Closures -> _StreamingCall methods (shared worker/monitor state on the
instance), codex passthrough and Bedrock Converse branches -> free
functions, _stream_final_text/_emit_stream_* -> module helpers. Bodies
are AST-identical modulo the self.<name> renames (verified by script);
stream event handling and wire shapes unchanged.
Adds two bounded fast modes on top of the static /fast toggle, default OFF:
- `auto`: every user turn opens a `agent.fast_auto_seconds` (default 60s)
window; requests inside it carry the provider fast param, later tool-loop
requests fall back to standard pricing.
- `cold`: the same window, but only on the first turn of a session (no prior
user/assistant/tool history).
agent/fast_mode.py holds the whole policy: `begin_turn()` at the
run_conversation ingress arms `agent._fast_until`; `effective_request_overrides()`
is consumed in the ONE place request_overrides feed the transports
(build_api_kwargs), so the fast param is a per-request kwarg only. System
prompt, tools and messages are untouched — the prompt cache is preserved.
resolve_fast_mode_overrides() is now the single gate for static and bounded
modes and accepts provider/base_url: OpenRouter, Nous, Copilot, Azure,
Bedrock and custom base_urls never receive service_tier/speed (#34308's
route gating). Both existing callers (CLI turn route, gateway turn route)
and the TUI config.set path pass the route.
Surfaces: config `agent.service_tier: auto|cold` + `agent.fast_auto_seconds`,
`/fast auto|cold` in CLI, gateway (picker gains both entries), TUI/desktop
config.set; status shows the mode; web dashboard select lists the real
values. Docs: configuration.md Fast Mode section with mode table + cost note,
slash-commands, cli-config.yaml.example, locale strings for the two picker
entries.
Salvages #89991 (bounded fast modes) and #34308 (route gating).
Fixes#64785, #74730.
Co-authored-by: Eva <239388517+100yenadmin@users.noreply.github.com>
Co-authored-by: kbaicai <kbaicai@qq.com>
The one-shot reasoning-off retry changes a request parameter that is part
of the provider cache key on config-sensitive providers (Anthropic renders
thinking/effort into the prompt; OpenAI lists reasoning.effort as
prefix-affecting), so that request is a deliberate single cache miss.
Pin the bound: the request AFTER it must carry the configured reasoning
again and the system prompt must be byte-identical across the whole retry
sequence. Sabotage-verified (sticky flag -> test fails on request 3).
Docstring on _consume_ephemeral_reasoning_off states the cost honestly.
GLM-5.3-flash on ollama-cloud with reasoning_effort=high can spend the ENTIRE
output cap on reasoning delivered in a separate field and return
finish_reason=length with no visible content (verified live: max_tokens=4096,
completion_tokens=4096, content empty).
The length-continuation path handled that shape badly:
1. the empty response was appended as an interim assistant fragment,
poisoning the transcript until the pre-call sanitizer healed it
(observed 3+ healings per turn on the reporting user's session);
2. every continuation re-ran with thinking ON, re-deriving the whole
thinking budget against a growing context, so 4 attempts still produced
nothing and the turn died with 'Response remains truncated after 4
continuation attempts'.
Now:
- interim assistant fragments with no visible content are never appended
(whichever way they got empty);
- a thinking-only truncation sets a one-shot reasoning-off override that
build_api_kwargs consumes for the next request, so the continuation
writes the answer instead of re-thinking it;
- the ceiling exit clears a pending override and, when every fragment was
empty, returns an actionable final_response instead of an invisible None.
When a watchdog (TTFB / stream-idle / stale-call) force-closes a Codex
Responses request, the worker thread can still be draining SSE frames.
`_consume_codex_event_stream` returns `status=terminal_status`, which defaults
to `"completed"`, and its only truncation guard is
`if not saw_terminal and not output`. A mid-stream kill leaves
`saw_terminal=False` but `output`/text non-empty, so the partial text came back
as a `finish_reason=stop` response and got persisted as a finished assistant
turn — a long reply just stops mid-sentence with no error surfaced.
Observed as a long generation dying at `1. Create (6/6)` and never emitting its
end marker, with the truncated text already stored in state.db.
Fix: publish a per-request retirement token so the worker can tell it has been
retired.
- `agent/chat_completion_helpers.py`: `interruptible_api_call` installs
`agent._active_codex_stream_request_token` before handing off to the worker
(codex_responses only) and clears it at all four kill sites plus the worker's
own `finally`. Retirement is cleared BEFORE `_close_request_client_once`,
which can raise — every other call site wraps it in try/except, and a leaked
token would let a later worker mistake itself for the owning attempt. The
request-local `_codex_request_retired` mirror also swallows the transport
error our own force-close causes, so the worker's local error cannot replace
the watchdog's retryable TimeoutError (same split as `_request_cancelled`).
- `agent/codex_runtime.py`: `run_codex_stream` captures the token and raises
`TimeoutError` from `interrupt_check` when it no longer owns the request —
raising rather than breaking, because a break returns the partial `final`.
The four stream callbacks also drop post-retirement frames so an abandoned
attempt cannot stream tokens into the live turn's bubble (the gateway caches
AIAgent instances per session).
