Agentic RAG with Planning and Tool Use: Build a Research Assistant Step by Step

25 Aug 2026 - Shyam Mohan

Agentic RAG with Planning and Tool Use: Build a Research Assistant Step by Step

Quick Answer / TL;DR

Agentic RAG with Planning and Tool Use extends traditional RAG by empowering Language Models (LLMs) to strategize, break down complex tasks, and dynamically select and execute tools (like web search, code interpreters, or custom APIs) to gather and process information. This approach enables the LLM to perform multi-step reasoning, iterate on responses, and generate far more accurate, comprehensive, and contextually rich answers than standard RAG, making it ideal for sophisticated applications like research assistants.

In the rapidly evolving landscape of artificial intelligence, Retrieval-Augmented Generation (RAG) has proven to be a cornerstone technique for grounding Large Language Models (LLMs) in external knowledge, mitigating hallucinations, and providing up-to-date information. However, traditional RAG often operates in a single-turn, reactive mode, merely retrieving documents and summarizing them. To tackle more complex, multi-faceted inquiries, we need a more proactive and intelligent approach: Agentic RAG with Planning and Tool Use. This method transforms a static Q&A system into a dynamic research assistant capable of autonomous problem-solving.

This guide will walk you through the process of building such an assistant, from conceptual understanding to practical implementation.

What You Will Learn

The core concepts of Agentic RAG, including planning, tool use, and multi-step reasoning.
How to define and integrate custom tools that an AI agent can leverage.
Strategies for designing an agent’s planning and execution loop.
How to build a robust research assistant that goes beyond simple information retrieval.
Best practices for evaluating and refining agentic systems.

The Evolution from Basic RAG to Agentic RAG
Understanding the Core Components of an Agentic RAG System
Prerequisites and Environment Setup
Step-by-Step: Building Your Agentic Research Assistant
Real-World Use Cases for Agentic Research Assistants
Conclusion
FAQ
Further Reading

The Evolution from Basic RAG to Agentic RAG

Traditional RAG involves fetching relevant documents from a vector database based on a user query and then passing these documents along with the query to an LLM for synthesis. While effective for direct questions, this approach falls short when facing:

Ambiguous or multi-part queries: “What’s the market outlook for AI in healthcare, and who are the key players?” requires multiple information-gathering steps.
Need for external, real-time data: “What’s the current stock price of NVIDIA?”
Complex reasoning: “Compare the pros and cons of two different machine learning algorithms, considering recent research papers.”
Iterative refinement: The initial retrieval might not be sufficient, requiring follow-up searches.

Agentic RAG with Planning and Tool Use addresses these limitations by empowering the LLM itself to act as an intelligent agent. This agent can:

Plan: Break down a complex query into smaller, manageable sub-tasks.
Choose Tools: Select the most appropriate tool(s) from a predefined set for each sub-task (e.g., search the web, query a local knowledge base, perform a calculation, read a document).
Execute Tools: Run the chosen tool with specific inputs.
Observe Results: Evaluate the output from the tool.
Reflect & Iterate: Based on the observations, decide the next step – which could be another planning phase, tool execution, or generating a final answer.

This iterative loop allows the LLM to mimic human-like problem-solving, leading to more thorough and accurate responses.

Now that we understand the power of Agentic RAG, let’s dive into its components.

Understanding the Core Components of an Agentic RAG System

An effective agentic system typically comprises several key elements:

The Language Model (LLM): The brain of the agent, responsible for planning, reasoning, and generating natural language.
Tools: Functions or APIs that the LLM can call to interact with the external world (e.g., search engines, databases, code interpreters, custom APIs). Each tool performs a specific, well-defined action.
Planning Module: A component (often just the LLM itself, prompted with a specific instruction set) that determines the sequence of actions needed to achieve a goal. It generates a “thought process” before acting.
Memory/Context Management: The ability to retain conversational history, intermediate thoughts, and tool outputs to inform subsequent decisions within a task.
Orchestrator/Agent Executor: The logic that manages the interaction loop: taking the LLM’s plan, executing the chosen tool, feeding results back to the LLM, and repeating until the task is complete.
Knowledge Base (RAG): The traditional RAG component, now integrated as a specific tool that the agent can choose to query when internal, static knowledge is required.

