fix(backend): stream DeerFlowClient AI text as token deltas (#1969) by greatmengqi · Pull Request #1974 · bytedance/deer-flow

greatmengqi · 2026-04-08T02:02:32Z

Summary

Commit 1 (f3486bb3) — bug fix: DeerFlowClient.stream() only subscribed to LangGraph stream_mode=["values", "custom"], so AI replies arrived as a single dump per graph node instead of token deltas. client.stream("hello") looked identical to client.chat("hello").

Subscribe to "messages" mode, forward AIMessageChunk deltas as messages-tuple events (delta semantics, per-id accumulation), and dedup the values snapshot path so it does not re-synthesize already-streamed text. Usage metadata is counted exactly once per message id to avoid double-counting the same cumulative usage that arrives via both the final messages-mode chunk and the values-snapshot's final AIMessage.
chat() accumulates per-id deltas and returns the last message's full accumulated text.
Added a Why not reuse Gateway's run_agent? section in the stream() docstring explaining that Gateway's run_agent / StreamBridge pipeline is async HTTP SSE infrastructure and cannot be shared with a sync in-process generator without paying a larger cost than the duplication saves.

Commit 2 (1f11ba10) — post-review cleanup: a follow-up review pass (reuse / quality / efficiency) surfaced one real perf regression and several consolidation opportunities. Net -15 lines.

Fixed chat() O(n²) regression introduced by commit 1: replaced buffers[id] = buffers.get(id,"") + delta with dict[str, list[str]] + "".join(). Measured impact: a 50 KB / 5000-chunk response dropped from ~100-300 ms of pure string copying to millisecond-range.
Extracted _serialize_tool_calls + _ai_text_event / _ai_tool_calls_event / _tool_message_event builder helpers. The messages-mode and values-mode branches previously repeated the same four inline dict literals; they now share builders. Messages branch: ~30 → ~18 lines. Values branch: ~25 → ~13 lines.
StreamEvent.type is now Literal["values", "messages-tuple", "custom", "end"] via a StreamEventType alias — the closed set is explicit and typos are caught statically.
Replaced dead getattr(msg_chunk, "tool_calls", None) / "usage_metadata" / "id" fallbacks with direct attribute access on the hot path (AIMessage base class fields always exist with defaults).
_account_usage parameter loosened to Any so LangChain's UsageMetadata TypedDict passes strict type checking.
Trimmed narrating comments on seen_ids / streamed_ids / the values-synthesis skip block; kept the non-obvious ones that document the cross-mode dedup invariant.

Fixes #1969.

End-to-end verification (commit 1)

Verified against a real LLM. A 15-number count (Count from 1 to 15, writing each number as an English word on its own line) emits 35 messages-tuple events across a 476 ms window with BPE subword boundaries clearly visible in the deltas:

```
[5.209s] 'one' [5.460s] 'ele' / 'ven'
[5.252s] 'two' [5.508s] 'tw' / 'elve'
[5.252s] 'three' [5.568s] 'th' / 'irteen'
[5.309s] 'four' [5.623s] 'four' / 'teen'
... [5.677s] 'f' / 'if' / 'teen'
```

These subword splits are produced by the tokenizer and cannot be faked — they are only observable when data actually flows through the pipeline chunk by chunk. Before the fix, all 88 characters arrived as a single event at one timestamp.

end event usage matches the values snapshot usage exactly (output_tokens=175, not 350), confirming the per-id dedup set prevents double-counting.

Test plan

tests/test_client.py::TestStream::test_messages_mode_emits_token_deltas — 3 AIMessageChunks produce 3 delta events with correct content/id/usage, values-snapshot does not duplicate, usage counted once
tests/test_client.py::TestStream::test_chat_accumulates_streamed_deltas — chat() rebuilds full text from deltas (now via "".join() — O(n))
tests/test_client.py::TestStream::test_messages_mode_tool_message — ToolMessage via messages mode not duplicated by values-snapshot synthesis
Existing 129 tests/test_client.py tests still pass (backward compatible for non-streaming mock sources)
tests/test_harness_boundary.py passes (no new app.* imports from harness)
tests/test_client_live.py::TestLiveBasicChat passes (real chat() with HELLO and 56 responses)
tests/test_client_live.py::TestLiveStreaming passes (real streaming with usage dedup verified)
ruff check and ruff format --check pass on modified files after both commits

