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* fix(backend): stream DeerFlowClient AI text as token deltas (#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 #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 * refactor(backend): simplify DeerFlowClient streaming helpers (#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 (#1969) 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. * test(backend): add refactor regression guards for stream() (#1969) 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 1f11ba10. 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 (#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 #1974. * test(backend): document none-id messages dedup limitation (#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 #1974 (client.py:515). * fix(frontend): UI polish - fix CSS typo, dark mode border, and hardcoded 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 * docs: clarify deployment sizing guidance (#1963) * fix(frontend): prevent stale 'new' thread ID from triggering 422 history 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. * fix(frontend): avoid using route new as thread id (#1967) Co-authored-by: luoxiao6645 <luoxiao6645@gmail.com> * Fix(subagent): Event loop conflict in SubagentExecutor.execute() (#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>
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@ -395,14 +395,16 @@ Both can be modified at runtime via Gateway API endpoints or `DeerFlowClient` me
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**Architecture**: Imports the same `deerflow` modules that LangGraph Server and Gateway API use. Shares the same config files and data directories. No FastAPI dependency.
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**Agent Conversation** (replaces LangGraph Server):
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- `chat(message, thread_id)` — synchronous, returns final text
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- `stream(message, thread_id)` — yields `StreamEvent` aligned with LangGraph SSE protocol:
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- `"values"` — full state snapshot (title, messages, artifacts)
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- `"messages-tuple"` — per-message update (AI text, tool calls, tool results)
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- `"end"` — stream finished
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- `chat(message, thread_id)` — synchronous, accumulates streaming deltas per message-id and returns the final AI text
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- `stream(message, thread_id)` — subscribes to LangGraph `stream_mode=["values", "messages", "custom"]` and yields `StreamEvent`:
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- `"values"` — full state snapshot (title, messages, artifacts); AI text already delivered via `messages` mode is **not** re-synthesized here to avoid duplicate deliveries
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- `"messages-tuple"` — per-chunk update: for AI text this is a **delta** (concat per `id` to rebuild the full message); tool calls and tool results are emitted once each
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- `"custom"` — forwarded from `StreamWriter`
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- `"end"` — stream finished (carries cumulative `usage` counted once per message id)
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- Agent created lazily via `create_agent()` + `_build_middlewares()`, same as `make_lead_agent`
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- Supports `checkpointer` parameter for state persistence across turns
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- `reset_agent()` forces agent recreation (e.g. after memory or skill changes)
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- See [docs/STREAMING.md](docs/STREAMING.md) for the full design: why Gateway and DeerFlowClient are parallel paths, LangGraph's `stream_mode` semantics, the per-id dedup invariants, and regression testing strategy
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**Gateway Equivalent Methods** (replaces Gateway API):
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@ -15,6 +15,7 @@ This directory contains detailed documentation for the DeerFlow backend.
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| Document | Description |
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|----------|-------------|
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| [STREAMING.md](STREAMING.md) | Token-level streaming design: Gateway vs DeerFlowClient paths, `stream_mode` semantics, per-id dedup |
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| [FILE_UPLOAD.md](FILE_UPLOAD.md) | File upload functionality |
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| [PATH_EXAMPLES.md](PATH_EXAMPLES.md) | Path types and usage examples |
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| [summarization.md](summarization.md) | Context summarization feature |
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@ -47,6 +48,7 @@ docs/
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├── PATH_EXAMPLES.md # Path usage examples
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├── summarization.md # Summarization feature
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├── plan_mode_usage.md # Plan mode feature
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├── STREAMING.md # Token-level streaming design
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├── AUTO_TITLE_GENERATION.md # Title generation
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├── TITLE_GENERATION_IMPLEMENTATION.md # Title implementation details
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└── TODO.md # Roadmap and issues
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351
backend/docs/STREAMING.md
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351
backend/docs/STREAMING.md
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@ -0,0 +1,351 @@
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# DeerFlow 流式输出设计
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本文档解释 DeerFlow 是如何把 LangGraph agent 的事件流端到端送到两类消费者（HTTP 客户端、嵌入式 Python 调用方）的：两条路径为什么**必须**并存、它们各自的契约是什么、以及设计里那些 non-obvious 的不变式。
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---
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## TL;DR
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- DeerFlow 有**两条并行**的流式路径：**Gateway 路径**（async / HTTP SSE / JSON 序列化）服务浏览器和 IM 渠道；**DeerFlowClient 路径**（sync / in-process / 原生 LangChain 对象）服务 Jupyter、脚本、测试。它们**无法合并**——消费者模型不同。
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- 两条路径都从 `create_agent()` 工厂出发，核心都是订阅 LangGraph 的 `stream_mode=["values", "messages", "custom"]`。`values` 是节点级 state 快照，`messages` 是 LLM token 级 delta，`custom` 是显式 `StreamWriter` 事件。**这三种模式不是详细程度的梯度，是三个独立的事件源**，要 token 流就必须显式订阅 `messages`。
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- 嵌入式 client 为每个 `stream()` 调用维护三个 `set[str]`：`seen_ids` / `streamed_ids` / `counted_usage_ids`。三者看起来相似但管理**三个独立的不变式**，不能合并。
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---
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## 为什么有两条流式路径
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两条路径服务的消费者模型根本不同：
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| 维度 | Gateway 路径 | DeerFlowClient 路径 |
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|---|---|---|
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| 入口 | FastAPI `/runs/stream` endpoint | `DeerFlowClient.stream(message)` |
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| 触发层 | `runtime/runs/worker.py::run_agent` | `packages/harness/deerflow/client.py::DeerFlowClient.stream` |
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| 执行模型 | `async def` + `agent.astream()` | sync generator + `agent.stream()` |
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| 事件传输 | `StreamBridge`（asyncio Queue）+ `sse_consumer` | 直接 `yield` |
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| 序列化 | `serialize(chunk)` → 纯 JSON dict，匹配 LangGraph Platform wire 格式 | `StreamEvent.data`，携带原生 LangChain 对象 |
