Supervisor Agents

A supervisor agent coordinates multiple subagents using agent.stream() or agent.generate(). You configure subagents on the supervisor's agents property, and the supervisor uses its instructions and each subagent's description to decide when and how to delegate tasks.

When to use supervisor agents

Use supervisor agents when a task requires multiple agents with different specializations to work together. The supervisor handles delegation decisions, context passing, and result synthesis.

Common use cases:

• Research and writing workflows where one agent gathers data and another produces content
• Multi-step tasks that need different expertise at each stage
• Tasks where you need fine-grained control over delegation behavior

Quick start

Define subagents with clear descriptions, then create a supervisor agent that references them:

src/mastra/agents/supervisor.ts

import { Agent } from '@mastra/core/agent'
import { Memory } from '@mastra/memory'
import { LibSQLStore } from '@mastra/libsql'

const researchAgent = new Agent({
  id: 'research-agent',
  description: 'Gathers factual information and returns bullet-point summaries.',
  model: 'openai/gpt-4o-mini',
})

const writingAgent = new Agent({
  id: 'writing-agent',
  description: 'Transforms research into well-structured articles.',
  model: 'openai/gpt-4o-mini',
})

const supervisor = new Agent({
  id: 'supervisor',
  instructions: `You coordinate research and writing using specialized agents.
    Delegate to research-agent for facts, then writing-agent for content.`,
  model: 'openai/gpt-5.1',
  agents: { researchAgent, writingAgent },
  memory: new Memory({
    storage: new LibSQLStore({ id: 'storage', url: 'file:mastra.db' }),
  }),
})

const stream = await supervisor.stream('Research AI in education and write an article', {
  maxSteps: 10,
})

for await (const chunk of stream.textStream) {
  process.stdout.write(chunk)
}

Delegation hooks

Delegation hooks let you intercept, modify, or reject delegations as they happen. Configure them under the delegation option, either in the agent's defaultOptions or per-call.

onDelegationStart

Called before the supervisor delegates to a subagent. Return an object to control the delegation:

• proceed: true — allow the delegation (default behavior)
• proceed: false — reject the delegation with a rejectionReason
• modifiedPrompt — rewrite the prompt sent to the subagent
• modifiedMaxSteps — limit the subagent's iteration count

src/mastra/agents/supervisor.ts

const stream = await supervisor.stream('Research AI trends', {
  maxSteps: 10,
  delegation: {
    onDelegationStart: async context => {
      console.log(`Delegating to: ${context.primitiveId}`)

      // Modify the prompt for a specific agent
      if (context.primitiveId === 'research-agent') {
        return {
          proceed: true,
          modifiedPrompt: `${context.prompt}\n\nFocus on 2024-2025 data.`,
          modifiedMaxSteps: 5,
        }
      }

      // Reject delegation after too many iterations
      if (context.iteration > 8) {
        return {
          proceed: false,
          rejectionReason: 'Max iterations reached. Synthesize current findings.',
        }
      }

      return { proceed: true }
    },
  },
})

The context object includes:

Property	Description
primitiveId	The ID of the subagent being delegated to
prompt	The prompt the supervisor is sending
iteration	Current iteration number

onDelegationComplete

Called after a delegation finishes. Use it to inspect results, provide feedback, or stop execution:

• context.bail() — stop the supervisor loop immediately
• Return { feedback: '...' } — add feedback that gets saved to the supervisor's memory and is visible to subsequent iterations

src/mastra/agents/supervisor.ts

const stream = await supervisor.stream('Research AI trends', {
  maxSteps: 10,
  delegation: {
    onDelegationComplete: async context => {
      console.log(`Completed: ${context.primitiveId}`)

      // Bail on errors
      if (context.error) {
        context.bail()
        return {
          feedback: `Delegation to ${context.primitiveId} failed: ${context.error}. Try a different approach.`,
        }
      }
    },
  },
})

The context object includes:

Property	Description
primitiveId	The ID of the subagent that ran
result	The subagent's response
error	Error if the delegation failed
bail()	Function to stop the supervisor loop

Message filtering

By default, subagents receive the full conversation context from the supervisor. Use messageFilter to control what messages are shared — for example, to remove sensitive data or limit context size.

src/mastra/agents/supervisor.ts

const stream = await supervisor.stream('Research AI trends', {
  maxSteps: 10,
  delegation: {
    messageFilter: ({ messages, primitiveId, prompt }) => {
      // Remove messages containing sensitive data
      return messages
        .filter(msg => {
          const content =
            typeof msg.content === 'string' ? msg.content : JSON.stringify(msg.content)
          return !content.includes('confidential')
        })
        .slice(-10) // Only pass the last 10 messages
    },
  },
})

The callback receives messages (the full conversation history), primitiveId (the subagent ID), and prompt (the delegation prompt). Return the filtered array of messages.

