Prompt Lifecycle

A prompt starts or contributes to foreground work in a session. The Agent may continue that work until it reports idle, including across multiple model exchanges and tool invocations. session/prompt only lasts until the Agent accepts the prompt. The Agent reports the accepted user message, running state, output, and completion through session/update notifications. Before sending prompts, Clients MUST first complete the initialization phase and session setup.

A typical prompt-driven flow enables rich interactions between the user, Agent, and any connected tools.

1. User Message

The Client sends a user message with 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)"
        }
      }
    ]
  }
}

sessionId

SessionId

The ID of the session to send this message to.

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

2. Prompt Accepted

Upon receiving a prompt request, the Agent MUST respond once it has accepted the prompt. The response body is empty because completion is reported through state_update notifications, not through the session/prompt response:

{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {}
}

After accepting the prompt, the Agent MUST report where the user message was inserted in session history. It can send either a user_message update with the full content array or streamed user_message_chunk updates. This update is the source of truth for the agent-owned messageId.

{
  "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

When foreground work starts or resumes, the Agent MUST send a state_update notification with state: "running":

{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "state_update",
      "state": "running"
    }
  }
}

The language model MAY respond with text content, tool calls, or both. 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",
        "planId": "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..."
        }
      ]
    }
  }
}

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 content leaves the existing message content unchanged, content: null clears it, and concrete content arrays replace the previous 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 other 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.
A chunk’s _meta, when present, is chunk-scoped.

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"
    }
  }
}

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".

4. Report Completion

When the Agent is ready to process a new prompt, it MUST report idle with a state_update notification. When the transition ends foreground work, the Agent MUST include the corresponding StopReason:

{
  "jsonrpc": "2.0",
  "method": "session/update",
  "params": {
    "sessionId": "sess_abc123def456",
    "update": {
      "sessionUpdate": "state_update",
      "state": "idle",
      "stopReason": "end_turn"
    }
  }
}

Agents MAY stop foreground work at any point by sending an idle state_update session update with the corresponding StopReason.

5. Tool Invocation and Status Reporting

Before proceeding with execution, the Agent MAY request permission from the Client via the session/request_permission method. While foreground work is blocked on a permission response or other user action, the Agent SHOULD report requires_action. When work resumes, the Agent SHOULD report running. 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. The Agent streams complete incremental content items 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. Display-only terminal bytes use terminal_output_chunk, not tool_call_content_chunk. See Display-only Terminals for their per-terminal byte ordering and snapshot semantics. While tools execute on the Agent, they MAY leverage Client capabilities negotiated during initialization. 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

6. Continue Conversation

The Agent sends the tool results back to the language model as another request. The cycle returns to step 3, continuing until the language model completes its response without requesting additional tool calls or foreground work is stopped by the Agent or cancelled by the Client.

Stop Reasons

When an Agent stops foreground work, it must specify the corresponding StopReason on an idle state_update session update:

end_turn

The Agent has no more work to perform after the language model finishes responding without requesting more tools

max_tokens

The maximum token limit is reached

max_turn_requests

The maximum number of model requests for the foreground work is exceeded

refusal

The Agent refuses to continue

cancelled

The Client cancels active work

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.

Session States

state_update reports foreground work.

running

Foreground work is in progress.

idle

The Agent is ready to process a new prompt.

requires_action

Foreground work is blocked on user action.

Background activity MAY continue and emit other session/update notifications while the Agent reports idle. These notifications do not change the state.

Cancellation

Clients MAY cancel active session work 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 active work 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 send an idle state_update session update with the cancelled stop reason.

API client libraries and tools often throw an exception when their operation is aborted, which may otherwise be surfaced as a generic failure.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 report the semantically meaningful cancelled stop reason on a state_update notification, 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 sending the idle state_update session update that reports cancellation. The Client SHOULD still accept tool call updates received after sending session/cancel.

After the Agent reports idle, the Client may send another session/prompt to continue the conversation, building on the established session context.

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Protocol

Libraries

Prompt Lifecycle