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docs: 0.114.0 release notes, navigation restructure, new guides, deploy video, and cleanup (#2653)
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- Add v0.114.0 release notes with highlights image and doc links - Restructure docs navigation (Strategy group, Releases tab, navbar links) - Update quickstart with deployment video and clearer instructions - Add/rename guides (Custom Manager Agent, Custom LLM) - Remove legacy concept/tool docs - Add new images and tool docs - Minor formatting and content improvements throughout
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Tony Kipkemboi
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---
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title: Using LangChain Tools
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description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
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icon: link
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---
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## Using LangChain Tools
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<Info>
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CrewAI seamlessly integrates with LangChain's comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.
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</Info>
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```python Code
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import os
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from dotenv import load_dotenv
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from crewai import Agent, Task, Crew
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from crewai.tools import BaseTool
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from pydantic import Field
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from langchain_community.utilities import GoogleSerperAPIWrapper
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# Set up your SERPER_API_KEY key in an .env file, eg:
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# SERPER_API_KEY=<your api key>
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load_dotenv()
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search = GoogleSerperAPIWrapper()
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class SearchTool(BaseTool):
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name: str = "Search"
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description: str = "Useful for search-based queries. Use this to find current information about markets, companies, and trends."
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search: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper)
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def _run(self, query: str) -> str:
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"""Execute the search query and return results"""
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try:
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return self.search.run(query)
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except Exception as e:
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return f"Error performing search: {str(e)}"
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# Create Agents
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researcher = Agent(
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role='Research Analyst',
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goal='Gather current market data and trends',
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backstory="""You are an expert research analyst with years of experience in
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gathering market intelligence. You're known for your ability to find
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relevant and up-to-date market information and present it in a clear,
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actionable format.""",
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tools=[SearchTool()],
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verbose=True
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)
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# rest of the code ...
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```
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## Conclusion
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Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
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When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms,
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and the flexibility of tool arguments to optimize your agents' performance and capabilities.
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---
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title: Using LlamaIndex Tools
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description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
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icon: toolbox
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---
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## Using LlamaIndex Tools
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<Info>
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CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more.
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</Info>
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Here are the available built-in tools offered by LlamaIndex.
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```python Code
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from crewai import Agent
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from crewai_tools import LlamaIndexTool
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# Example 1: Initialize from FunctionTool
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from llama_index.core.tools import FunctionTool
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your_python_function = lambda ...: ...
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og_tool = FunctionTool.from_defaults(
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your_python_function,
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name="<name>",
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description='<description>'
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)
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tool = LlamaIndexTool.from_tool(og_tool)
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# Example 2: Initialize from LlamaHub Tools
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from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
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wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
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wolfram_tools = wolfram_spec.to_tool_list()
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tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
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# Example 3: Initialize Tool from a LlamaIndex Query Engine
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query_engine = index.as_query_engine()
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query_tool = LlamaIndexTool.from_query_engine(
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query_engine,
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name="Uber 2019 10K Query Tool",
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description="Use this tool to lookup the 2019 Uber 10K Annual Report"
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)
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# Create and assign the tools to an agent
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agent = Agent(
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role='Research Analyst',
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goal='Provide up-to-date market analysis',
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backstory='An expert analyst with a keen eye for market trends.',
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tools=[tool, *tools, query_tool]
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)
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# rest of the code ...
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```
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## Steps to Get Started
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To effectively use the LlamaIndexTool, follow these steps:
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<Steps>
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<Step title="Package Installation">
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Make sure that `crewai[tools]` package is installed in your Python environment:
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<CodeGroup>
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```shell Terminal
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pip install 'crewai[tools]'
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```
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</CodeGroup>
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</Step>
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<Step title="Install and Use LlamaIndex">
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Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
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</Step>
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</Steps>
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