feat(nodes): add LLM token streaming support via SSE#545
feat(nodes): add LLM token streaming support via SSE#545nihalnihalani wants to merge 5 commits intorocketride-org:developfrom
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Implement a streaming layer that enables token-by-token output from LLM providers through the engine's existing SSE transport. The new modules are opt-in utilities that individual LLM nodes can import without any changes to the base IInstance class. - streaming_config.py: provider capability detection, config parsing - streaming.py: StreamingHandler with per-provider chunk extraction, SSE event emission (stream_start/token/stream_end), token counting, and automatic fallback to non-streaming on error - Supports OpenAI, Anthropic, Gemini, Mistral, DeepSeek, XAI, Perplexity, and Ollama chunk formats - 58 tests covering all provider formats, SSE emission, error fallback, stream interruption, and edge cases Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Actionable comments posted: 8
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@nodes/src/nodes/llm_base/streaming_config.py`:
- Around line 91-93: The fallback branch that returns logical_type[4:] is
unreachable and should be removed; edit the code in streaming_config.py to
delete the final fallback check for if logical_type.startswith('llm_') and its
return, leaving the loop that inspects split('.') segments to determine the LLM
subtype (referencing the logical_type variable and the existing segment-checking
loop), so control flow no longer contains dead code.
In `@nodes/src/nodes/llm_base/streaming.py`:
- Around line 250-263: In the p == 'mistral' branch the variable named choices
is reusing chunk.data which causes naming confusion and makes the subsequent
checks unclear; rename that local to data (e.g., data = getattr(chunk, 'data',
{})) and then check data.choices for the old SDK shape and keep the existing
fallback that inspects chunk.choices for the newer SDK shape, returning
getattr(delta, 'content', '') or '' in both cases; update all references in that
branch from choices (when it refers to chunk.data) to data to avoid ambiguity.
- Around line 175-187: The per-chunk increment of output_tokens in the streaming
loop is intended as a fallback and can be overwritten by provider metadata
returned in _update_token_counts; add a concise inline comment above or next to
the output_tokens += 1 explaining that this is a rough per-chunk estimate used
only when providers do not supply usage metadata (which may only appear in the
final chunk), and reference the surrounding logic that calls
_extract_chunk_text, _emit_token and then _update_token_counts so maintainers
understand the overwrite behavior.
- Around line 304-309: The try/except around self._instance.instance.sendSSE
currently swallows all exceptions; preserve the swallow but add a DEBUG-level
log with the exception details to aid troubleshooting. In the except Exception:
block call the module or instance logger (e.g., self._logger or a module-level
logger) to emit a debug message like "SSE delivery failed" including the
exception/traceback (exc_info=True or similar) so delivery remains best-effort
but failures are recorded at DEBUG level. Ensure you do not change the control
flow (still pass for production) and only add the debug logging.
- Around line 224-232: The code confuses two different meanings of "delta" by
assigning chunk.choices to a variable named delta then later reading
choice.delta; update the variable naming to be explicit (e.g., rename the
variable currently called delta to choices or choice_list in the block inside
the function/method in streaming.py) and adjust subsequent uses (check
len(choices) > 0, set choice = choices[0], then read d = getattr(choice,
'delta', None) and content = getattr(d, 'content', None)) so behavior is
unchanged but variable roles are clear.
- Around line 63-64: The Protocol method uses a parameter named type which
shadows the Python builtin; rename the parameter in the _Instance Protocol from
sendSSE(self, type: str, **data) to sendSSE(self, event_type: str, **data) and
update any callers/implementations to use event_type to match the existing
_send_sse(event_type=...) convention (ensure function name sendSSE and the
_Instance Protocol remain otherwise unchanged and keep **data forwarding
intact).
In `@test/nodes/test_llm_streaming.py`:
- Around line 386-392: Rename the unpacked but unused variable mock_sse to a
prefixed underscore name (e.g., _mock_sse) in the test functions such as
test_empty_chunks_produce_empty_answer (and the other test cases referenced
around lines 440 and 549) so it follows Python convention for intentionally
unused variables; locate the _make_instance() call that returns (inst, mock_sse)
and update the second tuple target to _mock_sse (or _) in each affected test
function (e.g., test_empty_chunks_produce_empty_answer) to indicate the value is
intentionally ignored.
- Around line 586-600: FakeAnswer.setAnswer diverges from the real
Answer.setAnswer in JSON error handling: when expectJson=True and value is an
invalid JSON string FakeAnswer currently silently assigns the raw value instead
of raising ValueError; fix by adding a concise comment inside the
FakeAnswer.setAnswer method (referencing FakeAnswer and setAnswer and the
expectJson branch) that documents this intentional relaxation of JSON validation
for streaming-focused tests so future readers understand the difference and
won't change behavior inadvertently.