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| 消费者 | 前端 `useStream` React hook、飞书/Slack/Telegram channel、LangGraph SDK 客户端 | Jupyter notebook、集成测试、内部 Python 脚本 |
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| 生命周期管理 | `RunManager`：run_id 跟踪、disconnect 语义、multitask 策略、heartbeat | 无；函数返回即结束 |
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| 断连恢复 | `Last-Event-ID` SSE 重连 | 无需要 |
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**两条路径的存在是 DRY 的刻意妥协**：Gateway 的全部基础设施（async + Queue + JSON + RunManager）**都是为了跨网络边界把事件送给 HTTP 消费者**。当生产者（agent）和消费者（Python 调用栈）在同一个进程时，这整套东西都是纯开销。
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### 为什么不能让 DeerFlowClient 复用 Gateway
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曾经考虑过三种复用方案，都被否决：
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1. **让 `client.stream()` 变成 `async def client.astream()`**
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breaking change。用户用不上的 `async for` / `asyncio.run()` 要硬塞进 Jupyter notebook 和同步脚本。DeerFlowClient 的一大卖点（"把 agent 当普通函数调用"）直接消失。
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2. **在 `client.stream()` 内部起一个独立事件循环线程，用 `StreamBridge` 在 sync/async 之间做桥接**
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引入线程池、队列、信号量。为了"消除重复"，把**复杂度**代替代码行数引进来。是典型的"wrong abstraction"——开销高于复用收益。
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3. **让 `run_agent` 自己兼容 sync mode**
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给 Gateway 加一条用不到的死分支，污染 worker.py 的焦点。
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所以两条路径的事件处理逻辑会**相似但不共享**。这是刻意设计，不是疏忽。
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---
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## LangGraph `stream_mode` 三层语义
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LangGraph 的 `agent.stream(stream_mode=[...])` 是**多路复用**接口：一次订阅多个 mode，每个 mode 是一个独立的事件源。三种核心 mode：
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```mermaid
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flowchart LR
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classDef values fill:#B8C5D1,stroke:#5A6B7A,color:#2C3E50
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classDef messages fill:#C9B8A8,stroke:#7A6B5A,color:#2C3E50
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classDef custom fill:#B5C4B1,stroke:#5A7A5A,color:#2C3E50
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subgraph LG["LangGraph agent graph"]
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direction TB
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Node1["node: LLM call"]
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Node2["node: tool call"]
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Node3["node: reducer"]
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end
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LG -->|"每个节点完成后"| V["values: 完整 state 快照"]
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Node1 -->|"LLM 每产生一个 token"| M["messages: (AIMessageChunk, meta)"]
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Node1 -->|"StreamWriter.write()"| C["custom: 任意 dict"]
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class V values
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class M messages
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class C custom
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```
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| Mode | 发射时机 | Payload | 粒度 |
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|---|---|---|---|
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| `values` | 每个 graph 节点完成后 | 完整 state dict（title、messages、artifacts）| 节点级 |
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| `messages` | LLM 每次 yield 一个 chunk；tool 节点完成时 | `(AIMessageChunk \| ToolMessage, metadata_dict)` | token 级 |
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| `custom` | 用户代码显式调用 `StreamWriter.write()` | 任意 dict | 应用定义 |
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### 两套命名的由来
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同一件事在**三个协议层**有三个名字：
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```
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Application HTTP / SSE LangGraph Graph
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐
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│ frontend │ │ LangGraph │ │ agent.astream│
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│ useStream │──"messages- │ Platform SDK │──"messages"──│ graph.astream│
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│ Feishu IM │ tuple"──────│ HTTP wire │ │ │
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└──────────────┘ └──────────────┘ └──────────────┘
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```
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- **Graph 层**（`agent.stream` / `agent.astream`）：LangGraph Python 直接 API，mode 叫 **`"messages"`**。
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- **Platform SDK 层**（`langgraph-sdk` HTTP client）：跨进程 HTTP 契约，mode 叫 **`"messages-tuple"`**。
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- **Gateway worker** 显式做翻译：`if m == "messages-tuple": lg_modes.append("messages")`（`runtime/runs/worker.py:117-121`）。
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**后果**：`DeerFlowClient.stream()` 直接调 `agent.stream()`（Graph 层），所以必须传 `"messages"`。`app/channels/manager.py` 通过 `langgraph-sdk` 走 HTTP SDK，所以传 `"messages-tuple"`。**这两个字符串不能互相替代**，也不能抽成"一个共享常量"——它们是不同协议层的 type alias，共享只会让某一层说不是它母语的话。
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---
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## Gateway 路径：async + HTTP SSE
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```mermaid
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sequenceDiagram
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participant Client as HTTP Client
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participant API as FastAPI<br/>thread_runs.py
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participant Svc as services.py<br/>start_run
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participant Worker as worker.py<br/>run_agent (async)
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participant Bridge as StreamBridge<br/>(asyncio.Queue)
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participant Agent as LangGraph<br/>agent.astream
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participant SSE as sse_consumer
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Client->>API: POST /runs/stream
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API->>Svc: start_run(body)
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Svc->>Bridge: create bridge
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Svc->>Worker: asyncio.create_task(run_agent(...))
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Svc-->>API: StreamingResponse(sse_consumer)
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API-->>Client: event-stream opens
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par worker (producer)
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Worker->>Agent: astream(stream_mode=lg_modes)
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loop 每个 chunk
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Agent-->>Worker: (mode, chunk)
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Worker->>Bridge: publish(run_id, event, serialize(chunk))
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end
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Worker->>Bridge: publish_end(run_id)
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and sse_consumer (consumer)
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SSE->>Bridge: subscribe(run_id)
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loop 每个 event
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Bridge-->>SSE: StreamEvent
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SSE-->>Client: "event: <name>\ndata: <json>\n\n"
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end
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end
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```
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关键组件：
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- `runtime/runs/worker.py::run_agent` — 在 `asyncio.Task` 里跑 `agent.astream()`，把每个 chunk 通过 `serialize(chunk, mode=mode)` 转成 JSON，再 `bridge.publish()`。
