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What is Knowledge?

Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks. Think of it as giving your agents a reference library they can consult while working.

Key benefits of using Knowledge:

  • Enhance agents with domain-specific information
  • Support decisions with real-world data
  • Maintain context across conversations
  • Ground responses in factual information

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Supported Knowledge Sources

CrewAI supports various types of knowledge sources out of the box:

Text Sources

  • Raw strings
  • Text files (.txt)
  • PDF documents

Structured Data

  • CSV files
  • Excel spreadsheets
  • JSON documents

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Supported Knowledge Parameters

ParameterTypeRequiredDescription
sourcesList[BaseKnowledgeSource]YesList of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.
collection_namestrNoName of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to “knowledge” if not provided.
storageOptional[KnowledgeStorage]NoCustom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.

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Quickstart Example

For file-Based Knowledge Sources, make sure to place your files in a knowledge directory at the root of your project. Also, use relative paths from the knowledge directory when creating the source.

Here’s an example using string-based knowledge:

Code
from crewai import Agent, Task, Crew, Process, LLM
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource

# Create a knowledge source
content = "Users name is John. He is 30 years old and lives in San Francisco."
string_source = StringKnowledgeSource(
    content=content,
)

# Create an LLM with a temperature of 0 to ensure deterministic outputs
llm = LLM(model="gpt-4o-mini", temperature=0)

# Create an agent with the knowledge store
agent = Agent(
    role="About User",
    goal="You know everything about the user.",
    backstory="""You are a master at understanding people and their preferences.""",
    verbose=True,
    allow_delegation=False,
    llm=llm,
)
task = Task(
    description="Answer the following questions about the user: {question}",
    expected_output="An answer to the question.",
    agent=agent,
)

crew = Crew(
    agents=[agent],
    tasks=[task],
    verbose=True,
    process=Process.sequential,
    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
)

result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})

Here’s another example with the CrewDoclingSource. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including MD, PDF, DOCX, HTML, and more.

You need to install docling for the following example to work: uv add docling

Code
from crewai import LLM, Agent, Crew, Process, Task
from crewai.knowledge.source.crew_docling_source import CrewDoclingSource

# Create a knowledge source
content_source = CrewDoclingSource(
    file_paths=[
        "https://lilianweng.github.io/posts/2024-11-28-reward-hacking",
        "https://lilianweng.github.io/posts/2024-07-07-hallucination",
    ],
)

# Create an LLM with a temperature of 0 to ensure deterministic outputs
llm = LLM(model="gpt-4o-mini", temperature=0)

# Create an agent with the knowledge store
agent = Agent(
    role="About papers",
    goal="You know everything about the papers.",
    backstory="""You are a master at understanding papers and their content.""",
    verbose=True,
    allow_delegation=False,
    llm=llm,
)
task = Task(
    description="Answer the following questions about the papers: {question}",
    expected_output="An answer to the question.",
    agent=agent,
)

crew = Crew(
    agents=[agent],
    tasks=[task],
    verbose=True,
    process=Process.sequential,
    knowledge_sources=[
        content_source
    ],  # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
)

result = crew.kickoff(
    inputs={
        "question": "What is the reward hacking paper about? Be sure to provide sources."
    }
)

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More Examples

Here are examples of how to use different types of knowledge sources:

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Text File Knowledge Source

from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource

# Create a text file knowledge source
text_source = TextFileKnowledgeSource(
    file_paths=["document.txt", "another.txt"]
)

# Create crew with text file source on agents or crew level
agent = Agent(
    ...
    knowledge_sources=[text_source]
)

crew = Crew(
    ...
    knowledge_sources=[text_source]
)

​
PDF Knowledge Source

from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource

# Create a PDF knowledge source
pdf_source = PDFKnowledgeSource(
    file_paths=["document.pdf", "another.pdf"]
)

# Create crew with PDF knowledge source on agents or crew level
agent = Agent(
    ...
    knowledge_sources=[pdf_source]
)

crew = Crew(
    ...
    knowledge_sources=[pdf_source]
)

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CSV Knowledge Source

from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource

# Create a CSV knowledge source
csv_source = CSVKnowledgeSource(
    file_paths=["data.csv"]
)

# Create crew with CSV knowledge source or on agent level
agent = Agent(
    ...
    knowledge_sources=[csv_source]
)

crew = Crew(
    ...
    knowledge_sources=[csv_source]
)

