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Author SHA1 Message Date
yang rui
3dc895df4d
feat(models): pace shared RPM budgets before dispatch (#5432) 
* feat(models): add shared RPM admission queues

* fix(models): address admission pacing review feedback

* docs: simplify request admission quick start guidance

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
 2026-09-15 07:14:35 +08:00
Tu Naichao
ae82f426bf
fix(summarization): stop fraction triggers from crashing the agent build (#4901) 
* fix(summarization): resolve fraction triggers from declared context_window, degrade instead of crashing the agent build

A fraction trigger/keep clause requires profile["max_input_tokens"], which any
third-party OpenAI-compatible model lacks, so SummarizationMiddleware
construction raised ValueError out of create_summarization_middleware and failed
the whole agent build (#3103).

- factory: translate a declared model context_window into the langchain
  profile (metadata-only, never reaches the provider payload); explicit
  caller/override profiles win
- summarization factory: drop unusable fraction trigger clauses (absolute
  clauses survive), fall a fraction keep back to the messages default, and
  disable compaction with an actionable warning only when no usable trigger
  clause remains — the agent build never dies from summarization config
- docs: config.example.yaml, ModelConfig.context_window, summarization.md

* refactor(summarization): share the default keep constant with the fraction fallback

The fraction-keep degradation fallback hardcoded ("messages", 20),
duplicating SummarizationConfig.keep's default_factory literal. Move the
value to a shared DEFAULT_KEEP constant so the two cannot drift apart.

* fix(summarization): keep trigger-null + fraction-keep constructing after degradation

A trigger of None with a fraction keep hit the all-clauses-dropped branch
(has_usable_trigger=False) and disabled compaction, and the accompanying
warning claimed configured triggers were all fraction-based when none were
configured. Only report nothing-usable when trigger clauses actually
existed; trigger:null keeps constructing the never-firing middleware with
the degraded keep, matching its behavior outside the degradation path.

* fix(summarization): address review — keep manual compaction, validate ContextSize, pin wiring

Review follow-ups on #4901:

- When every configured trigger is a dropped fraction clause, keep
  constructing the never-firing middleware (trigger=None) instead of
  returning None: manual /compact runs with force=True and never consults
  trigger clauses, so it must keep working for a profile-less model
  rather than reporting 'compaction is disabled'. The warning now says
  auto-compaction will not fire while manual compaction remains.
- ContextSize gains a config-load validator: fraction values must be in
  (0,1] (a percent-style 80 instead of 0.8 previously produced a threshold
  the context could never reach — a silently inert trigger), absolute
  values must be positive.
- New un-monkeypatched integration test pins the shipped wiring
  (context_window declared -> real factory attaches profile -> fraction
  clause survives -> middleware constructs), which the stubbed
  middleware-side tests and kwarg-capturing factory-side tests each
  stopped short of.
- Docs (summarization.md + config.example.yaml) clarify that the fraction
  resolves against the summary/anchor model's context_window
  (summarization.model_name when set, else the run model), including the
  mismatch caveat for a larger-window summary model.

* fix(summarization): reject non-finite ContextSize values at config load

YAML .nan / .inf pass pydantic's float parsing, and nan <= 0 is False,
so the positivity check alone let them through as dead thresholds
(count >= nan is always False) — the same silent-inert-trigger class the
range validator was added to close. Guard with math.isfinite first,
consistent with the existing non-finite guards on mem0 timeout_seconds
and poll_after_seconds.

* fix(summarization): merge context_window into inferred profile, require whole message counts

- construct the model first, then merge max_input_tokens into the
  provider-inferred langchain profile: passing profile= to the
  constructor replaced the whole inferred metadata (tool_calling,
  structured_output, io capabilities, output limits) with the single
  key. An explicitly configured profile is still never clobbered.
- reject non-integral ContextSize values for type=messages at config
  load: langchain slices the message list with them, so a float index
  raised TypeError mid-compaction.