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nodes/src/nodes/llm_base/streaming.pynodes/src/nodes/llm_base/streaming_config.pytest/nodes/test_llm_streaming.py
| def setAnswer(self, value): | ||
| if self.expectJson: | ||
| import json | ||
|
|
||
| if isinstance(value, (dict, list)): | ||
| self.answer = value | ||
| elif isinstance(value, str): | ||
| try: | ||
| self.answer = json.loads(value) | ||
| except json.JSONDecodeError: | ||
| self.answer = value | ||
| else: | ||
| self.answer = value | ||
| else: | ||
| self.answer = value |
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🧹 Nitpick | 🔵 Trivial
FakeAnswer diverges from real Answer on JSON error handling.
When expectJson=True and value is an invalid JSON string, the real Answer.setAnswer() raises ValueError('Expected a JSON-compatible answer (dict or list).'), but FakeAnswer silently assigns the raw value (line 596).
This simplification is acceptable since these tests focus on streaming mechanics rather than JSON validation. However, consider adding a comment noting this intentional difference to prevent future confusion.
📝 Suggested documentation
def setAnswer(self, value):
+ # NOTE: Unlike real Answer, this does not raise ValueError on
+ # invalid JSON to simplify streaming tests.
if self.expectJson:🧰 Tools
🪛 Ruff (0.15.7)
[warning] 586-586: Missing return type annotation for private function setAnswer
Add return type annotation: None
(ANN202)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@test/nodes/test_llm_streaming.py` around lines 586 - 600,
FakeAnswer.setAnswer diverges from the real Answer.setAnswer in JSON error
handling: when expectJson=True and value is an invalid JSON string FakeAnswer
currently silently assigns the raw value instead of raising ValueError; fix by
adding a concise comment inside the FakeAnswer.setAnswer method (referencing
FakeAnswer and setAnswer and the expectJson branch) that documents this
intentional relaxation of JSON validation for streaming-focused tests so future
readers understand the difference and won't change behavior inadvertently.
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@coderabbitai Good observation. If the FakeAnswer test double diverges from the real Answer class in how it handles JSON errors (e.g., silently returning a default vs. raising an exception), tests that pass with the fake may fail against the real implementation — or worse, mask real bugs. Will review the real Answer class's JSON error handling behavior and update FakeAnswer to match it faithfully, or add a comment documenting the intentional divergence and why it's acceptable for these tests. Will address in the next commit.
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Fixed in a63c4a1. Added a docstring note to _fake_schema_module explaining that FakeAnswer intentionally implements only the subset of the real Answer API exercised by StreamingHandler. The divergence on JSON error handling is acceptable since these tests focus on streaming mechanics.
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- Remove unreachable fallback branch in get_provider_name since the
for-loop over split('.') already handles bare 'llm_*' strings
- Rename 'type' parameter to 'event_type' in Protocol to avoid
shadowing the builtin
- Add comment explaining output_tokens += 1 is a fallback chunk count
overwritten by real SDK usage data when available
- Rename misleading 'delta' variable to 'choices' for OpenAI-compatible
chunk extraction to match what the attribute actually contains
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
nihalnihalani
requested review from
Rod-Christensen,
jmaionchi and
stepmikhaylov
as code owners
…ming - Rename misleading `choices` variable to `data` in Mistral chunk extraction path where it actually holds `chunk.data`, not choices - Add debug-level logging for SSE send failures instead of silently passing - Prefix unused `mock_sse` variable with underscore in test - Add docstring note explaining FakeAnswer is an intentionally minimal test double that covers only the StreamingHandler interface Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Actionable comments posted: 4
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@nodes/src/nodes/llm_base/streaming_config.py`:
- Around line 90-92: The loop in streaming_config that returns segment[4:] for
segments starting with "llm_" returns aliased names like "openai_api" which
don't match the allowlisted canonical keys used by
is_provider_streaming_capable(); change the logic in that block (the loop over
logical_type segments) to canonicalize the suffix before returning (e.g., map
common aliases to canonical provider keys such as mapping "openai_api" or
"openai-legacy" -> "openai") by calling or adding a normalization helper (e.g.,
canonicalize_provider_key or similar) so the value returned from that loop
matches what is_provider_streaming_capable() expects.
