Extends the fanout enum with 'every_n:<N>' (N >= 2): advisors run on the
first iteration of each user turn and every Nth tool iteration after it;
off-cadence iterations REUSE the cached guidance from the last on-cadence
run via the same cache mechanism the user_turn fanout uses, so the
aggregator still gets advice on every step. The cadence counter is scoped
per user turn (resets on a new user message) and only advances when the
advisory state actually changes, so streaming retries never consume a
cadence slot. Mapping form {mode: every_n, n: N} normalizes to the
canonical string. Unknown/degenerate values fall back to per_iteration.
Addresses issue #63393 (advisor fan-out multiplies turn latency/cost by
the tool-iteration count). Redesigned from PR #63448: the submitted shape
skipped references entirely on off-cadence iterations (aggregator ran
advice-less); this version keeps the last advice in play, credited for
the idea and cadence framing.
Config-gated, default-off (default fanout remains per_iteration).
Co-authored-by: webtecnica <75556242+webtecnica@users.noreply.github.com>
Adds test_moa_gemini_aggregator_sanitize_uses_real_model: drives a full MoA
tool-call turn (virtual-provider mode) with a Gemini aggregator and asserts
the strict-API sanitize pass is invoked with the resolved aggregator model
(gemini-3-pro-preview), never the virtual preset name once a slot is
resolved — the exact path that stripped extra_content/thought_signature and
made Gemini aggregators 400 (#65092).
Writing the test surfaced a gap in the salvaged #66212 fix: in virtual-
provider MoA mode (provider=moa, no moa_config threaded through
run_conversation) the conversation-loop branch never fired because it only
consulted moa_config. Extend it to fall back to the facade's
last_aggregator_slot — the same source the handle_max_iterations fix uses —
so both MoA entry modes resolve the real aggregator model.
Also adds the contributors/emails mapping for the #15676 credit base.
Follow-up to the salvaged core of #53802: a naive MoAClient(preset) rebuild
restores a working facade but silently drops the reference_callback relay
wired in agent_init, so moa.reference / moa.aggregating display events stop
reaching every frontend for the rest of the session.
Introduce agent.moa_loop.build_moa_facade(agent, preset) as the single
construction point for the MoA facade and use it at:
- initial client construction (agent_init.py)
- turn-start fallback restore (restore_primary_runtime)
- transient transport recovery (try_recover_primary_transport — previously
fell through to _create_openai_client with MoA's empty client_kwargs and
died with 'api_key client option must be set')
- mid-session model switches (switch_model)
The relay reads agent.tool_progress_callback at emit time, so callbacks
attached after construction are picked up automatically.
Adds test_moa_restored_facade_still_emits_reference_events covering event
delivery through a restored facade.
When Hermes fails over from a non-Gemini provider (xAI, Anthropic, etc.) to
Gemini mid-conversation, the existing assistant tool_calls in history carry
no Gemini ``extra_content.google.thought_signature`` (the originating provider
never emits one). The native adapter's ``_translate_tool_call_to_gemini``
omitted ``thoughtSignature`` entirely in that case, so Gemini 3 thinking
models rejected every replayed turn with::
HTTP 400 INVALID_ARGUMENT
Function call is missing a thought_signature in functionCall parts.
Additional data, function call default_api:<tool_name>, position N.
The Cloud Code Assist sibling adapter already handles this exact case by
emitting a sentinel ``"skip_thought_signature_validator"`` (see
``agent/gemini_cloudcode_adapter.py:106``, originally added in #11270 and
documented as matching ``opencode-gemini-auth``'s approach). This change
mirrors that fallback in the native adapter so the two paths behave
identically when replaying cross-provider history.
Verified live against ``generativelanguage.googleapis.com/v1beta`` with
``gemini-3-pro-preview``: synthetic 2-turn conversation with no real
``thoughtSignature`` returns 400 without the sentinel and 200 with it.
