openai/chatkit-python
Publicmirrored from https://github.com/openai/chatkit-pythonAvailable
docs/guides/update-client-during-response.md
164lines · modecode
| 1 | # Update the client during a response |
| 2 | |
| 3 | Keep your UI responsive while the server is working: stream progress text, trigger client-side effects, and run client tools mid-response without blocking everything else. |
| 4 | |
| 5 | This guide covers three patterns: |
| 6 | |
| 7 | - Progress updates for lightweight status while tools run |
| 8 | - Client effects for fire-and-forget UI behavior |
| 9 | - Client tools for round-trips to the browser/app during inference |
| 10 | |
| 11 | ## Show progress while tools run |
| 12 | |
| 13 | Use `ProgressUpdateEvent` when you need lightweight, real-time status. These updates stream immediately to the client and disappear after the turn—they are not stored in the thread. |
| 14 | |
| 15 | ### From tools |
| 16 | |
| 17 | Inside a tool, use `AgentContext.stream` to enqueue progress events. They are delivered to the client immediately and are not persisted as thread items. |
| 18 | |
| 19 | ```python |
| 20 | from agents import RunContextWrapper, function_tool |
| 21 | from chatkit.agents import AgentContext |
| 22 | from chatkit.types import ProgressUpdateEvent |
| 23 | |
| 24 | |
| 25 | @function_tool() |
| 26 | async def ingest_files(ctx: RunContextWrapper[AgentContext], paths: list[str]): |
| 27 | await ctx.context.stream(ProgressUpdateEvent(icon="upload", text="Uploading...")) |
| 28 | await upload(paths) |
| 29 | |
| 30 | await ctx.context.stream( |
| 31 | ProgressUpdateEvent(icon="search", text="Indexing and chunking...") |
| 32 | ) |
| 33 | await index_files(paths) |
| 34 | |
| 35 | await ctx.context.stream(ProgressUpdateEvent(icon="check", text="Done")) |
| 36 | ``` |
| 37 | |
| 38 | `stream_agent_response` will forward these events for you alongside any assistant text or tool call updates. |
| 39 | |
| 40 | ### From custom pipelines |
| 41 | |
| 42 | If you are not using the Agents SDK, yield `ProgressUpdateEvent` directly from your `respond` or `action` methods while your backend works: |
| 43 | |
| 44 | ```python |
| 45 | async def respond(...): |
| 46 | yield ProgressUpdateEvent(icon="search", text="Searching tickets...") |
| 47 | results = await search_tickets() |
| 48 | |
| 49 | yield ProgressUpdateEvent(icon="code", text="Generating summary...") |
| 50 | yield from await stream_summary(results) |
| 51 | ``` |
| 52 | |
| 53 | Use short, action-oriented messages and throttle updates to meaningful stages instead of every percent to avoid noisy streams. |
| 54 | |
| 55 | ## Trigger client-side effects without blocking |
| 56 | |
| 57 | Send `ClientEffectEvent` to trigger fire-and-forget UI work (such as refreshing a view, opening a modal, or showing a toast) without creating thread items or pausing the model stream. |
| 58 | |
| 59 | Client effects are ephemeral: they stream immediately to ChatKit.js, trigger your registered effect handler, and are not persisted to the thread history. Use client tool calls instead when you need a round-trip response from the client. |
| 60 | |
| 61 | ### Stream a client effect from your server |
| 62 | |
| 63 | Yield client effects directly from the `respond` or `action` method: |
| 64 | |
| 65 | ```python |
| 66 | async def respond(...): |
| 67 | yield ClientEffectEvent( |
| 68 | name="highlight_text", |
| 69 | data={"index": 142, "length": 35}, |
| 70 | ) |
| 71 | ``` |
| 72 | |
| 73 | Or from tools, through `AgentContext`: |
| 74 | |
| 75 | ```python |
| 76 | from agents import RunContextWrapper, function_tool |
| 77 | from chatkit.agents import AgentContext |
| 78 | from chatkit.types import ClientEffectEvent |
| 79 | |
| 80 | |
| 81 | @function_tool() |
| 82 | async def highlight_text(ctx: RunContextWrapper[AgentContext], index: int, length: int): |