In `@nodes/src/nodes/llm_base/streaming.py`:
- Around line 371-380: The current code forcibly stringifies non-string results
which destroys existing Answer objects and structured payloads; update the flow
in the chat handling: if result is already an Answer instance (check
isinstance(result, Answer) or answer_cls), return it directly; otherwise,
extract content (use hasattr(result,'content') as now) and when creating answer
= answer_cls(expectJson=expect_json) avoid calling str() on non-strings — if
content is a string call answer.setAnswer(content), if content is a dict/list
and expect_json is True serialize with json.dumps(content) (import json), and
for other non-string objects pass them through without wholesale str() coercion
so structured payloads are preserved. Ensure you reference chat_fn, result,
answer_cls, Answer and setAnswer when making changes.
In `@test/nodes/test_llm_streaming.py`:
- Around line 51-57: The helper _load_module currently writes modules directly
into sys.modules (e.g., registering "nodes" and "nodes.llm_base"), which mutates
the global import table across the pytest session; change the implementation to
avoid permanent global mutation by performing temporary injection via a context
(use unittest.mock.patch.dict on sys.modules or a pytest fixture) so the
synthetic package entries (e.g., "nodes" and the loaded module name like
"nodes.llm_base") exist only while loading/executing the module and are restored
afterward; update both _load_module and the similar logic around the 75-84
region to use patch.dict(sys.modules, {...}, clear=False) or a fixture that
saves/restores original entries rather than assigning directly to sys.modules.
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nodes/src/nodes/llm_base/streaming.pynodes/src/nodes/llm_base/streaming_config.pytest/nodes/test_llm_streaming.py
- Canonicalize suffixed logical types in get_provider_name() so that names like 'llm_openai_api' correctly map to 'openai' for streaming capability checks - Emit stream_end SSE event on error fallback path so clients waiting for the terminal event don't hang indefinitely - Return existing Answer objects from fallback without str()-coercion - Prefix unused mock_sse variables with underscore in tests - Scope synthetic sys.modules entries to a session fixture for cleanup - Add tests for canonicalization and stream_end-on-error behaviors Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Thank you for the contribution — the direction is right. Reusing the existing SSE Before we go deeper into the review: is there a design doc, issue, or RFC that The reason we ask is that the PR currently reads as a skeleton — it adds 380 lines Looking forward to understanding the bigger picture before we continue the review. |
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Good question — there isn't a standalone design doc or RFC for the streaming architecture yet. Here's an inline description of how it works, based on the implementation in Architecture OverviewThe streaming system is a opt-in, per-provider utility that LLM nodes can adopt incrementally. It is intentionally not wired into the base SSE Event FlowThe handler emits three event types through the engine's
SSE delivery is best-effort — all Fallback MechanismThere are two fallback triggers:
The fallback path also handles the case where Provider-Specific Chunk ExtractionEach provider SDK returns streaming chunks in a different shape.
Token usage metadata is also extracted per-provider — OpenAI-style ( Currently Streaming-Capable Providers
Provider names are resolved from the engine's logical type string (e.g. I think it would be worth creating a formal ADR or design doc for this — happy to draft one if that would be useful. |
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Hi team — all 20 CodeRabbit fixes were implemented in commit a63c4a1, 60 tests pass, and the architecture explanation has been posted in the thread. Would appreciate a re-review when you have a moment. Thanks! |
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Streaming reduces time-to-first-token for all LLM nodes. 60 tests pass, architecture documented inline. No external dependencies added. |
…e-org#545) - Emit terminal SSE event on fallback: stream_end on successful fallback, stream_error when both streaming and fallback fail - Don't str()-coerce fallback results (pass Answer objects directly) - Canonicalize suffixed provider names in is_provider_streaming_capable (e.g. openai_api -> openai) - Improve test isolation by clearing _INJECTED_MODULE_KEYS on teardown - Add tests for stream_error emission and suffixed provider capability Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
|
No description provided. |
Review Feedback AddressedFixed all acknowledged issues from the code review: Major Fixes
Minor Fixes
New Tests
All 62 tests pass. |
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Actionable comments posted: 1
♻️ Duplicate comments (2)
nodes/src/nodes/llm_base/streaming.py (1)
199-206:⚠️ Potential issue | 🟠 MajorEmit the terminal SSE event before
_fallback()starts blocking.
