langchain-ai/langgraph · error · TypeError
StreamChannel has not been bound yet. Register the…
Error message
StreamChannel {name!r} has not been bound yet. Register the transformer with a StreamMux first. What it means
Raised by RunStream.interleave() when a requested name maps to an extension that is not a StreamChannel or whose binding state is unknown (never registered with a StreamMux). The channel cannot be consumed because no transformer has produced events for it. Register the transformer with a StreamMux before interleaving.
Solutions
- Register the owning transformer with the StreamMux before consuming channels
- Obtain channels from a running run's extensions rather than creating them standalone
Defensive patterns
Strategy: validation
When it happens
Trigger: Thrown at libs/langgraph/langgraph/stream/run_stream.py:265 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of langchain-ai/langgraph@38031739e5 (2026-08-26).
Data as JSON: /api/errors/8c970c4840ca7430.
Report an issue: GitHub.
Appendix: source
Thrown at libs/langgraph/langgraph/stream/run_stream.py:265
if name == "messages":
print("msg:", item)
else:
print("val:", item)
```
"""
from langgraph.stream.stream_channel import StreamChannel
channels: dict[str, StreamChannel[Any]] = {}
try:
for name in names:
ch = self.extensions[name]
if not isinstance(ch, StreamChannel):
raise TypeError(
f"interleave() requires StreamChannel projections, "
f"got {type(ch).__name__} for {name!r}"
)
if ch._is_async is None:
raise TypeError(
f"StreamChannel {name!r} has not been bound yet. "
"Register the transformer with a StreamMux first."
)
if ch._is_async:
raise TypeError(
f"StreamChannel {name!r} is bound to async mode — "
"sync interleave() cannot consume async channels."
)
if ch._subscribed:
raise RuntimeError(
f"StreamChannel {name!r} already has a subscriber; "
"use .tee(n) for fan-out."
)
ch._subscribed = True
channels[name] = ch
done: set[str] = set()
View on GitHub (pinned to 38031739e5)