StreamEmitter API reference (Python)

When to use this

Use StreamEmitter when you want your tools to send live progress updates back to the caller during app.stream(...) / app.astream(...) execution. Unlike traditional tool results (which return only after completion), StreamEmitter allows tools to report intermediate steps, retries, and errors in real-time.

Available only during streaming. StreamEmitter is automatically injected into tool functions when the graph is run in streaming mode. During invoke() / ainvoke() (non-streaming), tools receive emit=None.

Import path

Python

from tenxgraph.core.state.stream_emitter import StreamEmitter

Constructor

StreamEmitter is created automatically by the graph runtime. You do not instantiate it directly. Instead, declare it as an optional parameter in your tool functions:

Python

def my_tool(
    location: str,
    emit: StreamEmitter | None = None,
) -> str:
    if emit:
        emit.progress("Starting work...", data={"step": 1})
    # ... work ...
    return "result"

Core Methods

`progress(message: str, data: dict | None = None) -> None`

Emit a progress update for the running tool.

When to use: Report intermediate steps, current progress, or status changes during tool execution.

Characteristics:

Status is set to "tool_progress"
Visible at all ResponseGranularity levels (default LOW)
Commonly used for retry attempts, step counts, or percentage completion

Parameters:

message (str): Human-readable description of the current step (e.g., “Fetching data…”, “Attempt 2 of 3”)
data (dict, optional): Extra metadata as key-value pairs (e.g., {"attempt": 2, "max_attempts": 3})

Example:

Python

def search_with_retries(query: str, emit: StreamEmitter | None = None) -> str:
    for attempt in range(3):
        if emit:
            emit.progress(
                f"Attempt {attempt + 1} of 3",
                data={"attempt": attempt + 1, "max_attempts": 3}
            )
        try:
            return _perform_search(query)
        except TemporaryError:
            if attempt == 2:
                raise

`error(message: str, data: dict | None = None) -> None`

Emit an error update for the running tool.

When to use: Report failures, warnings, or issues during execution without stopping the tool. The tool continues and eventually returns a result.

Characteristics:

Status is set to "tool_failed"
Purely informational; does not interrupt execution
The tool result is still returned normally after this call

Parameters:

message (str): Human-readable description of the error
data (dict, optional): Extra metadata

Example:

Python

def fetch_data(url: str, emit: StreamEmitter | None = None) -> str:
    try:
        return requests.get(url).text
    except requests.ConnectTimeout:
        if emit:
            emit.error(
                "Connection timeout, using cached data",
                data={"retry_count": 3, "cache_age_seconds": 3600}
            )
        return get_cached_data(url)

`message(message: str, data: dict | None = None) -> None`

Emit a plain message update from the running tool.

When to use: Send arbitrary informational messages that don’t fit the “progress” or “error” categories.

Characteristics:

Status is set to "tool_message"
General-purpose status/informational updates

Parameters:

message (str): Message text
data (dict, optional): Extra metadata

Example:

Python

def process_file(filename: str, emit: StreamEmitter | None = None) -> str:
    if emit:
        emit.message(f"Processing file: {filename}")
    # process
    if emit:
        emit.message("File processing complete", data={"lines_processed": 1000})
    return "done"

`update(data: dict) -> None`

Emit a generic data update from the running tool.

When to use: Send custom metrics, counters, or structured data updates without a message.

Characteristics:

Status is set via data["status"] if provided, otherwise omitted
Purely data-driven; no built-in message field

Parameters:

data (dict): Arbitrary key-value pairs to include in the stream chunk

Example:

Python

def batch_processor(items: list, emit: StreamEmitter | None = None) -> int:
    processed = 0
    for item in items:
        _process(item)
        processed += 1
        if emit:
            emit.update({
                "status": "batch_progress",
                "processed_count": processed,
                "total_count": len(items),
                "percentage": (processed / len(items)) * 100,
            })
    return processed

Attributes

StreamEmitter carries useful metadata about the current execution:

tool_name (str): Name of the tool being executed
tool_call_id (str): Unique identifier for this tool invocation
node_name (str): Name of the graph node executing the tool
thread_id (str | None): Active thread/session identifier
run_id (str | None): Active run identifier

These are automatically populated; you typically do not need to access them directly.

Stream Chunk Output

When you emit via StreamEmitter, a StreamChunk is added to the stream output with the following structure:

Python

{
    "event": "message" | "error" | "update",  # StreamEvent enum
    "data": {
        "status": "tool_progress" | "tool_failed" | "tool_message" | ...,
        "tool_name": "my_tool",
        "tool_call_id": "call_abc123",
        "node": "TOOL",
        "message": "...",  # if progress/error/message emitted
        "thread_id": "thread_xyz",
        "run_id": "run_123",
        # ... plus any extra data you passed
    },
    "thread_id": "thread_xyz",
    "run_id": "run_123",
}

Frontend clients consume these chunks from app.stream(...) / app.astream(...) to display live updates.