Iteration monitoring

onIterationComplete is called after each iteration of the supervisor loop. Use it to log progress, inject feedback, or stop execution early.

src/mastra/agents/supervisor.ts

const stream = await supervisor.stream('Research AI trends', {
  maxSteps: 10,
  onIterationComplete: async context => {
    console.log(`Iteration ${context.iteration}/${context.maxIterations}`)
    console.log(`Finish reason: ${context.finishReason}`)

    // Inject feedback to guide the agent
    if (!context.text.includes('recommendations')) {
      return {
        continue: true,
        feedback: 'Please include specific recommendations in your analysis.',
      }
    }

    // Stop early when the response is sufficient
    if (context.text.length > 1000 && context.finishReason === 'stop') {
      return { continue: false }
    }

    return { continue: true }
  },
})

Return { continue: true } to keep iterating, or { continue: false } to stop. Include optional feedback to add guidance that's visible to the next iteration.

Memory isolation

The supervisor pattern implements memory isolation — subagents receive the full conversation context for better decision-making, but only their specific delegation prompt and response are saved to their memory.

How it works:

1. Full context forwarded — When the supervisor delegates, the subagent receives all messages from the supervisor's conversation
2. Scoped memory saves — Only the delegation prompt and the subagent's response are saved to the subagent's memory
3. Fresh thread per invocation — Each delegation uses a unique thread ID, ensuring clean separation

This ensures subagents have the context they need without cluttering their memory with the entire supervisor conversation.

Tool approval propagation

Tool approvals propagate through the delegation chain. When a subagent uses a tool with requireApproval: true or calls suspend(), the approval request surfaces to the supervisor level.

const sensitiveDataTool = createTool({
  id: 'get-user-data',
  requireApproval: true,
  execute: async input => {
    return await database.getUserData(input.userId)
  },
})

const dataAgent = new Agent({
  id: 'data-agent',
  tools: { sensitiveDataTool },
})

const supervisor = new Agent({
  id: 'supervisor',
  agents: { dataAgent },
  memory: new Memory(),
})

const stream = await supervisor.stream('Get data for user 123')

for await (const chunk of stream.fullStream) {
  if (chunk.type === 'tool-call-approval') {
    console.log('Tool requires approval:', chunk.payload.toolName)
  }
}

Task completion scoring

Task completion scorers validate whether the task is complete after each iteration. If validation fails, the supervisor continues iterating. Feedback from failed scorers is included in the conversation context so subagents can see what was missing.

import { createScorer } from '@mastra/core/evals'

const taskCompleteScorer = createScorer({
  id: 'task-complete',
  name: 'Task Completeness',
}).generateScore(async context => {
  const text = (context.run.output || '').toString()
  const hasAnalysis = text.includes('analysis')
  const hasRecommendations = text.includes('recommendation')
  return hasAnalysis && hasRecommendations ? 1 : 0
})

const stream = await supervisor.stream('Research AI in education', {
  maxSteps: 10,
  isTaskComplete: {
    scorers: [taskCompleteScorer],
    strategy: 'all',
    onComplete: async result => {
      console.log('Task complete:', result.complete)
    },
  },
})

Writing effective instructions

Clear instructions are essential for effective delegation. Your supervisor's instructions should specify available resources, when to use each one, how to coordinate them, and success criteria.

Each subagent should have a clear description that explains what it does, what format it returns, and when to use it. The supervisor uses these descriptions to make delegation decisions.

const supervisor = new Agent({
  instructions: `You coordinate research and writing tasks.

Available resources:
- researchAgent: Gathers factual data and sources (returns bullet points)
- writingAgent: Transforms research into narrative content (returns full paragraphs)

Delegation strategy:
1. For research requests: Delegate to researchAgent first
2. For writing requests: Delegate to writingAgent
3. For complex requests: Delegate to researchAgent first, then writingAgent

Success criteria:
- All user questions are fully answered
- Response is well-formatted and complete`,
  agents: { researchAgent, writingAgent },
})

Related

• Agent Networks
• Migration: .network() to Supervisor Pattern
• Guide: Research Coordinator
• Agent.stream() Reference
• Agent.generate() Reference
• Agent Approval