It is capable of explaining biological terms, species and everything related to the field of science known as biology
Deploy / Run AgentAPI & SDK Integration
Load, run, and interact with this agent programmatically using standard HTTP curl requests or the SVAHNAR SDK.
Secure Environment
Runs in isolated, zero-retention execution nodes with encrypted data transport.
Models Used (1)
Tools Attached (0)
No tools attached to this agent.
MCP Connections (0)
No Model Context Protocol servers connected.
Specifications
AGENT TYPE
Single Agent
SUB-AGENTS
1 agent
SUPERVISOR
No
RUNS
1,200+ runs
Configuration (YAML)
create_vertical_agent_network:
agent-1:
LLM_config:
params:
model: gpt-4o-mini
max_tokens: 1000
temperature: 0.5
request_timeout: 600
agent_name: Agent_1
incoming_edge:
- Start
outgoing_edge: []
agent_function:
- You are an expert in biology
Developer Quick Start
1. Install the SDK
pip install svahnar2. Configure your API key
export SVAHNAR_API_KEY="your_api_key_here"3. Save the Configuration YAML
Copy the YAML configuration box above and save it locally as agent.yaml.
4. Deploy the Agent (Python)
from svahnar import Svahnar
from pathlib import Path
client = Svahnar()
# Deploy the agent configuration
agent = client.agents.create(
name="Biology Agent",
description="It is capable of explaining biological terms, species and everything related to the field of science known as biology",
deploy_to="Organization",
yaml_content=Path("agent.yaml")
)
print(f"Created Agent ID: {agent.id}")5. Run the Agent (Python)
from svahnar import Svahnar
client = Svahnar()
# Run the agent using its ID
response = client.agents.run(
agent_id="YOUR_DEPLOYED_AGENT_ID", # Replace with your agent ID from Step 4
message="Your input message here"
)
print(response)For advanced features, Human-in-the-Loop configurations, and guides, visit the Developer Documentation.