These components work in synergy to enable complex, multi-step information retrieval and synthesis. Let’s get our hands dirty with the practical setup.

Prerequisites and Environment Setup

Before we begin building, ensure you have the necessary environment and libraries.

1. Python Environment: It’s recommended to use a virtual environment.

python -m venv agentic_rag_env
source agentic_rag_env/bin/activate # On Windows use `agentic_rag_env\Scripts\activate`

2. Install Required Libraries: We’ll use langchain as our primary framework for building agents, openai for LLM access, and duckduckgo_search for web search. For the RAG component, we’ll need chromadb and sentence-transformers.

pip install langchain langchain-openai duckduckgo-search chromadb sentence-transformers

3. API Keys: You’ll need an OpenAI API key (or similar LLM provider key) to interact with the LLM. Set it as an environment variable.

export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"

With our environment ready, let’s start constructing our Agentic RAG with Planning and Tool Use research assistant.

Step-by-Step: Building Your Agentic Research Assistant

This section guides you through the process of creating an agent that can dynamically decide whether to use web search, a local knowledge base (RAG), or just its internal LLM knowledge.

Step 1: Define and Implement Your Tools

Tools are the agent’s connection to the external world. For our research assistant, we’ll define two primary tools:

Web Search Tool: To fetch real-time, broad information from the internet.
RAG Knowledge Base Tool: To query a specific, curated document collection.

Let’s start with the web search tool using DuckDuckGoSearchRun from langchain_community.

# tools.py
from langchain_community.tools import DuckDuckGoSearchRun
from langchain.pydantic_v1 import BaseModel, Field
from typing import Type

# 1. Web Search Tool
class WebSearchInput(BaseModel):
    query: str = Field(description="search query to look up on the internet")

web_search_tool = DuckDuckGoSearchRun(
    name="WebSearch",
    description="Useful for general web searches when you need up-to-date information or broad context.",
    args_schema=WebSearchInput
)

print("Web Search Tool initialized.")

Next, let’s create a placeholder for our RAG knowledge base tool. For simplicity, we’ll create a small in-memory Chroma database. In a real application, this would be populated with extensive domain-specific documents.

# tools.py (continued)
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
import os

# Initialize embeddings and vector store
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Chroma(embedding_function=embeddings, persist_directory="./chroma_db")

# Example documents for our RAG knowledge base
sample_documents = [
    "Agentic AI systems leverage planning and tools to achieve complex goals.",
    "Retrieval-Augmented Generation (RAG) improves LLM factual consistency.",
    "Tool use allows LLMs to interact with APIs, databases, and external systems.",
    "Planning involves breaking down a problem into sequential steps.",
    "The core idea of Agentic RAG is to make the LLM a proactive problem solver."
]

# Add documents to the vector store if it's empty
if vectorstore._collection.count() == 0:
    print("Populating RAG knowledge base with sample documents...")
    docs = [Document(page_content=d) for d in sample_documents]
    vectorstore.add_documents(docs)
    vectorstore.persist()
    print("RAG knowledge base populated.")
else:
    print("RAG knowledge base already populated.")


class RAGToolInput(BaseModel):
    query: str = Field(description="query for the local knowledge base about AI agents and RAG")

class RAGKnowledgeBaseTool(DuckDuckGoSearchRun): # Inherit to reuse some structure, but override behavior
    name = "RAGKnowledgeBase"
    description = "Useful for answering specific questions about Agentic AI, RAG, planning, and tool use from a curated internal knowledge base."
    args_schema: Type[BaseModel] = RAGToolInput

    def _run(self, query: str) -> str:
        """Use the RAG vector store to find relevant information."""
        print(f"Executing RAG query: '{query}'")
        results = vectorstore.similarity_search(query, k=3)
        return "\n".join([doc.page_content for doc in results])

rag_knowledge_base_tool = RAGKnowledgeBaseTool()

print("RAG Knowledge Base Tool initialized.")