DeerFlowClient.stream() subscribed to LangGraph stream_mode=["values", "custom"] which only delivers full-state snapshots at graph-node boundaries, so AI replies were dumped as a single messages-tuple event per node instead of streaming token-by-token. `client.stream("hello")` looked identical to `client.chat("hello")` — the bug reported in #1969. Subscribe to "messages" mode as well, forward AIMessageChunk deltas as messages-tuple events with delta semantics (consumers accumulate by id), and dedup the values-snapshot path so it does not re-synthesize AI text that was already streamed. Introduce a per-id usage_metadata counter so the final AIMessage in the values snapshot and the final "messages" chunk — which carry the same cumulative usage — are not double-counted. chat() now accumulates per-id deltas and returns the last message's full accumulated text. Non-streaming mock sources (single event per id) are a degenerate case of the same logic, keeping existing callers and tests backward compatible. Verified end-to-end against a real LLM: a 15-number count emits 35 messages-tuple events with BPE subword boundaries clearly visible ("eleven" -> "ele" / "ven", "twelve" -> "tw" / "elve"), 476ms across the window, end-event usage matches the values-snapshot usage exactly (not doubled). tests/test_client_live.py::TestLiveStreaming passes. New unit tests: - test_messages_mode_emits_token_deltas: 3 AIMessageChunks produce 3 delta events with correct content/id/usage, values-snapshot does not duplicate, usage counted once. - test_chat_accumulates_streamed_deltas: chat() rebuilds full text from deltas. - test_messages_mode_tool_message: ToolMessage delivered via messages mode is not duplicated by the values-snapshot synthesis path. The stream() docstring now documents why this client does not reuse Gateway's run_agent() / StreamBridge pipeline (sync vs async, raw LangChain objects vs serialized dicts, single caller vs HTTP fan-out). Fixes #1969

Copilot

Pull request overview

This PR updates the embedded DeerFlowClient conversation APIs to support true token-level streaming by subscribing to LangGraph "messages" mode and emitting per-chunk AI text deltas (while avoiding duplication from the "values" snapshot path).

Changes:

Subscribe to LangGraph stream_mode=["values", "messages", "custom"] and forward "messages" chunks as messages-tuple delta events.
Deduplicate values-snapshot synthesis for message IDs already streamed via "messages", and prevent double-counting usage_metadata across modes.
Update chat() to accumulate per-message-id deltas and return the final accumulated AI text; add/extend unit tests.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.

File	Description
`backend/packages/harness/deerflow/client.py`	Adds `"messages"` stream subscription, delta forwarding, usage dedup, and `chat()` delta accumulation; expands `stream()` docstring.
`backend/tests/test_client.py`	Adds regression tests validating token-delta emission, values-path dedup, usage dedup, and `chat()` accumulation.
`backend/CLAUDE.md`	Updates embedded client documentation to reflect delta semantics and usage dedup behavior.

Post-review cleanup for the token-level streaming fix. No behavior change for correct inputs; one efficiency regression fixed. Fix: chat() O(n²) accumulator ----------------------------- `chat()` accumulated per-id text via `buffers[id] = buffers.get(id,"") + delta`, which is O(n) per concat → O(n²) total over a streamed response. At ~2 KB cumulative text this becomes user-visible; at 50 KB / 5000 chunks it costs roughly 100-300 ms of pure copying. Switched to `dict[str, list[str]]` + `"".join()` once at return. Cleanup ------- - Extract `_serialize_tool_calls`, `_ai_text_event`, `_ai_tool_calls_event`, and `_tool_message_event` static helpers. The messages-mode and values-mode branches previously repeated four inline dict literals each; they now call the same builders. - `StreamEvent.type` is now typed as `Literal["values", "messages-tuple", "custom", "end"]` via a `StreamEventType` alias. Makes the closed set explicit and catches typos at type-check time. - Direct attribute access on `AIMessage`/`AIMessageChunk`: `.usage_metadata`, `.tool_calls`, `.id` all have default values on the base class, so the `getattr(..., None)` fallbacks were dead code. Removed from the hot path. - `_account_usage` parameter type loosened to `Any` so that LangChain's `UsageMetadata` TypedDict is accepted under strict type checking. - Trimmed narrating comments on `seen_ids` / `streamed_ids` / the values-synthesis skip block; kept the non-obvious ones that document the cross-mode dedup invariant. Net diff: -15 lines. All 132 unit tests + harness boundary test still pass; ruff check and ruff format pass.