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Excel Knowledge Source

from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource

# Create an Excel knowledge source
excel_source = ExcelKnowledgeSource(
    file_paths=["spreadsheet.xlsx"]
)

# Create crew with Excel knowledge source on agents or crew level
agent = Agent(
    ...
    knowledge_sources=[excel_source]
)

crew = Crew(
    ...
    knowledge_sources=[excel_source]
)

​
JSON Knowledge Source

from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource

# Create a JSON knowledge source
json_source = JSONKnowledgeSource(
    file_paths=["data.json"]
)

# Create crew with JSON knowledge source on agents or crew level
agent = Agent(
    ...
    knowledge_sources=[json_source]
)

crew = Crew(
    ...
    knowledge_sources=[json_source]
)

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Knowledge Configuration

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Chunking Configuration

Knowledge sources automatically chunk content for better processing. You can configure chunking behavior in your knowledge sources:

from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource

source = StringKnowledgeSource(
    content="Your content here",
    chunk_size=4000,      # Maximum size of each chunk (default: 4000)
    chunk_overlap=200     # Overlap between chunks (default: 200)
)

The chunking configuration helps in:

  • Breaking down large documents into manageable pieces
  • Maintaining context through chunk overlap
  • Optimizing retrieval accuracy

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Embeddings Configuration

You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents. The embedder parameter supports various embedding model providers that include:

  • openai: OpenAI’s embedding models
  • google: Google’s text embedding models
  • azure: Azure OpenAI embeddings
  • ollama: Local embeddings with Ollama
  • vertexai: Google Cloud VertexAI embeddings
  • cohere: Cohere’s embedding models
  • voyageai: VoyageAI’s embedding models
  • bedrock: AWS Bedrock embeddings
  • huggingface: Hugging Face models
  • watson: IBM Watson embeddings

Here’s an example of how to configure the embedder for the knowledge store using Google’s text-embedding-004 model:

from crewai import Agent, Task, Crew, Process, LLM
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
import os

# Get the GEMINI API key
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")

# Create a knowledge source
content = "Users name is John. He is 30 years old and lives in San Francisco."
string_source = StringKnowledgeSource(
    content=content,
)

# Create an LLM with a temperature of 0 to ensure deterministic outputs
gemini_llm = LLM(
    model="gemini/gemini-1.5-pro-002",
    api_key=GEMINI_API_KEY,
    temperature=0,
)

# Create an agent with the knowledge store
agent = Agent(
    role="About User",
    goal="You know everything about the user.",
    backstory="""You are a master at understanding people and their preferences.""",
    verbose=True,
    allow_delegation=False,
    llm=gemini_llm,
    embedder={
        "provider": "google",
        "config": {
            "model": "models/text-embedding-004",
            "api_key": GEMINI_API_KEY,
        }
    }
)

task = Task(
    description="Answer the following questions about the user: {question}",
    expected_output="An answer to the question.",
    agent=agent,
)

crew = Crew(
    agents=[agent],
    tasks=[task],
    verbose=True,
    process=Process.sequential,
    knowledge_sources=[string_source],
    embedder={
        "provider": "google",
        "config": {
            "model": "models/text-embedding-004",
            "api_key": GEMINI_API_KEY,
        }
    }
)

result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})

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Clearing Knowledge

If you need to clear the knowledge stored in CrewAI, you can use the crewai reset-memories command with the --knowledge option.

Command
crewai reset-memories --knowledge

This is useful when you’ve updated your knowledge sources and want to ensure that the agents are using the most recent information.

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Agent-Specific Knowledge

While knowledge can be provided at the crew level using crew.knowledge_sources, individual agents can also have their own knowledge sources using the knowledge_sources parameter:

Code
from crewai import Agent, Task, Crew
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource

# Create agent-specific knowledge about a product
product_specs = StringKnowledgeSource(
    content="""The XPS 13 laptop features:
    - 13.4-inch 4K display
    - Intel Core i7 processor
    - 16GB RAM
    - 512GB SSD storage
    - 12-hour battery life""",
    metadata={"category": "product_specs"}
)

# Create a support agent with product knowledge
support_agent = Agent(
    role="Technical Support Specialist",
    goal="Provide accurate product information and support.",
    backstory="You are an expert on our laptop products and specifications.",
    knowledge_sources=[product_specs]  # Agent-specific knowledge
)

# Create a task that requires product knowledge
support_task = Task(
    description="Answer this customer question: {question}",
    agent=support_agent
)

# Create and run the crew
crew = Crew(
    agents=[support_agent],
    tasks=[support_task]
)

# Get answer about the laptop's specifications
result = crew.kickoff(
    inputs={"question": "What is the storage capacity of the XPS 13?"}
)

Benefits of agent-specific knowledge:

  • Give agents specialized information for their roles
  • Maintain separation of concerns between agents
  • Combine with crew-level knowledge for layered information access

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Custom Knowledge Sources

CrewAI allows you to create custom knowledge sources for any type of data by extending the BaseKnowledgeSource class. Let’s create a practical example that fetches and processes space news articles.