# Combine all tools for the agent
all_tools = [web_search_tool, rag_knowledge_base_tool]

This setup provides our agent with two distinct ways to access information. The agent’s Planning Module will decide which tool is most appropriate based on the user’s query and the current state of its reasoning.

Step 2: Set Up Your Knowledge Base (RAG Component)

While we initialized a basic Chroma database in Step 1 for demonstration, a real RAG component would involve:

Data Ingestion: Loading documents from various sources (PDFs, web pages, databases).
Text Splitting: Breaking down documents into smaller, manageable chunks.
Embedding: Converting text chunks into numerical vector representations.
Vector Storage: Storing these embeddings in a vector database (e.g., Chroma, Pinecone, Weaviate).

For a deep dive into building a more comprehensive RAG system, refer to resources on data loading, chunking strategies, and vector database integration. Our current RAGKnowledgeBaseTool already encapsulates the query part of this system.

Step 3: Design the Agent’s Planning and Orchestration Logic

The heart of an agentic system is its ability to plan. LangChain’s create_react_agent function provides a convenient way to implement the ReAct (Reasoning and Acting) framework, where the LLM observes, thinks, and then acts.

The agent’s “thought process” is guided by a prompt that instructs it on how to reason, what tools it has available, and how to format its output.

# agent_core.py
from langchain_openai import ChatOpenAI
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
from tools import all_tools # Import the tools we defined earlier

# 1. Initialize the LLM
llm = ChatOpenAI(model="gpt-4o", temperature=0) # Using a powerful model for better reasoning

# 2. Define the Agent Prompt
# We'll use a prompt from LangChain Hub and customize it if needed
# The hub provides a good starting point for ReAct agents
prompt = hub.pull("hwchase17/react")

# We can customize the prompt to emphasize specific behaviors,
# but the standard ReAct prompt is usually sufficient for a start.
# Example customization (uncomment if you want to modify):
# custom_template = """
# You are an expert research assistant. You have access to the following tools:
# {tools}

# Use the following format:

# Question: the input question you must answer
# Thought: you should always think about what to do
# Action: the action to take, should be one of [{tool_names}]
# Action Input: the input to the action
# Observation: the result of the action
# ... (this Thought/Action/Action Input/Observation can repeat N times)
# Thought: I now know the final answer
# Final Answer: the final answer to the original input question

# Begin!

# Question: {input}
# Thought:{agent_scratchpad}
# """
# prompt = PromptTemplate.from_template(custom_template)


# 3. Create the Agent
agent = create_react_agent(llm, all_tools, prompt)

# 4. Create the Agent Executor
agent_executor = AgentExecutor(agent=agent, tools=all_tools, verbose=True, handle_parsing_errors=True)

print("Agent and Executor initialized.")

The verbose=True flag is crucial for debugging, as it prints the agent’s internal “Thought,” “Action,” and “Observation” steps, allowing you to trace its reasoning process.

Step 4: Assemble and Test the Agent

Now that all components are in place, let’s assemble our research assistant and put it to the test.

# main.py
from agent_core import agent_executor
import os

if __name__ == "__main__":
    print("Agentic Research Assistant Ready!")
    print("Type 'exit' to quit.")

    while True:
        user_query = input("\nEnter your research query: ")
        if user_query.lower() == 'exit':
            break

        try:
            print(f"\nProcessing query: '{user_query}'")
            # The agent_executor will handle planning, tool execution, and result synthesis
            response = agent_executor.invoke({"input": user_query})
            print("\n--- Final Answer ---")
            print(response["output"])
            print("--------------------\n")
        except Exception as e:
            print(f"\nAn error occurred: {e}")
            print("Please try refining your query or check your setup.")

