Documentation Index
Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
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Create agents using studio.create_agent() with customizable LLM providers, roles, and features.
Quick Start
from lyzr import Studio
studio = Studio(api_key="your-api-key")
agent = studio.create_agent(
name="Support Bot",
provider="gpt-4o",
role="Customer support agent",
goal="Help customers resolve their issues",
instructions="Be empathetic, clear, and solution-oriented"
)
Function Signature
studio.create_agent(
name: str,
provider: str = None,
role: str = None,
goal: str = None,
instructions: str = None,
description: str = None,
temperature: float = 0.7,
top_p: float = 0.9,
response_model: BaseModel = None,
memory: int = None,
contexts: List[Context] = None,
rai_policy: RAIPolicy = None,
file_output: bool = False,
image_model: ImageModelConfig = None,
reflection: bool = False,
bias_check: bool = False,
llm_judge: bool = False,
groundedness_facts: List[str] = None,
llm_credential_id: str = None,
additional_model_params: Dict = None,
) -> Agent
Parameters
Required Parameters
| Parameter | Type | Description |
|---|
name | str | Agent name (1-200 characters) |
Core Parameters
| Parameter | Type | Default | Description |
|---|
provider | str | None | LLM provider and model. Examples: "gpt-4o", "claude-sonnet-4.5", "openai/gpt-4o" |
role | str | None | Agent’s role or persona (e.g., “Customer support agent”) |
goal | str | None | Agent’s primary objective (e.g., “Help users resolve issues”) |
instructions | str | None | Detailed instructions for agent behavior |
description | str | None | Agent description (max 1000 characters) |
Model Parameters
| Parameter | Type | Default | Description |
|---|
temperature | float | 0.7 | Creativity/randomness (0.0-2.0). Lower = more focused, higher = more creative |
top_p | float | 0.9 | Nucleus sampling (0.0-1.0). Controls diversity of responses |
additional_model_params | dict | None | Additional provider-specific model parameters |
Feature Parameters
| Parameter | Type | Default | Description |
|---|
response_model | BaseModel | None | Pydantic model for structured outputs |
memory | int | None | Number of messages to remember (1-50) |
contexts | List[Context] | None | Background information contexts |
rai_policy | RAIPolicy | None | Responsible AI policy for guardrails |
file_output | bool | False | Enable file generation (PDF, DOCX, etc.) |
image_model | ImageModelConfig | None | Image generation model configuration |
Evaluation Parameters
| Parameter | Type | Default | Description |
|---|
reflection | bool | False | Enable self-reflection to reduce hallucinations |
bias_check | bool | False | Enable bias detection in responses |
llm_judge | bool | False | Enable third-party LLM to evaluate responses |
groundedness_facts | List[str] | None | Facts for response validation |
Advanced Parameters
| Parameter | Type | Default | Description |
|---|
llm_credential_id | str | Auto | Custom credential ID for LLM (from Studio UI) |
Examples
Basic Agent
agent = studio.create_agent(
name="Assistant",
provider="gpt-4o",
role="General assistant",
goal="Help users with questions",
instructions="Be helpful and concise"
)
Agent with Custom Temperature
# More creative agent
agent = studio.create_agent(
name="Creative Writer",
provider="gpt-4o",
role="Creative writer",
goal="Generate creative content",
instructions="Be imaginative and original",
temperature=1.2 # Higher creativity
)
# More focused agent
agent = studio.create_agent(
name="Code Reviewer",
provider="gpt-4o",
role="Code reviewer",
goal="Review code for bugs",
instructions="Be precise and thorough",
temperature=0.2 # Lower, more deterministic
)
Agent with Structured Output
from pydantic import BaseModel
class SentimentAnalysis(BaseModel):
sentiment: str
confidence: float
keywords: list[str]
agent = studio.create_agent(
name="Sentiment Analyzer",
provider="gpt-4o",
role="Sentiment analyst",
goal="Analyze text sentiment",
instructions="Analyze the sentiment of the provided text",
response_model=SentimentAnalysis
)
result: SentimentAnalysis = agent.run("I love this product!")