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Space News Knowledge Source Example

from crewai import Agent, Task, Crew, Process, LLM
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
import requests
from datetime import datetime
from typing import Dict, Any
from pydantic import BaseModel, Field

class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
    """Knowledge source that fetches data from Space News API."""

    api_endpoint: str = Field(description="API endpoint URL")
    limit: int = Field(default=10, description="Number of articles to fetch")

    def load_content(self) -> Dict[Any, str]:
        """Fetch and format space news articles."""
        try:
            response = requests.get(
                f"{self.api_endpoint}?limit={self.limit}"
            )
            response.raise_for_status()

            data = response.json()
            articles = data.get('results', [])

            formatted_data = self._format_articles(articles)
            return {self.api_endpoint: formatted_data}
        except Exception as e:
            raise ValueError(f"Failed to fetch space news: {str(e)}")

    def _format_articles(self, articles: list) -> str:
        """Format articles into readable text."""
        formatted = "Space News Articles:\n\n"
        for article in articles:
            formatted += f"""
                Title: {article['title']}
                Published: {article['published_at']}
                Summary: {article['summary']}
                News Site: {article['news_site']}
                URL: {article['url']}
                -------------------"""
        return formatted

    def add(self) -> None:
        """Process and store the articles."""
        content = self.load_content()
        for _, text in content.items():
            chunks = self._chunk_text(text)
            self.chunks.extend(chunks)

        self._save_documents()

# Create knowledge source
recent_news = SpaceNewsKnowledgeSource(
    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
    limit=10,
)

# Create specialized agent
space_analyst = Agent(
    role="Space News Analyst",
    goal="Answer questions about space news accurately and comprehensively",
    backstory="""You are a space industry analyst with expertise in space exploration,
    satellite technology, and space industry trends. You excel at answering questions
    about space news and providing detailed, accurate information.""",
    knowledge_sources=[recent_news],
    llm=LLM(model="gpt-4", temperature=0.0)
)

# Create task that handles user questions
analysis_task = Task(
    description="Answer this question about space news: {user_question}",
    expected_output="A detailed answer based on the recent space news articles",
    agent=space_analyst
)

# Create and run the crew
crew = Crew(
    agents=[space_analyst],
    tasks=[analysis_task],
    verbose=True,
    process=Process.sequential
)

# Example usage
result = crew.kickoff(
    inputs={"user_question": "What are the latest developments in space exploration?"}
)

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Key Components Explained

  1. Custom Knowledge Source (SpaceNewsKnowledgeSource):

    • Extends BaseKnowledgeSource for integration with CrewAI
    • Configurable API endpoint and article limit
    • Implements three key methods:
      • load_content(): Fetches articles from the API
      • _format_articles(): Structures the articles into readable text
      • add(): Processes and stores the content

  2. Agent Configuration:

    • Specialized role as a Space News Analyst
    • Uses the knowledge source to access space news

  3. Task Setup:

    • Takes a user question as input through {user_question}
    • Designed to provide detailed answers based on the knowledge source

  4. Crew Orchestration:

    • Manages the workflow between agent and task
    • Handles input/output through the kickoff method

This example demonstrates how to:

  • Create a custom knowledge source that fetches real-time data
  • Process and format external data for AI consumption
  • Use the knowledge source to answer specific user questions
  • Integrate everything seamlessly with CrewAI’s agent system

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About the Spaceflight News API

The example uses the Spaceflight News API, which:

  • Provides free access to space-related news articles
  • Requires no authentication
  • Returns structured data about space news
  • Supports pagination and filtering

You can customize the API query by modifying the endpoint URL:

# Fetch more articles
recent_news = SpaceNewsKnowledgeSource(
    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
    limit=20,  # Increase the number of articles
)

# Add search parameters
recent_news = SpaceNewsKnowledgeSource(
    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news
    limit=10,
)

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Best Practices

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