                        │
│  Agent Executes (async)                                     │
│    ↓                                                        │
│  Result → Result Queue                                      │
│    ↓                                                        │
│  Listener Task picks up result                              │
│    ↓                                                        │
│  [agent_return_direct=True?]                                │
│    ↓ Yes              ↓ No                                  │
│  Return result    Supervisor reviews                        │
│                       ↓                                     │
│                   [Satisfied?]                              │
│                     ↓ No                                    │
│                   Request revision                          │
│                     ↓ Yes                                   │
│  Add to completed_tasks[]                                   │
│    ↓                                                        │
│  Call result_callback (if set)                              │
│    ↓                                                        │
│  Return final result                                        │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Parameters:

• max_thread_workers: Maximum concurrent tasks (default: 5)
• check_interval: Interval for checking completed tasks (default: 1.0s)
• _last_n_completed_tasks: Number of completed tasks to keep in context (default: 10)

8. OMAN (Orchestrated Multi-Agent Network) Detailed

OMAN coordinates multiple MAS instances, each specializing in different domains:

Architecture:

┌─────────────────────────────────────────────────────────────┐
│                    OMAN ARCHITECTURE                        │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌──────────────────────────────────────────────────────┐  │
│  │           OMAN SUPERVISOR                            │  │
│  │  - Routes queries to appropriate MAS                 │  │
│  │  - Maintains network-level memory                    │  │
│  │  - Coordinates cross-MAS communication               │  │
│  └──────────────────────────────────────────────────────┘  │
│                          │                                  │
│         ┌────────────────┼────────────────┐                │
│         ↓                ↓                ↓                │
│  ┌──────────┐     ┌──────────┐     ┌──────────┐          │
│  │  MAS 1   │     │  MAS 2   │     │  MAS 3   │          │
│  │ (Finance)│     │(Research)│     │(Customer)│          │
│  │          │     │          │     │ Support  │          │
│  │ Agent A  │     │ Agent D  │     │ Agent G  │          │
│  │ Agent B  │     │ Agent E  │     │ Agent H  │          │
│  │ Agent C  │     │ Agent F  │     │ Agent I  │          │
│  └──────────┘     └──────────┘     └──────────┘          │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Setup Example:

from masai.OMAN.oman import OrchestratedMultiAgentNetwork
from masai.MultiAgents.MultiAgent import MultiAgentSystem

# Create specialized MAS instances
finance_mas = MultiAgentSystem(agentManager=finance_manager)
research_mas = MultiAgentSystem(agentManager=research_manager)
support_mas = MultiAgentSystem(agentManager=support_manager)

# Create OMAN
oman = OrchestratedMultiAgentNetwork(
    mas_instances=[finance_mas, research_mas, support_mas],
    network_memory_order=5,
    oman_llm_config={
        "model_name": "gpt-4",
        "category": "openai",
        "temperature": 0.2,
        "memory_order": 5
    },
    extra_context={"environment": "production", "company": "MyCompany"}
)

# Use OMAN
result = oman.delegate_task("Analyze Q4 financial performance")

OMAN Routing Process:

1. OMAN supervisor receives query
2. Analyzes query against each MAS's agent capabilities
3. Selects most appropriate MAS based on specialization
4. Delegates query to selected MAS
5. MAS processes query using its agents
6. Result returned through OMAN supervisor
7. Network memory updated with task outcome

Use Cases:

• Enterprise Systems: Multiple departments with specialized agents
• Multi-Domain Applications: Finance + Research + Support in one system
• Scalable Architecture: Add new MAS instances without modifying existing ones

---

Troubleshooting & Best Practices

1. Tool Loop Prevention

Problem: Agent repeatedly calls the same tool without making progress

Causes:

• Tool returns insufficient information
• LLM doesn't recognize task completion
• Tool errors not properly handled

Solutions:

# 1. Increase max_tool_loops
from masai.Config import config
config.max_tool_loops = 5  # Default: 3

# 2. Improve tool descriptions
@tool
def search_database(query: str) -> dict:
    """
    Searches database for records.

    Args:
        query: Search query string

    Returns:
        dict with 'count' (int) and 'results' (list) keys.
        Returns empty list if no results found.
    """
    # Implementation

# 3. Add error handling in tools
@tool
def api_call(endpoint: str) -> dict:
    """Makes API call with error handling"""
    try:
        response = requests.get(endpoint)
        response.raise_for_status()
        return {"success": True, "data": response.json()}
    except Exception as e:
        return {"success": False, "error": str(e)}

2. Reflection Counter Management

Problem: Agent gets stuck in reflection loops

Solution:

# Adjust reflection limit
config.MAX_REFLECTION_COUNT = 5  # Default: 3

# Improve reflector temperature for more decisive answers
config_dict = {
    "reflector_temperature": 0.3  # Lower = more deterministic
}

3. Memory Optimization Strategies

Strategy 1: Short Conversations

manager.create_agent(
    agent_name="quick_agent",
    tools=tools,
    agent_details=details,
    memory_order=5,
    long_context=False  # No summarization needed
)

Strategy 2: Long Conversations with Summarization

manager.create_agent(
    agent_name="research_agent",
    tools=tools,
    agent_details=details,
    memory_order=10,
    long_context=True,
    long_context_order=30  # Keep 30 summaries
)

Strategy 3: Very Long Conversations with Vector Store

from masai.Memory.InMemoryStore import InMemoryDocStore

memory_store = InMemoryDocStore(embedding_model="all-MiniLM-L6-v2")

manager.create_agent(
    agent_name="longterm_agent",
    tools=tools,
    agent_details=details,
    memory_order=10,
    long_context=True,
    long_context_order=20,
    in_memory_store=memory_store,
    top_k=5  # Retrieve 5 most relevant memories
)

4. When to Use long_context vs LTIMStore

Feature	long_context	LTIMStore
Purpose	Sequential conversation history	Semantic search over old conversations
Storage	List of summaries	Vector embeddings
Retrieval	All recent summaries	Top-k most relevant
Best For	Conversations with clear progression	Research tasks with topic jumps
Overhead	Low (summarization only)	Medium (embedding computation)
Use When	Conversation length < 100 messages	Conversation length > 100 messages

5. Performance Tuning

Reduce Latency:

# Use faster models for frequent operations
config_dict = {
    "router_model_name": "gemini-2.0-flash",  # Fast routing
    "evaluator_model_name": "gemini-2.0-flash",  # Fast evaluation
    "reflector_model_name": "gemini-pro"  # Quality reflection
}

# Reduce memory_order for faster context processing
memory_order=5  # Instead of 20

# Enable Redis caching for expensive tools
cache = ToolCache(timeout=60)
@tool
@cache.masai_cache
def expensive_tool(query: str) -> dict:
    # Implementation

Reduce Costs:

# Use cheaper models where possible
config_dict = {
    "router_model_name": "gpt-3.5-turbo",
    "evaluator_model_name": "gpt-3.5-turbo",
    "reflector_model_name": "gpt-4"  # Only use expensive model where needed
}

# Reduce memory_order to minimize token usage
memory_order=3
long_context_order=10

6. Error Handling Patterns

Pattern 1: Tool Error Handling

@tool
def robust_tool(query: str) -> dict:
    """Tool with comprehensive error handling"""
    try:
        result = perform_operation(query)
        return {"success": True, "data": result}
    except ValueError as e:
        return {"success": False, "error": f"Invalid input: {e}"}
    except ConnectionError as e:
        return {"success": False, "error": f"Connection failed: {e}"}
    except Exception as e:
        return {"success": False, "error": f"Unexpected error: {e}"}