print(result.sentiment) # "positive"
print(result.confidence) # 0.95
Agent with Memory
agent = studio.create_agent(
name="Conversational Bot",
provider="gpt-4o",
role="Conversational assistant",
goal="Have natural conversations",
instructions="Remember context from previous messages",
memory=30 # Remember last 30 messages
)
# Conversation maintains context
agent.run("My name is Alice")
agent.run("What's my name?") # Remembers "Alice"
Agent with Contexts
# Create contexts with background information
company_ctx = studio.create_context(
name="company_info",
value="Acme Corp - Founded 2020, 50k customers, SaaS platform"
)
pricing_ctx = studio.create_context(
name="pricing",
value="Basic: $10/mo, Pro: $50/mo, Enterprise: Custom"
)
agent = studio.create_agent(
name="Sales Bot",
provider="gpt-4o",
role="Sales representative",
goal="Answer product questions",
instructions="Use company context to answer questions",
contexts=[company_ctx, pricing_ctx]
)
Agent with RAI Policy
from lyzr import PIIType, PIIAction, SecretsAction
# Create safety 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
}
)
agent = studio.create_agent(
name="Support Bot",
provider="gpt-4o",
role="Customer support",
goal="Help customers safely",
instructions="Never expose sensitive data",
rai_policy=policy
)
Agent with File Output
agent = studio.create_agent(
name="Report Generator",
provider="gpt-4o",
role="Report writer",
goal="Generate reports",
instructions="Create detailed PDF reports",
file_output=True
)
response = agent.run("Create a sales report for Q1")
if response.has_files():
for artifact in response.files:
artifact.download(f"./{artifact.name}")
Agent with Image Generation
from lyzr.image_models import DallE
agent = studio.create_agent(
name="Image Creator",
provider="gpt-4o",
role="Image generator",
goal="Create images from descriptions",
instructions="Generate high-quality images",
image_model=DallE.DALL_E_3
)
response = agent.run("Create an image of a sunset over mountains")
Agent with Reflection
agent = studio.create_agent(
name="Accurate Bot",
provider="gpt-4o",
role="Factual assistant",
goal="Provide accurate information",
instructions="Always verify your responses",
reflection=True, # Self-reflection for accuracy
bias_check=True # Check for bias
)
Agent with Groundedness
agent = studio.create_agent(
name="Fact Checker",
provider="gpt-4o",
role="Fact checker",
goal="Validate claims against known facts",
instructions="Only state facts that can be verified",
groundedness_facts=[
"Company was founded in 2020",
"Headquarters is in San Francisco",
"CEO is John Smith"
]
)
Agent with Custom Credentials
# Use custom credentials configured in Studio UI
agent = studio.create_agent(
name="Custom Agent",
provider="gpt-4o",
role="Assistant",
goal="Help users",
instructions="Be helpful",
llm_credential_id="my_custom_openai_credential"
)
You can specify providers in two formats:
The ADK auto-resolves the provider:
agent = studio.create_agent(provider="gpt-4o") # → OpenAI
agent = studio.create_agent(provider="claude-sonnet-4.5") # → Anthropic
agent = studio.create_agent(provider="gemini-2.5-pro") # → Google
agent = studio.create_agent(provider="openai/gpt-4o")
agent = studio.create_agent(provider="anthropic/claude-sonnet-4.5")
agent = studio.create_agent(provider="google/gemini-2.5-pro")
Return Value
Returns an Agent object with methods like:
run() - Execute the agentupdate() - Update configurationdelete() - Delete the agentclone() - Clone the agentadd_tool() - Add a local tooladd_memory() - Add memoryadd_context() - Add contextadd_rai_policy() - Add RAI policy
See Running Agents and Agent Features for more details.