Test added: ``test_build_native_request_emits_sentinel_for_cross_provider_tool_call``.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
When MoA mode is active with a Gemini model as the aggregator,
agent.model holds the virtual preset name (e.g. "closed"), not the
actual aggregator model name. The _sanitize_tool_calls_for_strict_api
call uses agent.model to decide whether to keep extra_content
(thought_signature) on tool_calls — since "closed" doesn't contain
"gemini", the thought_signature is stripped and the Gemini aggregator
rejects the next request with HTTP 400 (INVALID_ARGUMENT):
"Function call is missing a thought_signature in functionCall parts."
Fix: resolve the actual aggregator model name from moa_config
(conversation_loop) or last_aggregator_slot (chat_completion_helpers)
and pass it to _sanitize_tool_calls_for_strict_api so the
_model_consumes_thought_signature check sees the real Gemini model.
Closes#65092
Bare ContextCompressor.__new__ doubles (test_compress_focus,
cross_session_guard, image_tokens, pre_compress_memory_context) skip
__init__ and lack the attribute — the documented compression-path
test-double pitfall. Guard with getattr default 1 + int type pin
(bool excluded).
Follow-up fixes on top of the salvaged #22566 mechanism:
- N-collector now counts only REAL actionable user turns via
_is_actionable_user_turn + _is_synthetic_compression_user_turn —
the same filter pair _find_last_user_message_idx uses post-#69291.
The contributor's bare role=='user' + _is_context_summary_content
check let blank platform echoes and continuation/todo rows consume
N slots, silently degrading the guarantee.
- Default flipped 3 -> 1 (behavior-preserving): a default of 3 was
measured to change the tail cut on transcripts whose budget covers
only the last turn. min_tail_user_messages=1 delegates to the
existing single-user anchor; N>1 is opt-in, and the call site is
gated so the default path is byte-identical to main.
- Hardened config parse in agent_init (bool rejected, fractional
floats rejected, floor 1) matching the max_attempts parser shape.
- Wired the recurring external-PR config gaps: hermes_cli/config.py
DEFAULT_CONFIG + cli-config.yaml.example (PR only had cli.py).
- Regression tests: blank echoes / synthetic rows don't count toward
N; tool-call/result pairs never split by the N-boundary (no-orphan
both directions); N-guarantee wins over tail_token_budget and the
_MAX_TAIL_MESSAGE_FLOOR (floor is a minimum, not a cap); default
parity pin; DEFAULT_CONFIG pin.
Add _ensure_last_n_user_messages_in_tail to guarantee the last N user
messages survive compression in the uncompressed tail, with surrounding
assistant/tool context preserved.
- Add min_tail_user_messages parameter (default 3) to ContextCompressor
- New _ensure_last_n_user_messages_in_tail method generalizes single-user protection
- Skip context-summary handoff banners when counting user messages
- User messages are clean boundaries — skip _align_boundary_backward
- Wire through cli.py, agent_init.py, and gateway cache busting keys
Config:
compression:
min_tail_user_messages: 3
Co-Authored-By: Claude <noreply@anthropic.com>
_collect_ghosted_skill_names() covers both ghost-skill shapes in the
compressed middle window: rows already demoted to a [SKILL_PRUNED: ...]
marker AND raw skill_view bodies (> _SKILL_VIEW_PRUNE_MIN_CHARS) that
survived Phase-1 inside an earlier protected tail and then aged into the
compression window — the summarizer paraphrases those instructions away
too. Shared threshold constant between the emit site and the scan.
Pinned by a live-probe-shaped test (real compress(), mocked aux LLM).
Salvage rework of PR #44166 (@dolphin-creator) onto current main:
- ONE canonical prune marker: _skill_pruned_marker(name) builds
'[SKILL_PRUNED: ... reload with skill_view(name='X')]'; both emit
sites and the survival presence check use the same string, fixing the
original PR's defect where the emitted marker was '[SKILL_PRUNED:'
but the presence check looked for '[SKILL_PRUNED]' (re-injection
duplicated markers that had survived).
