Follow-up on the salvaged #64611 commit: the original guard blocked the
install tree unconditionally, which would have broken the legitimate
'developing Hermes from a source clone' CLI flow (launching hermes inside
the repo and getting its AGENTS.md as project context).
Refined policy:
- resolve_context_cwd(): validates configured paths (missing dir -> None +
warning) but honors an EXPLICIT install-tree cwd verbatim — deliberate
user choice.
- build_context_files_prompt(): blocks only the cwd=None -> os.getcwd()
FALLBACK into the install tree, with a new allow_install_tree_fallback
param. system_prompt.py passes it for platform cli/tui (launch dir is
the user's real shell cwd there); desktop/gateway surfaces keep the
guard (their fallback dir is self-spawned, never user-picked).
- Warning log names the resolved dir and the terminal.cwd remedy.
E2E-verified all five scenarios: desktop fallback blocked, in-tree CLI dev
keeps AGENTS.md, explicit install-tree cwd honored, invalid TERMINAL_CWD
falls to None then blocked, normal workspace loads.
_extract_parallel_scope_path used Path.cwd() (process cwd) instead of the
tool's actual execution cwd, and os.path.abspath() instead of os.path.realpath(),
so symlink aliases and relative/absolute path pairs that resolve to the same
physical file were treated as distinct targets and placed in the same parallel
segment. On case-insensitive platforms (Windows) os.path.normcase() was also
absent, allowing Foo.txt and foo.txt to race.
Changes:
- agent/tool_dispatch_helpers.py: introduce _canonical_path(raw_path,
execution_cwd) applying expanduser->abspath->realpath->normcase; thread
execution_cwd through _extract_parallel_scope_path and
_plan_tool_batch_segments
- agent/tool_executor.py: pass get_active_env(effective_task_id).cwd as
execution_cwd to _plan_tool_batch_segments; add pathlib.Path import
- run_agent.py: pass active env cwd to _plan_tool_batch_segments at the
second call site inside _execute_tool_calls
- tests/run_agent/test_tool_batch_segmentation.py: add 5 regression tests
covering relative/absolute same target, symlink alias, execution_cwd vs
process cwd, symlink parent + nonexistent write target, and Windows
case-insensitive alias (skipped on non-Windows)
Fixes a file-corruption / lost-update race introduced by the mixed
tool-batch segmentation feature (perf commit #64460).
- Refresh _OFFICIAL_DOCS_PRICING fireworks entries against current
docs.fireworks.ai/serverless/pricing: qwen3p6-plus is gone (replaced
by qwen3p7-plus); add glm-5p2/5p1, kimi-k2p7-code, deepseek-v4-flash,
minimax-m3/m2p7, gpt-oss-120b/20b, and the routers/*-fast tiers with
their distinct higher rates.
- Picker pricing via get_pricing_for_provider('fireworks'): pure dict
transform over the shared models.dev in-memory/disk cache (1h TTL) +
_pricing_cache memoization — no new network call on the picker path.
- Wire pricing display into the generic api-key-provider setup flow so
Fireworks model pickers show $/M columns like OpenRouter/Nous do.
- Invariant tests: plugin fallback_models all priced, fast tiers price
higher than standard, every row carries cache_read < input.
Fireworks-hosted sessions previously showed estimated_cost_usd = 0
because (a) _OFFICIAL_DOCS_PRICING had no Fireworks entries and (b)
resolve_billing_route() had no branch for provider="fireworks",
falling through to billing_mode="unknown".
Adds entries for the three Fireworks models hermes operators are
most likely to route through (Kimi K2.6, DeepSeek V4 Pro, Qwen3.6-Plus)
and a routing branch that triggers on either explicit
provider="fireworks" or api.fireworks.ai base_url match. Mirrors the
recently-merged MiniMax addition pattern; pricing snapshot sourced
from https://docs.fireworks.ai/serverless/pricing and the per-model
pages on fireworks.ai.
