Pre-built Agent

Fact Checker Agent

AGENT ID: d6d5318f-2357-4840-b4d2-51e9501ce4c7

It will check for the authenticity of a fact asked by user that if the fact is correct or not.

Deploy / Run Agent

API & 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)

gpt-4o-mini

Tools Attached (1)

Tavily

MCP Connections (0)

No Model Context Protocol servers connected.

Specifications

AGENT TYPE

Single Agent

SUB-AGENTS

1 agent

SUPERVISOR

RUNS

1,200+ runs

Configuration (YAML)

create_vertical_agent_network:
  agent-1:
    tools:
      tool_assigned:
      - name: Tavily
        config:
          API_key: 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
    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 a fact checker. Your task is to verify and check if the user asked or
      what user claims is correct or not. You can make web search and check for the
      authenticity of user query

Developer Quick Start

1. Install the SDK

pip install svahnar

2. 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="Fact Checker Agent",
    description="It will check for the authenticity of a fact asked by user that if the fact is correct or not.",
    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.