Real-Time Streaming

Overview

Shannon provides real-time event streaming so you can monitor task execution as it happens. This is essential for:

Providing live feedback to users
Debugging workflow execution
Building interactive UIs
Monitoring long-running tasks

Streaming Technologies

Shannon supports two streaming protocols:

Protocol	Use Case	Features
SSE (Server-Sent Events)	One-way server→client	Simple, HTTP-based, auto-reconnect
WebSocket	Bidirectional	Full duplex, lower latency

SSE is recommended for most use cases due to its simplicity and built-in reconnection handling.

Server-Sent Events (SSE)

Using cURL

curl -N http://localhost:8080/api/v1/stream/sse?workflow_id={task_id}

Output:

event: WORKFLOW_STARTED
data: {"workflow_id":"task-123","agent_id":"orchestrator","message":"Task processing started"}

event: DATA_PROCESSING
data: {"workflow_id":"task-123","message":"Preparing context"}

event: PROGRESS
data: {"workflow_id":"task-123","agent_id":"planner","message":"Created a plan with 3 steps"}

event: AGENT_STARTED
data: {"workflow_id":"task-123","agent_id":"research-agent","message":"Processing query"}

event: LLM_PROMPT
data: {"workflow_id":"task-123","agent_id":"research-agent","message":"Searching for information..."}

event: TOOL_INVOKED
data: {"workflow_id":"task-123","agent_id":"research-agent","message":"Calling web_search with query='topic A'"}

event: TOOL_OBSERVATION
data: {"workflow_id":"task-123","agent_id":"research-agent","message":"Found 5 results"}

event: LLM_OUTPUT
data: {"workflow_id":"task-123","agent_id":"research-agent","message":"Research complete: Found relevant information"}

event: AGENT_COMPLETED
data: {"workflow_id":"task-123","agent_id":"research-agent","message":"Task done"}

event: WORKFLOW_COMPLETED
data: {"workflow_id":"task-123","message":"Final synthesized result ready"}

Using Python SDK

from shannon import ShannonClient

client = ShannonClient(base_url="http://localhost:8080")

# Submit task
handle = client.submit_task(
    query="Research AI trends and create summary"
)

# Stream events
for event in client.stream(handle.workflow_id):
    print(f"[{event.type}] {event.message}")

    if event.type == "WORKFLOW_COMPLETED":
        print(f"Task completed: {event.message}")
        break

Event Types

Shannon emits 33 event types across different categories. Here are the most commonly used ones:

Core Workflow Events

Event Type	Description	Example Message
`WORKFLOW_STARTED`	Task processing started	`"Task processing started"`
`WORKFLOW_COMPLETED`	Workflow finished successfully	`"All done"`
`AGENT_STARTED`	Agent began processing	`"Processing query"`
`AGENT_COMPLETED`	Agent finished	`"Task done"`
`STATUS_UPDATE`	Status update/progress	`"Planning next step"`
`ERROR_OCCURRED`	Error during execution	`"Failed to connect: timeout"`

LLM & Tool Events

Event Type	Description	Example Message
`LLM_PROMPT`	Prompt sent to LLM	`"What is 5 + 5?"`
`LLM_OUTPUT`	Complete LLM response	`"5 + 5 equals 10"`
`LLM_PARTIAL`	Streaming chunk (often filtered)	`"5 + 5"`
`TOOL_INVOKED`	Tool execution started	`"Calling database_query"`
`TOOL_OBSERVATION`	Tool result/output	`"Query returned 42 rows"`

Progress & Status Events

Event Type	Description	Example Message
`PROGRESS`	Step completion update	`"Created a plan with 3 steps"`
`DATA_PROCESSING`	Processing/analyzing data	`"Preparing context"`
`DELEGATION`	Task delegated to another agent	`"Handing off to simple task"`

Multi-Agent Events (requires feature gates)

Event Type	Description	Example Message
`MESSAGE_SENT`	Agent sent message (requires `p2p_v1`)	`"Please analyze section 3"`
`MESSAGE_RECEIVED`	Agent received message (requires `p2p_v1`)	`"Received task"`
`TEAM_RECRUITED`	Agent recruited (requires `dynamic_team_v1`)	`"Summarize section 3"`
`TEAM_RETIRED`	Agent retired (requires `dynamic_team_v1`)	`"Task completed"`
`BUDGET_THRESHOLD`	Token budget threshold reached	`"Budget threshold: 80%"`

For a complete list of all 27 event types, see the Event Types Reference.