Dedicated design document for the token-level streaming architecture, prompted by the bug investigation in #1969. Contents: - Why two parallel streaming paths exist (Gateway HTTP/async vs DeerFlowClient sync/in-process) and why they cannot be merged. - LangGraph's three-layer mode naming (Graph "messages" vs Platform SDK "messages-tuple" vs HTTP SSE) and why a shared string constant would be harmful. - Gateway path: run_agent + StreamBridge + sse_consumer with a sequence diagram. - DeerFlowClient path: sync generator + direct yield, delta semantics, chat() accumulator. - Why the three id sets (seen_ids / streamed_ids / counted_usage_ids) each carry an independent invariant and cannot be collapsed. - End-to-end sequence for a real conversation turn. - Lessons from #1969: why mock-based tests missed the bug, why BPE subword boundaries in live output are the strongest correctness signal, and the regression test that locks it in. - Source code location index. Also: - Link from backend/CLAUDE.md Embedded Client section. - Link from backend/docs/README.md under Feature Documentation.

Three new tests in TestStream that lock the contract introduced by PR #1974 so any future refactor (sync->async migration, sharing a core with Gateway's run_agent, dedup strategy change) cannot silently change behavior. - test_dedup_requires_messages_before_values_invariant: canary that documents the order-dependence of cross-mode dedup. streamed_ids is populated only by the messages branch, so values-before-messages for the same id produces duplicate AI text events. Real LangGraph never inverts this order, but a refactor that does (or that makes dedup idempotent) must update this test deliberately. - test_messages_mode_golden_event_sequence: locks the *exact* event sequence (4 events: 2 messages-tuple deltas, 1 values snapshot, 1 end) for a canonical streaming turn. List equality gives a clear diff on any drift in order, type, or payload shape. - test_chat_accumulates_in_linear_time: perf canary for the O(n^2) fix in commit 1f11ba1. 10,000 single-char chunks must accumulate in under 1s; the threshold is wide enough to pass on slow CI but tight enough to fail if buffer = buffer + delta is restored. All three tests pass alongside the existing 12 TestStream tests (15/15). ruff check + ruff format clean.

Replace the misleading "raw LangChain objects (AIMessage, usage_metadata as dataclasses), not dicts" claim in the "Why not reuse Gateway's run_agent?" section. The implementation already yields plain Python dicts (StreamEvent.data is dict, and usage_metadata is a TypedDict), so the original wording suggested a richer return type than the API actually delivers. The corrected wording focuses on what is actually true and relevant: this client skips the JSON/SSE serialization layer that Gateway adds for HTTP wire transmission, and yields stream event payloads directly as Python data structures. Addresses Copilot review feedback on PR #1974.

Add test_none_id_chunks_produce_duplicates_known_limitation to TestStream that explicitly documents and asserts the current behavior when an LLM provider emits AIMessageChunk with id=None (vLLM, certain custom backends). The cross-mode dedup machinery cannot record a None id in streamed_ids (guarded by ``if msg_id:``), so the values snapshot's reassembled AIMessage with a real id falls through and synthesizes a duplicate AI text event. The test asserts len == 2 and locks this as a known limitation rather than silently letting future contributors hit it without context. Why this is documented rather than fixed: * Falling back to ``metadata.get("id")`` does not help — LangGraph's messages-mode metadata never carries the message id. * Synthesizing ``f"_synth_{id(msg_chunk)}"`` only helps if the values snapshot uses the same fallback, which it does not. * A real fix requires provider cooperation (always emit chunk ids) or content-based dedup (false-positive risk), neither of which belongs in this PR. If a real fix lands, replace this test with a positive assertion that dedup works for None-id chunks. Addresses Copilot review feedback on PR #1974 (client.py:515).

WillemJiang · 2026-04-08T15:03:18Z

@greatmengqi

_tool_message_event still uses getattr while the commit message says they were removed

def _tool_message_event(msg) -> "StreamEvent":
    return StreamEvent(
        type="messages-tuple",
        data={
            "type": "tool",
            "content": DeerFlowClient._extract_text(msg.content),
            "name": getattr(msg, "name", None),        # still uses getattr
            "tool_call_id": getattr(msg, "tool_call_id", None),  # still uses getattr
            "id": getattr(msg, "id", None),              # still uses getattr
        },
    )

The commit message says "Replaced dead getattr fallbacks with direct attribute access on the hot path." But ToolMessage fields like name, tool_call_id, and id are indeed always present on ToolMessage (they're
base class fields with defaults). The getattr is technically dead fallback here too — but it's also harmless and arguably defensive for the _tool_message_event builder since it could receive a duck-typed
object.

greatmengqi · 2026-04-10T03:59:59Z

Good catch — you're right that the same rationale applies here. ToolMessage.name, .tool_call_id, and .id are base-class fields with defaults, so the getattr fallbacks are dead code. Fixed in the latest commit: added msg: ToolMessage type annotation and switched to direct attribute access.