- Phase-1 prune (_prune_old_tool_results) now threads a protected-skill
set: skills whose skill_view call is within the last 10 messages, in
the protected tail, or named in a tail user message keep their full
bodies. Pass-4 pressure demotion deliberately overrides the guard so
the #61932 dead-end shape cannot return.
- P2 deterministic marker survival: skill names are extracted from the
summarizer INPUT (and the previous summary) before the aux LLM call
and any dropped canonical markers are re-injected afterward under a
'## Pruned Skills' section — routed through _redact_compaction_text,
appended to the summary body only (never in front of SUMMARY_PREFIX
or scaffolding start-of-content markers; classify_summary_content is
unaffected). Same treatment on the static fallback path, re-applied
after its size cap since truncation cuts exactly where markers land.
- Summarizer prompt gains a '## Pruned Skills' copy-verbatim section.
Fixes#32106.
Surgical reapply of the marker-alignment and dedup-guidance halves of
PR #44166 commits 52341f6ca3 / 3d8a31432d / ae07412e4b onto current main:
- the [SKILL_PRUNED: ...] marker embeds the exact reload call
skill_view(name='<skill>') so the model can act without guessing
- SKILLS_GUIDANCE Skill Safety Rule gains rule 4 (DEDUP): after one
reload, remaining markers for the same skill are historical artifacts
Fixes#32106 (part).
The Vertex AI provider (added same-day, commit c73e74386) was never added to
either of the two provider registries that agent/auxiliary_client.py and the
MoA slot-resolution chain depend on, breaking Vertex outside the main
conversation loop:
1. hermes_cli/auth.py::PROVIDER_REGISTRY had no "vertex" entry. The
plugin-auto-extend loop that normally fills gaps explicitly skips
non-api_key auth types (`if _pp.auth_type != "api_key": continue`), and
Vertex was never hand-declared like "bedrock" is. Because
resolve_provider_client() in agent/auxiliary_client.py gates everything
on `pconfig = PROVIDER_REGISTRY.get(provider)` and returns (None, None)
immediately when pconfig is None, its `elif pconfig.auth_type == "vertex"`
branch was permanently dead code — every auxiliary Vertex call (vision,
title generation, reflection, context compression, MoA reference/
aggregator slots) failed outright, not just a MoA-specific edge case.
2. hermes_cli/providers.py::HERMES_OVERLAYS also had no "vertex" entry, so
hermes_cli.providers.get_provider("vertex") returned None. This backs
_preserve_provider_with_base_url() in agent/auxiliary_client.py, which a
MoA slot's resolved (base_url, api_key) pair needs to keep its "vertex"
identity instead of silently collapsing to "custom" — losing the
identity _refresh_provider_credentials() needs to re-mint an expired
OAuth2 token (~1h lifetime) on a 401, and permanently breaking every
subsequent call in that MoA preset for the rest of the session.
Fix mirrors the existing "bedrock"/aws_sdk entries in both registries
exactly, plus adds a "vertex" branch to _refresh_provider_credentials() (it
had branches for openai-codex/nous/anthropic/xai-oauth but not vertex,
so a 401 fell through to `return False` without evicting the stale cached
client).
- hermes_cli/auth.py: hand-declared vertex ProviderConfig(auth_type="vertex")
in PROVIDER_REGISTRY, matching bedrock's shape.
- hermes_cli/providers.py: vertex HermesOverlay(auth_type="vertex") in
HERMES_OVERLAYS + "Google Vertex AI" label override.
- agent/auxiliary_client.py: vertex branch in _refresh_provider_credentials
that re-mints the token via get_vertex_config() and evicts the stale
cached client.
- 8 new regression tests across tests/hermes_cli/test_vertex_provider.py and
tests/agent/test_auxiliary_client.py: registry membership, end-to-end
resolve_provider_client("vertex", ...) building a working client (proving
the previously-dead branch is now reachable), and the 401-refresh/cache-
eviction path.