WebSocket Streaming

Connect via WebSocket

import asyncio
import websockets
import json

async def stream_task():
    uri = f"ws://localhost:8080/api/v1/stream/ws?workflow_id={workflow_id}"

    # Pass API key via headers (gateway requires header-based auth)
    async with websockets.connect(
        uri, extra_headers={'X-API-Key': 'sk_test_123456'}
    ) as websocket:
        while True:
            message = await websocket.recv()
            event = json.loads(message)
            print(f"Event: {event['type']}")

            if event['type'] == 'WORKFLOW_COMPLETED':
                break

asyncio.run(stream_task())

WebSocket streaming is currently server-to-client only. Use the REST API /api/v1/tasks/{id}/cancel to cancel tasks.

Filtering Events

Filter events by type to reduce noise:

# Only show important events
for event in client.stream(workflow_id):
    if event.type in ['PROGRESS', 'AGENT_COMPLETED', 'WORKFLOW_COMPLETED']:
        print(f"{event.type}: {event.message}")

Progress Tracking

Calculate task progress from events:

def track_progress(workflow_id):
    agents_started = 0
    agents_completed = 0

    for event in client.stream(workflow_id):
        if event.type == 'PROGRESS':
            # Track progress updates from planner
            print(f"Progress: {event.message}")

        elif event.type == 'AGENT_STARTED':
            agents_started += 1
            print(f"Agent started: {event.agent_id}")

        elif event.type == 'AGENT_COMPLETED':
            agents_completed += 1
            if agents_started > 0:
                progress = (agents_completed / agents_started) * 100
                print(f"Progress: {progress:.1f}% ({agents_completed}/{agents_started})")

        elif event.type == 'WORKFLOW_COMPLETED':
            print("✅ Task complete!")
            break

track_progress(handle.workflow_id)

Output:

Progress: Created a plan with 3 steps
Agent started: research-agent
Agent started: analysis-agent
Agent started: writer-agent
Progress: 33.3% (1/3)
Progress: 66.7% (2/3)
Progress: 100.0% (3/3)
✅ Task complete!

Real-Time UI Example

React Component

import { useEffect, useState } from 'react';

function TaskMonitor({ taskId, apiKey }) {
  const [events, setEvents] = useState([]);
  const [progress, setProgress] = useState(0);

  useEffect(() => {
    const params = new URLSearchParams({ workflow_id: taskId });

    // Note: Browser EventSource doesn't support custom headers.
    // Production: initiate SSE from a backend that injects auth headers,
    // or use GATEWAY_SKIP_AUTH=1 in development.

    const eventSource = new EventSource(
      `http://localhost:8080/api/v1/stream/sse?${params}`
    );

    const handleEvent = (e) => {
      const event = JSON.parse(e.data);
      setEvents(prev => [...prev, event]);

      // Update progress based on workflow lifecycle
      if (event.type === 'WORKFLOW_STARTED') {
        setProgress(10);
      } else if (event.type === 'PROGRESS') {
        setProgress(prev => Math.min(prev + 20, 80));
      } else if (event.type === 'WORKFLOW_COMPLETED') {
        setProgress(100);
      }
    };

    // Listen to specific event types (Shannon uses named SSE events)
    const eventTypes = [
      'WORKFLOW_STARTED',
      'WORKFLOW_COMPLETED',
      'AGENT_STARTED',
      'AGENT_COMPLETED',
      'STATUS_UPDATE',
      'PROGRESS',
      'ERROR_OCCURRED',
      'LLM_OUTPUT',
      'TOOL_INVOKED',
      'TOOL_OBSERVATION'
    ];

    eventTypes.forEach(type => {
      eventSource.addEventListener(type, handleEvent);
    });

