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")

​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