PlannerAgent 0.1.0

Flexible LangGraph Plan-and-Execute Agent with customizable knowledge bases, prompts, and tools

PlannerAgent

Flexible LangGraph Plan-and-Execute Agent with customizable knowledge bases, prompts, and tools

Overview

PlannerAgent is a Python package that implements a sophisticated Plan-and-Execute agent architecture using LangGraph. The agent breaks down complex queries into actionable steps, executes them iteratively, and replans dynamically based on results.

How It Works

User Query → Summarize → Plan → Execute → Replan → Answer
                                   ↑         ↓
                                   ┴─────────┘
                              (Iterate until complete)

The agent:

1. Summarizes conversation context
2. Plans multi-step execution strategy
3. Executes each step with appropriate tools
4. Replans based on execution results
5. Iterates until objective is achieved
6. Returns final answer

Features

Async Architecture

• Fully asynchronous implementation
• Non-blocking I/O for efficient execution
• Seamless integration with async frameworks like FastAPI

Multi-Model Support

• Mix and match OpenAI and Anthropic models
• Separate models for planning, replanning, and execution
• Cost optimization through strategic model selection

Flexible Tool Integration

• Built-in search tools (Tavily)
• Database query tools with MongoDB
• Easy custom tool integration
• Type-safe tool validation

Knowledge Base

• JSON-based knowledge base support
• Automatic knowledge injection into prompts
• Customizable knowledge format

Persistent State

• MongoDB checkpointing for state persistence
• Resume conversations across sessions
• Full conversation history tracking

Streaming Support

• Real-time execution updates
• Progress monitoring
• Event-driven architecture

Installation

From PyPI

pip install PlannerAgent

From Source

git clone https://github.com/mmirz94/PlannerAgent.git
cd PlannerAgent
pip install -e .

Dependencies

Required:

• Python ≥ 3.11
• LangGraph
• LangChain (OpenAI, Anthropic, Community)
• Pydantic
• MongoDB drivers (Motor, PyMongo)

Optional:

• pytest (for testing)
• black, ruff (for development)

Quick Start

Basic Usage

import asyncio
from PlannerAgent.agent import PlanAndExecuteAgent

# Create agent with minimal configuration
agent = PlanAndExecuteAgent(
    kb_path="path/to/knowledge_base.json",
    planner_model="gpt-4o",
    planner_provider="openai",
    replanner_model="gpt-4o",
    replanner_provider="openai",
    agent_model="gpt-4o",
    agent_provider="openai",
)

# Run the agent
result = asyncio.run(
    agent.run(
        user_input="What are the latest AI developments?",
        user_id="user123"
    )
)

print(result.response)

With Custom Tools

import asyncio
from langchain_core.tools import tool
from langchain_tavily import TavilySearch
from PlannerAgent.agent import PlanAndExecuteAgent

# Define custom tool
@tool
def calculate(expression: str) -> float:
    """Evaluate a mathematical expression."""
    return eval(expression)

# Create search tool
search_tool = TavilySearch(max_results=3)

# Initialize agent with tools
agent = PlanAndExecuteAgent(
    kb_path="knowledge_base.json",
    planner_model="gpt-4o",
    planner_provider="openai",
    replanner_model="gpt-4o",
    replanner_provider="openai",
    agent_model="gpt-4o",
    agent_provider="openai",
    tools=[search_tool, calculate]
)

# Run with custom tools
result = asyncio.run(
    agent.run(
        user_input="Calculate 25 * 4 and search for the answer's significance",
        user_id="user123"
    )
)
print(result.response)

Multi-Model Configuration

import asyncio
from PlannerAgent.agent import PlanAndExecuteAgent

# Use powerful Claude for planning, fast GPT for execution
agent = PlanAndExecuteAgent(
    kb_path="knowledge_base.json",
    planner_model="claude-sonnet-4-5-20250929",
    planner_provider="anthropic",
    replanner_model="claude-sonnet-4-5-20250929",
    replanner_provider="anthropic",
    agent_model="gpt-4o-mini",  # Faster, cheaper for execution
    agent_provider="openai",
)

# Run with multi-model setup
result = asyncio.run(
    agent.run(
        user_input="Analyze the latest market trends",
        user_id="user123"
    )
)
print(result.response)

With MongoDB Persistence

import asyncio
from PlannerAgent.agent import PlanAndExecuteAgent

agent = PlanAndExecuteAgent(
    mongo_db_connection_string="mongodb://localhost:27017",
    db_name="planneragent",
    kb_path="knowledge_base.json",
    planner_model="gpt-4o",
    planner_provider="openai",
    replanner_model="gpt-4o",
    replanner_provider="openai",
    agent_model="gpt-4o",
    agent_provider="openai",
)

