> ## Documentation Index > Fetch the complete documentation index at: https://docs.mcp-agent.com/llms.txt > Use this file to discover all available pages before exploring further. # Planner (Orchestrator) > Break complex objectives into coordinated steps Orchestrator workflow diagram ## When to use it * The user request is ambiguous or multi-step and you want the system to decide *what* to do before executing. * Different parts of the task require different agents or MCP servers (retrieve context, analyse, write, verify). * You need visibility into the plan, intermediate results, and token spend for each subtask. * You want to switch between full upfront planning and iterative planning based on feedback. The orchestrator mirrors Anthropic’s “orchestrator-workers” pattern: a planner decomposes the objective, assigns workers, collects intermediate artifacts, and synthesises the final response. ## Core roles [`create_orchestrator`](https://github.com/lastmile-ai/mcp-agent/blob/main/src/mcp_agent/workflows/factory.py#L214) returns an [`Orchestrator`](https://github.com/lastmile-ai/mcp-agent/blob/main/src/mcp_agent/workflows/orchestrator/orchestrator.py) AugmentedLLM composed of: * **Planner** – an LLM that produces a [`Plan`](https://github.com/lastmile-ai/mcp-agent/blob/main/src/mcp_agent/workflows/orchestrator/orchestrator_models.py#L52) with sequential `Step`s and parallel `Task`s. You can provide your own planner agent or use the default prompt. * **Workers** – the `available_agents` you pass in. Each step selects whichever agent (or MCP server) best fits the subtask. * **Synthesizer** – combines the outputs of each step into the final result. Provide your own synthesizer agent to bias tone or format. ## Quick start ```python theme={null} from mcp_agent.app import MCPApp from mcp_agent.workflows.factory import AgentSpec, OrchestratorOverrides, create_orchestrator app = MCPApp(name="orchestrator_example") async def main(): async with app.run() as running_app: orchestrator = create_orchestrator( available_agents=[ AgentSpec( name="researcher", instruction="Gather supporting evidence from trusted sources.", server_names=["fetch"], ), AgentSpec( name="writer", instruction="Summarise findings as bullet points and a short conclusion.", ), AgentSpec( name="editor", instruction="Check for policy violations and tighten language.", ), ], plan_type="iterative", # or "full" overrides=OrchestratorOverrides( planner_instruction="You are a project manager. Break the goal into 2-4 sequential steps.", synthesizer_instruction="Return Markdown with headings and a highlights section.", ), provider="openai", context=running_app.context, ) summary = await orchestrator.generate_str( "Research the latest MCP Agent updates and draft a changelog." ) return summary ``` Use `await orchestrator.execute(objective)` when you want full access to the [`PlanResult`](https://github.com/lastmile-ai/mcp-agent/blob/main/src/mcp_agent/workflows/orchestrator/orchestrator_models.py#L69): each step, the tasks that ran, agent selections, and intermediate outputs. ## Execution modes * `plan_type="full"` builds the entire plan upfront, then executes it step by step. Use this when the task is well understood and you want deterministic execution. * `plan_type="iterative"` plans one step at a time, feeding the latest results back into the planner. Ideal for exploratory work or when new context appears during execution. * Guardrails: * `request_params.max_iterations` caps the number of planner loops. * `request_params.maxTokens` defaults to 16K to accommodate plans and long syntheses. * `request_params.use_history` is disabled—context is managed manually between steps. ## Customising prompts and roles * Pass `planner` or `synthesizer` arguments to supply pre-built agents (with their own tools or model preferences). * Use `OrchestratorOverrides` to replace any prompt template: * `get_full_plan_prompt` / `get_iterative_plan_prompt`: plug in your own prompt builders if you want richer task schemas or intermediate metadata. * `get_task_prompt`: control the system prompt given to each worker before it runs. * `get_synthesize_plan_prompt`: change the final aggregation format (JSON, Markdown, HTML). * The orchestrator automatically loads tool availability from the context; include `server_names` on your `AgentSpec`s so planner prompts explain what each worker can do. ## Observability and debugging * Enable tracing (`otel.enabled: true`) to capture spans for planning, each step’s parallel execution, and synthesis. Each task records which agent ran, token usage, and outputs. * Call `await orchestrator.get_token_node()` to inspect the token/cost tree—each planner iteration and worker invocation is a child node. * The `PlanResult` returned by `execute` contains `step_results` with every intermediate output. Persist it for audit trails or UX side panels. ## Integration tips * Wrap the orchestrator with `@app.async_tool` to expose it as an MCP tool that other agents (or Anthropic Claude) can call. * Combine with the [Parallel pattern](/mcp-agent-sdk/effective-patterns/map-reduce) by using a `ParallelLLM` as a worker inside a step. * For durable execution, point `execution_engine` to `temporal` and follow the [Temporal examples](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/temporal/orchestrator.py). * Need policy, budgeting, or knowledge extraction? Reach for the [Deep Research pattern](/mcp-agent-sdk/effective-patterns/deep-research), which builds on this orchestrator. ## Example projects * [workflow\_orchestrator\_worker](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/workflows/workflow_orchestrator_worker) – student essay grader that plans retrieval, analysis, and report writing. * [Temporal orchestrator](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/temporal/orchestrator.py) – durable planner with pause/resume. * [workflow\_deep\_orchestrator](https://github.com/lastmile-ai/mcp-agent/tree/main/examples/workflows/workflow_deep_orchestrator) – extended planner with dashboards, budgets, and policy enforcement. ## Related reading * [Deep Research pattern](/mcp-agent-sdk/effective-patterns/deep-research) * [Workflow & decorators guide](/mcp-agent-sdk/core-components/workflows)