Tests cover: (a) full Fireworks model id resolves to the snapshot
entry, (b) base_url alone is sufficient to route, (c) end-to-end
estimate returns "estimated" status with the expected dollar amount.
A follow-up upstream issue is open proposing a dynamic pricing
source (e.g. litellm's pricing JSON) as a permanent fix to the
PR-per-model treadmill that this snapshot keeps adding to.
* feat(analytics): record auxiliary model usage per task in session accounting
Auxiliary LLM calls (vision, compression, title_generation, web_extract,
session_search, ...) discarded their token usage, leaving dashboard
analytics blind to aux model spend (issue #23270).
- hermes_state.py: session_model_usage gains a task PK dimension
(''=main loop) via v22 table-rebuild migration (SQLite can't alter a
PK); record_auxiliary_usage() writes per-(model,provider,task) deltas
WITHOUT touching sessions counters (gateway overwrites those with
absolute main-loop totals — folding aux in would double-count or be
clobbered). Aux rows never inherit the session's main-loop route.
- agent/aux_accounting.py: ContextVar ambient accounting context
(mirrors the portal_tags conversation context); record_aux_usage()
normalizes usage via usage_pricing.normalize_usage, estimates cost,
and is strictly best-effort. moa_reference/moa_aggregator excluded —
conversation_loop already folds MoA usage+cost into the main delta.
- agent/auxiliary_client.py: _validate_llm_response is the recording
chokepoint — every successful non-streaming aux response passes
through it exactly once, sync and async, including fallback paths
(model read from the response itself stays accurate across
fallbacks).
- run_agent.py: run_conversation publishes/resets the accounting
context; agent/title_generator.py republishes on its bare thread.
- hermes_cli/web_server.py: /api/analytics/usage folds aux rows into
by_model (aux-only models finally appear) and adds a by_task
summary; /api/analytics/models surfaces aux rows on the Models page.
Design per review of PR #62850 by @eeksock (thread-local + separate
auxiliary_usage table): rebuilt on ContextVar (async-safe — thread-local
cross-attributes concurrent coroutines on one event loop) and the
existing session_model_usage table instead of a parallel accounting
path, extended beyond vision to every aux task, and wired the analytics
endpoints so the dashboard actually shows it. Credit to @eeksock for
the approach and @tboatman for the detailed root-cause analysis.
* test(moa): match _validate_llm_response mock to new accounting-hint signature
* test(aux): accept accounting-hint kwargs in remaining _validate_llm_response mocks
Extends the conversation=<id> Portal tag (salvaged from PR #65183 by
@J-SUPHA) from main-loop-only to every LLM call in a conversation:
- agent/portal_tags.py: ContextVar-based conversation context.
nous_portal_tags() falls back to the ambient id when no explicit
session_id is passed, so every aux tag site (auxiliary_client,
chat_completion_helpers summary path, web_tools) inherits the tag
with zero per-call-site plumbing. Ambient id wins over explicit
per-segment ids since it carries the lineage root.
- hermes_state.py: SessionDB.get_conversation_root() — public wrapper
over the lineage walk; returns the ROOT session id, so one
user-facing conversation keeps a single conversation= value across
context-compression rotation, and delegate subagent trees tag as
their parent conversation.
- run_agent.py: run_conversation() publishes the root id for the turn
and resets it in finally. _conversation_root_id() resolves via
_parent_session_id for subagents.
- agent/moa_loop.py: MoA reference fan-out workers now run under
propagate_context_to_thread so advisor slots attribute to the acting
conversation (also fixes approval-callback propagation on that path).
- agent/title_generator.py: bare title thread republishes the context
from its session id (spawned after turn reset).
Tests: ContextVar semantics, cross-context isolation, thread-hop
propagation, lineage-root resolution incl. cycle guard.
Thinking-only retries flush the scrubber then stream again without
reset(). flush() was leaving _last_emitted_ended_newline False, so the
next stream's opening <think> looked mid-line and leaked into the UI.