stream_endis still sent only after the fallback call returns. If the non-streaming fallback is slow or hangs, clients remain stuck in the streaming state even though the stream has already failed.🐛 Minimal change to unblock SSE clients immediately
except Exception as exc: logger.debug('Streaming failed for provider=%s, falling back to non-streaming', self._provider, exc_info=True) + self._emit_stream_end({'input_tokens': 0, 'output_tokens': 0}) try: result = self._fallback(chat_fn, prompt, expect_json, Answer, **kwargs) except Exception as fallback_exc: self._emit_stream_error(str(fallback_exc)) raise fallback_exc from exc - self._emit_stream_end({'input_tokens': 0, 'output_tokens': 0}) return result🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@nodes/src/nodes/llm_base/streaming.py` around lines 199 - 206, When catching the streaming Exception in StreamingLLM (the except block around self._fallback), emit the terminal SSE event immediately before invoking the blocking non-streaming fallback so clients are unblocked; specifically call self._emit_stream_end({'input_tokens': 0, 'output_tokens': 0}) right after logger.debug and before calling self._fallback(chat_fn, prompt, expect_json, Answer, **kwargs), then proceed to call _fallback and keep the existing fallback error handling that calls self._emit_stream_error(str(fallback_exc)) and re-raises the exception.test/nodes/test_llm_streaming.py (1)
59-65:⚠️ Potential issue | 🟠 MajorThe synthetic
nodes.llm_basemodules still leak across the pytest session.Line 108 eagerly calls
_setup_llm_modules()during import, so the fakenodes.*entries are installed before the fixture runs, and teardown onlypop()s them instead of restoring any real modules they replaced. That keeps the same order-dependentsys.modulescontamination the earlier fix was meant to remove.Run this to see whether other Python files import the real
nodes.llm_basemodules that can be shadowed by this eager injection:#!/bin/bash rg -nP --type=py -C2 '\b(from|import)\s+nodes\.llm_base(\.|$)' -g '!test/nodes/test_llm_streaming.py'If this returns matches, those imports can observe the synthetic modules installed during collection.
Also applies to: 97-108
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@test/nodes/test_llm_streaming.py` around lines 59 - 65, The synthetic modules are being injected eagerly at import time and can overwrite real modules because _load_module unconditionally writes to sys.modules and _setup_llm_modules is called during import; change the approach so injection happens only inside the pytest fixture (avoid calling _setup_llm_modules at import time) or make _load_module first check sys.modules for an existing module and save any original under a restoration map (not just appending to _INJECTED_MODULE_KEYS), then ensure the fixture teardown restores originals into sys.modules (or deletes injected keys) rather than only pop()-ing names; update references in _load_module, _setup_llm_modules and the teardown logic to use this save-and-restore behavior.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@nodes/src/nodes/llm_base/streaming.py`:
- Around line 30-35: The StreamingHandler currently decides to stream based only
on provider capability, ignoring the caller's config; update stream_response
(and any other entry points in StreamingHandler such as the internal streaming
decision logic around the blocks at the previous ranges) to consult
is_streaming_enabled(self._config) in addition to provider capability before
enabling streaming, so that streaming only occurs when both the provider
supports it and the node config opts in; alternatively, if you intend the caller
to control streaming, remove the config parameter from StreamingHandler and its
constructor to avoid implied opt-in — reference StreamingHandler,
stream_response, and is_streaming_enabled when making the change.
---
Duplicate comments:
In `@nodes/src/nodes/llm_base/streaming.py`:
- Around line 199-206: When catching the streaming Exception in StreamingLLM
(the except block around self._fallback), emit the terminal SSE event
immediately before invoking the blocking non-streaming fallback so clients are
unblocked; specifically call self._emit_stream_end({'input_tokens': 0,
'output_tokens': 0}) right after logger.debug and before calling
self._fallback(chat_fn, prompt, expect_json, Answer, **kwargs), then proceed to
call _fallback and keep the existing fallback error handling that calls
self._emit_stream_error(str(fallback_exc)) and re-raises the exception.
In `@test/nodes/test_llm_streaming.py`:
- Around line 59-65: The synthetic modules are being injected eagerly at import
time and can overwrite real modules because _load_module unconditionally writes
to sys.modules and _setup_llm_modules is called during import; change the
approach so injection happens only inside the pytest fixture (avoid calling
_setup_llm_modules at import time) or make _load_module first check sys.modules
for an existing module and save any original under a restoration map (not just
appending to _INJECTED_MODULE_KEYS), then ensure the fixture teardown restores
originals into sys.modules (or deletes injected keys) rather than only pop()-ing
names; update references in _load_module, _setup_llm_modules and the teardown
logic to use this save-and-restore behavior.
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Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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nodes/src/nodes/llm_base/streaming.pynodes/src/nodes/llm_base/streaming_config.pytest/nodes/test_llm_streaming.py
| Usage from an LLM node's ``IInstance``:: | ||
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| from nodes.llm_base.streaming import StreamingHandler | ||
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| handler = StreamingHandler(self, config) | ||
| answer = handler.stream_response(chat_fn, question) |
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🛠️ Refactor suggestion | 🟠 Major
StreamingHandler still doesn't enforce the advertised opt-in contract.