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- `runtime/stream_bridge` — 抽象 Queue。`publish/subscribe` 解耦生产者和消费者，支持 `Last-Event-ID` 重连、心跳、多订阅者 fan-out。
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- `app/gateway/services.py::sse_consumer` — 从 bridge 订阅，格式化为 SSE wire 帧。
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- `runtime/serialization.py::serialize` — mode-aware 序列化；`messages` mode 下 `serialize_messages_tuple` 把 `(chunk, metadata)` 转成 `[chunk.model_dump(), metadata]`。
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**`StreamBridge` 的存在价值**：当生产者（`run_agent` 任务）和消费者（HTTP 连接）在不同的 asyncio task 里运行时，需要一个可以跨 task 传递事件的中介。Queue 同时还承担断连重连的 buffer 和多订阅者的 fan-out。
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---
|
||||
|
||||
## DeerFlowClient 路径：sync + in-process
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant User as Python caller
|
||||
participant Client as DeerFlowClient.stream
|
||||
participant Agent as LangGraph<br/>agent.stream (sync)
|
||||
|
||||
User->>Client: for event in client.stream("hi"):
|
||||
Client->>Agent: stream(stream_mode=["values","messages","custom"])
|
||||
loop 每个 chunk
|
||||
Agent-->>Client: (mode, chunk)
|
||||
Client->>Client: 分发 mode<br/>构建 StreamEvent
|
||||
Client-->>User: yield StreamEvent
|
||||
end
|
||||
Client-->>User: yield StreamEvent(type="end")
|
||||
```
|
||||
|
||||
对比之下，sync 路径的每个环节都是显著更少的移动部件：
|
||||
|
||||
- 没有 `RunManager` —— 一次 `stream()` 调用对应一次生命周期，无需 run_id。
|
||||
- 没有 `StreamBridge` —— 直接 `yield`，生产和消费在同一个 Python 调用栈，不需要跨 task 中介。
|
||||
- 没有 JSON 序列化 —— `StreamEvent.data` 直接装原生 LangChain 对象（`AIMessage.content`、`usage_metadata` 的 `UsageMetadata` TypedDict）。Jupyter 用户拿到的是真正的类型，不是匿名 dict。
|
||||
- 没有 asyncio —— 调用者可以直接 `for event in ...`，不必写 `async for`。
|
||||
|
||||
---
|
||||
|
||||
## 消费语义：delta vs cumulative
|
||||
|
||||
LangGraph `messages` mode 给出的是 **delta**：每个 `AIMessageChunk.content` 只包含这一次新 yield 的 token，**不是**从头的累计文本。
|
||||
|
||||
这个语义和 LangChain 的 `fs2 Stream` 风格一致：**上游发增量，下游负责累加**。Gateway 路径里前端 `useStream` React hook 自己维护累加器；DeerFlowClient 路径里 `chat()` 方法替调用者做累加。
|
||||
|
||||
### `DeerFlowClient.chat()` 的 O(n) 累加器
|
||||
|
||||
```python
|
||||
chunks: dict[str, list[str]] = {}
|
||||
last_id: str = ""
|
||||
for event in self.stream(message, thread_id=thread_id, **kwargs):
|
||||
if event.type == "messages-tuple" and event.data.get("type") == "ai":
|
||||
msg_id = event.data.get("id") or ""
|
||||
delta = event.data.get("content", "")
|
||||
if delta:
|
||||
chunks.setdefault(msg_id, []).append(delta)
|
||||
last_id = msg_id
|
||||
return "".join(chunks.get(last_id, ()))
|
||||
```
|
||||
|
||||
**为什么不是 `buffers[id] = buffers.get(id,"") + delta`**：CPython 的字符串 in-place concat 优化仅在 refcount=1 且 LHS 是 local name 时生效；这里字符串存在 dict 里被 reassign，优化失效，每次都是 O(n) 拷贝 → 总体 O(n²)。实测 50 KB / 5000 chunk 的回复要 100-300ms 纯拷贝开销。用 `list` + `"".join()` 是 O(n)。
|
||||
|
||||
---
|
||||
|
||||
## 三个 id set 为什么不能合并
|
||||
|
||||
`DeerFlowClient.stream()` 在一次调用生命周期内维护三个 `set[str]`：
|
||||
|
||||
```python
|
||||
seen_ids: set[str] = set() # values 路径内部 dedup
|
||||
streamed_ids: set[str] = set() # messages → values 跨模式 dedup
|
||||
counted_usage_ids: set[str] = set() # usage_metadata 幂等计数
|
||||
```
|
||||
|
||||
乍看像是"三份几乎一样的东西"，实际每个管**不同的不变式**。
|
||||
|
||||
| Set | 负责的不变式 | 被谁填充 | 被谁查询 |
|
||||
|---|---|---|---|
|
||||
| `seen_ids` | 连续两个 `values` 快照里同一条 message 只生成一个 `messages-tuple` 事件 | values 分支每处理一条消息就加入 | values 分支处理下一条消息前检查 |
|
||||
| `streamed_ids` | 如果一条消息已经通过 `messages` 模式 token 级流过，values 快照到达时**不要**再合成一次完整 `messages-tuple` | messages 分支每发一个 AI/tool 事件就加入 | values 分支看到消息时检查 |
|
||||
| `counted_usage_ids` | 同一个 `usage_metadata` 在 messages 末尾 chunk 和 values 快照的 final AIMessage 里各带一份，**累计总量只算一次** | `_account_usage()` 每次接受 usage 就加入 | `_account_usage()` 每次调用时检查 |
|
||||
|
||||
### 为什么不能只用一个 set
|
||||
|
||||
关键观察：**同一个 message id 在这三个 set 里的加入时机不同**。
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant M as messages mode
|
||||
participant V as values mode
|
||||
participant SS as streamed_ids
|
||||
participant SU as counted_usage_ids
|
||||
participant SE as seen_ids
|
||||
|
||||
Note over M: 第一个 AI text chunk 到达
|
||||
M->>SS: add(msg_id)
|
||||
Note over M: 最后一个 chunk 带 usage
|
||||
M->>SU: add(msg_id)
|
||||
Note over V: snapshot 到达，包含同一条 AI message
|
||||
V->>SE: add(msg_id)
|
||||
V->>SS: 查询 → 已存在，跳过文本合成
|
||||
V->>SU: 查询 → 已存在，不重复计数
|
||||
```
|
||||
|
||||
- `seen_ids` **永远在 values 快照到达时**加入，所以它是 "values 已处理" 的标记。一条只出现在 messages 流里的消息（罕见但可能），`seen_ids` 里永远没有它。
|
||||
- `streamed_ids` **在 messages 流的第一个有效事件时**加入。一条只通过 values 快照到达的非 AI 消息（HumanMessage、被 truncate 的 tool 消息），`streamed_ids` 里永远没有它。
|
||||
- `counted_usage_ids` **只在看到非空 `usage_metadata` 时**加入。一条完全没有 usage 的消息（tool message、错误消息）永远不会进去。
|
||||
|
||||
**集合包含关系**：`counted_usage_ids ⊆ (streamed_ids ∪ seen_ids)` 大致成立，但**不是严格子集**，因为一条消息可以在 messages 模式流完 text 但**在最后那个带 usage 的 chunk 之前**就被 values snapshot 赶上——此时它已经在 `streamed_ids` 里，但还不在 `counted_usage_ids` 里。把它们合并成一个 dict-of-flags 会让这个微妙的时序依赖**从类型系统里消失**，变成注释里的一句话。三个独立的 set 把不变式显式化了：每个 set 名对应一个可以口头回答的问题。
|
||||
|
||||
---
|
||||
|
||||
## 端到端：一次真实对话的事件时序
|
||||
|
||||
假设调用 `client.stream("Count from 1 to 15")`，LLM 给出 "one\ntwo\n...\nfifteen"（88 字符），tokenizer 把它拆成 ~35 个 BPE chunk。下面是事件到达序列的精简版：
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant U as User
|
||||
participant C as DeerFlowClient
|
||||
participant A as LangGraph<br/>agent.stream
|
||||
|
||||
U->>C: stream("Count ... 15")
|
||||
C->>A: stream(mode=["values","messages","custom"])
|
||||
|
||||
A-->>C: ("values", {messages: [HumanMessage]})
|
||||
C-->>U: StreamEvent(type="values", ...)
|
||||
|
||||
Note over A,C: LLM 开始 yield token
|
||||
loop 35 次，约 476ms
|
||||
A-->>C: ("messages", (AIMessageChunk(content="ele"), meta))
|
||||
C->>C: streamed_ids.add(ai-1)
|
||||
C-->>U: StreamEvent(type="messages-tuple",<br/>data={type:ai, content:"ele", id:ai-1})
|
||||
end
|
||||
|
||||
Note over A: LLM finish_reason=stop，最后一个 chunk 带 usage
|
||||
A-->>C: ("messages", (AIMessageChunk(content="", usage_metadata={...}), meta))
|
||||
C->>C: counted_usage_ids.add(ai-1)<br/>(无文本，不 yield)
|
||||
|
||||
A-->>C: ("values", {messages: [..., AIMessage(complete)]})
|
||||
C->>C: ai-1 in streamed_ids → 跳过合成
|
||||
C->>C: 捕获 usage (已在 counted_usage_ids，no-op)
|
||||
C-->>U: StreamEvent(type="values", ...)
|
||||
|
||||
C-->>U: StreamEvent(type="end", data={usage:{...}})
|
||||
```
|
||||
|
||||
关键观察：
|
||||
|
||||
1. 用户看到 **35 个 messages-tuple 事件**，跨越约 476ms，每个事件带一个 token delta 和同一个 `id=ai-1`。
|
||||
2. 最后一个 `values` 快照里的 `AIMessage` **不会**再触发一个完整的 `messages-tuple` 事件——因为 `ai-1 in streamed_ids` 跳过了合成。
|
||||
3. `end` 事件里的 `usage` 正好等于那一份 cumulative usage，**不是它的两倍**——`counted_usage_ids` 在 messages 末尾 chunk 上已经吸收了，values 分支的重复访问是 no-op。
|
||||
4. 消费者拿到的 `content` 是**增量**："ele" 只包含 3 个字符，不是 "one\ntwo\n...ele"。想要完整文本要按 `id` 累加，`chat()` 已经帮你做了。
|
||||
|
||||
---
|
||||
|
||||
## 为什么这个设计容易出 bug，以及测试策略
|
||||
|
||||
本文档的直接起因是 bytedance/deer-flow#1969：`DeerFlowClient.stream()` 原本只订阅 `["values", "custom"]`，**漏了 `"messages"`**。结果 `client.stream("hello")` 等价于一次性返回，视觉上和 `chat()` 没区别。
|
||||
|
||||
这类 bug 有三个结构性原因：
|
||||
|
||||
1. **多协议层命名**：`messages` / `messages-tuple` / HTTP SSE `messages` 是同一概念的三个名字。在其中一层出错不会在另外两层报错。
|
||||
2. **多消费者模型**：Gateway 和 DeerFlowClient 是两套独立实现，**没有单一的"订阅哪些 mode"的 single source of truth**。前者订阅对了不代表后者也订阅对了。
|
||||
3. **mock 测试绕开了真实路径**：老测试用 `agent.stream.return_value = iter([dict_chunk, ...])` 喂 values 形状的 dict 模拟 state 快照。这样构造的输入**永远不会进入 `messages` mode 分支**，所以即使 `stream_mode` 里少一个元素，CI 依然全绿。
|
||||
|
||||
### 防御手段
|
||||
|
||||
真正的防线是**显式断言 "messages" mode 被订阅 + 用真实 chunk shape mock**：
|
||||
|
||||
```python
|
||||
# tests/test_client.py::test_messages_mode_emits_token_deltas
|
||||
agent.stream.return_value = iter([
|
||||
("messages", (AIMessageChunk(content="Hel", id="ai-1"), {})),
|
||||
("messages", (AIMessageChunk(content="lo ", id="ai-1"), {})),
|
||||
("messages", (AIMessageChunk(content="world!", id="ai-1"), {})),
|
||||
("values", {"messages": [HumanMessage(...), AIMessage(content="Hello world!", id="ai-1")]}),
|
||||
])
|
||||
# ...
|
||||
assert [e.data["content"] for e in ai_text_events] == ["Hel", "lo ", "world!"]
