from pydantic_ai import Agent
from subagents_pydantic_ai import SubAgentCapability, SubAgentConfig

agent = Agent(
    "openai:gpt-4.1",
    capabilities=[SubAgentCapability(
        subagents=[
            SubAgentConfig(
                name="researcher",
                description="Researches topics and gathers information",
                instructions="You are a research assistant. Investigate thoroughly.",
            ),
            SubAgentConfig(
                name="writer",
                description="Writes content based on research",
                instructions="You are a technical writer. Write clear, concise content.",
            ),
        ],
    )],
)

result = await agent.run("Research Python async patterns and write a blog post about it")

from pydantic_ai import Agent
from subagents_pydantic_ai import create_subagent_toolset, SubAgentConfig

toolset = create_subagent_toolset(
    subagents=[
        SubAgentConfig(name="researcher", description="Researches topics", instructions="..."),
    ],
)
agent = Agent("openai:gpt-4.1", toolsets=[toolset])

# Agent calls: task(description="...", subagent_type="researcher", mode="sync")
# Parent waits for result before continuing

# Agent calls: task(description="...", subagent_type="researcher", mode="async")
# Returns task_id immediately, agent continues working
# Later: check_task(task_id) to get result

# Agent calls: task(description="...", subagent_type="researcher", mode="auto")
# System decides based on:
# - Task complexity (simple → sync, complex → async)
# - Independence (can run without user context → async)
# - Subagent preferences (from config)

from pydantic_ai_backends import create_console_toolset

def my_toolsets_factory(deps):
    """Factory that creates toolsets for subagents."""
    return [
        create_console_toolset(),  # File operations
        create_search_toolset(),   # Web search
    ]

toolset = create_subagent_toolset(
    subagents=subagents,
    toolsets_factory=my_toolsets_factory,
)

from subagents_pydantic_ai import (
    create_subagent_toolset,
    create_agent_factory_toolset,
    DynamicAgentRegistry,
)

registry = DynamicAgentRegistry()

agent = Agent(
    "openai:gpt-4o",
    deps_type=Deps,
    toolsets=[
        # Pass registry so task() can resolve dynamically created agents
        create_subagent_toolset(registry=registry),
        create_agent_factory_toolset(
            registry=registry,
            allowed_models=["openai:gpt-4o", "openai:gpt-4o-mini"],
            max_agents=5,
        ),
    ],
)

# Now the agent can:
# 1. create_agent(name="analyst", ...) — creates a new agent in registry
# 2. task(description="...", subagent_type="analyst") — delegates to it

SubAgentConfig(
    name="analyst",
    description="Analyzes data",
    instructions="Ask for clarification when data is ambiguous.",
    can_ask_questions=True,
    max_questions=3,
)

import yaml
from subagents_pydantic_ai import SubAgentSpec

# Load from YAML
with open("subagents.yaml") as f:
    specs = [SubAgentSpec(**s) for s in yaml.safe_load(f)]

# Convert to SubAgentConfig dicts
configs = [spec.to_config() for spec in specs]

# Use with capability
agent = Agent("openai:gpt-4.1", capabilities=[
    SubAgentCapability(subagents=configs),
])

# Config -> Spec -> Config
spec = SubAgentSpec.from_config(existing_config)
config = spec.to_config()

SubAgentConfig(
    name="coder",
    description="Writes and reviews code",
    instructions="Follow project coding rules.",
    context_files=["/CODING_RULES.md"],  # Loaded by consumer library
    extra={"memory": "project", "cost_budget": 100},  # Custom metadata
)