Agent Features

Enhance agents with features like memory, local tools, contexts, RAI guardrails, file output, image generation, and evaluation capabilities.

Quick Start

from lyzr import Studio

studio = Studio(api_key="your-api-key")

agent = studio.create_agent(
    name="Assistant",
    provider="gpt-4o",
    role="Helpful assistant",
    goal="Help users",
    instructions="Be helpful and accurate"
)

# Add features
agent = agent.add_memory(30)
agent.add_tool(my_function)

Memory

Enable conversation memory to maintain context across messages.

Add Memory

agent.add_memory(max_messages: int = 10) -> Agent

Parameter	Type	Default	Description
`max_messages`	int	10	Messages to remember (1-50)

# Add memory for 30 messages
agent = agent.add_memory(30)

# Conversation maintains context
agent.run("My name is John", session_id="session_1")
agent.run("What's my name?", session_id="session_1")
# Agent remembers: "Your name is John"

Check Memory

agent.has_memory() -> bool

if agent.has_memory():
    print("Memory is enabled")

Get Memory Config

agent.get_memory_config() -> dict | None

config = agent.get_memory_config()
if config:
    print(f"Max messages: {config['max_messages_context_count']}")

Remove Memory

agent.remove_memory() -> Agent

agent = agent.remove_memory()

Local Tools

Add Python functions as tools that the agent can execute.

Add Tool

agent.add_tool(tool) -> Agent

Parameter	Type	Description
`tool`	callable or Tool	Python function or Tool object

Function-Based Tool

def get_weather(city: str) -> str:
    """Get current weather for a city"""
    # Your implementation
    return f"Weather in {city}: 72°F, Sunny"

def search_database(query: str, limit: int = 10) -> str:
    """Search the database for records"""
    # Your implementation
    return f"Found {limit} results for '{query}'"

# Add tools to agent
agent.add_tool(get_weather)
agent.add_tool(search_database)

# Agent can now call these functions
response = agent.run("What's the weather in New York?")

Tool Object

from lyzr import Tool

tool = Tool(
    name="calculate",
    description="Perform mathematical calculations",
    parameters={
        "type": "object",
        "properties": {
            "expression": {
                "type": "string",
                "description": "Math expression to evaluate"
            }
        },
        "required": ["expression"]
    },
    function=lambda expression: str(eval(expression))
)

agent.add_tool(tool)

Multiple Tools

def read_file(path: str) -> str:
    """Read contents of a file"""
    with open(path) as f:
        return f.read()

def write_file(path: str, content: str) -> str:
    """Write content to a file"""
    with open(path, "w") as f:
        f.write(content)
    return f"Written to {path}"

def list_files(directory: str) -> str:
    """List files in a directory"""
    import os
    return str(os.listdir(directory))

# Add all tools
agent.add_tool(read_file)
agent.add_tool(write_file)
agent.add_tool(list_files)

# Agent orchestrates tool usage
response = agent.run("Read config.json and summarize it")

Contexts

Add background information as key-value pairs.

Add Context

agent.add_context(context: Context) -> Agent

# Create context
company_info = studio.create_context(
    name="company",
    value="Acme Corp - Founded 2020, 50k customers, SaaS platform"
)

# Add to agent
agent = agent.add_context(company_info)

List Contexts

agent.list_contexts() -> List[dict]

contexts = agent.list_contexts()
for ctx in contexts:
    print(f"Context: {ctx}")

Remove Context

agent.remove_context(context: Context) -> Agent

agent = agent.remove_context(company_info)

Multiple Contexts

company = studio.create_context(
    name="company",
    value="Acme Corp - SaaS platform"
)

pricing = studio.create_context(
    name="pricing",
    value="Basic: $10/mo, Pro: $50/mo, Enterprise: Custom"
)

support_hours = studio.create_context(
    name="support_hours",
    value="24/7 for Pro and Enterprise, 9-5 PST for Basic"
)

# Add all contexts
agent = agent.add_context(company)
agent = agent.add_context(pricing)
agent = agent.add_context(support_hours)

RAI Guardrails

Add Responsible AI policies for content safety.

