Schemas

from swarms.schemas import Step

step = Step(
    step_id="unique-step-id",
    time=1.234,
    response=agent_response
)

from swarms.schemas import ManySteps

run_data = ManySteps(
    agent_id="financial-agent-1",
    agent_name="FinancialAnalyst",
    task="Analyze Q4 earnings",
    max_loops=3,
    steps=[step1, step2, step3],
    total_tokens=5000
)

from swarms.schemas import MCPConnection

mcp = MCPConnection(
    server_name="filesystem",
    command="npx",
    args=["-y", "@modelcontextprotocol/server-filesystem", "/path/to/data"],
    env={"API_KEY": "secret"}
)

from swarms.schemas import MultipleMCPConnections

connections = MultipleMCPConnections(
    connections=[mcp1, mcp2, mcp3]
)

from swarms.schemas.base_schemas import ModelCard

model = ModelCard(
    id="gpt-4",
    object="model",
    created=1234567890,
    owned_by="openai"
)

from swarms.schemas.base_schemas import ChatMessageInput

message = ChatMessageInput(
    role="user",
    content="Analyze this financial report"
)

# Multi-modal message
multimodal_message = ChatMessageInput(
    role="user",
    content=[
        {"type": "text", "text": "What's in this image?"},
        {"type": "image_url", "image_url": {"url": "https://..."}}
    ]
)

from swarms.schemas.base_schemas import ChatCompletionRequest

request = ChatCompletionRequest(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    temperature=0.7,
    max_tokens=1000,
    stream=False
)

from swarms.schemas.base_schemas import ChatCompletionResponse

response = ChatCompletionResponse(
    model="gpt-4",
    object="chat.completion",
    choices=[
        {
            "index": 0,
            "message": {"role": "assistant", "content": "Hello! How can I help?"},
            "input": "Hello!"
        }
    ],
    created=1234567890
)

from swarms.schemas.base_schemas import AgentChatCompletionResponse

response = AgentChatCompletionResponse(
    id="agent-abc123",
    agent_name="FinancialAnalyst",
    object="chat.completion",
    choices=choice_data,
    created=1234567890
)

from swarms.schemas.base_schemas import UsageInfo

usage = UsageInfo(
    prompt_tokens=150,
    completion_tokens=300,
    total_tokens=450
)

from swarms.schemas import Step, ManySteps
from swarms.schemas.base_schemas import AgentChatCompletionResponse
import time

# Track individual steps
steps = []

for i in range(3):
    step_start = time.time()
    
    # Simulate agent execution
    response = AgentChatCompletionResponse(
        agent_name="FinancialAnalyst",
        choices={"message": {"role": "assistant", "content": f"Analysis {i+1}"}}
    )
    
    step = Step(
        step_id=f"step-{i}",
        time=time.time() - step_start,
        response=response
    )
    steps.append(step)

# Create complete run record
run_record = ManySteps(
    agent_id="financial-agent-1",
    agent_name="FinancialAnalyst",
    task="Analyze Q4 earnings reports",
    max_loops=3,
    run_id="run-abc123",
    steps=steps,
    full_history="Complete execution history...",
    total_tokens=5000,
    stopping_token="<END>",
    interactive=False,
    dynamic_temperature_enabled=True
)

print(f"Run ID: {run_record.run_id}")
print(f"Total steps: {len(run_record.steps)}")
print(f"Total tokens: {run_record.total_tokens}")

from swarms.schemas import MCPConnection, MultipleMCPConnections

# Define individual connections
filesystem_mcp = MCPConnection(
    server_name="filesystem",
    command="npx",
    args=["-y", "@modelcontextprotocol/server-filesystem", "/data"]
)

web_search_mcp = MCPConnection(
    server_name="brave-search",
    command="npx",
    args=["-y", "@modelcontextprotocol/server-brave-search"],
    env={"BRAVE_API_KEY": "your-api-key"}
)

# Combine multiple connections
connections = MultipleMCPConnections(
    connections=[filesystem_mcp, web_search_mcp]
)

# Use with agent
from swarms import Agent

agent = Agent(
    agent_name="MultiTool-Agent",
    mcp_connections=connections,
    model_name="gpt-4"
)

# Serialization
step_dict = step.model_dump()
step_json = step.model_dump_json(indent=2)

# Deserialization
step_from_dict = Step(**step_dict)
step_from_json = Step.model_validate_json(step_json)