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
 2026-09-03 17:05:09 +08:00
Amorend
85c3909c2e
feat: show real-time context window usage (#3125) (#3183) 
* feat: show real-time context window usage in chat UI (#3125)

Adds a `context_usage` block to `GET /api/threads/{id}/token-usage`
(token count from the live checkpoint, the thread model's
`context_window`, and a percentage), introduces a new
`ModelConfig.context_window` distinct from the per-call `max_tokens`
output cap, and surfaces the percentage in the chat header — inside
`TokenUsageIndicator` when token-usage tracking is on, or as a
standalone badge when it's off so context capacity stays visible
independent of cost tracking.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* feat: per-category breakdown for context window usage

Replace the single-number context_usage payload with a Claude-Code-style
breakdown — messages, system prompt, skills, system/MCP tools (active +
deferred), custom agents, memory injection, autocompact buffer, and free
space — and surface it in the chat UI with a segmented progress bar and
per-row table.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* docs(config): document context_window across model examples

Add `context_window` to every example model in config.example.yaml so the
new chat-UI "% context used" indicator works out of the box for whichever
example a user adopts. Each value is the published default at the time of
writing; users are pointed at the official model spec to verify. Bumps
config_version to 11 so `make config-upgrade` flags outdated user configs.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* style: ruff format (line-length 240)

No behavior change — collapses two multi-line expressions that fit on
one line under the project's 240-char limit. Picked up by `make format`.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* review: address Copilot bot comments on #3183

- token-usage-indicator: switch `{contextPercentage && (...)}` to an
  explicit `!= null` check. (The string `"0"` is actually truthy in JS so
  the original code wasn't buggy, but the explicit check is clearer.)
- context-usage-breakdown: drop the `useMemo` around segments/totals — the
  computation is O(n) over a handful of rows and the previous memo deps
  omitted `t.contextUsage.categories`, so the bar's tooltips/aria-labels
  could stay in the old language after a locale switch.
- context_usage._split_tools: snapshot MCP names from
  `get_cached_mcp_tools()` directly instead of re-reading
  `extensions_config.json` after `get_available_tools()` already loaded
  it. Removes redundant file I/O on every `/token-usage` poll.
  (`get_available_tools()` still emits its own INFO logs — silencing
  those is out of scope here.)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* style(frontend): prettier --write context-usage-breakdown

CI's `pnpm format` (prettier --check) caught two lines previously
formatted by hand. Collapses one comma to fit on one line; no behavior
change.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* fix(gateway): correct context-usage breakdown + add exact token counting

The context-usage indicator shipped two bugs that silently zeroed whole
breakdown rows (both caught by try/except, so the feature looked alive but
produced wrong numbers):

1. _count_system_prompt passed app_config= to get_deferred_tools_prompt_section,
   which only accepts deferred_names -> TypeError swallowed -> system_prompt
   row always 0, and used_tokens/percentage undercounted by the full prompt.
   Also subtracted the deferred section twice (the rendered prompt already
   excluded it). Fix: derive deferred names deterministically and pass them to
   apply_prompt_template; drop the redundant subtraction.

2. _split_tools imported a non-existent get_deferred_registry -> ImportError
   swallowed -> all four tool-category rows always 0. Fix: classify via the
   public is_mcp_tool predicate + tool_search.enabled (mirrors
   build_deferred_tool_setup); the MCP tag is set by get_available_tools.

Added token_usage.counting (approximate|exact). 'exact' routes text/schema/
message counting through the model tokenizer (tiktoken cl100k_base) via the
existing memory-module machinery (lazy load + cache + cooldown + CJK-aware
fallback), so CJK-heavy threads stop being undercounted by chars//4.

Regression + e2e tests added; 6621 backend tests pass.

* fix(gateway): harden context usage accounting

* fix(gateway): count promoted MCP tools as active in context usage

Promoted tools (deferred MCP tools the thread has fetched via tool_search)
have their full schema bound on every subsequent turn by
DeferredToolFilterMiddleware, so they consume context like any active tool.
The breakdown previously left them in the reserved *_deferred rows, under-
counting the thread's used_tokens.

