feat: send agent prompt -> prompt agent

backend/src/agent.py

@@ -5,7 +5,6 @@ import asyncio
 import traceback
 
 from temporary import generate_tmp_id
-from stream import Stream
 
 # Tool related classes must be moved to a different module
 
@@ -66,7 +65,10 @@ class Agent:
         agent_run.close()
         raise NotImplementedError("Subclasses must implement run()")
 
-    # Deprecate this. This is just run with extra baggage.
+    # Async generator: constructs a run, executes it, and yields every message
+    # the run emits (raw inference.Message objects: role + content) in arrival
+    # order. No projection: no delta collapsing, no tool filtering, no dropping
+    # of reasoning/usage. Consumers route or accumulate as they see fit.
     async def prompt(self, instruction, input=None, **kwargs):
         run = AgentRun.create(
             self.app,
@@ -79,39 +81,46 @@ class Agent:
             },
         )
 
-        done_event = asyncio.Event()
+        queue = asyncio.Queue()
-        out_stream = Stream(self.app)
-        tool_calls = []
-        tool_names = None
-        if 'tools' in kwargs:
-            tool_names = [t['function']['name'] for t in kwargs['tools'] if isinstance(t, dict)]
 
-        def on_chunk(chunk):
-            out_stream.feed(chunk)
         def on_inference(msg):
-            kwargs = {'content': msg.content}
+            queue.put_nowait(('message', msg))
 
-            if msg.role == 'assistant':
+        def on_done(_data):
-                msg.content.listen(on_chunk, lambda: None)
+            queue.put_nowait(('done', None))
-            elif msg.role == 'tool' and tool_names:
-                if msg.content.tool_name in tool_names:
-                    tool_calls.append(msg.content)
-        def on_done(ev):
-            done_event.set()
-            out_stream.close()
 
+        # Register listeners before scheduling the task. AgentRun.on replays
+        # past events of that name, so this both avoids a race and guarantees
+        # no early messages are lost.
         run.on('inference', on_inference)
         run.on('done', on_done)
-        await self.run(run)
 
-        if tool_names:
+        run.task = asyncio.create_task(self.run(run))
-            await done_event.wait()
+
-            return {
+        def on_task_done(task):
-                'output': out_stream,
+            # Safety net: an agent that raises (or forgets run.close()) would
-                'tool_calls': tool_calls,
+            # otherwise leave the consumer awaiting forever.
-            }
+            queue.put_nowait(('task', task))
-        else:
+
-            return out_stream
+        run.task.add_done_callback(on_task_done)
+
+        try:
+            while True:
+                kind, payload = await queue.get()
+                if kind == 'message':
+                    yield payload
+                elif kind == 'done':
+                    break
+                else:  # task
+                    if payload.cancelled():
+                        break
+                    exc = payload.exception()
+                    if exc:
+                        raise exc
+                    break
+        finally:
+            if not run.task.done():
+                run.task.cancel()
 
 
     async def received_message(self, message):

backend/src/app.py

@@ -47,7 +47,8 @@ class App(ChatMixin, AgentMixin):
         ]
 
         from features.calc import Calc
-        self.tool_classes = [Calc]
+        from features.web import WebSearch, WebFetch
+        self.tool_classes = [Calc, WebSearch, WebFetch]
 
     # --- Connection Management (Synchronous) ---
 

backend/src/app_mixins/agent.py

@@ -180,10 +180,15 @@ class AgentMixin:
             runs = [r for r in runs if r.user_id == user_id]
         return [r.serialize() for r in runs]
 
-    async def send_agent_prompt(self, agent_id, instruction, input=None, options={}):
+    async def prompt_agent(self, agent_id, instruction, input=None, options={}):
+        # Mirror agent.prompt's generator so run ownership and lifecycle stay
+        # inside the mixin: callers just iterate and receive the same raw
+        # inference.Message objects. get_agent_control is awaited lazily on first
+        # iteration, keeping the control object alive for the generator's
+        # lifetime.
         control = await self.get_agent_control(agent_id)
-        response = await control.prompt(instruction, input, **options)
+        async for msg in control.prompt(instruction, input, **options):
-        return response
+            yield msg
 
     async def call_agent_action(self, agent_id, action, args):
         control = await self.get_agent_control(agent_id)

