Studio

The main entry point for the Lyzr ADK

The Studio class is the main entry point for the Lyzr ADK. It manages authentication, connections, and provides methods to create and manage all resources.

Quick Start

from lyzr import Studio

# Initialize with API key
studio = Studio(api_key="your-api-key")

# Create an agent
agent = studio.create_agent(
    name="My Agent",
    provider="gpt-4o",
    role="Assistant",
    goal="Help users",
    instructions="Be helpful"
)

# Run the agent
response = agent.run("Hello!")
print(response.response)

Initialization

Studio(
    api_key: str = None,      # API key (or use LYZR_API_KEY env var)
    log: str = "warning"      # Logging level
)

Parameters

Parameter	Type	Required	Default	Description
api_key	str	No	None	Lyzr API key. If not provided, reads from LYZR_API_KEY environment variable
log	str	No	"warning"	Logging level: "debug", "info", "warning", "error", "none"

Example

from lyzr import Studio

# With explicit API key
studio = Studio(api_key="sk-xxx")

# Using environment variable
import os
os.environ["LYZR_API_KEY"] = "sk-xxx"
studio = Studio()

# With debug logging
studio = Studio(api_key="sk-xxx", log="debug")

---

Agent Methods

create_agent()

Create a new agent.

agent = studio.create_agent(
    name: str,                          # Agent name
    provider: str = None,               # LLM provider/model (e.g., "gpt-4o")
    role: str = None,                   # Agent role
    goal: str = None,                   # Agent goal
    instructions: str = None,           # Agent instructions
    temperature: float = 0.7,           # Creativity (0.0-2.0)
    top_p: float = 0.9,                 # Nucleus sampling (0.0-1.0)
    response_model: BaseModel = None,   # Pydantic model for structured output
    memory: int = None,                 # Number of messages to remember
    contexts: List[Context] = None,     # Background contexts
    rai_policy: RAIPolicy = None,       # Responsible AI policy
    file_output: bool = False,          # Enable file generation
    image_model: ImageModelConfig = None,  # Image generation model
    llm_credential_id: str = None,      # Custom LLM credential ID
)

agent = studio.create_agent(
    name="Support Bot",
    provider="gpt-4o",
    role="Customer support",
    goal="Resolve issues quickly",
    instructions="Be empathetic and professional"
)

get_agent()

Get an agent by ID.

agent = studio.get_agent(agent_id: str)

agent = studio.get_agent("agent_123")
print(agent.name)

list_agents()

List all agents.

agents = studio.list_agents()

agents = studio.list_agents()
for agent in agents:
    print(f"{agent.id}: {agent.name}")

delete_agent()

Delete an agent by ID.

success = studio.delete_agent(agent_id: str)

studio.delete_agent("agent_123")

bulk_delete_agents()

Delete multiple agents.

success = studio.bulk_delete_agents(agent_ids: List[str])

studio.bulk_delete_agents(["agent_1", "agent_2", "agent_3"])

---

Knowledge Base Methods

create_knowledge_base()

Create a new knowledge base for RAG.

kb = studio.create_knowledge_base(
    name: str,                          # KB name
    vector_store: str = "qdrant",       # Vector store provider
    embedding_model: str = "text-embedding-3-large",  # Embedding model
    llm_model: str = "gpt-4o",          # LLM for query processing
    description: str = None,            # KB description
)

kb = studio.create_knowledge_base(
    name="Product Documentation",
    vector_store="qdrant",
    embedding_model="text-embedding-3-large"
)

get_knowledge_base()

Get a knowledge base by ID.

kb = studio.get_knowledge_base(kb_id: str)

kb = studio.get_knowledge_base("kb_123")
print(kb.name)

list_knowledge_bases()

List all knowledge bases.

kbs = studio.list_knowledge_bases()

kbs = studio.list_knowledge_bases()
for kb in kbs:
    print(f"{kb.id}: {kb.name}")

delete_knowledge_base()

Delete a knowledge base.

success = studio.delete_knowledge_base(kb_id: str)

studio.delete_knowledge_base("kb_123")

---

Context Methods

create_context()

Create a context (key-value background information).

context = studio.create_context(
    name: str,      # Context name/key
    value: str      # Context value
)

context = studio.create_context(
    name="company_info",
    value="Acme Corp - Founded 2020, serves 50k customers"
)

get_context()

Get a context by ID.

context = studio.get_context(context_id: str)

list_contexts()

List all contexts.

contexts = studio.list_contexts()

---

RAI Policy Methods

create_rai_policy()

Create a Responsible AI policy.

from lyzr import PIIType, PIIAction, SecretsAction

policy = studio.create_rai_policy(
    name: str,                                      # Policy name
    description: str,                               # Policy description
    toxicity_threshold: float = 0.4,                # Toxicity threshold (0.0-1.0)
    prompt_injection: bool = False,                 # Enable prompt injection detection
    secrets_detection: SecretsAction = SecretsAction.DISABLED,  # Secrets handling
    pii_detection: Dict[PIIType, PIIAction] = None, # PII handling
    banned_topics: List[str] = None,                # Topics to ban
    nsfw_check: bool = False,                       # Enable NSFW detection
    nsfw_threshold: float = 0.8,                    # NSFW threshold
)

from lyzr import PIIType, PIIAction, SecretsAction

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
    }
)

get_rai_policy()

Get a RAI policy by ID.

policy = studio.get_rai_policy(policy_id: str)

list_rai_policies()

List all RAI policies.

policies = studio.list_rai_policies()

---

Memory Methods

create_memory_credential()

Create a memory credential for Lyzr memory.

memory = studio.create_memory_credential(
    provider: str = "lyzr",     # Memory provider
    config: dict = None         # Provider-specific config
)

memory = studio.create_memory_credential(provider="lyzr")

---

Module Access

You can also access methods through dedicated modules:

studio = Studio(api_key="sk-xxx")

# Agent methods
agent = studio.agents.create(...)
agent = studio.agents.get(agent_id)
agents = studio.agents.list()

# Knowledge base methods
kb = studio.knowledge_bases.create(...)
kb = studio.knowledge_bases.get(kb_id)

# Context methods
ctx = studio.contexts.create(...)
ctx = studio.contexts.get(context_id)

# RAI methods
policy = studio.rai.create_policy(...)
policy = studio.rai.get_policy(policy_id)

# Memory methods
memory = studio.memory.create_credential(...)

---

Context Manager

Use Studio as a context manager for automatic cleanup:

with Studio(api_key="sk-xxx") as studio:
    agent = studio.create_agent(
        name="Assistant",
        provider="gpt-4o"
    )
    response = agent.run("Hello!")
# Connection automatically closed

---

Logging

Control ADK logging verbosity:

# Debug - show all details
studio = Studio(log="debug")

# Info - general information
studio = Studio(log="info")

# Warning - warnings and errors only (default)
studio = Studio(log="warning")

# Error - errors only
studio = Studio(log="error")

# None - disable logging
studio = Studio(log="none")