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A prompt turn represents a complete interaction cycle between the Client and Agent, starting with a user message and continuing until the Agent completes its response. This may involve multiple exchanges with the language model and tool invocations. Before sending prompts, Clients MUST first complete the initialization phase and session setup.

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The Prompt Turn Lifecycle

A prompt turn follows a structured flow that enables rich interactions between the user, Agent, and any connected tools.

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1. User Message

The turn begins when the Client sends a session/prompt: 
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "session/prompt",
  "params": {
    "sessionId": "sess_abc123def456",
    "prompt": [
      {
        "type": "text",
        "text": "Can you analyze this code for potential issues?"
      },
      {
        "type": "resource",
        "resource": {
          "uri": "file:///home/user/project/main.py",
          "mimeType": "text/x-python",
          "text": "def process_data(items):\n    for item in items:\n        print(item)"
        }
      }
    ]
  }
}
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sessionId
SessionId
The ID of the session to send this message to.
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prompt
ContentBlock[]
The contents of the user message, e.g. text, images, files, etc.Clients MUST restrict types of content according to the Prompt Capabilities established during initialization.

Learn more about Content

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2. Agent Processing

Upon receiving the prompt request, the Agent processes the user’s message and sends it to the language model, which MAY respond with text content, tool calls, or both. If the Agent reports the accepted user message through session/update, it can send either a user_message update with the full content array or streamed user_message_chunk updates. 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "user_message",
      "messageId": "msg_user_8f7a1",
      "content": [
        {
          "type": "text",
          "text": "Can you analyze this code for potential issues?"
        }
      ]
    }
  }
}

​
3. Agent Reports Output

The Agent reports the model’s output to the Client via session/update notifications. This may include the Agent’s plan for accomplishing the task: 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "plan_update",
      "plan": {
        "type": "items",
        "id": "plan-1",
        "entries": [
          {
            "content": "Check for syntax errors",
            "priority": "high",
            "status": "pending"
          },
          {
            "content": "Identify potential type issues",
            "priority": "medium",
            "status": "pending"
          },
          {
            "content": "Review error handling patterns",
            "priority": "medium",
            "status": "pending"
          },
          {
            "content": "Suggest improvements",
            "priority": "low",
            "status": "pending"
          }
        ]
      }
    }
  }
}

Learn more about Agent Plans
The Agent can report the model’s text response as an agent_message update with the full content array: 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "agent_message",
      "messageId": "msg_agent_c42b9",
      "content": [
        {
          "type": "text",
          "text": "I'll analyze your code for potential issues. Let me examine it..."
        }
      ]
    }
  }
}

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Message IDs

The Agent MUST include an opaque messageId on message updates and message chunks. User, agent, and thought messages can each be reported either as a message update with a full content array or as streamed chunks. user_message, agent_message, and agent_thought updates are upserts keyed by messageId: omitted fields leave the existing message unchanged, null clears a field, and concrete values replace the previous value. content arrays replace the whole message content; send [] or null to clear content. Chunk updates with the same messageId append content; a changed messageId indicates a new message. Clients apply message updates and chunks in the order they are received for each messageId:
  • A message update without content leaves the current content unchanged, so Agents can update _meta or future optional fields without resending content.
  • A message update with content replaces all content currently stored for that message, including content accumulated from earlier chunks.
  • A message update with content: [] or content: null clears the message content.
  • A chunk appends its content to whatever content is current for that message, whether that content came from an earlier message update or earlier chunks.
For example, if an Agent sends agent_message with content: [A], then sends agent_message_chunk with B, the rendered message content is [A, B]. If it later sends another agent_message with content: [C], the rendered content becomes [C]; the earlier full content and chunks are replaced. Subsequent chunks append to [C]. For streaming agent text, the Agent can use agent_message_chunk updates: 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "agent_message_chunk",
      "messageId": "msg_agent_c42b9",
      "content": {
        "type": "text",
        "text": " Let me examine it..."
      }
    }
  }
}
The Agent can report internal reasoning with the same message-update or chunk patterns. agent_thought updates patch fields for the same thought messageId; agent_thought_chunk updates append new content. 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "agent_thought",
      "messageId": "msg_thought_a12",
      "content": [
        {
          "type": "text",
          "text": "Need to inspect the loop body before suggesting a fix."
        }
      ]
    }
  }
}
If the model requested tool calls, these are also reported immediately: 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "tool_call_update",
      "toolCallId": "call_001",
      "title": "Analyzing Python code",
      "kind": "other",
      "status": "pending"
    }
  }
}

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Session Usage Updates

The Agent MAY also report current session context and cumulative cost state with a usage_update: 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "usage_update",
      "used": 53000,
      "size": 200000,
      "cost": {
        "amount": 0.045,
        "currency": "USD"
      }
    }
  }
}
used and size are required and non-null token counts for the current session context. cost is optional and, if present, amount and currency are required. currency is an ISO 4217 currency code like "USD".

