File: //opt/PraisonAI/docs/features/langchain.mdx
---
title: "LangChain Agents"
sidebarTitle: "LangChain Agents"
description: "Learn how to use LangChain tools and utilities with PraisonAI agents."
icon: "link-simple"
---
## Quick Start
<Tabs>
<Tab title="Code">
<Steps>
<Step title="Install Package">
First, install the required packages:
```bash
pip install praisonaiagents langchain-community wikipedia
```
</Step>
<Step title="Set API Key">
Set your OpenAI API key as an environment variable in your terminal:
```bash
export OPENAI_API_KEY=your_api_key_here
```
</Step>
<Step title="Create a file">
Create a new file `app.py` with the basic setup:
<CodeGroup>
```python Single Tool
from praisonaiagents import Agent, Task, PraisonAIAgents
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],
self_reflect=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 = PraisonAIAgents(
agents=[agent],
tasks=[task],
verbose=True
)
agents.start()
```
```python Multiple Tools
from praisonaiagents import Agent, Task, PraisonAIAgents
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],
self_reflect=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],
self_reflect=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 = PraisonAIAgents(
agents=[agent, agent2],
tasks=[task, task2],
verbose=True
)
agents.start()
```
</CodeGroup>
</Step>
<Step title="Start Agents">
Type this in your terminal to run your agents:
```bash
python app.py
```
</Step>
</Steps>
</Tab>
<Tab title="No Code">
<Steps>
<Step title="Install Package">
(Upcoming Feature)
Install the PraisonAI package:
```bash
pip install "praisonai"
```
</Step>
<Step title="Set API Key">
Set your OpenAI API key as an environment variable in your terminal:
```bash
export OPENAI_API_KEY=your_api_key_here
```
</Step>
<Step title="Create a file">
Create a new file `agents.yaml` with the basic setup:
```yaml
framework: praisonai
process: sequential
roles:
researcher:
name: SearchAgent
role: Research Assistant
goal: Search for information from multiple sources
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
```
</Step>
<Step title="Start Agents">
Type this in your terminal to run your agents:
```bash
praisonai agents.yaml
```
</Step>
</Steps>
</Tab>
</Tabs>
<Note>
**Requirements**
- Python 3.10 or higher
- OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys)
- LangChain compatible tools and utilities
</Note>
## Understanding LangChain Integration
<Card title="What is LangChain Integration?" icon="question">
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
</Card>
## Features
<CardGroup cols={2}>
<Card title="Tool Integration" icon="plug">
Seamlessly use LangChain tools with PraisonAI agents.
</Card>
<Card title="Multiple Sources" icon="database">
Access various data sources through LangChain utilities.
</Card>
<Card title="Community Tools" icon="users">
Leverage the extensive LangChain community ecosystem.
</Card>
<Card title="Custom Tools" icon="wrench">
Create and integrate custom LangChain tools.
</Card>
</CardGroup>
## Next Steps
<CardGroup cols={2}>
<Card title="Memory Integration" icon="brain" href="../concepts/memory">
Learn how to combine LangChain tools with agent memory
</Card>
<Card title="Custom Tools" icon="wrench" href="./tools">
Create your own custom tools for agents
</Card>
</CardGroup>
<Note>
For optimal results, ensure all required dependencies are installed and API keys are properly configured for each tool.
</Note>