> ## Documentation Index > Fetch the complete documentation index at: https://docs.karta.sh/llms.txt > Use this file to discover all available pages before exploring further. # Streaming events > How Karta streams turns across native sessions, the Managed Agents adapter, and the OpenAI-compatible Responses adapter. Karta is streaming-first. A turn is a sequence of typed events. The native session API, the CLI, the Python SDK, the widget, and the OpenAI-compatible adapters all use that model, then shape it for the surface you called. Use the native event stream when you want the fullest Karta contract. Use an adapter stream when you need a client library that already speaks another wire format. ## Choose the stream | Surface | Stream event names | Use when | | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------- | | Native sessions API and Python SDK | `text`, `tool_use`, `input_required`, `done`, and related raw event types | You control the integration and want the complete Karta event model. | | Managed Agents adapter and widget | `session.status_running`, `agent.message`, `agent.tool_use`, `agent.tool_result`, `session.status_idle`, `session.error` | You are building an embedded or full-fidelity chat UI. | | OpenAI Responses adapter | `response.created`, `response.output_text.delta`, `response.completed`, `response.failed` | You want to reuse OpenAI Responses client code. | Non-streaming calls still run through the same turn. Karta accumulates the stream and returns the final response object. ## Native event envelope Native session streams use Server-Sent Events. The SSE `event:` name matches the event object's `type`. ```text theme={null} event: text data: {"type":"text","source":"opencode","session_id":"ses_...","data":{"part":{"type":"text","text":"Quick"}}} event: done data: {"type":"done","source":"karta","session_id":"ses_...","data":{"usage":{"input_tokens":42,"output_tokens":128,"total_tokens":170}}} ``` In the SDK, iterate over the same event objects: ```python theme={null} async for event in app.stream("Explain quicksort"): if event.type == "text": print(event.data['part']['text'], end="", flush=True) elif event.type == "tool_use": print(f"\n[tool] {event.data['tool_name']}") elif event.type == "done": print(f"\n[usage] {event.data['usage']}") ``` ## Native event types The native stream is the most detailed stream. Event payloads vary by harness, but these event types are the public contract you should branch on. ### text An agent text chunk. The text is at `event.data['part']['text']` (the Claude SDK shape). ### tool\_use The agent invoked a tool. Common payload fields include the tool name, input, status, result, and tool-use id. ### reasoning An extended-thinking / reasoning block, when the model is configured to emit it. ### step\_start An agent step began. Payload includes a `step_index` and a description. ### step\_finish An agent step completed, with its result. ### input\_required The turn is paused awaiting an approval decision (tool use, file write, and similar requests). Payload includes `request_id`, `kind`, `tool`, `target`, `message`, and `options`. Resolve it with `approve_once`, `approve_session`, or `deny`. See [Inputs](/api-reference/inputs). ### error An agent, model, or policy error. Payload includes a human-readable `message` and may include a machine-readable `code`. In streaming mode, mid-turn failures arrive as events rather than replacing the stream with a new HTTP response. ### done The turn is complete. Payload carries `usage` (`input_tokens`, `output_tokens`, `total_tokens`) and the assembled message. When the effective model that ran the turn is known, the payload also carries `model` (the resolved model id, e.g. `claude-sonnet-5`); it is omitted when unknown. ## Native API example Create or resume a session, then stream a message: ```bash theme={null} SID=$(curl -s https://api.karta.sh/v1/sessions \ -H "Authorization: Bearer $KARTA_API_KEY" \ -H "Content-Type: application/json" \ -d '{}' | jq -r '.id') ``` ```bash theme={null} curl -N https://api.karta.sh/v1/sessions/$SID/messages \ -H "Authorization: Bearer $KARTA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"text":"Explain quicksort","stream":true}' ``` ## Managed Agents stream The Managed Agents adapter is the full-fidelity browser and widget stream. It maps native events into stable UI event names: | Event | Meaning | | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `session.status_running` | The turn has started. | | `agent.message` | Agent text. Treat repeated events with the same `part_id` as replace-by-id text. | | `agent.thinking` | Reasoning text, when enabled and visible for this agent. | | `agent.tool_use` | A tool invocation started. | | `agent.tool_result` | A tool invocation completed or errored. | | `session.status_idle` | The turn ended or paused. `stop_reason` tells you whether it ended normally or requires action. On normal completion it carries `usage` and, when known, the effective `model`. | | `session.error` | The turn failed. | Approvals arrive as `session.status_idle` with `stop_reason: "requires_action"`, plus the `request_id`, `tool`, `target`, and `options` needed to render a decision UI. ## Responses stream The OpenAI-compatible Responses adapter emits the portable Responses sequence: ```text theme={null} event: response.created data: {"type":"response.created","response":{"id":"resp_...","status":"in_progress"}} event: response.output_text.delta data: {"type":"response.output_text.delta","response_id":"resp_...","delta":"Quick"} event: response.completed data: {"type":"response.completed","response":{"id":"resp_...","status":"completed","output_text":"Quick..."}} ``` Use `previous_response_id` to continue the same Karta session through the Responses API. Send and stream over HTTP, with full SSE framing. The same stream, re-shaped into OpenAI and Anthropic wire formats.