Follow-ups on top of the cherry-picked #27748 mechanism:
- move the cap constant to module level with full rationale comment
(class attribute aliases it so subclasses/tests can override)
- bound the iterative-update path too: the PREVIOUS SUMMARY block is
passed through _bound_summary_input so a pathological rehydrated
handoff cannot blow up the prompt (previous summary + new turns each
capped)
- extra regression tests: byte-identical small-input passthrough
(identity), direct bound+marker unit check, bound-after-per-message-
truncation shape (hundreds of under-_CONTENT_MAX turns), iterative
path bounded, marker vs classify_summary_content non-collision
- contributor email mapping for @robgfl45
The phase-1 tool-result prune only runs inside compress(), which fires
near 50% of the context window, so it never triggers on large-window
models; old tool outputs then ride in history and are re-sent every turn.
Add prune_tool_results_only(): the same no-LLM prune on a separate, low
proactive_prune_tokens trigger, run as an elif to the compression branch.
Opt-in (default 0), protects the recent tail by message count.
Add the method to the ContextEngine base as a no-op default so pluggable
engines inherit it safely (the post-tool-call path never AttributeErrors on
a non-built-in engine); the built-in compressor supplies the real prune.
Register both keys under the top-level compression config with defaults and
document them.
Follow-up to the #54805 cherry-pick (skill guard rules):
- turn_context.py: snapshot_preflight_display_tokens gets a
getattr+callable guard (SimpleNamespace compressor doubles / plugin
context engines lack the ContextCompressor-only method) and the
snapshot value is type-pinned to a real int (bool excluded) before
arming the rollback — MagicMock compressors return truthy Mocks.
- turn_finalizer.py: interrupted pinned 'is True', the
_turn_received_provider_response read pinned 'is not True' (MagicMock
auto-attrs are truthy), and the compressor rollback method call gets a
getattr+callable guard. Rollback stays display-only: it never touches
_ineffective_compression_count or any durable guard, and preserves the
-1 post-compaction sentinel.
Interrupted turns can seed a speculative display token count before the provider receives the request. Restore that display-only seed when interruption wins the race, while preserving completed post-compaction state and treating a successful provider response independently of optional usage metadata.
Constraint: #54776 remains reproducible on current main, while review #4702305384 identifies anti-thrashing rollback as stale and usage receipt as an unreliable response-completion signal.
Rejected: Restore anti-thrashing counters from a preflight snapshot | current main derives their verdict from real provider usage after a completed compaction boundary.
Confidence: high
Scope-risk: narrow
Directive: Keep interrupted preflight rollback display-only, and never infer provider completion from the presence of usage metadata.
Tested: ./.venv/bin/python -m pytest -q tests/run_agent/test_413_compression.py (29 passed); turn-finalizer/conversation-loop tests (31 passed); context-compressor targeted tests (12 passed); infinite-compaction targeted tests (3 passed); ruff; git diff --check.
Not-tested: End-to-end interactive interrupt through CLI or gateway transport.
A lock-loser compression pass returns its input unchanged, which the
automatic compression sites misread as 'cannot compress further': the
preflight loop armed the insufficient-progress blocker, the pre-API gate
burned a shared attempt, and a lock-contended 413/overflow retried into
the attempt cap and returned compression_exhausted — which the gateway
answers with a full session auto-reset (#9893/#35809). A temporary
concurrent-compression defer wiped the session.
Consume the landed #69870 lock-skip signal on every automatic path
(preflight in turn_context, pre-API pressure gate, 413 handler, overflow
handler, post-tool compaction): when a pass no-ops AND the type-pinned
lock-skip flag is set, refund the attempt (never count it toward the cap
or the insufficient-progress blocker), and when the turn cannot proceed
(provider already proved the request does not fit) end it with a soft
compression_deferred result — distinct from compression_exhausted — so
the gateway keeps the session intact and the next message retries after
the concurrent compressor finishes.