To run this:

Save tools.py with the tool definitions.
Save agent_core.py with the agent and executor logic.
Save main.py with the main execution loop.
Ensure your OPENAI_API_KEY is set as an environment variable.
Run python main.py in your terminal.

Example Queries to Test:

“What is Agentic RAG and how does it differ from traditional RAG?” (Should primarily use RAGKnowledgeBase)
“What are the recent advancements in AI agent planning, and who are the leading researchers in this field?” (Likely starts with WebSearch, then potentially RAGKnowledgeBase for definitions)
“Explain the concept of tool use in LLMs.” (Should use RAGKnowledgeBase)
“What’s the current market capitalization of Google (Alphabet Inc.)?” (Should use WebSearch)
“Compare the benefits of using a local vector store like Chroma vs. a cloud-based one like Pinecone for RAG.” (Might use both, depending on how detailed its internal knowledge is or if it needs to search for up-to-date comparisons.)

Observe the Thought, Action, Action Input, and Observation steps printed in the console. This demonstrates the agent’s dynamic planning and tool orchestration. The agent will decide whether to use its WebSearch tool for current, broad information or its RAGKnowledgeBase tool for specific, curated knowledge related to AI agents and RAG. This is the core of Agentic RAG with Planning and Tool Use.

Real-World Use Cases for Agentic Research Assistants

The capabilities of an agentic research assistant extend far beyond simple Q&A. Here are a few practical applications:

Market Research: Automatically gather data on industry trends, competitor analysis, product reviews, and synthesize reports.
Scientific Literature Review: Scan new research papers, identify key findings, track specific methodologies, and summarize advancements in a field.
Legal Case Preparation: Retrieve relevant statutes, case precedents, and legal opinions, helping lawyers quickly build arguments.
Financial Analysis: Pull real-time market data, company news, analyst reports, and generate summaries for investment decisions.
Competitive Intelligence: Monitor competitor product launches, marketing campaigns, and strategic announcements across various sources.
Personalized Learning: Act as a tutor, explaining complex topics, finding supplementary materials, and guiding users through learning paths.

By combining structured retrieval with dynamic web access and the LLM’s reasoning, Agentic RAG unlocks significantly more powerful and autonomous applications.

Conclusion

We’ve journeyed from understanding the limitations of traditional RAG to constructing a sophisticated Agentic RAG with Planning and Tool Use research assistant. By empowering LLMs with the ability to plan, select tools, and iterate, we move beyond static question-answering towards dynamic, intelligent problem-solving agents. This approach not only enhances the accuracy and comprehensiveness of responses but also opens up a new paradigm for building AI systems that can proactively engage with complex information landscapes. The future of AI interaction lies in these autonomous, tool-augmented agents, capable of independent reasoning and action.

FAQ

Q1: What is the main difference between traditional RAG and Agentic RAG? A1: Traditional RAG primarily focuses on retrieving relevant documents for a single query. Agentic RAG empowers the LLM to plan multi-step actions, choose and use various tools (including a RAG knowledge base), and iterate to solve complex problems, mimicking human-like reasoning.

Q2: Why is “planning” crucial in Agentic RAG? A2: Planning allows the LLM to break down complex tasks into smaller, manageable sub-goals, strategize the optimal sequence of actions, and recover from failures, leading to more robust and accurate task completion.

Q3: Can I add custom tools to an Agentic RAG system? A3: Absolutely. The strength of agentic systems lies in their extensibility. You can define and integrate any custom function or API as a tool, allowing the agent to interact with proprietary databases, internal systems, or perform specific calculations.

Q4: How does tool use prevent LLM hallucinations? A4: Tool use helps mitigate hallucinations by allowing the LLM to fetch and verify information from authoritative external sources (like a web search engine or a specific knowledge base) rather than relying solely on its potentially outdated or incorrect internal training data.

Q5: What are the primary challenges in building Agentic RAG systems? A5: Key challenges include designing effective prompts for planning and tool selection, handling tool execution failures, managing context over long chains of actions, and ensuring the agent’s behavior remains aligned with user intent and safety guidelines.