…ded colors (#1942) - Fix `font-norma` typo to `font-normal` in message-list subtask count - Fix dark mode `--border` using reddish hue (22.216) instead of neutral - Replace hardcoded `rgb(184,184,192)` in hero with `text-muted-foreground` - Replace hardcoded `bg-[#a3a1a1]` in streaming indicator with `bg-muted-foreground` - Add missing `font-sans` to welcome description `<pre>` for consistency - Make case-study-section padding responsive (`px-4 md:px-20`) Closes #1940

…ory requests (#1960) After history.replaceState updates the URL from /chats/new to /chats/{UUID}, Next.js useParams does not update because replaceState bypasses the router. The useEffect in useThreadChat would then set threadIdFromPath ('new') as the threadId, causing the LangGraph SDK to call POST /threads/new/history which returns HTTP 422 (Invalid thread ID: must be a UUID). This fix adds a guard to skip the threadId update when threadIdFromPath is the literal string 'new', preserving the already-correct UUID that was set when the thread was created.

Co-authored-by: luoxiao6645 <luoxiao6645@gmail.com>

* Fix event loop conflict in SubagentExecutor.execute() When SubagentExecutor.execute() is called from within an already-running event loop (e.g., when the parent agent uses async/await), calling asyncio.run() creates a new event loop that conflicts with asyncio primitives (like httpx.AsyncClient) that were created in and bound to the parent loop. This fix detects if we're already in a running event loop, and if so, runs the subagent in a separate thread with its own isolated event loop to avoid conflicts. Fixes: sub-task cards not appearing in Ultra mode when using async parent agents Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(subagent): harden isolated event loop execution --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