|
||||
assert len(ai_text_events) == 3 # values snapshot must NOT re-synthesize
|
||||
assert "messages" in agent.stream.call_args.kwargs["stream_mode"]
|
||||
```
|
||||
|
||||
**为什么这比"抽一个共享常量"更有效**：共享常量只能保证"用它的人写对字符串"，但新增消费者的人可能根本不知道常量在哪。行为断言强制任何改动都要穿过**实际执行路径**，改回 `["values", "custom"]` 会立刻让 `assert "messages" in ...` 失败。
|
||||
|
||||
### 活体信号：BPE 子词边界
|
||||
|
||||
回归的最终验证是让真实 LLM 数 1-15，然后看是否能在输出里看到 tokenizer 的子词切分：
|
||||
|
||||
```
|
||||
[5.460s] 'ele' / 'ven' eleven 被拆成两个 token
|
||||
[5.508s] 'tw' / 'elve' twelve 拆两个
|
||||
[5.568s] 'th' / 'irteen' thirteen 拆两个
|
||||
[5.623s] 'four'/ 'teen' fourteen 拆两个
|
||||
[5.677s] 'f' / 'if' / 'teen' fifteen 拆三个
|
||||
```
|
||||
|
||||
子词切分是 tokenizer 的外部事实，**无法伪造**。能看到它就说明数据流**逐 chunk** 地穿过了整条管道，没有被任何中间层缓冲成整段。这种"活体信号"在流式系统里是比单元测试更高置信度的证据。
|
||||
|
||||
---
|
||||
|
||||
## 相关源码定位
|
||||
|
||||
| 关心什么 | 看这里 |
|
||||
|---|---|
|
||||
| DeerFlowClient 嵌入式流 | `packages/harness/deerflow/client.py::DeerFlowClient.stream` |
|
||||
| `chat()` 的 delta 累加器 | `packages/harness/deerflow/client.py::DeerFlowClient.chat` |
|
||||
| Gateway async 流 | `packages/harness/deerflow/runtime/runs/worker.py::run_agent` |
|
||||
| HTTP SSE 帧输出 | `app/gateway/services.py::sse_consumer` / `format_sse` |
|
||||
| 序列化到 wire 格式 | `packages/harness/deerflow/runtime/serialization.py` |
|
||||
| LangGraph mode 命名翻译 | `packages/harness/deerflow/runtime/runs/worker.py:117-121` |
|
||||
| 飞书渠道的增量卡片更新 | `app/channels/manager.py::_handle_streaming_chat` |
|
||||
| Channels 自带的 delta/cumulative 防御性累加 | `app/channels/manager.py::_merge_stream_text` |
|
||||
| Frontend useStream 支持的 mode 集合 | `frontend/src/core/api/stream-mode.ts` |
|
||||
| 核心回归测试 | `backend/tests/test_client.py::TestStream::test_messages_mode_emits_token_deltas` |
|
||||
@ -25,7 +25,7 @@ import uuid
|
||||
from collections.abc import Generator, Sequence
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
from typing import Any, Literal
|
||||
|
||||
from langchain.agents import create_agent
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
@ -55,6 +55,9 @@ from deerflow.uploads.manager import (
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
StreamEventType = Literal["values", "messages-tuple", "custom", "end"]
|
||||
|
||||
|
||||
@dataclass
|
||||
class StreamEvent:
|
||||
"""A single event from the streaming agent response.
|
||||
@ -69,7 +72,7 @@ class StreamEvent:
|
||||
data: Event payload. Contents vary by type.
|
||||
"""
|
||||
|
||||
type: str
|
||||
type: StreamEventType
|
||||
data: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
@ -254,13 +257,53 @@ class DeerFlowClient:
|
||||
|
||||
return get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled)
|
||||
|
||||
@staticmethod
|
||||
def _serialize_tool_calls(tool_calls) -> list[dict]:
|
||||
"""Reshape LangChain tool_calls into the wire format used in events."""
|
||||
return [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in tool_calls]
|
||||
|
||||
@staticmethod
|
||||
def _ai_text_event(msg_id: str | None, text: str, usage: dict | None) -> "StreamEvent":
|
||||
"""Build a ``messages-tuple`` AI text event, attaching usage when present."""
|
||||
data: dict[str, Any] = {"type": "ai", "content": text, "id": msg_id}
|
||||
if usage:
|
||||
data["usage_metadata"] = usage
|
||||
return StreamEvent(type="messages-tuple", data=data)
|
||||
|
||||
@staticmethod
|
||||
def _ai_tool_calls_event(msg_id: str | None, tool_calls) -> "StreamEvent":
|
||||
"""Build a ``messages-tuple`` AI tool-calls event."""
|
||||
return StreamEvent(
|
||||
type="messages-tuple",
|
||||
data={
|
||||
"type": "ai",
|
||||
"content": "",
|
||||
"id": msg_id,
|
||||
"tool_calls": DeerFlowClient._serialize_tool_calls(tool_calls),
|
||||
},
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _tool_message_event(msg: ToolMessage) -> "StreamEvent":
|
||||
"""Build a ``messages-tuple`` tool-result event from a ToolMessage."""
|
||||
return StreamEvent(
|
||||
type="messages-tuple",
|
||||
data={
|
||||
"type": "tool",
|
||||
"content": DeerFlowClient._extract_text(msg.content),
|
||||
"name": msg.name,
|
||||
"tool_call_id": msg.tool_call_id,
|
||||
"id": msg.id,
|
||||
},
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _serialize_message(msg) -> dict:
|
||||
"""Serialize a LangChain message to a plain dict for values events."""
|
||||
if isinstance(msg, AIMessage):
|
||||
d: dict[str, Any] = {"type": "ai", "content": msg.content, "id": getattr(msg, "id", None)}
|
||||
if msg.tool_calls:
|
||||
d["tool_calls"] = [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in msg.tool_calls]
|
||||
d["tool_calls"] = DeerFlowClient._serialize_tool_calls(msg.tool_calls)
|
||||
if getattr(msg, "usage_metadata", None):
|
||||
d["usage_metadata"] = msg.usage_metadata
|
||||
return d
|
||||
@ -438,6 +481,53 @@ class DeerFlowClient:
|
||||
consumers can switch between HTTP streaming and embedded mode
|
||||
without changing their event-handling logic.
|
||||
|
||||
Token-level streaming
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
This method subscribes to LangGraph's ``messages`` stream mode, so
|
||||
``messages-tuple`` events for AI text are emitted as **deltas** as
|
||||
the model generates tokens, not as one cumulative dump at node
|
||||
completion. Each delta carries a stable ``id`` — consumers that
|
||||
want the full text must accumulate ``content`` per ``id``.
|
||||
``chat()`` already does this for you.
|
||||
|
||||
Tool calls and tool results are still emitted once per logical
|
||||
message. ``values`` events continue to carry full state snapshots
|
||||
after each graph node finishes; AI text already delivered via the
|
||||
``messages`` stream is **not** re-synthesized from the snapshot to
|
||||
avoid duplicate deliveries.
|
||||
|
||||
Why not reuse Gateway's ``run_agent``?
