LangChain Agent

Learn how to use LangChain tools and utilities with PraisonAI agents.

from praisonaiagents import Agent
from langchain_community.tools import WikipediaQueryRun

agent = Agent(
    name="LangChain Agent",
    instructions="Answer using LangChain community tools.",
    tools=[WikipediaQueryRun()],
)
agent.start("Give a short overview of reinforcement learning from Wikipedia")

The user requests external knowledge; the agent queries LangChain tools and returns a summary.

Quick Start

Code

1. Install Package

   First, install the required packages:

   pip install praisonaiagents langchain-community wikipedia youtube-search
   
2. Set API Key

   Set your OpenAI API key as an environment variable in your terminal:

   export OPENAI_API_KEY=your_api_key_here
   
3. Create a file

   Create a new file app.py with the basic setup:

   Single Tool

   from praisonaiagents import Agent
   from langchain_community.utilities import WikipediaAPIWrapper
   
   wiki_agent = Agent(
     instructions="You are a wikipedia research Agent",
     tools=[WikipediaAPIWrapper]
   )
   
   wiki_agent.start("Research 'Artificial Intelligence' on Wikipedia")
   

   from praisonaiagents import Agent, AgentTeam
   from langchain_community.utilities import WikipediaAPIWrapper
   from langchain_community.tools import YouTubeSearchTool
   
   youtube_agent = Agent(
       instructions="Search for information about 'AI advancements' on YouTube",
       tools=[YouTubeSearchTool]
   )
   
   wiki_agent = Agent(
       instructions="Research 'Artificial Intelligence' on Wikipedia",
       tools=[WikipediaAPIWrapper]
   )
   
   agents = AgentTeam(agents=[youtube_agent, wiki_agent])
   agents.start()
   
4. Start Agents

   Type this in your terminal to run your agents:

   python app.py
   

No Code

1. Install Package

   (Upcoming Feature) Install the PraisonAI package:

   pip install "praisonai"
   
2. Set API Key

   Set your OpenAI API key as an environment variable in your terminal:

   export OPENAI_API_KEY=your_api_key_here
   
3. Create a file

   Create a new file agents.yaml with the basic setup:

   framework: praisonai
   process: sequential
   agents:  # Canonical: use 'agents' instead of 'roles'
     researcher:
       name: SearchAgent
       role: Research Assistant
       goal: Search for information from multiple sources
       instructions:  # Canonical: use 'instructions' instead of 'backstory' I am an AI assistant that can search YouTube and Wikipedia.
       tools:
         - youtube_search
         - wikipedia
       tasks:
         search_task:
           name: search_task
           description: Search for information about 'AI advancements' on both YouTube and Wikipedia
           expected_output: Combined information from YouTube videos and Wikipedia articles
   
4. Start Agents

   Type this in your terminal to run your agents:

   praisonai agents.yaml
   

Requirements

• Python 3.10 or higher
• OpenAI API key. Generate OpenAI API key here
• LangChain compatible tools and utilities

Understanding LangChain Integration

What is LangChain Integration?

LangChain integration enables agents to:

• Use LangChain’s extensive tool ecosystem
• Access various data sources and APIs
• Leverage pre-built utilities and wrappers
• Combine multiple tools in a single agent
• Extend agent capabilities with community tools

Features

Tool Integration

Seamlessly use LangChain tools with PraisonAI agents.

Multiple Sources

Access various data sources through LangChain utilities.

Community Tools

Leverage the extensive LangChain community ecosystem.

Custom Tools

Create and integrate custom LangChain tools.

LangChain Tools with Wrappers

• LangChain tools with wrappers can be directly used as Tools without modification.
• PraisonAI will automatically detect the tool and use it.
• Some exceptions apply where it doesn’t work.

Example Wrapper Usage

pip install langchain-community google-search-results
export SERPAPI_API_KEY=your_api_key_here
export OPENAI_API_KEY=your_api_key_here

from praisonaiagents import Agent, AgentTeam
from langchain_community.utilities import SerpAPIWrapper

data_agent = Agent(instructions="Search about AI job trends in 2025", tools=[SerpAPIWrapper])
editor_agent = Agent(instructions="Write a blog article")

agents = AgentTeam(agents=[data_agent, editor_agent])
agents.start()

Example without Wrapper Usage

pip install langchain-community 
export BRAVE_SEARCH_API=your_api_key_here
export OPENAI_API_KEY=your_api_key_here

from praisonaiagents import Agent, AgentTeam
from langchain_community.tools import BraveSearch
import os

def search_brave(query: str):
    """Searches using BraveSearch and returns results."""
    api_key = os.environ['BRAVE_SEARCH_API']
    tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": 3})
    return tool.run(query)

data_agent = Agent(instructions="Search about AI job trends in 2025", tools=[search_brave])
editor_agent = Agent(instructions="Write a blog article")
agents = AgentTeam(agents=[data_agent, editor_agent])
agents.start()

Next Steps

Memory Integration

Learn how to combine LangChain tools with agent memory

Custom Tools

Create your own custom tools for agents

For optimal results, ensure all required dependencies are installed and API keys are properly configured for each tool.