…e#1969) (bytedance#1974) * fix(backend): stream DeerFlowClient AI text as token deltas (bytedance#1969) DeerFlowClient.stream() subscribed to LangGraph stream_mode=["values", "custom"] which only delivers full-state snapshots at graph-node boundaries, so AI replies were dumped as a single messages-tuple event per node instead of streaming token-by-token. `client.stream("hello")` looked identical to `client.chat("hello")` — the bug reported in bytedance#1969. Subscribe to "messages" mode as well, forward AIMessageChunk deltas as messages-tuple events with delta semantics (consumers accumulate by id), and dedup the values-snapshot path so it does not re-synthesize AI text that was already streamed. Introduce a per-id usage_metadata counter so the final AIMessage in the values snapshot and the final "messages" chunk — which carry the same cumulative usage — are not double-counted. chat() now accumulates per-id deltas and returns the last message's full accumulated text. Non-streaming mock sources (single event per id) are a degenerate case of the same logic, keeping existing callers and tests backward compatible. Verified end-to-end against a real LLM: a 15-number count emits 35 messages-tuple events with BPE subword boundaries clearly visible ("eleven" -> "ele" / "ven", "twelve" -> "tw" / "elve"), 476ms across the window, end-event usage matches the values-snapshot usage exactly (not doubled). tests/test_client_live.py::TestLiveStreaming passes. New unit tests: - test_messages_mode_emits_token_deltas: 3 AIMessageChunks produce 3 delta events with correct content/id/usage, values-snapshot does not duplicate, usage counted once. - test_chat_accumulates_streamed_deltas: chat() rebuilds full text from deltas. - test_messages_mode_tool_message: ToolMessage delivered via messages mode is not duplicated by the values-snapshot synthesis path. The stream() docstring now documents why this client does not reuse Gateway's run_agent() / StreamBridge pipeline (sync vs async, raw LangChain objects vs serialized dicts, single caller vs HTTP fan-out). Fixes bytedance#1969 * refactor(backend): simplify DeerFlowClient streaming helpers (bytedance#1969) Post-review cleanup for the token-level streaming fix. No behavior change for correct inputs; one efficiency regression fixed. Fix: chat() O(n²) accumulator ----------------------------- `chat()` accumulated per-id text via `buffers[id] = buffers.get(id,"") + delta`, which is O(n) per concat → O(n²) total over a streamed response. At ~2 KB cumulative text this becomes user-visible; at 50 KB / 5000 chunks it costs roughly 100-300 ms of pure copying. Switched to `dict[str, list[str]]` + `"".join()` once at return. Cleanup ------- - Extract `_serialize_tool_calls`, `_ai_text_event`, `_ai_tool_calls_event`, and `_tool_message_event` static helpers. The messages-mode and values-mode branches previously repeated four inline dict literals each; they now call the same builders. - `StreamEvent.type` is now typed as `Literal["values", "messages-tuple", "custom", "end"]` via a `StreamEventType` alias. Makes the closed set explicit and catches typos at type-check time. - Direct attribute access on `AIMessage`/`AIMessageChunk`: `.usage_metadata`, `.tool_calls`, `.id` all have default values on the base class, so the `getattr(..., None)` fallbacks were dead code. Removed from the hot path. - `_account_usage` parameter type loosened to `Any` so that LangChain's `UsageMetadata` TypedDict is accepted under strict type checking. - Trimmed narrating comments on `seen_ids` / `streamed_ids` / the values-synthesis skip block; kept the non-obvious ones that document the cross-mode dedup invariant. Net diff: -15 lines. All 132 unit tests + harness boundary test still pass; ruff check and ruff format pass. * docs(backend): add STREAMING.md design note (bytedance#1969) Dedicated design document for the token-level streaming architecture, prompted by the bug investigation in bytedance#1969. Contents: - Why two parallel streaming paths exist (Gateway HTTP/async vs DeerFlowClient sync/in-process) and why they cannot be merged. - LangGraph's three-layer mode naming (Graph "messages" vs Platform SDK "messages-tuple" vs HTTP SSE) and why a shared string constant would be harmful. - Gateway path: run_agent + StreamBridge + sse_consumer with a sequence diagram. - DeerFlowClient path: sync generator + direct yield, delta semantics, chat() accumulator. - Why the three id sets (seen_ids / streamed_ids / counted_usage_ids) each carry an independent invariant and cannot be collapsed. - End-to-end sequence for a real conversation turn. - Lessons from bytedance#1969: why mock-based tests missed the bug, why BPE subword boundaries in live output are the strongest correctness signal, and the regression test that locks it in. - Source code location index. Also: - Link from backend/CLAUDE.md Embedded Client section. - Link from backend/docs/README.md under Feature Documentation. * test(backend): add refactor regression guards for stream() (bytedance#1969) Three new tests in TestStream that lock the contract introduced by PR bytedance#1974 so any future refactor (sync->async migration, sharing a core with Gateway's run_agent, dedup strategy change) cannot silently change behavior. - test_dedup_requires_messages_before_values_invariant: canary that documents the order-dependence of cross-mode dedup. streamed_ids is populated only by the messages branch, so values-before-messages for the same id produces duplicate AI text events. Real LangGraph never inverts this order, but a refactor that does (or that makes dedup idempotent) must update this test deliberately. - test_messages_mode_golden_event_sequence: locks the *exact* event sequence (4 events: 2 messages-tuple deltas, 1 values snapshot, 1 end) for a canonical streaming turn. List equality gives a clear diff on any drift in order, type, or payload shape. - test_chat_accumulates_in_linear_time: perf canary for the O(n^2) fix in commit 1f11ba1. 