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4. Check for Completion

If there are no pending tool calls, the turn ends and the Agent MUST respond to the original session/prompt request with a StopReason: 
{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "stopReason": "end_turn"
  }
}
Agents MAY stop the turn at any point by returning the corresponding StopReason.

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5. Tool Invocation and Status Reporting

Before proceeding with execution, the Agent MAY request permission from the Client via the session/request_permission method. Once permission is granted (if required), the Agent SHOULD invoke the tool and report a status update marking the tool as in_progress: 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "tool_call_update",
      "toolCallId": "call_001",
      "status": "in_progress"
    }
  }
}
As the tool runs, the Agent MAY send additional updates, providing real-time feedback about tool execution progress. When the tool produces incremental content, the Agent can stream each item with tool_call_content_chunk: 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "tool_call_content_chunk",
      "toolCallId": "call_001",
      "content": {
        "type": "content",
        "content": {
          "type": "text",
          "text": "Checked syntax..."
        }
      }
    }
  }
}
Clients append each tool_call_content_chunk to the current content for that toolCallId. A later tool_call_update with content replaces the accumulated content. While tools execute on the Agent, they MAY leverage Client capabilities such as the file system (fs) methods to access resources within the Client’s environment. When the tool completes, the Agent sends another update with the final status and any content: 
{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "tool_call_update",
      "toolCallId": "call_001",
      "status": "completed",
      "content": [
        {
          "type": "content",
          "content": {
            "type": "text",
            "text": "Analysis complete:\n- No syntax errors found\n- Consider adding type hints for better clarity\n- The function could benefit from error handling for empty lists"
          }
        }
      ]
    }
  }
}

Learn more about Tool Calls

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6. Continue Conversation

The Agent sends the tool results back to the language model as another request. The cycle returns to step 2, continuing until the language model completes its response without requesting additional tool calls or the turn gets stopped by the Agent or cancelled by the Client.

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Stop Reasons

When an Agent stops a turn, it must specify the corresponding StopReason:
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end_turn
The language model finishes responding without requesting more tools
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max_tokens
The maximum token limit is reached
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max_turn_requests
The maximum number of model requests in a single turn is exceeded
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refusal
The Agent refuses to continue
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cancelled
The Client cancels the turn
In v2, custom or future stop reasons can be used when Clients can display a generic stopped state. Custom stop reasons MUST begin with _; unknown non-underscore stop reasons are reserved for future ACP variants.

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Cancellation

Clients MAY cancel an ongoing prompt turn at any time by sending a session/cancel notification: 
{
  "jsonrpc": "2.0",
  "method": "session/cancel",
  "params": {
    "sessionId": "sess_abc123def456"
  }
}
The Client SHOULD preemptively mark all non-finished tool calls pertaining to the current turn as cancelled as soon as it sends the session/cancel notification. The Client MUST respond to all pending session/request_permission requests with the cancelled outcome. When the Agent receives this notification, it SHOULD stop all language model requests and all tool call invocations as soon as possible. After all ongoing operations have been successfully aborted and pending updates have been sent, the Agent MUST respond to the original session/prompt request with the cancelled stop reason.
API client libraries and tools often throw an exception when their operation is aborted, which may propagate as an error response to session/prompt.Clients often display unrecognized errors from the Agent to the user, which would be undesirable for cancellations as they aren’t considered errors.Agents MUST catch these errors and return the semantically meaningful cancelled stop reason, so that Clients can reliably confirm the cancellation.
The Agent MAY send session/update notifications with content or tool call updates after receiving the session/cancel notification, but it MUST ensure that it does so before responding to the session/prompt request. The Client SHOULD still accept tool call updates received after sending session/cancel.
Once a prompt turn completes, the Client may send another session/prompt to continue the conversation, building on the context established in previous turns.