The new compression_skipped_due_to_lock() reader is type-pinned
(is True or isinstance(str)) per the MagicMock auto-attribute rule, and
compress_context() now also clears the signal at the very top of every
attempt (per-attempt state rule, #58629/#69853) so a stale value can
never make a later breaker/codex no-op look like lock contention.
Salvaged from PR #49874; rebuilt on main's #69870
_compression_skipped_due_to_lock signal instead of the PR's parallel
_compression_deferred_by_lock triple.
MoA reference_max_tokens is preset-level — one cap for all reference
models. When mixing a verbose model with a terse one, a single cap is
either too tight for the terse model or too loose for the verbose one.
Now each reference slot can optionally carry its own max_tokens:
reference_models:
- provider: openrouter
model: deepseek/deepseek-v4-pro
max_tokens: *** # per-slot cap, overrides preset-level
- provider: openai-codex
model: gpt-5.5
# no max_tokens → falls back to preset-level reference_max_tokens
_clean_slot (moa_config.py) preserves an optional max_tokens field on
the slot dict, coerced via _coerce_int_or_none. _run_reference
(moa_loop.py) reads slot-level max_tokens first, falling back to the
preset-level cap passed by the caller. Slots without the field are
unaffected — backward compatible.
Type hints on slot-handling functions updated from dict[str, str] to
dict[str, Any] to reflect the now-heterogeneous slot shape.
_build_call_kwargs omitted max_tokens for every provider except
anthropic-compat endpoints and NVIDIA NIM. Gemini's native
generateContent maps max_tokens -> maxOutputTokens and, when it is
omitted, applies a fixed 65,535-token ceiling (not "the model's full
budget"), so dropping the value made MoA's reference_max_tokens a
silent no-op for gemini advisors — they ran effectively uncapped
(observed ~2900 output tokens against a configured cap of 600),
inflating per-turn MoA latency.
Forward max_tokens for the gemini-native path (provider name or native
base_url). Gemini supports maxOutputTokens, so the cap is safe here;
providers that reject max_tokens (Copilot, GPT-5 max_completion_tokens,
ZAI vision) are unaffected — they still omit it as before.
Per review feedback from teknium1: reference_max_tokens is an advisors-only
contract. The aggregator is the acting model and must not be capped by the
reference budget. Changed _is_moa from startswith('moa_') to exact match on
'moa_reference'. Added regression test proving aggregator does NOT receive
max_tokens.
Copilot review pointed out that hardcoding kwargs['max_tokens'] would
400 on models requiring max_completion_tokens (GPT-5 family, Copilot).
The existing auxiliary_max_tokens_param() helper already selects the
correct parameter name per model — use it instead of hardcoding.
Test updated to parametrize expected_key so the Copilot gpt-5.5 case
correctly asserts max_completion_tokens instead of max_tokens.
Addresses Copilot review comments on both files.
PR #56756 added reference_max_tokens to cap MoA advisor output and cut
turn latency. The value is correctly threaded through five layers of MoA
code (moa_config → conversation_loop → aggregate_moa_context →
_run_references_parallel → _run_reference → call_llm(task='moa_reference',
max_tokens=800, ...)).
However, _build_call_kwargs() in auxiliary_client.py silently drops
max_tokens for all OpenAI-compatible providers (PR #34845, which fixed
endpoints and NVIDIA NIM keep it. This means reference_max_tokens never
reached the API for the vast majority of providers.
The bug affects every OpenAI-compatible MoA reference/aggregator slot:
Z.AI (coding plan), OpenRouter, OpenAI, GitHub Copilot, and local
providers. Only Anthropic-compat endpoints (MiniMax, /anthropic URLs)
worked — by coincidence, not MoA-aware design.
Fix: thread the 'task' parameter through all six _build_call_kwargs()
call sites. When task starts with 'moa_', max_tokens is always included
in the request kwargs regardless of provider. Non-MoA auxiliary tasks
(compression, titles, vision, etc.) keep PR #34845 behavior unchanged.
