AI Agents with Tools

Learn how to create AI agents that can use tools to interact with external systems and perform actions.

See also: Five-Layer Agent Stack — Tools are part of Layer 3 (the Harness).

Diagram

Feature	Knowledge	Tools
Purpose	Static reference information	Dynamic interaction capabilities
Access	Read-only reference	Execute actions and commands
Updates	Manual through files	Real-time through tool calls
Storage	Knowledge base	Assigned to specific agents
Persistence	Permanent until changed	Available during agent execution

Quick Start

Tools are functions that agents can use to interact with external systems and perform actions. They are essential for creating agents that can do more than just process text.

Creating Custom Tool

Step

Create any function that you want to use as a tool, that performs a specific task.

python

from duckduckgo_search import DDGS
from typing import List, Dict

# Tool Implementation
def internet_search_tool(query: str) -> List[Dict]:
    """
    Perform Internet Search using DuckDuckGo

    Args:
        query (str): The search query string

    Returns:
        List[Dict]: List of search results containing title, URL, and snippet
    """
    results = []
    ddgs = DDGS()
    for result in ddgs.text(keywords=query, max_results=5):
        results.append({
            "title": result.get("title", ""),
            "url": result.get("href", ""),
            "snippet": result.get("body", "")
        })
    return results

Step

Assign the tool to an agent

python

data_agent = Agent(
    name="DataCollector",
    role="Search Specialist",
    goal="Perform internet searches to collect relevant information.",
    backstory="Expert in finding and organising internet data.",
    tools=[internet_search_tool], ## Add the tool to the agent i.e the function name
)

That's it!

Implementing Tools Full Code Example

Code

Install PraisonAI
Install the core package:
Terminal
```
pip install praisonaiagents duckduckgo-search
```
Configure Environment
Terminal
```
export OPENAI_API_KEY=your_openai_key
```
Generate your OpenAI API key from OpenAI Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the Models for more information.

Create Agent with Tool

Create app.py

python

from praisonaiagents import Agent, Task, AgentTeam
from duckduckgo_search import DDGS
from typing import List, Dict

# 1. Tool Implementation
def internet_search_tool(query: str) -> List[Dict]:
    """
    Perform Internet Search using DuckDuckGo

    Args:
        query (str): The search query string

    Returns:
        List[Dict]: List of search results containing title, URL, and snippet
    """
    results = []
    ddgs = DDGS()
    for result in ddgs.text(keywords=query, max_results=5):
        results.append({
            "title": result.get("title", ""),
            "url": result.get("href", ""),
            "snippet": result.get("body", "")
        })
    return results

# 2. Assign the tool to an agent
data_agent = Agent(
    name="DataCollector",
    role="Search Specialist",
    goal="Perform internet searches to collect relevant information.",
    backstory="Expert in finding and organising internet data.",
    tools=[internet_search_tool],
    reflection=False
)

# 3. Task Definition
collect_task = Task(
    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
    expected_output="List of search results with titles, URLs, and snippets.",
    agent=data_agent,
    name="collect_data",
)

# 4. Start Agents
agents = AgentTeam(
    agents=[data_agent],
    tasks=[collect_task],
    process="sequential"
)

agents.start()

Start Agents
Execute your script:
Terminal
```
python app.py
```

No Code

Install PraisonAI
Install the core package and duckduckgo_search package:
Terminal
```
pip install praisonai duckduckgo_search
```

Create Custom Tool

Create a new file tools.py with the following content:

python

from duckduckgo_search import DDGS
from typing import List, Dict

# 1. Tool
def internet_search_tool(query: str) -> List[Dict]:
    """
    Perform Internet Search
    """
    results = []
    ddgs = DDGS()
    for result in ddgs.text(keywords=query, max_results=5):
        results.append({
            "title": result.get("title", ""),
            "url": result.get("href", ""),
            "snippet": result.get("body", "")
        })
    return results

Create Agent

Create a new file agents.yaml with the following content:

yaml

framework: praisonai
topic: create movie script about cat in mars
agents:  # Canonical: use 'agents' instead of 'roles'
  scriptwriter:
    instructions:  # Canonical: use 'instructions' instead of 'backstory' Expert in dialogue and script structure, translating concepts into
      scripts.
    goal: Write a movie script about a cat in Mars
    role: Scriptwriter
    tools:
      - internet_search_tool # <-- Tool assigned to Agent here
    tasks:
      scriptwriting_task:
        description: Turn the story concept into a production-ready movie script,
          including dialogue and scene details.
        expected_output: Final movie script with dialogue and scene details.