With More Detailed Instructions

Code

1. Install Package

   First, install the required packages:

   pip install praisonaiagents langchain-community wikipedia
   
2. Set API Key

   Set your OpenAI API key as an environment variable in your terminal:

   export OPENAI_API_KEY=your_api_key_here
   
3. Create a file

   Create a new file app.py with the basic setup:

   Single Tool

   from praisonaiagents import Agent, Task, AgentTeam
   from langchain_community.utilities import WikipediaAPIWrapper
   
   # Create an agent with Wikipedia tool
   agent = Agent(
       name="WikiAgent",
       role="Research Assistant",
       goal="Search Wikipedia for accurate information",
       backstory="I am an AI assistant specialized in Wikipedia research",
       tools=[WikipediaAPIWrapper],
       reflection=False
   )
   
   # Create a research task
   task = Task(
       name="wiki_search",
       description="Research 'Artificial Intelligence' on Wikipedia",
       expected_output="Comprehensive information from Wikipedia articles",
       agent=agent
   )
   
   # Create and start the workflow
   agents = AgentTeam(
       agents=[agent],
       tasks=[task]
   )
   
   agents.start()
   

   from praisonaiagents import Agent, Task, AgentTeam
   from langchain_community.tools import YouTubeSearchTool
   from langchain_community.utilities import WikipediaAPIWrapper
   
   # Create YouTube search agent
   agent = Agent(
       name="SearchAgent",
       role="Research Assistant",
       goal="Search for information from YouTube",
       backstory="I am an AI assistant that can search YouTube for relevant videos.",
       tools=[YouTubeSearchTool],
       reflection=False
   )
   
   # Create Wikipedia research agent
   agent2 = Agent(
       name="WikiAgent",
       role="Research Assistant",
       goal="Search for information from Wikipedia",
       backstory="I am an AI assistant that can search Wikipedia for accurate information.",
       tools=[WikipediaAPIWrapper],
       reflection=False
   )
   
   # Create YouTube search task
   task = Task(
       name="search_task",
       description="Search for information about 'AI advancements' on YouTube",
       expected_output="Relevant information from YouTube videos",
       agent=agent
   )
   
   # Create Wikipedia research task
   task2 = Task(
       name="wiki_task",
       description="Search for information about 'AI advancements' on Wikipedia",
       expected_output="Comprehensive information from Wikipedia articles",
       agent=agent2
   )
   
   # Create and start the workflow
   agents = AgentTeam(
       agents=[agent, agent2],
       tasks=[task, task2]
   )
   
   agents.start()
   
4. Start Agents

   Type this in your terminal to run your agents:

   python app.py
   

No Code

1. Install Package

   (Upcoming Feature) Install the PraisonAI package:

   pip install "praisonai"
   
2. Set API Key

   Set your OpenAI API key as an environment variable in your terminal:

   export OPENAI_API_KEY=your_api_key_here
   
3. Create a file

   Create a new file agents.yaml with the basic setup:

   framework: praisonai
   process: sequential
   agents:  # Canonical: use 'agents' instead of 'roles'
     researcher:
       name: SearchAgent
       role: Research Assistant
       goal: Search for information from multiple sources
       instructions:  # Canonical: use 'instructions' instead of 'backstory' I am an AI assistant that can search YouTube and Wikipedia.
       tools:
         - youtube_search
         - wikipedia
       tasks:
         search_task:
           name: search_task
           description: Search for information about 'AI advancements' on both YouTube and Wikipedia
           expected_output: Combined information from YouTube videos and Wikipedia articles
   
4. Start Agents

   Type this in your terminal to run your agents:

   praisonai agents.yaml
   

Best Practices

Prefer wrapper utilities over raw tools

Pass LangChain *Wrapper utilities (e.g. WikipediaAPIWrapper, SerpAPIWrapper) directly in the tools=[...] list. PraisonAI auto-detects callable tools and generates a schema from the signature, so wrappers work without extra glue code. Reach for a plain Python function only when a tool needs API-key setup that a wrapper does not expose.

Wrap non-standard tools in a function

Some LangChain tools require constructor arguments (like an API key) before they can run. Wrap those in a small typed function so the agent gets a clean schema:

from langchain_community.tools import BraveSearch
import os

def search_brave(query: str):
    """Search the web with BraveSearch."""
    tool = BraveSearch.from_api_key(api_key=os.environ["BRAVE_SEARCH_API"], search_kwargs={"count": 3})
    return tool.run(query)

Write clear docstrings and type hints

PraisonAI builds the tool schema from each function’s signature and docstring. Add a one-line docstring and annotate every parameter (query: str) so the agent knows when and how to call the tool.

Install only the tools you need

LangChain community tools pull in their own dependencies (wikipedia, youtube-search, google-search-results). Install just the extras a page uses and set the matching API keys before running, so agents fail fast on config rather than mid-run.

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Related

Custom Tools

Build your own agent tools

Tools Overview

Browse PraisonAI tool documentation