Verified end-to-end:
- Z.AI GLM-5.2 with max_tokens=50 → returned exactly 50 tokens
- Z.AI GLM-5.2 with max_tokens=20 → returned exactly 20 tokens
- Z.AI GLM-5.2 uncapped → returned 315 tokens
- 7 new regression tests covering 4 providers, Anthropic wire, non-MoA
tasks, and prefix-matching boundary
- 288 auxiliary_client tests pass (was 281, +7 new), 84 MoA tests pass
- Zero regressions
aggregate_moa_context's single max_tokens parameter was applied to
both the reference fan-out (_run_references_parallel) and the
aggregator's own synthesis call_llm. #53580 explicitly removed a
hardcoded cap from the aggregator call because it truncated long
aggregator syntheses; #56756 (reference_max_tokens, added to speed up
the advisor fan-out) reintroduced the same shared cap by passing it to
both calls, silently regressing #53580's fix.
Rename the parameter to reference_max_tokens (matching the caller's
own moa_config key) and stop forwarding it to the aggregator's
call_llm invocation, which now always runs uncapped as intended.
An assistant message with an empty or whitespace-only text content block —
produced by context compression or certain tool-call flows — is rejected by
the Anthropic Messages API with HTTP 400 "text content blocks must contain
non-whitespace text". Because the blank block is stored in session history and
replayed verbatim every turn, the session is permanently wedged behind the same
400.
The Bedrock adapter already guards this via _safe_text() (#9486); the native
Anthropic path never got the same treatment. _sanitize_replay_block() rebuilt
text blocks with the raw stored text, and the _convert_assistant_message()
guard only caught a fully empty block list, not a list still containing a
whitespace-only text block.
Add a _safe_text() helper mirroring the Bedrock one and apply it at both points:
the ordered-blocks replay path and a final in-place walk of the converted
content list. Both are self-healing — sessions that stored blank blocks recover
on the next API call. Only text blocks are coerced; thinking/tool_use/image
blocks are untouched.
Fixes#69512
Correction to the previous commit (PR #68402): the claim that api_server
never intercepts MEDIA: tags is inaccurate on current main.
_resolve_media_to_data_urls() (gateway/platforms/api_server.py) DOES
inline image MEDIA: tags (<=5MB, image extensions only) as base64 data
URLs on the four main endpoints (_handle_session_chat,
_handle_session_chat_stream, _handle_chat_completions, _handle_responses).
The real gaps elphamale's PR points at are narrower:
- the /v1/runs output path (_handle_runs) never calls the resolver;
- non-image filetypes are never resolved anywhere (_MEDIA_IMG_EXT is
image-only).
Reword the hint to teach both halves: images via MEDIA: work on the
chat/completions/responses endpoints; non-image files and anything on
the runs endpoint must fall back to plain file paths in the response
text. Update the test to pin the scoped guidance instead of a blanket
prohibition.
Every PLATFORM_HINTS entry for a messaging platform (Telegram, WhatsApp,
Discord, Slack, Signal, WebUI, desktop) teaches the model the MEDIA:/path
convention because an interception mechanism actually resolves it there
(native attachment delivery, or a validated/inlined data URL). The cli
entry, which has no such mechanism, explicitly tells the model NOT to use
it and to state the path in plain text instead.
The api_server entry had neither instruction. Its /v1/runs handler never
routes the final response through any MEDIA: resolver (confirmed against
source: none of the four call sites of the api_server module's media-tag
resolver are inside its runs-endpoint handler), so a MEDIA:/path tag there
renders as inert literal text in the API response — exposing a raw host
filesystem path to the caller with no delivery ever taking place. Nothing
platform-specific told the model not to use a convention it's correctly
taught for several sibling platforms in this same dict, so the general
cross-platform habit could surface here too, unlike cli where an explicit
prohibition already closes the gap.