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
Gateway (``runtime/runs/worker.py``) has a complete streaming
|
||||
pipeline: ``run_agent`` → ``StreamBridge`` → ``sse_consumer``. It
|
||||
looks like this client duplicates that work, but the two paths
|
||||
serve different audiences and **cannot** share execution:
|
||||
|
||||
* ``run_agent`` is ``async def`` and uses ``agent.astream()``;
|
||||
this method is a sync generator using ``agent.stream()`` so
|
||||
callers can write ``for event in client.stream(...)`` without
|
||||
touching asyncio. Bridging the two would require spinning up
|
||||
an event loop + thread per call.
|
||||
* Gateway events are JSON-serialized by ``serialize()`` for SSE
|
||||
wire transmission. This client yields in-process stream event
|
||||
payloads directly as Python data structures (``StreamEvent``
|
||||
with ``data`` as a plain ``dict``), without the extra
|
||||
JSON/SSE serialization layer used for HTTP delivery.
|
||||
* ``StreamBridge`` is an asyncio-queue decoupling producers from
|
||||
consumers across an HTTP boundary (``Last-Event-ID`` replay,
|
||||
heartbeats, multi-subscriber fan-out). A single in-process
|
||||
caller with a direct iterator needs none of that.
|
||||
|
||||
So ``DeerFlowClient.stream()`` is a parallel, sync, in-process
|
||||
consumer of the same ``create_agent()`` factory — not a wrapper
|
||||
around Gateway. The two paths **should** stay in sync on which
|
||||
LangGraph stream modes they subscribe to; that invariant is
|
||||
enforced by ``tests/test_client.py::test_messages_mode_emits_token_deltas``
|
||||
rather than by a shared constant, because the three layers
|
||||
(Graph, Platform SDK, HTTP) each use their own naming
|
||||
(``messages`` vs ``messages-tuple``) and cannot literally share
|
||||
a string.
|
||||
|
||||
Args:
|
||||
message: User message text.
|
||||
thread_id: Thread ID for conversation context. Auto-generated if None.
|
||||
@ -448,8 +538,8 @@ class DeerFlowClient:
|
||||
StreamEvent with one of:
|
||||
- type="values" data={"title": str|None, "messages": [...], "artifacts": [...]}
|
||||
- type="custom" data={...}
|
||||
- type="messages-tuple" data={"type": "ai", "content": str, "id": str}
|
||||
- type="messages-tuple" data={"type": "ai", "content": str, "id": str, "usage_metadata": {...}}
|
||||
- type="messages-tuple" data={"type": "ai", "content": <delta>, "id": str}
|
||||
- type="messages-tuple" data={"type": "ai", "content": <delta>, "id": str, "usage_metadata": {...}}
|
||||
- type="messages-tuple" data={"type": "ai", "content": "", "id": str, "tool_calls": [...]}
|
||||
- type="messages-tuple" data={"type": "tool", "content": str, "name": str, "tool_call_id": str, "id": str}
|
||||
- type="end" data={"usage": {"input_tokens": int, "output_tokens": int, "total_tokens": int}}
|
||||
@ -466,13 +556,47 @@ class DeerFlowClient:
|
||||
context["agent_name"] = self._agent_name
|
||||
|
||||
seen_ids: set[str] = set()
|
||||
# Cross-mode handoff: ids already streamed via LangGraph ``messages``
|
||||
# mode so the ``values`` path skips re-synthesis of the same message.
|
||||
streamed_ids: set[str] = set()
|
||||
# The same message id carries identical cumulative ``usage_metadata``
|
||||
# in both the final ``messages`` chunk and the values snapshot —
|
||||
# count it only on whichever arrives first.
|
||||
counted_usage_ids: set[str] = set()
|
||||
cumulative_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}
|
||||
|
||||
def _account_usage(msg_id: str | None, usage: Any) -> dict | None:
|
||||
"""Add *usage* to cumulative totals if this id has not been counted.
|
||||
|
||||
``usage`` is a ``langchain_core.messages.UsageMetadata`` TypedDict
|
||||
or ``None``; typed as ``Any`` because TypedDicts are not
|
||||
structurally assignable to plain ``dict`` under strict type
|
||||
checking. Returns the normalized usage dict (for attaching
|
||||
to an event) when we accepted it, otherwise ``None``.
|
||||
"""
|
||||
if not usage:
|
||||
return None
|
||||
if msg_id and msg_id in counted_usage_ids:
|
||||
return None
|
||||
if msg_id:
|
||||
counted_usage_ids.add(msg_id)
|
||||
input_tokens = usage.get("input_tokens", 0) or 0
|
||||
output_tokens = usage.get("output_tokens", 0) or 0
|
||||
total_tokens = usage.get("total_tokens", 0) or 0
|
||||
cumulative_usage["input_tokens"] += input_tokens
|
||||
cumulative_usage["output_tokens"] += output_tokens
|
||||
cumulative_usage["total_tokens"] += total_tokens
|
||||
return {
|
||||
"input_tokens": input_tokens,
|
||||
"output_tokens": output_tokens,
|
||||
"total_tokens": total_tokens,
|
||||
}
|
||||
|
||||
for item in self._agent.stream(
|
||||
state,
|
||||
config=config,
|
||||
context=context,
|
||||
stream_mode=["values", "custom"],
|
||||
stream_mode=["values", "messages", "custom"],
|
||||
):
|
||||
if isinstance(item, tuple) and len(item) == 2:
|
||||
mode, chunk = item
|
||||
@ -484,6 +608,36 @@ class DeerFlowClient:
|
||||
yield StreamEvent(type="custom", data=chunk)
|
||||
continue
|
||||
|
||||
if mode == "messages":
|
||||
# LangGraph ``messages`` mode emits ``(message_chunk, metadata)``.
|
||||
if isinstance(chunk, tuple) and len(chunk) == 2:
|
||||
msg_chunk, _metadata = chunk
|
||||
else:
|
||||
msg_chunk = chunk
|
||||
|
||||
msg_id = getattr(msg_chunk, "id", None)
|
||||
|
||||
if isinstance(msg_chunk, AIMessage):
|
||||
text = self._extract_text(msg_chunk.content)
|
||||
counted_usage = _account_usage(msg_id, msg_chunk.usage_metadata)
|
||||
|
||||
if text:
|
||||
if msg_id:
|
||||
streamed_ids.add(msg_id)
|
||||
yield self._ai_text_event(msg_id, text, counted_usage)
|
||||
|
||||
if msg_chunk.tool_calls:
|
||||
if msg_id:
|
||||
streamed_ids.add(msg_id)
|
||||
yield self._ai_tool_calls_event(msg_id, msg_chunk.tool_calls)
|
||||
|
||||
elif isinstance(msg_chunk, ToolMessage):
|
||||
if msg_id:
|
||||
streamed_ids.add(msg_id)
|
||||
yield self._tool_message_event(msg_chunk)
|
||||
continue
|
||||
|
||||
# mode == "values"
|
||||
messages = chunk.get("messages", [])
|
||||
|
||||
for msg in messages:
|
||||
@ -493,47 +647,25 @@ class DeerFlowClient:
|
||||
if msg_id:
|
||||
seen_ids.add(msg_id)