Add RAI Policy

agent.add_rai_policy(policy: RAIPolicy) -> Agent

from lyzr import PIIType, PIIAction, SecretsAction

# Create policy
policy = studio.create_rai_policy(
    name="SafePolicy",
    description="Content safety guardrails",
    toxicity_threshold=0.3,
    secrets_detection=SecretsAction.MASK,
    pii_detection={
        PIIType.CREDIT_CARD: PIIAction.BLOCK,
        PIIType.EMAIL: PIIAction.REDACT,
        PIIType.SSN: PIIAction.BLOCK
    }
)

# Add to agent
agent = agent.add_rai_policy(policy)

Check RAI Policy

agent.has_rai_policy() -> bool

if agent.has_rai_policy():
    print("RAI guardrails enabled")

Remove RAI Policy

agent.remove_rai_policy() -> Agent

agent = agent.remove_rai_policy()

File Output

Enable agents to generate files (PDF, DOCX, etc.).

Enable File Output

agent.enable_file_output() -> Agent

agent = agent.enable_file_output()

response = agent.run("Create a PDF report about Q1 sales")

if response.has_files():
    for artifact in response.files:
        print(f"Generated: {artifact.name}")
        artifact.download(f"./downloads/{artifact.name}")

Check File Output

agent.has_file_output() -> bool

Disable File Output

agent.disable_file_output() -> Agent

Image Generation

Enable agents to generate images.

Set Image Model

agent.set_image_model(image_model: ImageModelConfig) -> Agent

from lyzr.image_models import DallE, Gemini

# Use DALL-E 3
agent = agent.set_image_model(DallE.DALL_E_3)

# Or use Gemini
agent = agent.set_image_model(Gemini.PRO)

Available Models

Class	Models
`DallE`	`DALL_E_3`, `DALL_E_2`, `GPT_IMAGE_1`, `GPT_IMAGE_1_5`
`Gemini`	`PRO`, `FLASH`

Generate Images

from lyzr.image_models import DallE

agent = agent.set_image_model(DallE.DALL_E_3)

response = agent.run("Create an image of a sunset over mountains")

if response.has_files():
    for artifact in response.files:
        artifact.download(f"./images/{artifact.name}")

Check Image Output

agent.has_image_output() -> bool

Disable Image Output

agent.disable_image_output() -> Agent

Evaluation Features

Enable self-reflection, bias checking, and LLM judging.

Reflection

Self-reflection helps reduce hallucinations by having the agent review its responses.

agent.enable_reflection() -> Agent
agent.disable_reflection() -> Agent
agent.has_reflection() -> bool

# Enable reflection
agent = agent.enable_reflection()

# Agent will self-reflect before responding
response = agent.run("Explain quantum computing")

Bias Check

Check responses for potential bias.

agent.enable_bias_check() -> Agent
agent.disable_bias_check() -> Agent
agent.has_bias_check() -> bool

agent = agent.enable_bias_check()

LLM Judge

Use a third-party LLM to evaluate responses.

agent.enable_llm_judge() -> Agent
agent.disable_llm_judge() -> Agent
agent.has_llm_judge() -> bool

agent = agent.enable_llm_judge()

Groundedness

Validate responses against known facts.

agent.add_groundedness_facts(facts: List[str]) -> Agent
agent.remove_groundedness() -> Agent
agent.has_groundedness() -> bool

agent = agent.add_groundedness_facts([
    "Company was founded in 2020",
    "Headquarters is in San Francisco",
    "CEO is John Smith",
    "Annual revenue is $50M"
])

# Agent validates responses against these facts
response = agent.run("When was the company founded?")

Combined Example

from lyzr import Studio, PIIType, PIIAction, SecretsAction
from lyzr.image_models import DallE

studio = Studio(api_key="your-api-key")