Fixes#62452. Two amplifiers turned one slow auxiliary route into a
per-turn multi-minute stall:
1. Fallback candidates inherited the exact effective_timeout the primary
was called with. When the primary's deadline was short (tuned or
already burned), an independently healthy fallback died on the same
clock — the reporter's 163k-token compression needed ~90s on the
fallback and got the primary's 30s, every turn. fallback_chain
entries may now declare their own 'timeout' (seconds); both fallback
candidate call sites (sync + async) resolve it via
_fallback_entry_timeout, label-scoped so only configured-chain
candidates are affected. No entry timeout → task-level timeout,
preserving existing behavior.
2. A session whose transcript structurally cannot be summarized within
the deadline re-attempted every 60s, re-burning the full timeout on
every subsequent turn. Consecutive timeout-class failures now
escalate the cooldown 60s → 300s → 900s (capped); any successful
summary or session reset clears the streak. Timeout classification
takes precedence over the streaming-closed 30s rung ('timed out'
also matches _is_connection_error) and now recognizes the SDK's
'Request timed out.' phrasing.
Fail-safe behavior is unchanged: all messages are preserved when every
candidate fails; the cooldown only spaces out retries.
The cross-turn stale-call circuit breaker (_check_stale_giveup in
agent/chat_completion_helpers.py) raises a RuntimeError when the
provider has been unresponsive for N consecutive stale attempts.
This error was classified as FailoverReason.unknown (retryable=True,
should_fallback=False), causing the retry loop to burn all max_retries
against the same dead provider — each retry hitting the circuit breaker
instantly with zero network overhead — before fallback was attempted.
Add a classification rule (section 7b in classify_api_error) that
recognizes the stale-breaker RuntimeError by its signature phrases and
classifies it as FailoverReason.timeout with retryable=False,
should_fallback=True. This makes the retry loop skip retries and go
straight to fallback provider activation on the first hit.
Test: test_stale_breaker_runtime_error_triggers_fallback_not_retry
Three fixes for the Bedrock Claude path:
1. Streaming fallback: When AnthropicBedrock SDK raises 'Unexpected event
order' (SDK misparses Bedrock error events as message_start), auto-switch
to native Converse API for the rest of the session instead of failing
after 3 retries.
2. Image base64 decode (#33317): data URL payloads were passed as base64
strings to source.bytes, but boto3 re-encodes at the wire layer. Now
decoded to raw bytes before passing to Converse API.
3. Bearer token routing (#28156): Users with AWS_BEARER_TOKEN_BEDROCK are
now routed through Converse API regardless of model, since the
AnthropicBedrock SDK only supports SigV4 signing.
3 new tests. 121 bedrock_adapter tests passing.
Two consecutive incomplete assistant interims with identical visible
content (content + reasoning) are collapsed even when opaque provider
state (encrypted reasoning item ids, message item phases) drifts per
continuation — previously that drift defeated dedup and caused message
storms (#52711). The latest opaque payload is written onto the existing
message in place, so continuation replay still uses fresh provider
state.
Salvage note: the original PR also made the 3-retry continuation cap
cumulative per turn (no reset on progress). That half is intentionally
dropped — a legitimate long turn alternating incomplete/progress would
hard-fail at 3 cumulative, changing semantics beyond the reported bug.
Map Codex Responses status=incomplete with incomplete_details.reason=content_filter to finish_reason=content_filter so the existing refusal/fallback path runs instead of burning incomplete continuation attempts.
Follow-up on @AlexFucuson9's cherry-picked multimodal flattening:
- http(s) image parts render as '[image: <url>]' so the summary keeps a
referenceable handle after compaction (base64 data: URLs still
collapse to '[image]' — no reusable reference, and leaking them is
the bug being fixed)
- unknown part types (document, future shapes) render as '[<type>]'
instead of being silently dropped
- test docstrings corrected: the original crash premise is stale
(redact_sensitive_text grew str() coercion in #52147); the live bug
is repr-noise + base64 leakage into the summarizer input
msg.get('content') can return a list of parts for multimodal messages
(containing text, images, etc.). The old code passed this list directly
to redact_sensitive_text(text: str), which raised AttributeError on
list.replace(), causing context compression to fail entirely for any
session with attached images.
