> ## Documentation Index > Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt > Use this file to discover all available pages before exploring further. # 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 ```python theme={null} 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 ```python theme={null} 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 ```python theme={null} 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. ```python theme={null} 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 ) ``` ```python theme={null} 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. ```python theme={null} agent = studio.get_agent(agent_id: str) ``` ```python theme={null} agent = studio.get_agent("agent_123") print(agent.name) ``` ### list\_agents() List all agents. ```python theme={null} agents = studio.list_agents() ``` ```python theme={null} agents = studio.list_agents() for agent in agents: print(f"{agent.id}: {agent.name}") ``` ### delete\_agent() Delete an agent by ID. ```python theme={null} success = studio.delete_agent(agent_id: str) ``` ```python theme={null} studio.delete_agent("agent_123") ``` ### bulk\_delete\_agents() Delete multiple agents. ```python theme={null} success = studio.bulk_delete_agents(agent_ids: List[str]) ``` ```python theme={null} studio.bulk_delete_agents(["agent_1", "agent_2", "agent_3"]) ``` *** ## Knowledge Base Methods ### create\_knowledge\_base() Create a new knowledge base for RAG. ```python theme={null} 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 ) ``` ```python theme={null} 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. ```python theme={null} kb = studio.get_knowledge_base(kb_id: str) ``` ```python theme={null} kb = studio.get_knowledge_base("kb_123") print(kb.name) ``` ### list\_knowledge\_bases() List all knowledge bases. ```python theme={null} kbs = studio.list_knowledge_bases() ``` ```python theme={null} kbs = studio.list_knowledge_bases() for kb in kbs: print(f"{kb.id}: {kb.name}") ``` ### delete\_knowledge\_base() Delete a knowledge base. ```python theme={null} success = studio.delete_knowledge_base(kb_id: str) ``` ```python theme={null} studio.delete_knowledge_base("kb_123") ``` *** ## Context Methods ### create\_context() Create a context (key-value background information). ```python theme={null} context = studio.create_context( name: str, # Context name/key value: str # Context value ) ``` ```python theme={null} context = studio.create_context( name="company_info", value="Acme Corp - Founded 2020, serves 50k customers" ) ``` ### get\_context() Get a context by ID. ```python theme={null} context = studio.get_context(context_id: str) ``` ### list\_contexts() List all contexts. ```python theme={null} contexts = studio.list_contexts() ``` *** ## RAI Policy Methods ### create\_rai\_policy() Create a Responsible AI policy. ```python theme={null} 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 ) ``` ```python theme={null} 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. ```python theme={null} policy = studio.get_rai_policy(policy_id: str) ``` ### list\_rai\_policies() List all RAI policies. ```python theme={null} policies = studio.list_rai_policies() ``` *** ## Memory Methods ### create\_memory\_credential() Create a memory credential for Lyzr memory. ```python theme={null} memory = studio.create_memory_credential( provider: str = "lyzr", # Memory provider config: dict = None # Provider-specific config ) ``` ```python theme={null} memory = studio.create_memory_credential(provider="lyzr") ``` *** ## Module Access You can also access methods through dedicated modules: ```python theme={null} 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: ```python theme={null} 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: ```python theme={null} # 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") ```