    // Fallback for unnamed events
    eventSource.onmessage = handleEvent;

    eventSource.onerror = () => {
      console.error('SSE connection error');
    };

    return () => {
      eventTypes.forEach(type => {
        eventSource.removeEventListener(type, handleEvent);
      });
      eventSource.close();
    };
  }, [taskId, apiKey]);

  return (
    <div>
      <progress value={progress} max={100} />
      <ul>
        {events.map((e, i) => (
          <li key={i}>
            <strong>[{e.type}]</strong>{' '}
            {e.agent_id && <span>({e.agent_id})</span>}{' '}
            {e.message}
          </li>
        ))}
      </ul>
    </div>
  );
}

Key Implementation Details:

Shannon emits named SSE events (e.g., event: AGENT_STARTED), so you must use addEventListener() for each event type
Browser EventSource API doesn’t support custom headers. Do not pass API keys via query params; use a backend proxy to inject headers or disable auth in development
Always clean up event listeners in the cleanup function to prevent memory leaks

Error Handling

Handle connection failures gracefully:

from shannon import ShannonClient
from shannon.errors import ShannonError, ConnectionError
import time

def robust_streaming(workflow_id, max_retries=3):
    client = ShannonClient(base_url="http://localhost:8080")

    for attempt in range(max_retries):
        try:
            for event in client.stream(workflow_id):
                print(f"Event: {event.type}")

                if event.type == 'WORKFLOW_COMPLETED':
                    # Get final status to retrieve result
                    status = client.get_status(workflow_id.replace('wf-', 'task-'))
                    return status.result

        except (ShannonError, ConnectionError) as e:
            print(f"Stream error (attempt {attempt + 1}): {e}")
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                raise

Authentication with Streaming

When authentication is enabled, provide API key:

SSE with API Key

curl -N \
  -H "X-API-Key: sk_test_123456" \
  http://localhost:8080/api/v1/stream/sse?workflow_id={task_id}

Python SDK with API Key

client = ShannonClient(
    base_url="http://localhost:8080",
    api_key="sk_test_123456"
)

# API key automatically included in all requests
for event in client.stream(workflow_id):
    print(event)

Performance Considerations

Event Buffering

Shannon buffers events to prevent overwhelming clients:

# config/shannon.yaml
streaming:
  buffer_size: 100  # Maximum queued events
  flush_interval_ms: 100  # Send batches every 100ms

Keepalive

SSE sends keepalive comments every 15 seconds to prevent timeout:

: keepalive
: keepalive
event: LLM_PROMPT
data: {"message":"..."}

Resource Cleanup

Always close streams when done:

# Iterate through events (automatically handles connection)
for event in client.stream(workflow_id):
    if event.type == 'WORKFLOW_COMPLETED':
        break
# No explicit close needed

Multi-Task Monitoring

Monitor multiple tasks simultaneously:

import asyncio

async def monitor_task(client, workflow_id):
    # Use sync stream() in async context
    for event in client.stream(workflow_id):
        print(f"[{workflow_id}] {event.type}")
        if event.type == 'WORKFLOW_COMPLETED':
            # Get final result from status
            task_id = workflow_id.replace('wf-', 'task-')
            status = client.get_status(task_id)
            return status.result

async def monitor_all(task_ids):
    client = AsyncShannonClient(base_url="http://localhost:8080")

    # Monitor all tasks in parallel
    results = await asyncio.gather(*[
        monitor_task(client, tid) for tid in task_ids
    ])

    return results

# Run
task_ids = ["task-1", "task-2", "task-3"]
results = asyncio.run(monitor_all(task_ids))

Desktop Application

Shannon provides a native desktop application built with Tauri for real-time task monitoring and management. The desktop app offers:

Live event streaming for active tasks
Task submission and management interface
Real-time metrics and visualizations
Session history browsing

The desktop application has replaced the previous web dashboard. See the Desktop App Guide for installation and usage instructions.