Fix: detect list content and extract text parts before redacting.
Image parts are replaced with '[image]' placeholder.
Follow-up on the salvaged fallback: silent degradation is how #64333 went
unnoticed (jobs dead on arrival, only errors.log knew). Warn once with a
resync hint, and cover both the skewed and healthy paths with tests.
Two related bugs in _AnthropicCompletionsAdapter.create() in
agent/auxiliary_client.py silently discard caller-supplied
reasoning_config and extra_body on the Anthropic-Messages
auxiliary-protocol path:
* Bug A: reasoning_config=None was hardcoded at L1000, so the
reasoning_config parameter on build_anthropic_kwargs was
unreachable for any auxiliary task. The main agent path
(agent/transports/anthropic.py) already reads
reasoning_config from caller params; this PR aligns the
auxiliary adapter with the same pattern.
* Bug B: create(**kwargs) accepts an OpenAI-style kwargs
payload from the caller but only forwards a hand-picked
subset to self._client.messages.create(). Any caller-supplied
extra_body (e.g. thinking control, metadata, service_tier,
vendor-specific fields) was dropped on the floor. The
codex/responses transport in the same file already merges
extra_body; the Anthropic branch is the gap.
This unlocks the caller-supplied extra_body path so auxiliary
callers can set per-vendor request fields (including
thinking: {type: "disabled"} for Anthropic-compatible vendors
that require an explicit disable on the wire), and lets the
reasoning_config kwarg flow into build_anthropic_kwargs like the
main agent does. Both changes are backward-compatible for
callers that don't pass the affected kwargs.
Affected providers (all routed through _AnthropicCompletionsAdapter
via _maybe_wrap_anthropic): anthropic (native), minimax /
minimax-cn, kimi-coding / kimi-coding-cn, z.ai / GLM, and any
custom /anthropic-suffixed endpoint. See PR description for
related issues (#35566, #7209, #16533, #32813, #29248).
With the TTFB watchdog now scaled (not disabled) for large requests on
main, the hard ceiling is a backstop against mid-stream wedges, not the
primary stall detector. The original 600s default clamped BELOW the
intentional 1200s stale floor for >100k-token requests, partially
reverting the floor that keeps healthy gateway-scale payloads alive.
Raise the default to 1500s so the ceiling only catches unbounded
growth, never healthy slow turns.
A large Codex request (estimated context >= 10k tokens) disables the no-byte
TTFB watchdog on purpose, and openai_codex_stale_timeout_floor *raises* the
stale timeout (up to 1200s at >100k tokens) so healthy gateway-scale payloads
aren't aborted mid-prefill. When the backend genuinely stalls — no first byte
AND no events, exactly the #64507 symptom — the request is only reclaimed at
that high stale floor, so the session can hang for 13+ minutes with an idle
slash_worker and no ended_at/end_reason while Desktop still shows it as active.
Add a flat, finite hard ceiling on total openai-codex request time that always
applies (min() of the computed stale timeout and the ceiling) regardless of the
TTFB-disable / stale-floor interaction. A stalled large request is now killed
at the ceiling and the retry loop / visible failure path takes over instead of
hanging indefinitely. Tunable via HERMES_CODEX_HARD_TIMEOUT_SECONDS (default
600s; 0 disables the ceiling to restore pre-fix behavior).
Closes#64507
_summarize_tool_result() built the web_extract summary straight from the
first `urls` entry. When web_search results are forwarded into web_extract
(a common chain), that entry is a dict ({"url"/"href": ...}) rather than a
URL string. With 2+ URLs the `url_desc += " (+N more)"` step then raised
`TypeError: unsupported operand type(s) for +=: 'dict' and 'str'`, aborting
the pre-compression pruning pass; with a single URL the raw dict repr
leaked into the summary text.
Unwrap the URL from dict entries before use — mirroring the existing
web_extract handling in agent/display.py (`_display_url`),
tools/web_tools.py (`_web_extract_url`) and acp_adapter/tools.py
(`build_tool_title`) — so `url_desc` is always a string and the
concatenation is always str + str.