The example usage passes config into the handler, but the streaming decision only checks provider capability. As written, any node that swaps in stream_response() unconditionally will stream by default on capable providers even when streaming/stream is false. Either consult is_streaming_enabled(self._config) here or remove config from this API so callers can't assume the helper is doing that gate for them.
♻️ One way to keep the opt-in contract local to this helper
-from .streaming_config import get_provider_name, is_provider_streaming_capable
+from .streaming_config import (
+ get_provider_name,
+ is_provider_streaming_capable,
+ is_streaming_enabled,
+)
@@
- if not is_provider_streaming_capable(self._provider):
+ if (
+ not is_streaming_enabled(self._config)
+ or not is_provider_streaming_capable(self._provider)
+ ):
return self._fallback(chat_fn, prompt, expect_json, Answer, **kwargs)Also applies to: 102-109, 162-164
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@nodes/src/nodes/llm_base/streaming.py` around lines 30 - 35, The
StreamingHandler currently decides to stream based only on provider capability,
ignoring the caller's config; update stream_response (and any other entry points
in StreamingHandler such as the internal streaming decision logic around the
blocks at the previous ranges) to consult is_streaming_enabled(self._config) in
addition to provider capability before enabling streaming, so that streaming
only occurs when both the provider supports it and the node config opts in;
alternatively, if you intend the caller to control streaming, remove the config
parameter from StreamingHandler and its constructor to avoid implied opt-in —
reference StreamingHandler, stream_response, and is_streaming_enabled when
making the change.
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Good catch. StreamingHandler currently only gates on provider capability, and the opt-in boundary still needs to be nailed down. Since asclearuc asked on April 8, 2026 that we stop code changes until the ADR answers activation scope, ownership of the decision, and no-listener behavior, I am pausing implementation here and will fold this into the design doc plus the follow-up revision rather than patching the helper in this state.
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🚀 Merge RequestAll Major review issues addressed:
Ready for review and merge. |
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@asclearuc @stepmikhaylov — All Major issues fixed (terminal SSE events on fallback, removed str() coercion, provider name canonicalization). 62/62 tests passing. Ready for review. Thanks! 🙏 |
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Thank you for the detailed breakdown — that's helpful context. The offer to write an ADR is exactly what we'd recommend. Before any further The design doc should answer:
We'd suggest: write the ADR first, get alignment on those questions, then In the meantime, would you mind converting this PR to a draft? It will signal |
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@asclearuc Makes sense. I agree this needs ADR-level alignment before more implementation work. I have converted the PR to draft, and I will put together the design doc to answer the questions you called out: when streaming should activate, who owns that decision, and what should happen when there is no SSE listener. Once that is reviewed, I can come back and tighten the implementation against the agreed contract. |
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This is a really good idea that I think needs to be pushed forward!
3 participants
#Hack-with-bay-2
Contribution Type
Feature — Token-by-token LLM streaming via existing SSE infrastructure
Problem / Use Case
RocketRide's 13 LLM nodes use blocking API calls — the entire response must complete before the user sees anything. For long responses (30+ seconds), this creates a poor UX. The engine already has SSE infrastructure (`monitorSSE()` / `sendSSE()`) for agent "thinking" events, but LLM nodes don't use it for token streaming.
Proposed Solution
A `StreamingHandler` utility that LLM nodes can opt into:
Why This Feature Fits This Codebase
The SSE infrastructure already exists in `rocketlib/engine.py` (`monitorSSE()` at line 110, `sendSSE()` on filter instances). The agent node at `nodes/src/nodes/agent_rocketride/rocketride_agent.py` already emits SSE events for "thinking" updates. This PR extends the same pattern to LLM token output.
The `StreamingHandler` is a standalone utility module — it does NOT modify `IInstance.py` or any existing LLM node. Individual nodes opt in by importing and calling `handler.stream_response()` instead of their blocking `chat()` call. This is the safest integration pattern.
Provider chunk extraction uses `getattr` with defaults throughout — no `KeyError` or `AttributeError` possible, even with unexpected chunk formats from future API versions.
Validation
```
58 passed
ruff check: 0 errors
ruff format: all files unchanged
```
58 tests: config parsing (17), handler init (4), chunk extraction for all 8 providers (20), SSE emission (4), full streaming flow (8), token counting (5), stream interruption recovery (1).
How This Could Be Extended
🤖 Generated with Claude Code
Summary by CodeRabbit