|
||||
|
||||
# Already streamed via ``messages`` mode; only (defensively)
|
||||
# capture usage here and skip re-synthesizing the event.
|
||||
if msg_id and msg_id in streamed_ids:
|
||||
if isinstance(msg, AIMessage):
|
||||
_account_usage(msg_id, getattr(msg, "usage_metadata", None))
|
||||
continue
|
||||
|
||||
if isinstance(msg, AIMessage):
|
||||
# Track token usage from AI messages
|
||||
usage = getattr(msg, "usage_metadata", None)
|
||||
if usage:
|
||||
cumulative_usage["input_tokens"] += usage.get("input_tokens", 0) or 0
|
||||
cumulative_usage["output_tokens"] += usage.get("output_tokens", 0) or 0
|
||||
cumulative_usage["total_tokens"] += usage.get("total_tokens", 0) or 0
|
||||
counted_usage = _account_usage(msg_id, msg.usage_metadata)
|
||||
|
||||
if msg.tool_calls:
|
||||
yield StreamEvent(
|
||||
type="messages-tuple",
|
||||
data={
|
||||
"type": "ai",
|
||||
"content": "",
|
||||
"id": msg_id,
|
||||
"tool_calls": [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in msg.tool_calls],
|
||||
},
|
||||
)
|
||||
yield self._ai_tool_calls_event(msg_id, msg.tool_calls)
|
||||
|
||||
text = self._extract_text(msg.content)
|
||||
if text:
|
||||
event_data: dict[str, Any] = {"type": "ai", "content": text, "id": msg_id}
|
||||
if usage:
|
||||
event_data["usage_metadata"] = {
|
||||
"input_tokens": usage.get("input_tokens", 0) or 0,
|
||||
"output_tokens": usage.get("output_tokens", 0) or 0,
|
||||
"total_tokens": usage.get("total_tokens", 0) or 0,
|
||||
}
|
||||
yield StreamEvent(type="messages-tuple", data=event_data)
|
||||
yield self._ai_text_event(msg_id, text, counted_usage)
|
||||
|
||||
elif isinstance(msg, ToolMessage):
|
||||
yield StreamEvent(
|
||||
type="messages-tuple",
|
||||
data={
|
||||
"type": "tool",
|
||||
"content": self._extract_text(msg.content),
|
||||
"name": getattr(msg, "name", None),
|
||||
"tool_call_id": getattr(msg, "tool_call_id", None),
|
||||
"id": msg_id,
|
||||
},
|
||||
)
|
||||
yield self._tool_message_event(msg)
|
||||
|
||||
# Emit a values event for each state snapshot
|
||||
yield StreamEvent(
|
||||
@ -550,10 +682,12 @@ class DeerFlowClient:
|
||||
def chat(self, message: str, *, thread_id: str | None = None, **kwargs) -> str:
|
||||
"""Send a message and return the final text response.
|
||||
|
||||
Convenience wrapper around :meth:`stream` that returns only the
|
||||
**last** AI text from ``messages-tuple`` events. If the agent emits
|
||||
multiple text segments in one turn, intermediate segments are
|
||||
discarded. Use :meth:`stream` directly to capture all events.
|
||||
Convenience wrapper around :meth:`stream` that accumulates delta
|
||||
``messages-tuple`` events per ``id`` and returns the text of the
|
||||
**last** AI message to complete. Intermediate AI messages (e.g.
|
||||
planner drafts) are discarded — only the final id's accumulated
|
||||
text is returned. Use :meth:`stream` directly if you need every
|
||||
delta as it arrives.
|
||||
|
||||
Args:
|
||||
message: User message text.
|
||||
@ -561,15 +695,21 @@ class DeerFlowClient:
|
||||
**kwargs: Override client defaults (same as stream()).
|
||||
|
||||
Returns:
|
||||
The last AI message text, or empty string if no response.
|
||||
The accumulated text of the last AI message, or empty string
|
||||
if no AI text was produced.
|
||||
"""
|
||||
last_text = ""
|
||||
# Per-id delta lists joined once at the end — avoids the O(n²) cost
|
||||
# of repeated ``str + str`` on a growing buffer for long responses.
|
||||
chunks: dict[str, list[str]] = {}
|
||||
last_id: str = ""
|
||||
for event in self.stream(message, thread_id=thread_id, **kwargs):
|
||||
if event.type == "messages-tuple" and event.data.get("type") == "ai":
|
||||
content = event.data.get("content", "")
|
||||
if content:
|
||||
last_text = content
|
||||
return last_text
|
||||
msg_id = event.data.get("id") or ""
|
||||
delta = event.data.get("content", "")
|
||||
if delta:
|
||||
chunks.setdefault(msg_id, []).append(delta)
|
||||
last_id = msg_id
|
||||
return "".join(chunks.get(last_id, ()))
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Public API — configuration queries
|
||||
|
||||
@ -10,7 +10,7 @@ from pathlib import Path
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage # noqa: F401
|
||||
from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, SystemMessage, ToolMessage # noqa: F401
|
||||
|
||||
from app.gateway.routers.mcp import McpConfigResponse
|
||||
from app.gateway.routers.memory import MemoryConfigResponse, MemoryStatusResponse
|
||||
@ -225,7 +225,9 @@ class TestStream:
|
||||
|
||||
agent.stream.assert_called_once()
|
||||
call_kwargs = agent.stream.call_args.kwargs
|
||||
assert call_kwargs["stream_mode"] == ["values", "custom"]
|
||||
# ``messages`` enables token-level streaming of AI text deltas;
|
||||
# see DeerFlowClient.stream() docstring and GitHub issue #1969.
|
||||
assert call_kwargs["stream_mode"] == ["values", "messages", "custom"]
|
||||
|
||||
assert events[0].type == "custom"
|
||||
assert events[0].data == {"type": "task_started", "task_id": "task-1"}
|
||||
@ -351,6 +353,123 @@ class TestStream:
|
||||
# Should not raise; end event proves it completed
|
||||
assert events[-1].type == "end"
|
||||
|
||||
def test_messages_mode_emits_token_deltas(self, client):
|
||||
"""stream() forwards LangGraph ``messages`` mode chunks as delta events.
|
||||
|
||||
Regression for bytedance/deer-flow#1969 — before the fix the client
|
||||
only subscribed to ``values`` mode, so LLM output was delivered as
|
||||
a single cumulative dump after each graph node finished instead of
|
||||
token-by-token deltas as the model generated them.
|
||||
"""
|
||||
# Three AI chunks sharing the same id, followed by a terminal
|
||||
# values snapshot with the fully assembled message — this matches
|
||||
# the shape LangGraph emits when ``stream_mode`` includes both
|
||||
# ``messages`` and ``values``.
|
||||
assembled = AIMessage(content="Hel lo world!", id="ai-1", usage_metadata={"input_tokens": 3, "output_tokens": 4, "total_tokens": 7})
|
||||
agent = MagicMock()
|
||||
agent.stream.return_value = iter(
|
||||
[
|
||||
("messages", (AIMessageChunk(content="Hel", id="ai-1"), {})),
|
||||
("messages", (AIMessageChunk(content=" lo ", id="ai-1"), {})),
|
||||
(
|
||||
"messages",
|
||||
(
|
||||
AIMessageChunk(
|
||||
content="world!",
|
||||
id="ai-1",
|
||||
usage_metadata={"input_tokens": 3, "output_tokens": 4, "total_tokens": 7},
|
||||
),
|
||||
{},
|
||||
),
|
||||
),
|
||||
("values", {"messages": [HumanMessage(content="hi", id="h-1"), assembled]}),
|
||||
]
|
||||
)
|
||||
|
||||
with (
|
||||
patch.object(client, "_ensure_agent"),
|
||||
patch.object(client, "_agent", agent),
|
||||
):
|
||||
events = list(client.stream("hi", thread_id="t-stream"))
|
||||
|
||||
# Three delta messages-tuple events, all with the same id, each
|
||||
# carrying only its own delta (not cumulative).
|
||||
ai_text_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and e.data.get("content")]
|
||||
assert [e.data["content"] for e in ai_text_events] == ["Hel", " lo ", "world!"]