Mirrors cli's prohibition, adapted for api_server's actual constraint: no
"state the path in plain text" fallback, since a typical API caller has no
filesystem access to the host at all. Points at "a registered file-delivery
tool" generically rather than naming any specific tool, since api_server
toolsets are deployment-defined.
The api_server adapter returned error code "invalid_api_key" for
API_SERVER_KEY authentication failures, which the Desktop error
classifier misidentified as a provider (OpenRouter/OpenAI) key
problem — showing "OpenRouter API key missing" when the real issue
was gateway auth.
Changes:
- gateway/platforms/api_server.py: return "gateway_auth_failed" code
with descriptive message for API_SERVER_KEY auth failures
- apps/desktop/src/store/notifications.ts: add "gateway_auth_failed"
handler before "invalid_api_key" to show correct error message
- agent/error_classifier.py: add "gateway_auth_failed" to auth patterns
- tests: update test_session_api.py to expect new error code
Fixes#39365
* fix(desktop): hide persisted agent-only history scaffolding
Filter verification-stop nudges and context-compaction handoffs at the
stored-history mapper boundary. Preserve a real reply when a compaction
handoff shares its stored message.
* test(desktop): build persisted E2E sessions through the real agent
Drive tui_gateway.entry over its stdio JSON-RPC transport against the mock
provider, wait for real completion events, and persist normal session history
through AIAgent and SessionDB. Migrate resume and hidden-history coverage,
including real compression and live verify-on-stop scaffolding, then remove
the unused direct SessionDB import scripts.
* fix(desktop): use the provisioned Python for real-session E2Es
Run the stdio gateway through uv's synced project environment outside the
Nix dev shell, while retaining the fully provisioned Nix Python when the
shell advertises HERMES_PYTHON_SRC_ROOT.
* fix(nix): expose the provisioned Python environment to uv
Mark the Nix-built Python environment active in the dev shell so the shared
E2E session builder can always run through `uv run --active --no-sync`.
* fix(timeline): persist typed display events
* fix(timeline): strip display-only fields from provider payloads, preserve through rewrites, fix /resume display history
Three review findings from PR #69771:
1. Provider payload leak: display_kind and display_metadata were forwarded
to the provider API as unknown message fields. Strict OpenAI-compatible
backends can reject the next request after a model switch or resumed
typed event. Strip both from the per-request api_msg copy in
conversation_loop alongside the existing api_content pop.
2. Rewrite/import data loss: _insert_message_rows preserved display_kind
but silently dropped display_metadata. After replace_messages,
archive_and_compact, or session import, async-delegation completion
events lost their task counts and fell back to generic display text.
Add display_metadata to the INSERT columns and bind tuple.
3. CLI /resume stale recap: startup --resume A set _resume_display_history
from A's lineage. A subsequent in-session /resume B loaded B only into
conversation_history via get_messages_as_conversation, leaving the stale
A display projection. _display_resumed_history preferentially read the
stale attribute, showing A's recap for B. Switch /resume to
get_resume_conversations and update _resume_display_history alongside
conversation_history.
Tests: 890 Python (5 files), 35 desktop TS — all green.
* feat(tui): render typed display events as ◈ markers in the Ink TUI
The TUI was not handling display_kind at all — model switch markers and
async delegation completions rendered as opaque user messages with the
full [System: ...] text, and hidden compaction handoffs were visible.
Wire display_kind through the full TUI chain:
- _history_to_messages (tui_gateway/server.py) forwards display_kind
and display_metadata to the gateway transcript payload.
- GatewayTranscriptMessage (gatewayTypes.ts) gains both fields.
- Msg.kind (types.ts) gains 'event' value.
- toTranscriptMessages (domain/messages.ts) maps:
- hidden → skip entirely
- model_switch → event "model changed"
- async_delegation_complete → event "N background agents finished"
(or "background agent work finished" without metadata)
- messageGroup (blockLayout.ts) routes event to its own group, with
SELF_SPACED + PAINTS_TRAILING_GAP so it owns its margins.
- messageLine.tsx renders event-kind as a dim ◈ marker with no gutter,
matching the CLI's ◈ event rendering.
