​
Installation

First, install the lyzr-agent-api package:

pip install lyzr-agent-api

For more detailed installation instructions, refer to the installation guide on PyPI.

​
Initializing the Client

To start using the API, initialize the AgentAPI client with your Lyzr API key:

Tip: You can generate your API key from the Lyzr Agent API Studio.
from lyzr_agent_api.client import AgentAPI

client = AgentAPI(x_api_key="your-lyzr-api-key")

​
Environment

​
Create an Environment

The environment is a fundamental building block of the Lyzr Agent API. It defines the modules, features, tools available, and other configurations for your agent.For more,click here.

from lyzr_agent_api.models.environment import EnvironmentConfig, FeatureConfig

environment_config = EnvironmentConfig(
    name="Test Environment",
    features=[
        FeatureConfig(
            type="SHORT_TERM_MEMORY",
            config={},
            priority=0,
        )
    ],
    tools=[],
    llm_config={
        "provider": "openai",
        "model": "gpt-4o-mini",
        "config": {
            "temperature": 0.5,
            "top_p": 0.9,
        },
        "env": {
            "OPENAI_API_KEY": "your-openai-api-key"
        }
    },
)
environment = client.create_environment_endpoint(json_body=environment_config) 
print(environment)
# The response will include the environment ID, e.g. {'environment_id': '6wjbwhekndjxxxxx'}

​
Update an Environment

This function updates the configuration of an existing environment by passing in the environment ID (env_id) and a JSON body containing the new environment settings (json_body).

response = client.update_environment_endpoint(
    env_id="env-id",
    json_body=environment_config,
)

​
Get an Environment

This function retrieves a list of all available environments. It returns the details of each environment, allowing you to view their configurations and statuses.

client.get_environments_endpoint()

​
Agent

​
Create an Agent

Once the environment is set up, create an agent within that environment. The agent uses the environment ID to operate and can be customized with a system prompt and a description:

from lyzr_agent_api.models.agents import AgentConfig

agent_config = AgentConfig(
    env_id="environment-id",  # Replace with the actual environment ID
    system_prompt="This is a system prompt.",
    name="Test Agent",
    agent_description="Description of the test agent",
)
agent = client.create_agent_endpoint(json_body=agent_config)
print(agent)
# The response will include the agent ID, e.g. {'agent_id': '66fcghvhxxxxxx'}

​
Update an Agent

This function updates the configuration of an existing agent by providing the agent ID (agent_id) and a JSON body containing the new settings (json_body). It allows you to modify an agent’s behavior or properties dynamically.

response = client.update_agent_endpoint(
    agent_id=agent_id,
    json_body=agent_config
)

​
Get an Agent

This function retrieves a list of all available agents. It returns the details and configurations of each agent, allowing you to review and manage them.

client.get_agents_endpoint()

​
Delete an Agent

This function deletes an agent by specifying its agent_id.

response = client.delete_agent_by_id(
    agent_id=agent_id     # Example agent ID to delete
)

​
Inference

​
Chat with the Agent

After creating the agent, you can initiate a chat session with it. Provide the user ID, agent ID, and a message to start the conversation:

from lyzr_agent_api.models.chat import ChatRequest

response = client.chat_with_agent(
    json_body=ChatRequest(
        user_id="user-id",
        agent_id="agent-id",  # Replace with the actual agent ID
        message="Hello",
        session_id="session-id",
    )
)

print(response)

​
Execute Task with Agent

from lyzr_agent_api.models.chat import ChatRequest

response = client.create_chat_task(
        json_body=ChatRequest(
       user_id="your-user-id",
       agent_id="your-agent-id",
       message="scrap the content of https://www.lyzr.ai/ and give summary of it.",
       session_id="your-session-id",
   )
    )

print(response) #e.g {'task_id':'hsjshxxxxxxxx'}

Get Task Status and Response

response_status = client.get_task_status(
        task_id=response.task_id,  # Use the task_id from the create response
    )

print(response_status)

​
Installation

First, install the lyzr-agent-api package:

pip install lyzr-agent-api

For more detailed installation instructions, refer to the installation guide on PyPI.