Adds regression tests covering multiple/single dict URLs, the href key,
malformed dicts, and the plain-string path.
Community reports of GPT 'infinitely thinking' are usually a slow or
overloaded provider plus silent retry machinery: the CLI/TUI/Desktop
spinner shows a generic 'cogitating...' verb for the whole wait and the
gateway heartbeat says only 'Working — N min'.
Add AIAgent._emit_wait_notice(): rewrites the live spinner/status line
(thinking_callback → CLI prompt_toolkit widget, thinking.delta → TUI +
Desktop) and updates the activity tracker (included in the gateway's
'⏳ Working — N min' heartbeat). Wired at the four wait points:
- non-streaming wait loop: after 30s with no response, the line becomes
'⏳ waiting on <model> — Ns with no response yet (provider may be slow
or overloaded; auto-reconnect at Ns)'
- streaming wait loop: same explanation after 30s with no chunks,
including the long-thinking case
- TTFB / stale-stream kills: '⚠ no response from provider in Ns —
reconnecting...'
- Codex continuation retries: '↻ model returned reasoning with no final
answer — asking it to continue (n/3)' instead of silence
Notices are fail-open (display errors never break the wait loop) and
gateway sessions without a display callback still get the improved
activity description.
Follow-up to the salvaged #63690: when the interim assistant message is
too empty to append (no content and no reasoning of any kind), the last
message in history is still the prior user/tool turn — appending the
user-role nudge there would create a user→user or tool→user sequence
that strict providers reject. Only append the nudge when the last
message is an assistant turn. Also add IpastorSan to AUTHOR_MAP.
grok-4.x on the xAI /v1/responses surface sometimes ends a turn with only
reasoning items — no message output item, no tool calls — and those
reasoning items carry no encrypted_content. Two compounding problems:
1. The model occasionally emits its final answer INSIDE the reasoning
channel, delimited by grok's internal "<response>" tag. The answer
exists but is classified reasoning-only → finish_reason=incomplete.
2. An interim assistant message holding only plain-text reasoning replays
as nothing in _chat_messages_to_responses_input, so every continuation
request is byte-identical to the one that just failed. The model
deterministically repeats the reasoning-only response until the retry
budget is exhausted and the turn dies with "Codex response remained
incomplete after 3 continuation attempts".
Fixes:
- _normalize_codex_response (xai_responses only): salvage the
<response>-delimited tail from the reasoning text and promote it to
assistant content; the untagged prefix stays as thinking text.
- Codex-incomplete continuation path: when the interim message has
nothing the input converter will replay (no content, no encrypted
reasoning items, no message items), append a user-role nudge so the
retry actually differs and explicitly asks for the final answer /
pending tool call. Mirrors the existing _get_continuation_prompt
pattern used for length truncation.
Observed live with grok-4.20 on xai-oauth (2026-07-13); sibling of the
grok-composer web_search incomplete-loop fix in transports/codex.py.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Follow-up to the salvaged #64449: the status-trusting branch flipped
github_responses to 'stop' alongside unknown relays. Copilot fronts the
same OpenAI model family as codex_backend and shows the same
reasoning-only 'still thinking' degeneration, so it stays on the
continuation path. Only unrecognized (other:*) backends trust
response.status='completed' as terminal.
The aggregator is MoA's acting model, but the main loop's reasoning
gates key off the virtual moa://local identity and never fire — so with
no per-slot reasoning_effort the aggregator silently ran at the backend
default, ignoring the user's reasoning config entirely (#64187).
New _aggregator_reasoning_config(): slot value > full acting-model
resolution via the shared chokepoint (agent.reasoning_overrides for the
slot's model > global agent.reasoning_effort; YAML False stays
'disabled'). Applied to both aggregator call sites (acting turn +
one-shot /moa synthesis).
Reference advisors intentionally keep slot-or-default: inheriting a
global xhigh into every advisor fan-out would silently multiply cost.
