Task Planning

from marsys.agents import Agent
from marsys.models import ModelConfig

# Planning is enabled by default
agent = Agent(
    model_config=ModelConfig(
        type="api",
        name="anthropic/claude-opus-4.6",
        provider="openrouter",
        max_tokens=12000
    ),
    name="Researcher",
    goal="Research and analyze topics",
    instruction="You are a thorough researcher..."
)

# Agent has access to planning tools automatically
result = await agent.auto_run("Research AI trends and summarize findings")

# Disable planning for simple, single-step agents
simple_agent = Agent(
    model_config=config,
    name="Calculator",
    goal="Perform calculations",
    instruction="...",
    plan_config=False  # Disable planning
)

from marsys.agents.planning import PlanningConfig, InjectionTrigger

custom_agent = Agent(
    model_config=config,
    name="Analyst",
    goal="Analyze complex datasets",
    instruction="...",
    plan_config=PlanningConfig(
        min_plan_items=3,
        max_plan_items=15,
        compact_mode=True,
        inject_triggers={
            InjectionTrigger.SESSION_START,
            InjectionTrigger.ERROR_RECOVERY,
        }
    )
)

from marsys.agents.planning import InjectionTrigger

# Only inject at session start and errors (skip step-by-step)
config = PlanningConfig(
    inject_triggers={
        InjectionTrigger.SESSION_START,
        InjectionTrigger.ERROR_RECOVERY,
    }
)

# Agent calls plan_create tool with:
{
    "goal": "Build authentication system",
    "items": [
        {
            "title": "Design user schema",
            "content": "Create database tables for users and sessions",
            "active_form": "Designing user schema"
        },
        {
            "title": "Implement signup endpoint",
            "content": "Create POST /auth/signup with validation",
            "active_form": "Implementing signup endpoint"
        },
        {
            "title": "Add authentication tests",
            "content": "Write unit and integration tests",
            "active_form": "Writing authentication tests"
        }
    ]
}

# Mark item as in progress
plan_update(item_id="abc123", status="in_progress")

# Mark as completed when done
plan_update(item_id="abc123", status="completed")

# Update content if scope changes
plan_update(item_id="def456", content="Updated scope: also add OAuth support")

# Agent calls plan_read to review current state
# Returns structured plan with all items and their statuses

┌─ Plan Created ────────────────────────────────────────
│ Goal: Research AI trends and create summary report
│ Items: 4
│   1. [pending] Search for recent AI papers
│   2. [pending] Analyze key findings
│   3. [pending] Synthesize insights
│   4. [pending] Write summary report
└────────────────────────────────────────────────────

[Researcher] ▸ Searching for recent AI papers...

┌─ Plan Updated ────────────────────────────────────────
│ ✓ Search for recent AI papers → completed
│ ▸ Analyze key findings → in_progress
└────────────────────────────────────────────────────

from marsys.coordination.communication.channels import WebChannel
from marsys.coordination.status.channels import StatusWebChannel

web_channel = WebChannel()
status_manager.add_channel(StatusWebChannel(web_channel))

# Web clients can poll:
# await web_channel.get_status_events()

# Save agent state (includes plan)
agent.save_state("/path/to/state.json")

# Load state later (plan is restored)
new_agent = Agent(
    model_config=config,
    name="Researcher",
    goal="...",
    instruction="...",
    plan_config=True  # Must enable planning
)
new_agent.load_state("/path/to/state.json")

from marsys.agents import Agent
from marsys.agents.planning import PlanningConfig
from marsys.models import ModelConfig

# Create agent with planning
researcher = Agent(
    model_config=ModelConfig(
        type="api",
        name="anthropic/claude-opus-4.6",
        provider="openrouter",
        max_tokens=12000
    ),
    name="Researcher",
    goal="Research topics thoroughly and provide comprehensive summaries",
    instruction="""You are a thorough researcher. When given a research task:

    1. Create a plan with clear steps
    2. Work through each step systematically
    3. Mark items complete as you finish them
    4. Provide a final summary when done

    Use your planning tools to stay organized.""",
    tools={"search_web": search_web, "fetch_url": fetch_url},
    plan_config=PlanningConfig(
        min_plan_items=2,
        max_plan_items=10,
        compact_mode=True
    )
)

# Run the research task
result = await researcher.auto_run(
    "Research the latest developments in quantum computing and summarize the key breakthroughs",
    max_steps=10
)