|
||||
assert all(e.data["id"] == "ai-1" for e in ai_text_events)
|
||||
|
||||
# The values snapshot MUST NOT re-synthesize an AI text event for
|
||||
# the already-streamed id (otherwise consumers see duplicated text).
|
||||
assert len(ai_text_events) == 3
|
||||
|
||||
# Usage metadata attached only to the chunk that actually carried
|
||||
# it, and counted into cumulative usage exactly once (the values
|
||||
# snapshot's duplicate usage on the assembled AIMessage must not
|
||||
# be double-counted).
|
||||
events_with_usage = [e for e in ai_text_events if "usage_metadata" in e.data]
|
||||
assert len(events_with_usage) == 1
|
||||
assert events_with_usage[0].data["usage_metadata"] == {"input_tokens": 3, "output_tokens": 4, "total_tokens": 7}
|
||||
end_event = events[-1]
|
||||
assert end_event.type == "end"
|
||||
assert end_event.data["usage"] == {"input_tokens": 3, "output_tokens": 4, "total_tokens": 7}
|
||||
|
||||
# The values snapshot itself is still emitted.
|
||||
assert any(e.type == "values" for e in events)
|
||||
|
||||
# stream_mode includes ``messages`` — the whole point of this fix.
|
||||
call_kwargs = agent.stream.call_args.kwargs
|
||||
assert "messages" in call_kwargs["stream_mode"]
|
||||
|
||||
def test_chat_accumulates_streamed_deltas(self, client):
|
||||
"""chat() concatenates per-id deltas from messages mode."""
|
||||
agent = MagicMock()
|
||||
agent.stream.return_value = iter(
|
||||
[
|
||||
("messages", (AIMessageChunk(content="Hel", id="ai-1"), {})),
|
||||
("messages", (AIMessageChunk(content="lo ", id="ai-1"), {})),
|
||||
("messages", (AIMessageChunk(content="world!", id="ai-1"), {})),
|
||||
("values", {"messages": [HumanMessage(content="hi", id="h-1"), AIMessage(content="Hello world!", id="ai-1")]}),
|
||||
]
|
||||
)
|
||||
|
||||
with (
|
||||
patch.object(client, "_ensure_agent"),
|
||||
patch.object(client, "_agent", agent),
|
||||
):
|
||||
result = client.chat("hi", thread_id="t-chat-stream")
|
||||
|
||||
assert result == "Hello world!"
|
||||
|
||||
def test_messages_mode_tool_message(self, client):
|
||||
"""stream() forwards ToolMessage chunks from messages mode."""
|
||||
agent = MagicMock()
|
||||
agent.stream.return_value = iter(
|
||||
[
|
||||
(
|
||||
"messages",
|
||||
(
|
||||
ToolMessage(content="file.txt", id="tm-1", tool_call_id="tc-1", name="bash"),
|
||||
{},
|
||||
),
|
||||
),
|
||||
("values", {"messages": [HumanMessage(content="ls", id="h-1"), ToolMessage(content="file.txt", id="tm-1", tool_call_id="tc-1", name="bash")]}),
|
||||
]
|
||||
)
|
||||
|
||||
with (
|
||||
patch.object(client, "_ensure_agent"),
|
||||
patch.object(client, "_agent", agent),
|
||||
):
|
||||
events = list(client.stream("ls", thread_id="t-tool-stream"))
|
||||
|
||||
tool_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "tool"]
|
||||
# The tool result must be delivered exactly once (from messages
|
||||
# mode), not duplicated by the values-snapshot synthesis path.
|
||||
assert len(tool_events) == 1
|
||||
assert tool_events[0].data["content"] == "file.txt"
|
||||
assert tool_events[0].data["name"] == "bash"
|
||||
assert tool_events[0].data["tool_call_id"] == "tc-1"
|
||||
|
||||
def test_list_content_blocks(self, client):
|
||||
"""stream() handles AIMessage with list-of-blocks content."""
|
||||
ai = AIMessage(
|
||||
@ -373,6 +492,253 @@ class TestStream:
|
||||
assert len(msg_events) == 1
|
||||
assert msg_events[0].data["content"] == "result"
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Refactor regression guards (PR #1974 follow-up safety)
|
||||
#
|
||||
# The three tests below are not bug-fix tests — they exist to lock
|
||||
# the *exact* contract of stream() so a future refactor (e.g. moving
|
||||
# to ``agent.astream()``, sharing a core with Gateway's run_agent,
|
||||
# changing the dedup strategy) cannot silently change behavior.
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def test_dedup_requires_messages_before_values_invariant(self, client):
|
||||
"""Canary: locks the order-dependence of cross-mode dedup.
|
||||
|
||||
``streamed_ids`` is populated only by the ``messages`` branch.
|
||||
If a ``values`` snapshot arrives BEFORE its corresponding
|
||||
``messages`` chunks for the same id, the values path falls
|
||||
through and synthesizes its own AI text event, then the
|
||||
messages chunk emits another delta — consumers see the same
|
||||
id twice.
|
||||
|
||||
Under normal LangGraph operation this never happens (messages
|
||||
chunks are emitted during LLM streaming, the values snapshot
|
||||
after the node completes), so the implicit invariant is safe
|
||||
in production. This test exists as a tripwire for refactors
|
||||
that switch to ``agent.astream()`` or share a core with
|
||||
Gateway: if the ordering ever changes, this test fails and
|
||||
forces the refactor to either (a) preserve the ordering or
|
||||
(b) deliberately re-baseline to a stronger order-independent
|
||||
dedup contract — and document the new contract here.
|
||||
"""
|
||||
agent = MagicMock()
|
||||
agent.stream.return_value = iter(
|
||||
[
|
||||
# values arrives FIRST — streamed_ids still empty.
|
||||
("values", {"messages": [HumanMessage(content="hi", id="h-1"), AIMessage(content="Hello", id="ai-1")]}),
|
||||
# messages chunk for the same id arrives SECOND.
|
||||
("messages", (AIMessageChunk(content="Hello", id="ai-1"), {})),
|
||||
]
|
||||
)
|
||||
|
||||
with (
|
||||
patch.object(client, "_ensure_agent"),
|
||||
patch.object(client, "_agent", agent),
|
||||
):
|
||||
events = list(client.stream("hi", thread_id="t-order-canary"))
|
||||
|
||||
ai_text_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and e.data.get("content")]
|
||||
# Current behavior: 2 events (values synthesis + messages delta).
|
||||
# If a refactor makes dedup order-independent, this becomes 1 —
|
||||
# update the assertion AND the docstring above to record the
|
||||
# new contract, do not silently fix this number.
|
||||
assert len(ai_text_events) == 2
|
||||
assert all(e.data["id"] == "ai-1" for e in ai_text_events)
|
||||
assert [e.data["content"] for e in ai_text_events] == ["Hello", "Hello"]
|
||||
|
||||
def test_messages_mode_golden_event_sequence(self, client):
|
||||
"""Locks the **exact** event sequence for a canonical streaming turn.
|
||||
|
||||
This is a strong regression guard: any future refactor that
|
||||
changes the order, type, or shape of emitted events fails this
|
||||
test with a clear list-equality diff, forcing either a
|
||||
preserved sequence or a deliberate re-baseline.
|
||||
|
||||
Input shape:
|
||||
messages chunk 1 — text "Hel", no usage
|
||||
messages chunk 2 — text "lo", with cumulative usage
|
||||
values snapshot — assembled AIMessage with same usage
|
||||
|
||||
Locked behavior:
|
||||
* Two messages-tuple AI text events (one per chunk), each
|
||||
carrying ONLY its own delta — not cumulative.
|
||||
* ``usage_metadata`` attached only to the chunk that
|
||||
delivered it (not the first chunk).
|
||||
* The values event is still emitted, but its embedded
|
||||
``messages`` list is the *serialized* form — no
|
||||
synthesized messages-tuple events for the already-
|
||||
streamed id.
|
||||
* ``end`` event carries cumulative usage counted exactly
|
||||
once across both modes.