Classification now treats a tool as deferred only when tool_search is enabled,
it is MCP-sourced, AND it has not been promoted. The promoted set is read from
the checkpoint's channel_values and scoped by catalog hash — matching the
runtime middleware, so a stale promotion from MCP-config drift cannot inflate
the active count.

The static system prompt still lists all deferred tool names (promotions only
affect schema binding, not the prompt), so _count_system_prompt's deferred
rendering is intentionally left unchanged.

8 new tests cover classification, catalog-hash scoping (match / drift /
compute-failure / malformed), and checkpoint extraction.

* fix(context): address review feedback

* fix(context): count structured message payloads

* fix(context): harden usage accounting

* fix(config): bump schema for context usage fields

* refactor: narrow context usage to core indicator

---------

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
 2026-07-31 21:57:22 +08:00
Huixin615
88e36d9686
fix(#3189): prevent write_file streaming timeout on long reports (#3195) 
* fix(#3189): prevent write_file streaming timeout on long reports

Adds a layered defense against StreamChunkTimeoutError caused by oversized
single-shot write_file tool calls:

- factory: default stream_chunk_timeout to 240s for OpenAI-compatible
  clients (overridable via ModelConfig.stream_chunk_timeout in config.yaml)
- sandbox/tools: server-side 80 KB length guard on non-append write_file
  calls (configurable via DEERFLOW_WRITE_FILE_MAX_BYTES env var, 0 disables);
  rejects oversized payloads with a structured error pointing the model at
  str_replace or append=True
- middleware: classify StreamChunkTimeoutError as transient but cap retries
  at 1 via per-exception _RETRY_BUDGET_OVERRIDES (same-payload retry on a
  chunk-gap timeout buffers the same way upstream; full 3-attempt loop
  would stack 6-12 min of dead air)
- middleware: surface an actionable user-facing message for stream-drop
  exceptions instead of leaking the raw langchain stack
- prompts: add a routing-style File Editing Workflow hint to both lead_agent
  and general_purpose subagent prompts, pointing the model at str_replace
  for incremental edits (mirrors Claude Code's Edit / Codex's apply_patch)
- tests: behavioural coverage for size guard, retry budget override,
  stream-drop user message, factory default injection

Refs #3189

* fix(#3189): drop stream_chunk_timeout for non-OpenAI providers

Address CR feedback on PR #3195:

- factory: pop `stream_chunk_timeout` from kwargs for any model_use_path other than `langchain_openai:ChatOpenAI` instead of returning early. `ModelConfig.stream_chunk_timeout` is part of the shared schema, so a user-supplied value on a non-OpenAI provider would otherwise be forwarded to its constructor and raise `TypeError: unexpected keyword argument`.

- factory: rewrite docstring to describe the actual `exclude_none=True` behaviour (explicit null is excluded and falls back to the default) instead of the misleading "None falling out via exclude_none=True keeps its value".

- tests: add regression coverage asserting the kwarg is stripped before reaching a non-OpenAI provider's constructor.

Refs: bytedance#3189

* fix(#3189): restrict stream-drop user copy to StreamChunkTimeoutError only

Per CR on #3195: narrow _STREAM_DROP_EXCEPTIONS to StreamChunkTimeoutError. Generic httpx RemoteProtocolError / ReadError fall back to the standard 'temporarily unavailable' copy, since they routinely fire on transient network blips where the 'split the output' guidance is misleading. Retry/backoff classification is unchanged — both remain transient/retriable. Tests updated to reflect new copy, plus a symmetric regression test for ReadError.

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
 2026-06-07 17:47:11 +08:00
shivam johri
194bab4691
feat(config): add when_thinking_disabled support for model configs (#1970) 
* feat(config): add when_thinking_disabled support for model configs

Allow users to explicitly configure what parameters are sent to the
model when thinking is disabled, via a new `when_thinking_disabled`
field in model config. This mirrors the existing `when_thinking_enabled`
pattern and takes full precedence over the hardcoded disable behavior
when set. Backwards compatible — existing configs work unchanged.