Fixes#64187.
Consolidation follow-up on top of @liuwei666888's compressor fix (#52431)
and @Frowtek's ACP render guard (#62588) — completes the class fix across
every display-layer consumer of tool-call arguments:
- agent/context_compressor.py: wrap _summarize_tool_result in a
never-raises backstop (same pattern as get_cute_tool_message and the
ACP guard). The per-branch _str_arg coercions keep summaries
informative; the wrapper guarantees compression can never crash-loop
on a summary branch we didn't anticipate. Also reject non-dict parsed
args (a JSON list/scalar would crash every args.get call).
- agent/display.py: build_tool_preview's process branch sliced
session_id/data without coercion — crashed build_tool_preview and
build_tool_label on the live tool-progress callback (probed live:
TypeError 'int' object is not subscriptable). Coerce like the
sibling branches.
- tests: backstop fuzz matrix (16 tools x 6 hostile value shapes),
fallback-shape contracts, display process-preview regressions,
non-dict args guard.
When LLMs return non-string parameter values (e.g. bool, int) in tool call
arguments, _summarize_tool_result() crashes with TypeError because it calls
len(), .count(), or slicing directly on args.get() return values.
This causes an infinite crash loop in the TUI — context compression
triggers on session resume, which crashes, which restarts, which triggers
compression again.
Add _str_arg() helper that coerces any value to str, and use it in all 5
vulnerable call sites:
- terminal: len(cmd)
- write_file: content.count()
- delegate_task: len(goal)
- execute_code: len(code)
- vision_analyze: question[:50]
The chat_completions path raises EmptyStreamError when a stream yields
no chunks and no finish_reason (#64420). _call_anthropic() had no
equivalent, and the eventless failure surfaces differently by client:
- Real Anthropic SDK: no message_start means no final-message snapshot,
so get_final_message() raises a bare AssertionError — not in the
retry loop's transient set, so it burned no retries and surfaced raw.
- OpenAI-compat shims: may fabricate a contentless Message with no
stop_reason (or return None), flowing out as a 'successful' empty turn.
Track whether any stream event arrived and normalize both shapes to
EmptyStreamError, giving the anthropic path the same transient retry
budget in the shared _call() retry loop. A real completed response
always carries a stop_reason, so the guard cannot fire on legitimate
turns (including eventless mocks with stop_reason='end_turn').
Follow-up to #64420.
An exhausted EmptyStreamError previously reported 'Connection to provider
failed' — misleading, since the connection succeeded (stream opened) and
the provider simply sent nothing. Add a dedicated third branch so users
debugging a misconfigured endpoint aren't sent chasing network issues.
Follow-up to #64420.
Follow-up to @michaelHMK's cherry-picked fix for #50892:
- agent/tool_dispatch_helpers.py: guard the segment planner's except-path
debug log against non-string arguments (the planner replaced the old
_should_parallelize_tool_batch body after #64460 and inherited the same
latent arguments[:200] slice).
- tests: the PR's executor-coercion hunks were dropped as redundant —
both executors now route through _parse_tool_arguments, which already
rejects null/non-object args with a structured error result instead of
coercing to {} (the 'we do not repair bad model outputs' contract).
Reworked the salvaged tests to pin the current behavior: None args are
rejected without dispatch, valid siblings still run, the planner treats
them as a barrier without raising, and the mainline run_conversation
path (which normalizes None to '{}' before dispatch) stays crash-free
under verbose logging.
Review nit from verification: the comment claimed newer FastMCP accepts a
JSON schema kwarg — the installed SDK's add_tool has no such parameter; the
synthesized __signature__ is what drives schema generation on both paths.
Fixes#64025
The hermes-tools MCP server was fetching each tool's JSON schema
into params_schema but never passing it to FastMCP's add_tool(),
so all published tools had an empty **kwargs signature. MCP clients
couldn't see parameters and arguments were dropped at dispatch.