|
||||
"""
|
||||
# Inline the usage literal at construction sites so Pyright can
|
||||
# narrow ``dict[str, int]`` to ``UsageMetadata`` (TypedDict
|
||||
# narrowing only works on literals, not on bound variables).
|
||||
# The local ``usage`` is reused only for assertion comparisons
|
||||
# below, where structural dict equality is sufficient.
|
||||
usage = {"input_tokens": 3, "output_tokens": 2, "total_tokens": 5}
|
||||
agent = MagicMock()
|
||||
agent.stream.return_value = iter(
|
||||
[
|
||||
("messages", (AIMessageChunk(content="Hel", id="ai-1"), {})),
|
||||
("messages", (AIMessageChunk(content="lo", id="ai-1", usage_metadata={"input_tokens": 3, "output_tokens": 2, "total_tokens": 5}), {})),
|
||||
(
|
||||
"values",
|
||||
{
|
||||
"messages": [
|
||||
HumanMessage(content="hi", id="h-1"),
|
||||
AIMessage(content="Hello", id="ai-1", usage_metadata={"input_tokens": 3, "output_tokens": 2, "total_tokens": 5}),
|
||||
]
|
||||
},
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
with (
|
||||
patch.object(client, "_ensure_agent"),
|
||||
patch.object(client, "_agent", agent),
|
||||
):
|
||||
events = list(client.stream("hi", thread_id="t-golden"))
|
||||
|
||||
actual = [(e.type, e.data) for e in events]
|
||||
expected = [
|
||||
("messages-tuple", {"type": "ai", "content": "Hel", "id": "ai-1"}),
|
||||
("messages-tuple", {"type": "ai", "content": "lo", "id": "ai-1", "usage_metadata": usage}),
|
||||
(
|
||||
"values",
|
||||
{
|
||||
"title": None,
|
||||
"messages": [
|
||||
{"type": "human", "content": "hi", "id": "h-1"},
|
||||
{"type": "ai", "content": "Hello", "id": "ai-1", "usage_metadata": usage},
|
||||
],
|
||||
"artifacts": [],
|
||||
},
|
||||
),
|
||||
("end", {"usage": usage}),
|
||||
]
|
||||
assert actual == expected
|
||||
|
||||
def test_chat_accumulates_in_linear_time(self, client):
|
||||
"""``chat()`` must use a non-quadratic accumulation strategy.
|
||||
|
||||
PR #1974 commit 2 replaced ``buffer = buffer + delta`` with
|
||||
``list[str].append`` + ``"".join`` to fix an O(n²) regression
|
||||
introduced in commit 1. This test guards against a future
|
||||
refactor accidentally restoring the quadratic path.
|
||||
|
||||
Threshold rationale (10,000 single-char chunks, 1 second):
|
||||
* Current O(n) implementation: ~50-200 ms total, including
|
||||
all mock + event yield overhead.
|
||||
* O(n²) regression at n=10,000: chat accumulation alone
|
||||
becomes ~500 ms-2 s (50 M character copies), reliably
|
||||
over the bound on any reasonable CI.
|
||||
|
||||
If this test ever flakes on slow CI, do NOT raise the threshold
|
||||
blindly — first confirm the implementation still uses
|
||||
``"".join``, then consider whether the test should move to a
|
||||
benchmark suite that excludes mock overhead.
|
||||
"""
|
||||
import time
|
||||
|
||||
n = 10_000
|
||||
chunks: list = [("messages", (AIMessageChunk(content="x", id="ai-1"), {})) for _ in range(n)]
|
||||
chunks.append(
|
||||
(
|
||||
"values",
|
||||
{
|
||||
"messages": [
|
||||
HumanMessage(content="go", id="h-1"),
|
||||
AIMessage(content="x" * n, id="ai-1"),
|
||||
]
|
||||
},
|
||||
)
|
||||
)
|
||||
agent = MagicMock()
|
||||
agent.stream.return_value = iter(chunks)
|
||||
|
||||
with (
|
||||
patch.object(client, "_ensure_agent"),
|
||||
patch.object(client, "_agent", agent),
|
||||
):
|
||||
start = time.monotonic()
|
||||
result = client.chat("go", thread_id="t-perf")
|
||||
elapsed = time.monotonic() - start
|
||||
|
||||
assert result == "x" * n
|
||||
assert elapsed < 1.0, f"chat() took {elapsed:.3f}s for {n} chunks — possible O(n^2) regression (see PR #1974 commit 2 for the original fix)"
|
||||
|
||||
def test_none_id_chunks_produce_duplicates_known_limitation(self, client):
|
||||
"""Documents a known dedup limitation: ``messages`` chunks with ``id=None``.
|
||||
|
||||
Some LLM providers (vLLM, certain custom backends) emit
|
||||
``AIMessageChunk`` instances without an ``id``. In that case
|
||||
the cross-mode dedup machinery cannot record the chunk in
|
||||
``streamed_ids`` (the implementation guards on ``if msg_id``
|
||||
before adding), and a subsequent ``values`` snapshot whose
|
||||
reassembled ``AIMessage`` carries a real id will fall through
|
||||
the dedup check and synthesize a second AI text event for the
|
||||
same logical message — consumers see duplicated text.
|
||||
|
||||
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 (it carries ``langgraph_node`` / ``langgraph_step`` /
|
||||
``checkpoint_ns`` / ``tags`` etc.). Synthesizing a fallback
|
||||
like ``f"_synth_{id(msg_chunk)}"`` only helps if the values
|
||||
snapshot uses the same fallback, which it does not. A real
|
||||
fix requires either provider cooperation (always emit chunk
|
||||
ids — out of scope for this PR) or content-based dedup (risks
|
||||
false positives for two distinct short messages with identical
|
||||
text).
|
||||
|
||||
This test makes the limitation **explicit and discoverable**
|
||||
so a future contributor debugging "duplicate text in vLLM
|
||||
streaming" finds the answer immediately. If a real fix lands,
|
||||
replace this test with a positive assertion that dedup works
|
||||
for the None-id case.
|
||||
|
||||
See PR #1974 Copilot review comment on ``client.py:515``.
|
||||
"""
|
||||
agent = MagicMock()
|
||||
agent.stream.return_value = iter(
|
||||
[
|
||||
# Realistic shape: chunk has no id (provider didn't set one),
|
||||
# values snapshot's reassembled AIMessage has a fresh id
|
||||
# assigned somewhere downstream (langgraph or middleware).
|
||||
("messages", (AIMessageChunk(content="Hello", id=None), {})),
|
||||
(
|
||||
"values",
|
||||
{
|
||||
"messages": [
|
||||
HumanMessage(content="hi", id="h-1"),
|
||||
AIMessage(content="Hello", id="ai-1"),
|
||||
]
|
||||
},
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
with (
|
||||
patch.object(client, "_ensure_agent"),
|
||||
patch.object(client, "_agent", agent),
|
||||
):
|
||||
events = list(client.stream("hi", thread_id="t-none-id-limitation"))
|
||||
|
||||
ai_text_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and e.data.get("content")]
|
||||
# KNOWN LIMITATION: 2 events for the same logical message.
|
||||
# 1) from messages chunk (id=None, NOT added to streamed_ids
|
||||
# because of ``if msg_id:`` guard at client.py line ~522)
|
||||
# 2) from values-snapshot synthesis (ai-1 not in streamed_ids,
|
||||
# so the skip-branch at line ~549 doesn't trigger)
|
||||
# If this becomes 1, someone fixed the limitation — update this
|
||||
# test to a positive assertion and document the fix.
|
||||
assert len(ai_text_events) == 2
|
||||
assert ai_text_events[0].data["id"] is None
|
||||
assert ai_text_events[1].data["id"] == "ai-1"
|
||||
assert all(e.data["content"] == "Hello" for e in ai_text_events)
|
||||
|
||||
|
||||
class TestChat:
|
||||
def test_returns_last_message(self, client):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user