​
Initializing the Client

To start using the API, initialize the AgentAPI client with your Lyzr API key:

Tip: You can generate your API key from the Lyzr Agent API Studio.
from lyzr_agent_api.client import AgentAPI

client = AgentAPI(x_api_key="your-lyzr-api-key")

​
Environment

​
Create an Environment

The environment is a fundamental building block of the Lyzr Agent API. It defines the modules, features, tools available, and other configurations for your agent.For more,click here.

from lyzr_agent_api.models.environment import EnvironmentConfig, FeatureConfig

environment_config = EnvironmentConfig(
    name="Test Environment",
    features=[
        FeatureConfig(
            type="SHORT_TERM_MEMORY",
            config={},
            priority=0,
        )
    ],
    tools=[],
    llm_config={
        "provider": "openai",
        "model": "gpt-4o-mini",
        "config": {
            "temperature": 0.5,
            "top_p": 0.9,
        },
        "env": {
            "OPENAI_API_KEY": "your-openai-api-key"
        }
    },
)
environment = client.create_environment_endpoint(json_body=environment_config) 
print(environment)
# The response will include the environment ID, e.g. {'environment_id': '6wjbwhekndjxxxxx'}

​
Update an Environment

This function updates the configuration of an existing environment by passing in the environment ID (env_id) and a JSON body containing the new environment settings (json_body).

response = client.update_environment_endpoint(
    env_id="env-id",
    json_body=environment_config,
)

​
Get an Environment

This function retrieves a list of all available environments. It returns the details of each environment, allowing you to view their configurations and statuses.

client.get_environments_endpoint()

​
Agent

​
Create an Agent

Once the environment is set up, create an agent within that environment. The agent uses the environment ID to operate and can be customized with a system prompt and a description:

from lyzr_agent_api.models.agents import AgentConfig

agent_config = AgentConfig(
    env_id="environment-id",  # Replace with the actual environment ID
    system_prompt="This is a system prompt.",
    name="Test Agent",
    agent_description="Description of the test agent",
)
agent = client.create_agent_endpoint(json_body=agent_config)
print(agent)
# The response will include the agent ID, e.g. {'agent_id': '66fcghvhxxxxxx'}

​
Update an Agent

This function updates the configuration of an existing agent by providing the agent ID (agent_id) and a JSON body containing the new settings (json_body). It allows you to modify an agent’s behavior or properties dynamically.

response = client.update_agent_endpoint(
    agent_id=agent_id,
    json_body=agent_config
)

​
Get an Agent

This function retrieves a list of all available agents. It returns the details and configurations of each agent, allowing you to review and manage them.

client.get_agents_endpoint()

​
Delete an Agent

This function deletes an agent by specifying its agent_id.

response = client.delete_agent_by_id(
    agent_id=agent_id     # Example agent ID to delete
)

​
Inference

​
Chat with the Agent

After creating the agent, you can initiate a chat session with it. Provide the user ID, agent ID, and a message to start the conversation:

from lyzr_agent_api.models.chat import ChatRequest

response = client.chat_with_agent(
    json_body=ChatRequest(
        user_id="user-id",
        agent_id="agent-id",  # Replace with the actual agent ID
        message="Hello",
        session_id="session-id",
    )
)

print(response)

​
Execute Task with Agent

from lyzr_agent_api.models.chat import ChatRequest

response = client.create_chat_task(
        json_body=ChatRequest(
       user_id="your-user-id",
       agent_id="your-agent-id",
       message="scrap the content of https://www.lyzr.ai/ and give summary of it.",
       session_id="your-session-id",
   )
    )

print(response) #e.g {'task_id':'hsjshxxxxxxxx'}

Get Task Status and Response

response_status = client.get_task_status(
        task_id=response.task_id,  # Use the task_id from the create response
    )

print(response_status)