Praison AI

Langchain Tools

Guide for integrating Langchain tools and utilities with PraisonAI, including direct tool integration and wrapper implementations

python
from praisonaiagents import Agent
from langchain_community.tools import WikipediaQueryRun

agent = Agent(
    name="LangChain Wrapper",
    instructions="Use wrapped LangChain tools when helpful.",
    tools=[WikipediaQueryRun()],
)
agent.start("Summarise the Wikipedia article on transformer models")

The user asks a research question; the agent calls a LangChain tool wrapped for PraisonAI.

Diagram

Input

Agent

Tool

Output

Diagram
Langchain ToolsAgentUserLangchain ToolsAgentUserRequestProcess requestResultResponse

Langchain Tools

Quick Start

  1. Install
    bash
    pip install praisonaiagents langchain langchain-community
  2. Wrap a LangChain tool
    python
    from praisonaiagents import Agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper

wiki_tool = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())

def wikipedia_search(query: str) -> str:
    return wiki_tool.run(query)

agent = Agent(
    name="WikiAgent",
    instructions="Search Wikipedia for information.",
    tools=[wikipedia_search],
)

agent.start("What is quantum computing?")

Integrate Langchain Direct Tools

bash
pip install youtube_search praisonai langchain_community langchain
python
# tools.py
from langchain_community.tools import YouTubeSearchTool
yaml
# agents.yaml
framework: crewai
topic: research about the causes of lung disease
agents:  # Canonical: use 'agents' instead of 'roles'
  research_analyst:
    instructions:  # Canonical: use 'instructions' instead of 'backstory' Experienced in analyzing scientific data related to respiratory health.
    goal: Analyze data on lung diseases
    role: Research Analyst
    tasks:
      data_analysis:
        description: Gather and analyze data on the causes and risk factors of lung
          diseases.
        expected_output: Report detailing key findings on lung disease causes.
    tools:
    - 'YouTubeSearchTool'

Integrate Langchain with Wrappers

bash
pip install wikipedia langchain_community
python
# tools.py
from langchain_community.utilities import WikipediaAPIWrapper
class WikipediaSearchTool(BaseTool):
    name: str = "WikipediaSearchTool"
    description: str = "Search Wikipedia for relevant information based on a query."

    def _run(self, query: str):
        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
        results = api_wrapper.load(query=query)
        return results
yaml
# agents.yaml
framework: crewai
topic: research about nvidia growth
agents:  # Canonical: use 'agents' instead of 'roles'
  data_collector:
    instructions:  # Canonical: use 'instructions' instead of 'backstory' An experienced researcher with the ability to efficiently collect and
      organize vast amounts of data.
    goal: Gather information on Nvidia's growth by providing the Ticket Symbol to YahooFinanceNewsTool
    role: Data Collector
    tasks:
      data_collection_task:
        description: Collect data on Nvidia's growth from various sources such as
          financial reports, news articles, and company announcements.
        expected_output: A comprehensive document detailing data points on Nvidia's
          growth over the years.
    tools:
    - 'WikipediaSearchTool'

Best Practices

Wrap tools as plain functions

Convert LangChain tools to plain Python functions with tool.run() for cleaner integration.

Use LangChain for specialized tools only

Prefer PraisonAI native tools for search and memory – use LangChain only when there is no native equivalent.

Handle LangChain exceptions

LangChain tools can raise ToolException – wrap calls in try/except and return error strings.

Prefer simple function signatures

Tools with str-to-str signatures integrate most reliably with PraisonAI agents.

Related

Custom Tools

Build your own agent tools

 Tools Overview

Browse PraisonAI tool documentation