backend/src/features/web.py

@@ -0,0 +1,61 @@
+"""Web tools: online search and page fetching.
+
+Both tools subclass :class:`~backend.src.tool.Tool` and rely on the base
+class's automatic ``ToolSpec`` generation (docstring -> description,
+``run`` signature -> parameters).
+"""
+
+import re
+import urllib.request
+from html import unescape
+
+from tool import Tool
+
+
+def _strip_html(html: str) -> str:
+    """Light HTML filter: drop scripts/styles, strip tags, collapse whitespace."""
+    html = re.sub(
+        r"<(script|style|noscript)[^>]*>.*?</\1>",
+        " ",
+        html,
+        flags=re.DOTALL | re.IGNORECASE,
+    )
+    text = re.sub(r"<[^>]+>", " ", html)
+    text = unescape(text)
+    return re.sub(r"\s+", " ", text).strip()
+
+
+class WebSearch(Tool):
+    """Search the web with DuckDuckGo. Returns titles, URLs and snippets."""
+
+    def run(self, query: str, max_results: int = 5):
+        # `ddgs` is the current package name; `duckduckgo_search` is the legacy one.
+        try:
+            from ddgs import DDGS
+        except ImportError:  # pragma: no cover - fallback for older installs
+            from duckduckgo_search import DDGS
+
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, max_results=max_results))
+
+        return [
+            {
+                "title": r.get("title", ""),
+                "url": r.get("href", ""),
+                "snippet": r.get("body", ""),
+            }
+            for r in results
+        ]
+
+
+class WebFetch(Tool):
+    """Fetch a web page and return its text content, lightly filtered."""
+
+    def run(self, url: str):
+        req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+        with urllib.request.urlopen(req, timeout=15) as resp:
+            html = resp.read().decode("utf-8", errors="replace")
+        return _strip_html(html)
+
+
+__all__ = ["WebSearch", "WebFetch"]

backend/src/server/openai.py

@@ -2,7 +2,6 @@ import json, time, traceback, asyncio, uuid
 from quart import Response, request
 from quart_auth import login_required, current_user
 from tool import ToolSpec, ToolParameter, ToolCall
-from agent import AgentRun
 from utils import encode_json
 
 # ---------------------------------------------------------------------------
@@ -62,43 +61,49 @@ async def execute_deferred(tc):
     tc.result = await tc.tool.execute_async(**tc.arguments)
 
 
-def attach_run_handlers(run, tool_names):
+def messages_to_queue(generator, tool_names):
-    """Wire the run's output events into a single queue.
+    """Drive a prompt generator into a single queue.
 
     The queue receives ('content'|'reasoning'|'tool_call'|'deferred_call', data)
     tuples and terminates with ('eof', None). Tool calls are routed (client vs
-    native/deferred) here so both response modes share the same logic.
+    native/deferred) here so both response modes share the same logic. The
+    returned ``state`` dict tracks whether any tool call was observed, which
+    drives ``finish_reason``.
     """
     queue = asyncio.Queue()
     loop = asyncio.get_event_loop()
+    state = {'tool_call': False, 'error': None}
 
     async def on_content(chunk):
         await queue.put(('content', chunk))
     async def on_reasoning(chunk):
         await queue.put(('reasoning', chunk))
-    async def on_eof(): pass
+    def on_eof(): pass
 
-    def on_inference(msg):
+    async def drain():
-        # assistant and reasoning are assumed to have a Stream
+        try:
-        # tool is assumed to have a ToolCall
+            async for msg in generator:
-        if msg.role == 'assistant':
+                # assistant and reasoning are assumed to have a Stream
-            msg.content.listen(on_content, on_eof)
+                # tool is assumed to have a ToolCall
-        elif msg.role == 'reasoning':
+                if msg.role == 'assistant':
-            msg.content.listen(on_reasoning, on_eof)
+                    msg.content.listen(on_content, on_eof)
-        elif msg.role == 'tool':
+                elif msg.role == 'reasoning':
-            deferred = route_tool_call(msg.content, tool_names)
+                    msg.content.listen(on_reasoning, on_eof)
-            msg_type = 'deferred_call' if deferred else 'tool_call'
+                elif msg.role == 'tool':
-            loop.create_task(queue.put((msg_type, msg.content)))
+                    deferred = route_tool_call(msg.content, tool_names)
-        elif msg.role == 'usage':
+                    msg_type = 'deferred_call' if deferred else 'tool_call'
-            loop.create_task(queue.put(('usage', msg.content)))
+                    state['tool_call'] = True
+                    await queue.put((msg_type, msg.content))
+                elif msg.role == 'usage':
+                    await queue.put(('usage', msg.content))
+        except Exception as e:
+            # Surface the failure to the consumer via the eof drain.
+            state['error'] = e
+        finally:
+            await queue.put(('eof', None))
 
-    def on_done(output):
+    loop.create_task(drain())
-        loop.create_task(queue.put(('eof', None)))
+    return queue, state
-
-    run.on('inference', on_inference)
-    run.on('done', on_done)
-
-    return queue
 
 
 def format_chunk(agent_id, delta, finish_reason=None, usage=None):
@@ -242,25 +247,23 @@ def register(server, app):
         # tool and gets deferred.
         tool_names = {spec.name for spec in tools}
 
-        # --- Get agent control and create AgentRun directly ---
+        # --- Get agent control and drive the run through the shared prompt generator ---
         control = await app.get_agent_control(agent_id)
 
-        run = AgentRun.create(
+        # The OpenAI inputs ride as kwargs so they land in run.input, which
-            app,
+        # ExpertAgent.run reads ("tools", "tool_policy", "messages").
-            agent_id=agent_id,
+        generator = control.prompt(
-            user_id=user_id,
+            None,
-            input={
+            input=None,
-                "messages": messages,
+            messages=messages,
-                "tools": tools,
+            tools=tools,
-                # Never execute tools inside the run: emit them instead so
+            # Never execute tools inside the run: emit them instead so
-                # the handler can send client tools back and defer native
+            # the handler can send client tools back and defer native
-                # ones.
+            # ones.
-                "tool_policy": "ignore",
+            tool_policy="ignore",
-            },
         )
 