Usage Patterns

Pattern 1: Retry with Progress

Track retry attempts and emit updates after each failure:

Python

def call_external_api(endpoint: str, emit: StreamEmitter | None = None) -> str:
    max_retries = 3
    for attempt in range(max_retries):
        try:
            if emit and attempt > 0:
                emit.progress(f"Retry attempt {attempt}", data={"attempt": attempt})
            return requests.get(endpoint).json()
        except requests.RequestException as e:
            if attempt == max_retries - 1:
                if emit:
                    emit.error(f"All {max_retries} retries failed: {e}")
                raise

Pattern 2: Long-Running Task with Status Milestones

For tasks that take time, emit updates at key milestones:

Python

def process_large_file(filepath: str, emit: StreamEmitter | None = None) -> dict:
    if emit:
        emit.progress("Opening file...")
    with open(filepath) as f:
        lines = f.readlines()
    
    if emit:
        emit.progress(f"Loaded {len(lines)} lines, starting processing...")
    
    results = []
    for i, line in enumerate(lines):
        results.append(_process_line(line))
        if (i + 1) % 100 == 0 and emit:
            emit.progress(f"Processed {i + 1} of {len(lines)} lines")
    
    if emit:
        emit.message("Processing complete", data={"total_processed": len(results)})
    
    return {"count": len(results), "results": results}

Pattern 3: Multi-Step Tool with Conditional Branching

Emit different updates based on tool behavior:

Python

def intelligent_search(query: str, emit: StreamEmitter | None = None) -> str:
    if emit:
        emit.progress("Analyzing query...", data={"step": 1})
    
    query_type = _classify_query(query)
    
    if query_type == "simple":
        if emit:
            emit.progress("Using fast path", data={"step": 2})
        return _fast_search(query)
    else:
        if emit:
            emit.progress("Using deep search path", data={"step": 2})
        
        # Complex search with fallbacks
        try:
            if emit:
                emit.progress("Querying primary index...", data={"step": 3})
            return _deep_search_primary(query)
        except IndexError:
            if emit:
                emit.error("Primary index unavailable, falling back...", data={"step": 3})
                emit.progress("Querying secondary index...", data={"step": 4})
            return _deep_search_secondary(query)

Behavior During `invoke()` vs `stream()`

During `app.stream()` / `app.astream()`:

emit: StreamEmitter is automatically created and injected
Tools can call emit.progress(), emit.error(), etc.
Emitted chunks appear in the stream output
Frontend clients see live updates in real-time

During `app.invoke()` / `app.ainvoke()`:

emit: None is injected (or the parameter is absent)
Tools should check if emit: before calling any emit methods
No streaming output is generated
Only the final tool result is returned

Thread Safety

StreamEmitter is thread-safe. All emit methods use loop.call_soon_threadsafe internally, so:

Sync tools (running in asyncio.to_thread) can call emit methods safely
Async tools can call emit methods safely
No need for locks or explicit synchronization

Example: Complete Tool with StreamEmitter

Python

from tenxgraph.core.graph import Agent, ToolNode
from tenxgraph.core.state import AgentState
from tenxgraph.core.state.stream_emitter import StreamEmitter
import time

def get_weather(
    location: str,
    tool_call_id: str | None = None,
    state: AgentState | None = None,
    emit: StreamEmitter | None = None,
) -> str:
    """Get the current weather for a specific location.
    
    Injectable parameters: tool_call_id and state are automatically injected.
    emit is only injected during streaming; it's None during invoke().
    """
    if emit:
        emit.progress("Fetching weather data...", data={"location": location})

    time.sleep(1)  # Simulate API call

    if emit:
        emit.progress("Processing weather data...", data={"location": location})

    if tool_call_id:
        print(f"Tool call ID: {tool_call_id}")
    
    if state and hasattr(state, "context"):
        print(f"Message count: {len(state.context)}")

    if emit:
        emit.progress("Finalizing response...", data={"location": location})

    return f"The weather in {location} is sunny"

# Use in a graph
tool_node = ToolNode([get_weather])

agent = Agent(
    model="gemini-2.5-flash",
    provider="google",
    system_prompt=[{
        "role": "system",
        "content": "You are a helpful weather assistant. Call get_weather when asked.",
    }],
    tool_node=tool_node,
)

# Streaming will show progress updates in real-time
config = {"thread_id": "12345", "is_stream": True}
for chunk in graph.stream(input_data, config=config):
    print(chunk.model_dump())

When to use this

Import path

Constructor

Core Methods

progress(message: str, data: dict | None = None) -> None

error(message: str, data: dict | None = None) -> None

message(message: str, data: dict | None = None) -> None

update(data: dict) -> None

Attributes

Stream Chunk Output

Usage Patterns

Pattern 1: Retry with Progress

Pattern 2: Long-Running Task with Status Milestones

Pattern 3: Multi-Step Tool with Conditional Branching

Behavior During invoke() vs stream()

During app.stream() / app.astream():

During app.invoke() / app.ainvoke():

Thread Safety

Example: Complete Tool with StreamEmitter

See Also

`progress(message: str, data: dict | None = None) -> None`

`error(message: str, data: dict | None = None) -> None`

`message(message: str, data: dict | None = None) -> None`

`update(data: dict) -> None`

Behavior During `invoke()` vs `stream()`

During `app.stream()` / `app.astream()`:

During `app.invoke()` / `app.ainvoke()`: