---
title: Knowledge
description: What is knowledge in CrewAI and how to use it.
icon: book
---

## 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.

<Info>
  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
</Info>

## Supported Knowledge Sources

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

<CardGroup cols={2}>
  <Card title="Text Sources" icon="text">
    - Raw strings
    - Text files (.txt)
    - PDF documents
  </Card>
  <Card title="Structured Data" icon="table">
    - CSV files
    - Excel spreadsheets
    - JSON documents
  </Card>
</CardGroup>

## Supported Knowledge Parameters

| Parameter                    | Type                                | Required | Description                                                                                                                                           |
| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |

## Quickstart Example

<Tip>
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.
</Tip>

Here's an example using string-based knowledge:

```python 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. 

<Note>
  You need to install `docling` for the following example to work: `uv add docling`
</Note>



```python 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."
    }
)
```

## More Examples

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

Note: Please ensure that you create the ./knowldge folder. All source files (e.g., .txt, .pdf, .xlsx, .json) should be placed in this folder for centralized management.

### Text File Knowledge Source
```python
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
```python
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]
)
```

### CSV Knowledge Source
```python
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]
)
```

### Excel Knowledge Source
```python
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
```python
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]
)
```

## Knowledge Configuration

### Chunking Configuration

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

```python
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

### 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:
<CodeGroup>
```python Example
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?"})
```
```text Output
# Agent: About User
## Task: Answer the following questions about the user: What city does John live in and how old is he?

# Agent: About User
## Final Answer: 
John is 30 years old and lives in San Francisco.
```
</CodeGroup>
## Clearing Knowledge

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

```bash 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.

## 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:

```python 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?"}
)
```

<Info>
  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
</Info>

## 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.

#### Space News Knowledge Source Example

<CodeGroup>

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

    def validate_content(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?"}
)
```

```output Output
# Agent: Space News Analyst
## Task: Answer this question about space news: What are the latest developments in space exploration?


# Agent: Space News Analyst
## Final Answer:
The latest developments in space exploration, based on recent space news articles, include the following:

1. SpaceX has received the final regulatory approvals to proceed with the second integrated Starship/Super Heavy launch, scheduled for as soon as the morning of Nov. 17, 2023. This is a significant step in SpaceX's ambitious plans for space exploration and colonization. [Source: SpaceNews](https://spacenews.com/starship-cleared-for-nov-17-launch/)

2. SpaceX has also informed the US Federal Communications Commission (FCC) that it plans to begin launching its first next-generation Starlink Gen2 satellites. This represents a major upgrade to the Starlink satellite internet service, which aims to provide high-speed internet access worldwide. [Source: Teslarati](https://www.teslarati.com/spacex-first-starlink-gen2-satellite-launch-2022/)

3. AI startup Synthetaic has raised $15 million in Series B funding. The company uses artificial intelligence to analyze data from space and air sensors, which could have significant applications in space exploration and satellite technology. [Source: SpaceNews](https://spacenews.com/ai-startup-synthetaic-raises-15-million-in-series-b-funding/)

4. The Space Force has formally established a unit within the U.S. Indo-Pacific Command, marking a permanent presence in the Indo-Pacific region. This could have significant implications for space security and geopolitics. [Source: SpaceNews](https://spacenews.com/space-force-establishes-permanent-presence-in-indo-pacific-region/)

5. Slingshot Aerospace, a space tracking and data analytics company, is expanding its network of ground-based optical telescopes to increase coverage of low Earth orbit. This could improve our ability to track and analyze objects in low Earth orbit, including satellites and space debris. [Source: SpaceNews](https://spacenews.com/slingshots-space-tracking-network-to-extend-coverage-of-low-earth-orbit/)

6. The National Natural Science Foundation of China has outlined a five-year project for researchers to study the assembly of ultra-large spacecraft. This could lead to significant advancements in spacecraft technology and space exploration capabilities. [Source: SpaceNews](https://spacenews.com/china-researching-challenges-of-kilometer-scale-ultra-large-spacecraft/)

7. The Center for AEroSpace Autonomy Research (CAESAR) at Stanford University is focusing on spacecraft autonomy. The center held a kickoff event on May 22, 2024, to highlight the industry, academia, and government collaboration it seeks to foster. This could lead to significant advancements in autonomous spacecraft technology. [Source: SpaceNews](https://spacenews.com/stanford-center-focuses-on-spacecraft-autonomy/)
```

</CodeGroup>
#### 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

#### About the Spaceflight News API

The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/docs/), 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:

```python
# 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,
)
```

## Best Practices

<AccordionGroup>
  <Accordion title="Content Organization">
    - Keep chunk sizes appropriate for your content type
    - Consider content overlap for context preservation
    - Organize related information into separate knowledge sources
  </Accordion>

  <Accordion title="Performance Tips">
    - Adjust chunk sizes based on content complexity 
    - Configure appropriate embedding models
    - Consider using local embedding providers for faster processing
  </Accordion>
</AccordionGroup>