# Resume conversation
config = {"configurable": {"thread_id": "conversation_123"}}
result = asyncio.run(
    agent.run(
        user_input="Continue from where we left off",
        user_id="user123",
        config=config
    )
)

Streaming Execution

import asyncio
from PlannerAgent.agent import PlanAndExecuteAgent

# Create agent
agent = PlanAndExecuteAgent(
    kb_path="knowledge_base.json",
    planner_model="gpt-4o",
    planner_provider="openai",
    replanner_model="gpt-4o",
    replanner_provider="openai",
    agent_model="gpt-4o",
    agent_provider="openai",
)

# Stream execution updates
async def stream_agent():
    async for event in agent.stream(
        user_input="Plan a research project",
        user_id="user123",
        stream_mode="updates"
    ):
        print(f"Step: {list(event.keys())}")
        # Output: Step: ['summarize_conversation']
        #         Step: ['planner']
        #         Step: ['agent']
        #         Step: ['replan']

asyncio.run(stream_agent())

Verbose Logging

Control logging output with the verbose parameter to monitor agent execution:

# Level 0 (default): No logging - clean output
agent = PlanAndExecuteAgent(
    kb_path="knowledge_base.json",
    planner_model="gpt-4o",
    planner_provider="openai",
    replanner_model="gpt-4o",
    replanner_provider="openai",
    agent_model="gpt-4o",
    agent_provider="openai",
    verbose=0  # Silent mode
)

# Level 1: Show state at each step
agent = PlanAndExecuteAgent(
    ...,
    verbose=1  # Show states only
)
# Output:
# --------------------------------------------------------------------------------
# PLANNING STEP - State:
# <state details>
# --------------------------------------------------------------------------------

# Level 2: Show state + token usage for debugging
agent = PlanAndExecuteAgent(
    ...,
    verbose=2  # Show states and token usage
)
# Output:
# --------------------------------------------------------------------------------
# PLANNING STEP - State:
# <state details>
# --------------------------------------------------------------------------------
# ********************************************************************************
# PLANNER - Token Usage:
# <token usage details>
# ********************************************************************************

Logging Levels:

• verbose=0: No logging (production mode)
• verbose=1: State logging only (monitor workflow)
• verbose=2: State + token usage (debugging/cost tracking)

API Documentation

PlanAndExecuteAgent

Main agent class implementing plan-and-execute architecture.

Parameters

• mongo_db_connection_string (Optional[str]): MongoDB connection string for state persistence. Default: None.

• db_name (Optional[str]): Database name for MongoDB storage. Default: None.

• kb_path (Optional[str]): Path to knowledge base JSON file. Default: None.

• planner_model (Optional[str]): Model name for planning step (e.g., "gpt-4o", "claude-sonnet-4-5-20250929").

• replanner_model (Optional[str]): Model name for replanning step.

• agent_model (Optional[str]): Model name for execution step.

• planner_provider (Literal["openai", "anthropic"]): LLM provider for planner. Default: "openai".

• replanner_provider (Literal["openai", "anthropic"]): LLM provider for replanner. Default: "openai".

• agent_provider (Literal["openai", "anthropic"]): LLM provider for agent. Default: "openai".

• tools (Optional[List[BaseTool]]): List of LangChain tools for the agent. Default: None.

• verbose (int): Logging verbosity level. Default: 0.

  • 0: No logging (production mode)
  • 1: State logging at each step
  • 2: State + token usage logging

Methods

async run(user_input: str, user_id: str, config: Optional[dict] = None) -> OutputState

Execute the agent and return final result.

Parameters:

• user_input (str): User's query or prompt
• user_id (str): Unique user identifier for performing user-based queries with query tool.
• config (Optional[dict]): Optional configuration (e.g., recursion limit, thread ID)

Returns:

• OutputState: Final response with status message and answer

Example:

import asyncio

result = asyncio.run(
    agent.run(
        user_input="Analyze Q4 sales data",
        user_id="user123",
        config={"recursion_limit": 50}
    )
)
print(result.response)

async stream(user_input: str, user_id: str, config: Optional[dict] = None, stream_mode: Literal["values", "updates"] = "updates")

Stream agent execution with real-time updates.

Parameters:

• user_input (str): User's query or prompt
• user_id (str): Unique user identifier
• config (Optional[dict]): Optional configuration
• stream_mode (Literal["values", "updates"]): Stream mode - "values" for full state, "updates" for deltas

Yields:

• dict: State updates during execution of each node.

