LangChain Agent - PraisonAI

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
No Code

Install Package

First, install the required packages:

pip install praisonaiagents langchain-community wikipedia youtube-search

Set API Key

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

export OPENAI_API_KEY=your_api_key_here

Create a file

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

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()

Start Agents

Type this in your terminal to run your agents:

python app.py

Install Package

(Upcoming Feature) Install the PraisonAI package:

pip install "praisonai"

Set API Key

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

export OPENAI_API_KEY=your_api_key_here

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

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
No Code

Install Package

First, install the required packages:

pip install praisonaiagents langchain-community wikipedia

Set API Key

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

export OPENAI_API_KEY=your_api_key_here

Create a file

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

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()

Start Agents

Type this in your terminal to run your agents:

python app.py

Install Package

(Upcoming Feature) Install the PraisonAI package:

pip install "praisonai"

Set API Key

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

export OPENAI_API_KEY=your_api_key_here

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

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.

Custom Tools

Build your own agent tools

Tools Overview

Browse PraisonAI tool documentation

​Quick Start

​Understanding LangChain Integration

What is LangChain Integration?

​Features

Tool Integration

Multiple Sources

Community Tools

Custom Tools

​LangChain Tools with Wrappers

​Example Wrapper Usage

​Example without Wrapper Usage

​Next Steps

Memory Integration

Custom Tools

​With More Detailed Instructions

​Best Practices

​Related

Custom Tools

Tools Overview

Quick Start

Understanding LangChain Integration

Features

LangChain Tools with Wrappers

Example Wrapper Usage

Example without Wrapper Usage

Next Steps

With More Detailed Instructions

Best Practices

Related