-        queue = attach_run_handlers(run, tool_names)
+        queue, state = messages_to_queue(generator, tool_names)
-        await control.run(run)
 
         usage = {'completion_tokens': 0, 'prompt_tokens': 0, 'total_tokens': 0, 'cost': 0}
 
@@ -303,10 +306,10 @@ def register(server, app):
                             usage = data
 
                         elif msg_type == 'eof':
+                            if state['error']:
+                                raise state['error']
                             # Determine finish_reason — was there a tool call?
-                            finish_reason = "tool_calls" if any(
+                            finish_reason = "tool_calls" if state['tool_call'] else "stop"
-                                e['name'] == 'tool_call' for e in run.events
-                            ) else "stop"
                             yield f"data: {json.dumps(format_chunk(agent_id, {}, finish_reason))}\n\n"
                             break
 
@@ -334,6 +337,8 @@ def register(server, app):
                 while True:
                     msg_type, data = await queue.get()
                     if msg_type == 'eof':
+                        if state['error']:
+                            raise state['error']
                         break
                     elif msg_type == 'content':
                         content += data

backend/src/server/websocket.py

@@ -1,7 +1,6 @@
 import json, asyncio, traceback
 from quart import websocket
 from utils import encode_json
-from stream import Stream      # isinstance check in send-agent-prompt
 
 def register(server, app, auth_manager):
     @server.websocket('/ws')
@@ -298,37 +297,30 @@ def register(server, app, auth_manager):
                     )
                     await send({'message': "chat thread created", 'threadId': thread_id, 'nonce': nonce})
 
-                elif action == "send-agent-prompt":
+                elif action == "prompt-agent":
                     if 'instruction' not in body and 'input' not in body:
-                        await send({'error': "instruction or input required"})
+                        await send({'error': "instruction or input required", 'nonce': nonce})
                         continue
 
-                    # Shape of response changes based on options lol
+                    # Multiple-result action, same shape as read-stream: every
-                    response = await app.send_agent_prompt(
+                    # generated message is its own packet carrying the request
+                    # nonce, so the client callback runs once per message. The
+                    # terminal eof packet resolves the action. Streams referenced
+                    # by open assistant/reasoning messages are read by the
+                    # client via the existing read-stream action.
+                    generator = app.prompt_agent(
                         await agent_id_from_body(),
                         body.get('instruction'),
                         body.get('input'),
                         body.get('options', {})
                     )
-                    print("prompt response", repr(response))
+                    try:
-                    tool_calls = None
+                        async for msg in generator:
-
+                            await send({'message': 'agent-message', 'data': msg, 'nonce': nonce})
-                    # Extract from dict
+                    finally:
-                    if isinstance(response, dict):
+                        # Disconnect / cancellation point: closes the run task.
-                        tool_calls = response['tool_calls']
+                        await generator.aclose()
-                        response = response['output']
+                    await send({'eof': True, 'nonce': nonce})
-
-                    if isinstance(response, Stream):
-                        if body.get('stream'):
-                            response = {"streamId": response.id}
-                        else:
-                            response = await response.content_wait()
-
-                    packet_out = {'message': "prompt sent", 'response': response, 'nonce': nonce}
-                    if tool_calls:
-                        packet_out['toolCalls'] = tool_calls
-
-                    await send(packet_out)
 
                 elif action == "read-stream":
                     stream_id = body['streamId']

frontend/src/views/Agent.vue

@@ -157,14 +157,35 @@
     }
 
     promptHistory.value.push(historyEntry);
-    let res = await backend.sendAction("send-agent-prompt", payload);
 
-    if (res.error) {
+    // prompt-agent is a multiple-result action (like read-stream): one packet
-      historyEntry.error = res.error;
+    // per generated inference.Message, followed by a terminal eof packet. The
-    } else {
+    // callback runs once per packet. Assistant/reasoning content is an open
-      historyEntry.output = res.response;
+    // stream ({streamId}); we read it live via read-stream.
+    historyEntry.output = "";
+    try {
+      await backend.sendAction("prompt-agent", payload, (packet, deferred) => {
+        if (packet.eof) {
+          deferred.resolve(packet);
+        } else {
+          const msg = packet.data;
+          if (msg.role !== 'assistant') return;
+          const content = msg.content;
+          if (content && content.streamId != null) {
+            const stream = backend.readStream(content.streamId);
+            stream.on('data', () => {
+              historyEntry.output = stream.partialContent;
+            });
+          } else if (typeof content === 'string') {
+            historyEntry.output += content;
+          }
+        }
+      });
+    } catch (e) {
+      historyEntry.error = e.message || String(e);
+    } finally {
+      historyEntry.loading = false;
     }
-    historyEntry.loading = false;
   }
 
   function promptKeyDown (event, stage) {