`TimeoutError` is not an httpx / ConnectionError / RuntimeError subclass, so it
passes through the four `except` clauses around the consume call untouched.
No token installed (auxiliary callers such as `handle_max_iterations` drive
`_run_codex_stream` directly) means every check passes — behavior unchanged.
Tests: 5 new cases. Retirement raises instead of returning partial output;
post-retirement deltas stop reaching callbacks; the no-token path keeps its
existing terminal-frame tolerance; the watchdog installs and clears the token;
non-codex api_modes install nothing. A `_LazyCreateStream` helper is needed
because `_FakeCreateStream` materializes events in __init__, which would run
the retirement side effect before consumption starts.
should_use_direct_api_call() contexts (gateway cron turns #62151, delegate_task
children #60203) were short-circuited onto the NON-streaming wire because the
interrupt worker wedges inside their nested thread pools. That dropped every
liveness property streaming provides: edge proxies kill the silent POST
(z.ai HTTP 524 — three retries later the child dies as "max_iterations"), and
the non-stream stale watchdog cannot tell a reasoning model's thinking phase
from a hung provider, so children die at exactly stale_timeout (#100260).
Keep those contexts on interruptible_streaming_api_call. The request now runs
INLINE on the conversation thread (no worker → the deadlock class stays
closed) while the existing poll loop — 30s heartbeat, stale-stream detector,
cross-thread interrupt abort — moves onto a monitor thread that only ever
aborts sockets, never dispatches (same shape as direct_api_call's watchdog
timer). Interactive sessions are unchanged: worker + poll loop as before.
should_use_direct_api_call() itself is untouched; only what it routes to.
Live A/B (real SSE server, real AIAgent.run_conversation):
before: subagent/cron wire stream=None, request on conversation thread
after: subagent/cron wire stream=True, request on conversation thread
cli unchanged (stream=True, spawned worker)
inline stale detector kills a one-chunk-then-silence stream at budget;
AIAgent.interrupt() from another thread unwinds the inline stream in 0.6s.
Co-authored-by: Expri-commits <184641533+Expri-commits@users.noreply.github.com>
Run models locally as a first-class provider. The CLI grows a managed
llama.cpp runtime (engine install, model download, server supervision);
the desktop app grows the full setup and management story on top of it.
GUI surfaces ship behind the desktop --local launch flag (hermes desktop
--local, or the flag on the packaged app); backend routes and the CLI
are always live.
Runtime (hermes_cli/local_runtime/):
- curated GGUF catalog with per-machine variant selection: hardware
probe (VRAM/RAM/UMA), fit planning with spill accounting, quant choice
by context window
- derived recommendation: quality-ranked picks gated by a predicted
decode-speed floor, bandwidth-aware on unified memory; the decision
table is pinned as a test (pick AND reason per memory class), and the
Recommended badge explains its pick in a tooltip fed by the resolver's
actual branch
- engine install + model download with resumable split parts, cumulative
plan-level progress, and staged-model integrity (a split GGUF counts
only when every part is present)
- server supervision: spawn/adopt/stop, router mode with per-model load
progress relayed over SSE, abandoned-request cleanup
Desktop:
- Settings -> Providers -> Local models: one-click quickstart (install
engine, download the recommended model, boot) plus per-model download/
activate/eject, fit-ranked catalog with context pills
- model pickers (composer dropdown + Cmd+K) show staged local models,
in-flight downloads as live progress rows, and load-into-memory bars
- local-setup campaign tip for eligible hardware; System resources
statusbar widget (GPU/VRAM/RAM); in-chat load progress during sends
- friendly dead-server errors, and failed agent builds retry on the next
send instead of wedging the session
Co-developed with NVIDIA field feedback on RTX 5090 and DGX Spark.
A superseded writer was fencing the payload-empty terminal chunk, so
completed streams were mislabeled as mid-stream drops.
Co-authored-by: Cursor <cursoragent@cursor.com>
Follow-ups on the salvaged cluster:
- sse_done.py: dispatch SSE events at blank-line boundaries and join
consecutive data: lines per the SSE spec (a split JSON event no longer
reads as two malformed fragments that disable synthesis)
- accept integer/string-truthy lastOne sentinels (1 / "true") in both the
proxy tracker and the agent stream reader
- server.py: guard the [DONE] append against client hangup at EOF and
widen the interrupt tuple with OSError
- contributor email mappings for loulanyue and jon-nielsen
vLLM >= 0.1.dev20051 merges finish_reason into the final content chunk.
When the SSE-echo guard is engaged at that moment (GLM-family tokenizers
emit standalone ':' / ' id' tokens mid-prose), the guard's content-shape
continue paths swallow the terminal chunk and finish_reason is never
captured, so a complete stream is misclassified as a mid-stream drop and
retried.
Extract finish_reason/usage at the top of the chunk loop body, before any
content-shape continue; the late tail-side extraction becomes redundant.
Addresses a second root cause of #94614 (the consume-gate fence is
covered by #94625; usage-side classification by #91376).
Teknium