Start Agents
Execute your script:
Terminal
```
praisonai agents.yaml
```

MCP Tools (Model Context Protocol)

MCP allows agents to use external tools via standardized protocols. This is the recommended way to add powerful tools to your agents.

python

from praisonaiagents import Agent, MCP

# Use MCP tools with environment variables
agent = Agent(
    instructions="Search the web for information",
    tools=MCP(
        command="npx",
        args=["-y", "@anthropic/mcp-server-brave-search"],
        env={"BRAVE_API_KEY": "your-api-key"}
    )
)

agent.start("Search for AI trends in 2025")

MCP Overview

Introduction to Model Context Protocol

MCP Transports

stdio, HTTP, WebSocket, and SSE transports

Built-in Search Tools

PraisonAI includes built-in search tools that work with multiple providers:

python

from praisonaiagents import Agent

# Tavily Search (recommended)
agent = Agent(
    instructions="Search and analyze information",
    tools=["tavily"]  # Requires TAVILY_API_KEY
)

# You.com Search
agent = Agent(
    instructions="Search the web",
    tools=["you"]  # Requires YOU_API_KEY
)

# Exa Search
agent = Agent(
    instructions="Find relevant content",
    tools=["exa"]  # Requires EXA_API_KEY
)

Tavily

AI-optimized search with web search, news, and content extraction

You.com

Web search with AI-powered results

Exa

Neural search for finding similar content

Fast Context (Code Search)

Fast Context provides parallel, cached code search for AI agents:

python

from praisonaiagents import Agent
from praisonaiagents.context.fast import FastContext

# Create agent with context management
agent = Agent(
    instructions="You are a code assistant",
    context=True,  # Enable context management
)

# Use FastContext directly for code search
fc = FastContext(workspace_path="/path/to/codebase")
result = fc.search("find authentication handlers")
context = result.to_context_string() if result.files else None

Fast Context

Rapid parallel code search with caching and multi-language support

In-build Tools in PraisonAI

Search Tools

Tools for searching and retrieving information from various sources

Tavily Tools

AI-optimized web search, news, and content extraction

You.com Tools

Web search with AI-powered results

Exa Tools

Neural search for finding similar content

Python Tools

Essential Python utilities for data manipulation and scripting

Spider Tools

Web crawling and scraping capabilities for data extraction

Arxiv Tools

Access and search academic papers from arXiv repository

Newspaper Tools

Extract and parse content from news articles and websites

DuckDB Tools

Fast analytical SQL database operations and queries

DuckDuckGo Tools

Web search functionality using DuckDuckGo’s API

SearxNG Tools

Privacy-focused web search using local SearxNG instance

Calculator Tools

Perform mathematical calculations and conversions

YAML Tools

Parse and manipulate YAML format data

JSON Tools

Handle JSON data structures and operations

Pandas Tools

Data analysis and manipulation using Pandas

YFinance Tools

Fetch financial market data from Yahoo Finance

Shell Tools

Execute shell commands and system operations

AST-Grep Tools

AST-based structural code search and rewrite

Wikipedia Tools

Access and search Wikipedia articles and data

XML Tools

Process and manipulate XML format data

File Tools

File system operations and management utilities

Excel Tools

Work with Excel spreadsheets and workbooks

CSV Tools

Handle CSV file operations and transformations

Tools Overview

Search Tools

Tools for searching and retrieving information from various sources

File Tools

Tools for reading, writing, and manipulating files

API Tools

Tools for interacting with external APIs and services

Advanced Tool Features

Tool Configuration

python

def configured_tool(
    query: str,
    max_results: int = 5,
    timeout: int = 10
) -> List[Dict]:
    """
    Example of a configurable tool

    Args:
        query (str): Search query
        max_results (int): Maximum number of results
        timeout (int): Request timeout in seconds

    Returns:
        List[Dict]: Search results
    """
    # Tool implementation
    pass

Tool Chaining

python

def chain_tools(input_data: str) -> Dict:
    """
    Example of chaining multiple tools

    Args:
        input_data (str): Input data

    Returns:
        Dict: Processed results
    """
    # 1. Search for data
    search_results = internet_search_tool(input_data)

    # 2. Process results
    processed_data = process_tool(search_results)

    # 3. Format output
    return format_tool(processed_data)

Tool Categories

Data Collection Tools

Web scraping
API integration
Database queries

Processing Tools

Data transformation
Text analysis
Image processing

Output Tools

File generation
Report creation
Data visualization

Tool Integration

Adding Tools to Agents

python

# Multiple tools
agent = Agent(
    name="MultiTool Agent",
    tools=[
        internet_search_tool,
        file_processing_tool,
        api_integration_tool
    ]
)

Tool Dependencies

python

# Tool with dependencies
def advanced_tool(data: Dict) -> Dict:
    """
    Tool that depends on external libraries

    Args:
        data (Dict): Input data

    Returns:
        Dict: Processed data
    """
    try:
        import required_library
        # Tool implementation
        return processed_result
    except ImportError:
        raise Exception("Required library not installed")

Tool Guidelines

Best Practices

Type Hints
Use Python type hints
Define clear input/output types
Document complex types

Documentation
Write clear docstrings
Explain parameters
Provide usage examples

Error Handling
Handle exceptions gracefully
Return meaningful errors
Validate inputs