# Create base agent
agent = studio.create_agent(
    name="Full-Featured Bot",
    provider="gpt-4o",
    role="Enterprise assistant",
    goal="Help with various tasks securely",
    instructions="Be helpful while maintaining security"
)

# Add memory
agent = agent.add_memory(50)

# Add tools
def search_docs(query: str) -> str:
    """Search internal documentation"""
    return f"Results for: {query}"

agent.add_tool(search_docs)

# Add context
company_ctx = studio.create_context(
    name="company",
    value="Acme Corp - Enterprise software"
)
agent = agent.add_context(company_ctx)

# Add RAI policy
policy = studio.create_rai_policy(
    name="EnterprisePolicy",
    description="Enterprise security",
    toxicity_threshold=0.2,
    secrets_detection=SecretsAction.BLOCK,
    pii_detection={
        PIIType.CREDIT_CARD: PIIAction.BLOCK,
        PIIType.SSN: PIIAction.BLOCK
    }
)
agent = agent.add_rai_policy(policy)

# Enable file and image output
agent = agent.enable_file_output()
agent = agent.set_image_model(DallE.DALL_E_3)

# Enable evaluation features
agent = agent.enable_reflection()
agent = agent.enable_bias_check()

# Add groundedness facts
agent = agent.add_groundedness_facts([
    "Product launched in 2020",
    "Over 1000 enterprise customers"
])

# Verify features
print(f"Memory: {agent.has_memory()}")
print(f"RAI: {agent.has_rai_policy()}")
print(f"File output: {agent.has_file_output()}")
print(f"Image output: {agent.has_image_output()}")
print(f"Reflection: {agent.has_reflection()}")
print(f"Bias check: {agent.has_bias_check()}")
print(f"Groundedness: {agent.has_groundedness()}")

# Use the fully-featured agent
response = agent.run("Create a product overview document")

Introduction

Key Concepts

Agent Features

Quick Start

Memory

Add Memory

Check Memory

Get Memory Config

Remove Memory

Local Tools

Add Tool

Function-Based Tool

Tool Object

Multiple Tools

Contexts

Add Context

List Contexts

Remove Context

Multiple Contexts

RAI Guardrails

Add RAI Policy

Check RAI Policy

Remove RAI Policy

File Output

Enable File Output

Check File Output

Disable File Output

Image Generation

Set Image Model

Available Models

Generate Images

Check Image Output

Disable Image Output

Evaluation Features

Reflection

Bias Check

LLM Judge

Groundedness

Combined Example

​Quick Start

​Memory

​Add Memory

​Check Memory

​Get Memory Config

​Remove Memory

​Local Tools

​Add Tool

​Function-Based Tool

​Tool Object

​Multiple Tools

​Contexts

​Add Context

​List Contexts

​Remove Context

​Multiple Contexts

​RAI Guardrails

​Add RAI Policy

​Check RAI Policy

​Remove RAI Policy

​File Output

​Enable File Output

​Check File Output

​Disable File Output

​Image Generation

​Set Image Model

​Available Models

​Generate Images

​Check Image Output

​Disable Image Output

​Evaluation Features

​Reflection

​Bias Check

​LLM Judge

​Groundedness

​Combined Example

Quick Start

Memory

Add Memory

Check Memory

Get Memory Config

Remove Memory

Local Tools

Add Tool

Function-Based Tool

Tool Object

Multiple Tools

Contexts

Add Context

List Contexts

Remove Context

Multiple Contexts

RAI Guardrails

Add RAI Policy

Check RAI Policy

Remove RAI Policy

File Output

Enable File Output

Check File Output

Disable File Output

Image Generation

Set Image Model

Available Models

Generate Images

Check Image Output

Disable Image Output

Evaluation Features

Reflection

Bias Check

LLM Judge

Groundedness

Combined Example