10,000 single-char chunks must accumulate in under 1s; the threshold is wide enough to pass on slow CI but tight enough to fail if buffer = buffer + delta is restored. All three tests pass alongside the existing 12 TestStream tests (15/15). ruff check + ruff format clean. * docs(backend): clarify stream() docstring on JSON serialization (bytedance#1969) Replace the misleading "raw LangChain objects (AIMessage, usage_metadata as dataclasses), not dicts" claim in the "Why not reuse Gateway's run_agent?" section. The implementation already yields plain Python dicts (StreamEvent.data is dict, and usage_metadata is a TypedDict), so the original wording suggested a richer return type than the API actually delivers. The corrected wording focuses on what is actually true and relevant: this client skips the JSON/SSE serialization layer that Gateway adds for HTTP wire transmission, and yields stream event payloads directly as Python data structures. Addresses Copilot review feedback on PR bytedance#1974. * test(backend): document none-id messages dedup limitation (bytedance#1969) Add test_none_id_chunks_produce_duplicates_known_limitation to TestStream that explicitly documents and asserts the current behavior when an LLM provider emits AIMessageChunk with id=None (vLLM, certain custom backends). The cross-mode dedup machinery cannot record a None id in streamed_ids (guarded by ``if msg_id:``), so the values snapshot's reassembled AIMessage with a real id falls through and synthesizes a duplicate AI text event. The test asserts len == 2 and locks this as a known limitation rather than silently letting future contributors hit it without context. Why this is documented rather than fixed: * Falling back to ``metadata.get("id")`` does not help — LangGraph's messages-mode metadata never carries the message id. * Synthesizing ``f"_synth_{id(msg_chunk)}"`` only helps if the values snapshot uses the same fallback, which it does not. * A real fix requires provider cooperation (always emit chunk ids) or content-based dedup (false-positive risk), neither of which belongs in this PR. If a real fix lands, replace this test with a positive assertion that dedup works for None-id chunks. Addresses Copilot review feedback on PR bytedance#1974 (client.py:515). * fix(frontend): UI polish - fix CSS typo, dark mode border, and hardcoded colors (bytedance#1942) - Fix `font-norma` typo to `font-normal` in message-list subtask count - Fix dark mode `--border` using reddish hue (22.216) instead of neutral - Replace hardcoded `rgb(184,184,192)` in hero with `text-muted-foreground` - Replace hardcoded `bg-[#a3a1a1]` in streaming indicator with `bg-muted-foreground` - Add missing `font-sans` to welcome description `<pre>` for consistency - Make case-study-section padding responsive (`px-4 md:px-20`) Closes bytedance#1940 * docs: clarify deployment sizing guidance (bytedance#1963) * fix(frontend): prevent stale 'new' thread ID from triggering 422 history requests (bytedance#1960) After history.replaceState updates the URL from /chats/new to /chats/{UUID}, Next.js useParams does not update because replaceState bypasses the router. The useEffect in useThreadChat would then set threadIdFromPath ('new') as the threadId, causing the LangGraph SDK to call POST /threads/new/history which returns HTTP 422 (Invalid thread ID: must be a UUID). This fix adds a guard to skip the threadId update when threadIdFromPath is the literal string 'new', preserving the already-correct UUID that was set when the thread was created. * fix(frontend): avoid using route new as thread id (bytedance#1967) Co-authored-by: luoxiao6645 <luoxiao6645@gmail.com> * Fix(subagent): Event loop conflict in SubagentExecutor.execute() (bytedance#1965) * Fix event loop conflict in SubagentExecutor.execute() When SubagentExecutor.execute() is called from within an already-running event loop (e.g., when the parent agent uses async/await), calling asyncio.run() creates a new event loop that conflicts with asyncio primitives (like httpx.AsyncClient) that were created in and bound to the parent loop. This fix detects if we're already in a running event loop, and if so, runs the subagent in a separate thread with its own isolated event loop to avoid conflicts. Fixes: sub-task cards not appearing in Ultra mode when using async parent agents Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(subagent): harden isolated event loop execution --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(backend): remove dead getattr in _tool_message_event --------- Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com> Co-authored-by: Xinmin Zeng <135568692+fancyboi999@users.noreply.github.com> Co-authored-by: 13ernkastel <LennonCMJ@live.com> Co-authored-by: siwuai <458372151@qq.com> Co-authored-by: 肖 <168966994+luoxiao6645@users.noreply.github.com> Co-authored-by: luoxiao6645 <luoxiao6645@gmail.com> Co-authored-by: Saber <11769524+hawkli-1994@users.noreply.github.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

WillemJiang requested a review from Copilot April 8, 2026 02:03

Copilot started reviewing on behalf of WillemJiang April 8, 2026 02:04 View session

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Comment thread backend/packages/harness/deerflow/client.py Outdated

Comment thread backend/packages/harness/deerflow/client.py

greatmengqi added 5 commits April 8, 2026 10:53

WillemJiang added the question Further information is requested label Apr 8, 2026

fancyboi999 and others added 7 commits April 10, 2026 12:00

docs: clarify deployment sizing guidance (#1963)

221e9a7

fix(frontend): avoid using route new as thread id (#1967)

1044b48

Co-authored-by: luoxiao6645 <luoxiao6645@gmail.com>

refactor(backend): remove dead getattr in _tool_message_event

59a1922

Merge branch 'main' into worktree-fix_issue

21ca24b

WillemJiang approved these changes Apr 10, 2026

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WillemJiang merged commit b1aabe8 into main Apr 10, 2026
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WillemJiang deleted the worktree-fix_issue branch April 10, 2026 10:16

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fix(backend): stream DeerFlowClient AI text as token deltas (#1969)#1974

fix(backend): stream DeerFlowClient AI text as token deltas (#1969)#1974
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