Pattern 2: Agent Error Handling

try:
    agent = manager.get_agent("my_agent")
    result = await agent.initiate_agent("Query")

    if "error" in result:
        # Handle agent-level errors
        logger.error(f"Agent error: {result['error']}")
    else:
        # Process successful result
        print(result['answer'])

except ValueError as e:
    # Handle agent not found
    logger.error(f"Agent not found: {e}")
except Exception as e:
    # Handle unexpected errors
    logger.error(f"Unexpected error: {e}")

---

Summary

MAS-AI provides a comprehensive framework for building intelligent multi-agent systems with:

✅ Modular Architecture: Router, Evaluator, Reflector, Planner components ✅ Flexible Memory: 5-level hierarchy from short-term to vector store ✅ Multiple Workflows: Sequential, Hierarchical, Decentralized ✅ LLM Agnostic: Support for OpenAI, Gemini, Anthropic, Groq, Ollama, HuggingFace ✅ Advanced Features: Redis caching, streaming, dynamic context, per-component config ✅ Scalable: From single agents to OMAN networks

Next Steps:

1. Read MASAI_PROMPT_TEMPLATES_AND_DATA_FLOW.md for prompt details
2. Review README.md for quick start examples
3. Check MASAI_CONTEXT_MANAGEMENT_ANALYSIS.md for context deep dive

---

Installation & Setup

Prerequisites

• Python 3.9 or higher
• pip package manager

Installation Options

Option 1: Install from PyPI (Recommended)

pip install masai-framework

Option 2: Install from Source

# Clone the repository
git clone https://github.com/shaunthecomputerscientist/mas-ai.git
cd mas-ai

# Install in development mode
pip install -e .

# Or install directly
pip install .

Quick Start

1. Create a model configuration file (model_config.json):

{
  "router": {
    "model_name": "gpt-4",
    "temperature": 0.2,
    "model_category": "openai"
  },
  "evaluator": {
    "model_name": "gpt-3.5-turbo",
    "temperature": 0.1,
    "model_category": "openai"
  },
  "reflector": {
    "model_name": "gpt-4",
    "temperature": 0.7,
    "model_category": "openai"
  }
}

2. Set up environment variables:

# For OpenAI
export OPENAI_API_KEY="your-api-key"

# For Gemini
export GOOGLE_API_KEY="your-api-key"

# For Anthropic
export ANTHROPIC_API_KEY="your-api-key"

3. Create your first agent:

from masai.AgentManager.AgentManager import AgentManager, AgentDetails
from masai.Tools.tools.baseTools import human_in_loop_input

# Initialize AgentManager
manager = AgentManager(
    context={"user_name": "Your Name"},
    logging=True,
    model_config_path="model_config.json"
)

# Define agent capabilities
agent_details = AgentDetails(
    capabilities=["reasoning", "analysis", "problem-solving"],
    description="A helpful AI assistant",
    style="friendly and informative"
)

# Create an agent
manager.create_agent(
    agent_name="assistant",
    tools=[human_in_loop_input],
    agent_details=agent_details
)

# Use the agent
response = await manager.get_agent("assistant").initiate_agent("Hello, how can you help me?")
print(response["answer"])

Verification

Test your installation:

# Test custom LangGraph implementation
from masai.langgraph.graph import StateGraph, END, START
from masai.langgraph.graph.state import CompiledStateGraph

print("✅ MASAI Framework installed successfully!")
print("✅ Custom LangGraph implementation ready!")

Troubleshooting

Common Issues:

1. Import Errors: Make sure you're using Python 3.9+ and have installed all dependencies
2. API Key Issues: Verify your API keys are set correctly in environment variables
3. Model Configuration: Ensure your model_config.json file is properly formatted

Getting Help:

• 📖 Check the documentation
• 🐛 Report issues on GitHub Issues
• 💬 Join our community discussions

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Last Updated: 2025-01-05 Framework Version: v0.1.33 with Custom LangGraph + Visualization Documentation Version: 2.2 (Complete Custom LangGraph with Graph Visualization)