Best Practices

1. Use SSE for Simple Monitoring

# ✅ Good: Simple SSE streaming
for event in client.stream(workflow_id):
    print(event.type)

2. Handle Disconnections

# ✅ Good: Retry logic
for attempt in range(3):
    try:
        for event in client.stream(workflow_id):
            process_event(event)
        break
    except (ShannonError, ConnectionError):
        if attempt == 2:
            raise
        time.sleep(2)

3. Filter Unnecessary Events

# ✅ Good: Only critical events
critical_events = ['PROGRESS', 'WORKFLOW_COMPLETED', 'ERROR_OCCURRED']
for event in client.stream(workflow_id):
    if event.type in critical_events:
        handle_event(event)

4. Set Timeouts

# ✅ Good: Timeout protection
import signal

def timeout_handler(signum, frame):
    raise TimeoutError("Stream timeout")

signal.signal(signal.SIGALRM, timeout_handler)
signal.alarm(300)  # 5 minute timeout

try:
    for event in client.stream(workflow_id):
        print(event)
finally:
    signal.alarm(0)  # Cancel alarm

Next Steps

Python SDK

Async streaming with SDK

API Reference

Streaming API details

Monitoring

Task monitoring guide

WebSocket API

WebSocket documentation

Getting Started

Core Concepts

Guides

Real-Time Streaming

Overview

Streaming Technologies

Server-Sent Events (SSE)

Using cURL

Using Python SDK

Event Types

Core Workflow Events

LLM & Tool Events

Progress & Status Events

Multi-Agent Events (requires feature gates)

WebSocket Streaming

Connect via WebSocket

Filtering Events

Progress Tracking

Real-Time UI Example

React Component

Error Handling

Authentication with Streaming

SSE with API Key

Python SDK with API Key

Performance Considerations

Event Buffering

Keepalive

Resource Cleanup

Multi-Task Monitoring

Desktop Application

Best Practices

1. Use SSE for Simple Monitoring

2. Handle Disconnections

3. Filter Unnecessary Events

4. Set Timeouts

Next Steps

Python SDK

API Reference

Monitoring

WebSocket API

Getting Started

Core Concepts

Guides

​Overview

​Streaming Technologies

​Server-Sent Events (SSE)

​Using cURL

​Using Python SDK

​Event Types

​Core Workflow Events

​LLM & Tool Events

​Progress & Status Events

​Multi-Agent Events (requires feature gates)

​WebSocket Streaming

​Connect via WebSocket

​Filtering Events

​Progress Tracking

​Real-Time UI Example

​React Component

​Error Handling

​Authentication with Streaming

​SSE with API Key

​Python SDK with API Key

​Performance Considerations

​Event Buffering

​Keepalive

​Resource Cleanup

​Multi-Task Monitoring

​Desktop Application

​Best Practices

​1. Use SSE for Simple Monitoring

​2. Handle Disconnections

​3. Filter Unnecessary Events

​4. Set Timeouts

​Next Steps

Python SDK

API Reference

Monitoring

WebSocket API

Overview

Streaming Technologies

Server-Sent Events (SSE)

Using cURL

Using Python SDK

Event Types

Core Workflow Events

LLM & Tool Events

Progress & Status Events

Multi-Agent Events (requires feature gates)

WebSocket Streaming

Connect via WebSocket

Filtering Events

Progress Tracking

Real-Time UI Example

React Component

Error Handling

Authentication with Streaming

SSE with API Key

Python SDK with API Key

Performance Considerations

Event Buffering

Keepalive

Resource Cleanup

Multi-Task Monitoring

Desktop Application

Best Practices

1. Use SSE for Simple Monitoring

2. Handle Disconnections

3. Filter Unnecessary Events

4. Set Timeouts

Next Steps