This fix:
- Adds _signature_from_schema() to convert JSON schemas to Python
function signatures with type annotations
- Attaches the generated signature/annotations to each handler closure
so FastMCP introspects the real parameter structure
- Filters out None values before dispatch to avoid forwarding unset
optional parameters
Impact: web_search, browser automation, vision, and other Hermes tools
are now properly callable from the codex_app_server runtime.
The upstream migration to inference.nousresearch.com broke routing for
inference-api.nousresearch.com. Restore the correct base_url and drop
the stale alias match in _is_nous_inference_route().
Fixes#60715
The just-merged auxiliary.<task>.reasoning_effort shorthand applied
ensemble-wide to MoA (one value for every advisor) — wrong granularity.
Per-slot preset config supersedes it:
moa:
presets:
deep_review:
reference_models:
- {provider: ..., model: ..., reasoning_effort: low}
- {provider: ..., model: ..., reasoning_effort: xhigh}
aggregator:
{provider: ..., model: ..., reasoning_effort: high}
- Remove reasoning_effort from the moa_reference/moa_aggregator
DEFAULT_CONFIG blocks; _get_task_extra_body now warns-and-ignores the
key on MoA tasks, pointing at the preset config
- Guard tests: MoA aux blocks must not regrow the key; task-level value
is rejected with the pointer warning
- Docs: configuration.md notes the MoA exception and links the MoA page
Every auxiliary task block (vision, web_extract, compression,
title_generation, curator, background_review, moa_reference, ...) now
accepts a reasoning_effort shorthand:
auxiliary:
compression:
reasoning_effort: low
vision:
reasoning_effort: none
_get_task_extra_body() folds it into extra_body.reasoning, which every
auxiliary wire already translates: chat.completions passes it through,
the Codex Responses adapter maps it to top-level reasoning/include, and
the Anthropic auxiliary adapter now forwards it into
build_anthropic_kwargs(reasoning_config=...) (previously hardcoded None).
An explicit extra_body.reasoning on the same task wins over the
shorthand. Invalid levels are ignored with a warning. Empty string
(the shipped default) is a no-op — zero behavior change.
Config: reasoning_effort added to all 16 auxiliary task blocks in
DEFAULT_CONFIG (no version bump — deep-merge handles new keys).
A model response containing several parallel-safe reads plus one unsafe
tool used to lose ALL concurrency: _should_parallelize_tool_batch was
all-or-nothing, so a single barrier call (terminal, clarify, unknown
tool, malformed args) forced the entire batch onto the sequential path.
_plan_tool_batch_segments now splits the batch into ordered segments:
maximal contiguous runs of parallel-safe calls execute on the existing
concurrent path, barrier calls on the sequential path, strictly in the
model's emission order. Invariants preserved:
- one tool result per call, appended in emission order (segments are
contiguous, so no result reordering across a barrier)
- side-effect boundaries: no call starts before an earlier barrier ends
- overlapping file targets split into separate ordered parallel runs
- turn-end budget enforcement + /steer injection run exactly once per
batch (segment executors run with finalize=False; the segmented
dispatcher owns the whole-turn finalize)
- interrupt during segment k drains segments k+1..n with cancelled
results, keeping one result per tool_call_id
Homogeneous batches keep their original single-path dispatch (zero
behavior delta); _should_parallelize_tool_batch remains as a thin view
over the planner for existing callers and tests.
Collapse the six per-surface copies of override-then-global resolution
(CLI startup, gateway, TUI, cron, /model switch, fallback activation)
onto one shared resolve_reasoning_config() in hermes_constants.
Also fixes the gateway resolving reasoning against config model.default
instead of the session's effective model: after a session-only /model
switch, the switched model's override now applies (gateway message paths
pass the resolved session model through _resolve_session_reasoning_config;
/reasoning status reads the session model override).
Cleanup: drop docs/PER_MODEL_REASONING.md (duplicates the website docs
page), drop the change-detector _config_version test (no bump needed —
deep-merge handles new keys), remove a stale plan-reference comment.
Adds chokepoint contract tests (13) and gateway session-effective-model
regression tests (2).