Closes #1675

* fix(config): address copilot review — gate when_thinking_disabled independently

- Switch truthiness check to `is not None` so empty dict overrides work
- Restructure disable path so when_thinking_disabled is gated independently
  of has_thinking_settings, allowing it to work without when_thinking_enabled
- Update test to reflect new behavior
 2026-04-09 18:49:00 +08:00
mxyhi
e119dc74ae
feat(codex): support explicit OpenAI Responses API config (#1235) 
* feat: support explicit OpenAI Responses API config

Co-authored-by: Codex <noreply@openai.com>

* Update backend/packages/harness/deerflow/config/model_config.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Codex <noreply@openai.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
 2026-03-22 20:39:26 +08:00
DanielWalnut
76803b826f
refactor: split backend into harness (deerflow.*) and app (app.*) (#1131) 
* refactor: extract shared utils to break harness→app cross-layer imports

Move _validate_skill_frontmatter to src/skills/validation.py and
CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py.
This eliminates the two reverse dependencies from client.py (harness layer)
into gateway/routers/ (app layer), preparing for the harness/app package split.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: split backend/src into harness (deerflow.*) and app (app.*)

Physically split the monolithic backend/src/ package into two layers:

- **Harness** (`packages/harness/deerflow/`): publishable agent framework
  package with import prefix `deerflow.*`. Contains agents, sandbox, tools,
  models, MCP, skills, config, and all core infrastructure.

- **App** (`app/`): unpublished application code with import prefix `app.*`.
  Contains gateway (FastAPI REST API) and channels (IM integrations).

Key changes:
- Move 13 harness modules to packages/harness/deerflow/ via git mv
- Move gateway + channels to app/ via git mv
- Rename all imports: src.* → deerflow.* (harness) / app.* (app layer)
- Set up uv workspace with deerflow-harness as workspace member
- Update langgraph.json, config.example.yaml, all scripts, Docker files
- Add build-system (hatchling) to harness pyproject.toml
- Add PYTHONPATH=. to gateway startup commands for app.* resolution
- Update ruff.toml with known-first-party for import sorting
- Update all documentation to reflect new directory structure

Boundary rule enforced: harness code never imports from app.
All 429 tests pass. Lint clean.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* chore: add harness→app boundary check test and update docs

Add test_harness_boundary.py that scans all Python files in
packages/harness/deerflow/ and fails if any `from app.*` or
`import app.*` statement is found. This enforces the architectural
rule that the harness layer never depends on the app layer.

Update CLAUDE.md to document the harness/app split architecture,
import conventions, and the boundary enforcement test.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add config versioning with auto-upgrade on startup

When config.example.yaml schema changes, developers' local config.yaml
files can silently become outdated. This adds a config_version field and
auto-upgrade mechanism so breaking changes (like src.* → deerflow.*
renames) are applied automatically before services start.

- Add config_version: 1 to config.example.yaml
- Add startup version check warning in AppConfig.from_file()
- Add scripts/config-upgrade.sh with migration registry for value replacements
- Add `make config-upgrade` target
- Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services
- Add config error hints in service failure messages

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix comments

* fix: update src.* import in test_sandbox_tools_security to deerflow.*

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: handle empty config and search parent dirs for config.example.yaml

Address Copilot review comments on PR #1131:
- Guard against yaml.safe_load() returning None for empty config files
- Search parent directories for config.example.yaml instead of only
  looking next to config.yaml, fixing detection in common setups

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: correct skills root path depth and config_version type coercion

- loader.py: fix get_skills_root_path() to use 5 parent levels (was 3)
  after harness split, file lives at packages/harness/deerflow/skills/
  so parent×3 resolved to backend/packages/harness/ instead of backend/
- app_config.py: coerce config_version to int() before comparison in
  _check_config_version() to prevent TypeError when YAML stores value
  as string (e.g. config_version: "1")
- tests: add regression tests for both fixes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: update test imports from src.* to deerflow.*/app.* after harness refactor

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-14 22:55:52 +08:00