Example:

import asyncio

async def stream_agent():
    async for event in agent.stream(
        user_input="Research topic",
        user_id="user123",
        stream_mode="updates"
    ):
        if "planner" in event:
            print(f"Plan: {event['planner']}")
        elif "agent" in event:
            print(f"Executing: {event['agent']}")

asyncio.run(stream_agent())

save_graph_diagram(save_path: str)

Save the agent's graph structure as an image.

Parameters:

• save_path (str): Path to save diagram (must end with .png or .jpg)

Raises:

• ValueError: If path doesn't end with .png or .jpg

Example:

agent.save_graph_diagram("diagrams/agent_flow.png")

Configuration

Environment Variables

# OpenAI (Azure)
AZURE_OPENAI_ENDPOINT=https://your-endpoint.openai.azure.com/
AZURE_OPENAI_API_KEY=your-key

# Anthropic
ANTHROPIC_API_KEY=your-key

# Search (optional)
TAVILY_API_KEY=your-key

# MongoDB (optional)
MONGODB_URI=mongodb://localhost:27017

Knowledge Base Format

JSON file with structured knowledge of:

• Domain-specific data
• Database schema and rules
• General rules to follow
• Examples

{
  "company_info": {
    "name": "Acme Corp",
    "industry": "Technology"
  },
  "policies": {
    "refund": "30-day money-back guarantee",
    "shipping": "Free shipping over $50"
  }
}

The agent automatically includes this knowledge in planning and execution.

Architecture

Agent Flow

The agent follows a cyclical plan-and-execute workflow:

1. Summarize Conversation - Condenses conversation history and current query
2. Planner - Creates a multi-step execution plan with appropriate tools
3. Agent (Execute) - Executes the current step using the specified tool
4. Replan - Evaluates progress and either:
   • Returns final answer if objective is complete
   • Continues to next execution step if more work is needed

Note: To regenerate this diagram, run python generate_graph_diagram.py

Components

• Nodes: Summarize, Plan, Execute, Replan
• Edges: Conditional routing based on completion status
• State: Pydantic models for type safety
• Tools: Extensible tool system with LangChain
• Checkpointing: MongoDB-backed state persistence

Examples

Example 1: Research Assistant

import asyncio
from langchain_tavily import TavilySearch
from PlannerAgent.agent import PlanAndExecuteAgent

# Create search tool
search = TavilySearch(max_results=5, include_raw_content=True)

# Initialize agent
agent = PlanAndExecuteAgent(
    kb_path="research_knowledge.json",
    planner_model="claude-sonnet-4-5-20250929",
    planner_provider="anthropic",
    replanner_model="claude-sonnet-4-5-20250929",
    replanner_provider="anthropic",
    agent_model="gpt-4o",
    agent_provider="openai",
    tools=[search]
)

# Research query
result = asyncio.run(
    agent.run(
        user_input="What are the latest advancements in quantum computing?",
        user_id="researcher_1"
    )
)
print(result.response)

Example 2: Data Analysis Agent

import asyncio
from langchain_core.tools import tool
from PlannerAgent.agent import PlanAndExecuteAgent

@tool
def query_database(sql: str) -> str:
    """Execute SQL query and return results."""
    # Your database logic here
    return "Query results: ..."

@tool
def create_visualization(data: str, chart_type: str) -> str:
    """Create data visualization."""
    # Your visualization logic
    return "chart_url"

# Initialize agent with custom data tools
agent = PlanAndExecuteAgent(
    kb_path="data_schema.json",
    planner_model="gpt-4o",
    planner_provider="openai",
    replanner_model="gpt-4o",
    replanner_provider="openai",
    agent_model="gpt-4o-mini",
    agent_provider="openai",
    tools=[query_database, create_visualization]
)

# Run data analysis
result = asyncio.run(
    agent.run(
        user_input="Analyze sales trends and create a bar chart",
        user_id="analyst_1"
    )
)
print(result.response)

License

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

Citation

If you use PlannerAgent in your research or project, please cite:

@software{planneragent2024,
  author = {Mirzaloo, Salar},
  title = {PlannerAgent: Flexible LangGraph Plan-and-Execute Agent},
  year = {2024},
  url = {https://github.com/mmirz94/PlannerAgent}
}

Support

• Email: [e-mail]
• Issues: GitHub Issues

Acknowledgments

Built with:

• LangGraph - Agent orchestration
• LangChain - LLM framework
• OpenAI - GPT models
• Anthropic - Claude models