| 83 | await ctx.context.stream( |
| 84 | ClientEffectEvent( |
| 85 | name="highlight_text", |
| 86 | data={"index": index, "length": length}, |
| 87 | ) |
| 88 | ) |
| 89 | ``` |
| 90 | |
| 91 | ### Handle the client effect in ChatKit.js |
| 92 | |
| 93 | Register a client effect handler when initializing ChatKit on the client: |
| 94 | |
| 95 | ```ts |
| 96 | const chatkit = useChatKit({ |
| 97 | // ... |
| 98 | onEffect: async ({name, data}) => { |
| 99 | if (name === "highlight_text") { |
| 100 | const {index, length} = data; |
| 101 | highlightArticleText({index, length}); |
| 102 | // No return value needed |
| 103 | } |
| 104 | }, |
| 105 | }); |
| 106 | ``` |
| 107 | |
| 108 | ## Call client tools mid-inference |
| 109 | |
| 110 | Client tool calls let the agent invoke browser/app callbacks mid-inference. Register the tool on both client and server; when triggered, ChatKit pauses the model, sends the tool request to the client, and resumes with the returned result. |
| 111 | |
| 112 | Use client effects instead when you do not need to wait for the client callback response for the rest of your response. |
| 113 | |
| 114 | ### Define a client tool in your agent |
| 115 | |
| 116 | Set `ctx.context.client_tool_call` inside a tool and configure the agent to stop at that tool. Only one client tool call can run per turn. Include client tools in `stop_at_tool_names` so the model pauses while the client callback runs and returns its result. |
| 117 | |
| 118 | ```python |
| 119 | from agents import Agent, RunContextWrapper, StopAtTools, function_tool |
| 120 | from chatkit.agents import AgentContext, ClientToolCall |
| 121 | |
| 122 | |
| 123 | @function_tool(description_override="Read the user's current canvas selection.") |
| 124 | async def get_selected_canvas_nodes(ctx: RunContextWrapper[AgentContext]) -> None: |
| 125 | ctx.context.client_tool_call = ClientToolCall( |
| 126 | name="get_selected_canvas_nodes", |
| 127 | arguments={"project": my_project()}, |
| 128 | ) |
| 129 | |
| 130 | |
| 131 | assistant = Agent[AgentContext]( |
| 132 | ..., |
| 133 | tools=[get_selected_canvas_nodes], |
| 134 | # StopAtTools pauses model generation so the pending client callback can run and resume the run. |
| 135 | tool_use_behavior=StopAtTools(stop_at_tool_names=[get_selected_canvas_nodes.name]), |
| 136 | ) |
| 137 | ``` |
| 138 | |
| 139 | ### Register the client tool in ChatKit.js |
| 140 | |
| 141 | Provide a matching callback when initializing ChatKit on the client. The function name must match the `ClientToolCall.name`, and its return value is sent back to the server to resume the run. |
| 142 | |
| 143 | ```ts |
| 144 | const chatkit = useChatKit({ |
| 145 | // ... |
| 146 | onClientTool: async ({name, params}) => { |
| 147 | if (name === "get_selected_canvas_nodes") { |
| 148 | const {project} = params; |
| 149 | const nodes = myCanvas.getSelectedNodes(project); |
| 150 | return { |
| 151 | nodes: nodes.map((node) => ({id: node.id, kind: node.type})), |
| 152 | }; |
| 153 | } |
| 154 | }, |
| 155 | }); |
| 156 | ``` |
| 157 | |
| 158 | ### Stream and resume |
| 159 | |
| 160 | In `respond`, stream via `stream_agent_response` as usual. ChatKit emits a pending client tool call item; the frontend runs your registered client tool, posts the output back, and the server continues the run. |
| 161 | |
| 162 | When the client posts the tool result, ChatKit stores it as a `ClientToolCallItem`. The continued inference after the client tool call handler returns the result feeds both the call and its output back to the model through `ThreadItemConverter.client_tool_call_to_input`, which emits a `function_call` plus matching `function_call_output` so the model sees the browser-provided context. |
| 163 | |
| 164 | |
| 165 | |