Tool Types

Search Tools
Web search
Database queries
Document search

File Tools
Read/write operations
File conversion
Data extraction

API Tools
REST API calls
GraphQL queries
Service integration

Best Practices Summary

Design Principles

Single Responsibility

Each tool should have one clear purpose and do it well. Avoid creating tools that try to do too many things.

python

# Good Example
def process_image(image: np.array) -> np.array:
    return processed_image

# Avoid
def process_and_save_and_upload(image):
    # Too many responsibilities
    pass

Clear Interfaces

Define explicit input/output types and maintain consistent parameter naming.

python

def search_tool(
    query: str,
    max_results: int = 10
) -> List[Dict[str, Any]]:
    """
    Search for information with clear parameters
    """
    pass

Documentation

Always include detailed docstrings and type hints.

python

def analyze_text(
    text: str,
    language: str = "en"
) -> Dict[str, float]:
    """
    Analyze text sentiment and emotions.

    Args:
        text: Input text to analyze
        language: ISO language code

    Returns:
        Dict with sentiment scores
    """
    pass

Performance Optimization

Efficient Processing

Optimize resource usage and processing time.

python

# Use generators for large datasets
def process_large_data():
    for chunk in data_generator():
        yield process_chunk(chunk)

Resource Management

Properly handle resource allocation and cleanup.

python

async with aiohttp.ClientSession() as session:
    # Resource automatically managed
    await process_data(session)

Caching

Implement caching for frequently accessed data.

python

@cache.memoize(timeout=300)
def expensive_operation(data: str) -> Dict:
    return process_expensive(data)

Async Operations

Use async/await for I/O-bound operations.

python

async def fetch_data(urls: List[str]):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_url(session, url) for url in urls]
        return await asyncio.gather(*tasks)

Security Best Practices

Input Validation

Always validate and sanitize inputs to prevent security vulnerabilities.

python

def process_user_input(data: str) -> str:
    if not isinstance(data, str):
        raise ValueError("Input must be string")
    return sanitize_input(data.strip())

Rate Limiting

Implement rate limiting for API calls to prevent abuse.

python

@rate_limit(calls=100, period=60)
async def api_call():
    return await make_request()

API Key Management

Securely handle API keys and credentials using environment variables.

python

# Use environment variables
api_key = os.getenv('API_KEY')
if not api_key:
    raise ConfigError("API key not found")

Error Masking

Hide sensitive information in error messages to prevent information leakage.

python

try:
    result = process_sensitive_data()
except Exception as e:
    # Log detailed error internally
    logger.error(f"Detailed error: {str(e)}")
    # Return sanitized error to user
    raise PublicError("Processing failed")

Forcing Tool Usage with tool_choice

Diagram

YAML

yaml

agents:
  researcher:
    role: Research Specialist
    tools:
      - search_web
    tool_choice: required  # Forces the agent to call a tool
    llm: gpt-4o-mini

Python

python

from praisonaiagents import Agent
from praisonaiagents.tools import search_web

# For direct Agent usage, tool_choice is passed via chat()
agent = Agent(
    name="researcher",
    tools=[search_web],
    llm="gpt-4o-mini"
)

# Force tool usage in a specific call
response = agent.chat("Search for AI news", tool_choice="required")

tool_choice string

Controls when the LLM calls tools: – auto – LLM decides whether to call tools (default) – required – LLM must call a tool before responding – none – LLM cannot call tools (text response only)

Tool Profiles

Tool profiles are composable, named sets of tools that eliminate duplication across agent configurations. The SDK ships with built-in profiles that auto-sync to all consumers.

Diagram

Built-in Profiles

Profile	Tools	Description
`file_ops`	`read_file`, `write_file`, `list_files`, `copy_file`, `move_file`, `delete_file`, `get_file_info`	File system operations
`shell`	`execute_command`, `list_processes`, `get_system_info`	Shell and system tools
`web`	`internet_search`, `search_web`, `web_crawl`	Web search and crawling
`memory`	`store_memory`, `search_memory`	Active memory store/search
`learning`	`store_learning`, `search_learning`	Categorized knowledge store/search
`schedule`	`schedule_add`, `schedule_list`, `schedule_remove`	Agent-centric scheduling
`code_intelligence`	`ast_grep_search`, `ast_grep_rewrite`, `ast_grep_scan`	Structural code search
`code_exec`	`execute_code`, `analyze_code`, `format_code`, `lint_code`	Code execution and analysis
`autonomy`	All of the above combined	Default for autonomous agents

Using Profiles

python

from praisonaiagents.tools.profiles import resolve_profiles

# Get tools from specific profiles
tool_names = resolve_profiles("file_ops", "web", "memory")

# Register custom profiles
from praisonaiagents.tools.profiles import register_profile, ToolProfile
register_profile(ToolProfile(
    name="my_tools",
    tools=["my_custom_tool"],
    description="My custom tool set"
))