- 4 new TUI tests for hidden/model_switch/async_delegation mapping.
TUI typecheck: clean. TUI lint: 0 errors (2 pre-existing warnings).
TUI tests: 9 passed (1 pre-existing failure on main, unrelated).
Claude-on-Bedrock already gets prompt caching via the AnthropicBedrock
SDK path. This adds it to the raw Converse API path used for non-Claude
models (Amazon Nova, and Claude when bearer-token auth forces Converse
routing, #28156) — a conservative model allowlist inserts cachePoint
blocks after tools, system, and the message before the newest turn, and
extracts cacheReadInputTokens/cacheWriteInputTokens into usage so cost
accounting picks them up through the existing Anthropic-style fallback.
Ref: relatorio-cache-performance-provedores-ia.md, P0 item 1.
'auto' is a sentinel meaning "inherit from main runtime / auto-detect",
not a literal model id -- already handled for cfg_model (config-derived)
in _resolve_task_provider_model, but not for the explicit `model` kwarg.
MoA reference/aggregator slots (agent/moa_loop.py's _slot_runtime) forward
a preset's `model:` field as this explicit argument rather than through
auxiliary.<task> config, so a MoA preset configured with `model: auto`
(a natural thing to try given the existing auxiliary.*.model: auto
convention) reached this function as the explicit `model` arg and took
the `model or cfg_model` branch, bypassing the cfg_model-only sentinel
check entirely -- sending the literal string "auto" to the wire as a
model id.
Normalize both the explicit `model` and `cfg_model` the same way, fixing
this at the single chokepoint every caller (MoA included) already goes
through, rather than patching moa_loop.py separately.
Follow-up to the #62614 salvage: try_refresh_matching (added by the
#69843 salvage after this PR's base) calls self.current() while already
holding the now-locking non-reentrant pool lock — a guaranteed deadlock
that git merges silently (no textual conflict). Use _current_unlocked()
and cover the method in the no-deadlock test.
Follow-up to review feedback:
- Acquire self._lock in the remaining public pool-state methods:
has_credentials, reset_statuses, remove_index, resolve_target, and
add_entry. All of them read or rebind self._entries (and the mutating
ones persist auth.json), so they now hold the same lock as select()
and the query methods. None are called from within the lock, so no
unlocked helpers are needed.
- Make the blocking test deterministic: an instrumented lock records the
acquire attempt, and the test first waits for the worker to actually
reach self._lock before asserting it blocks. Previously an unlocked
method could pass if the worker thread was scheduled late.
- Extend the lock test matrix to all nine public methods; the five newly
locked ones fail the test without this fix.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
`has_available()`, `peek()`, `current()` and `entries()` read (and, via
`_available_entries()`, mutate and persist) `self._entries` without holding
`self._lock`, while every other entry point — `select()`,
`mark_exhausted_and_rotate()`, `acquire_lease()`, `try_refresh_current()` —
guards the exact same access with the lock.
`_available_entries()` is not read-only: it prunes aged-out DEAD manual
entries (rebinding `self._entries` at the prune step) and calls `_persist()`
(writes auth.json). The gateway runs platform adapters in threads and cron
runs jobs in a ThreadPoolExecutor, so a status probe via `has_available()`
or `peek()` can race a concurrent `select()`/rotation: torn iteration of
`self._entries`, interleaved auth.json writes, or a lost token rotation.
Fix: take `self._lock` in all four query methods. Because the lock is
non-reentrant and `peek()` composes `current()` + `_available_entries()`,
add a lock-free `_current_unlocked()` helper and route the already-locked
internal callers (`_select_unlocked`, `mark_exhausted_and_rotate`,
`_try_refresh_current_unlocked`) through it to avoid self-deadlock.
Added regression tests: a no-deadlock check (peek re-entrancy) and a
lock-held-blocks-the-call check for each of the four methods.
Two related races in credential-pool cooldown state:
1. Lost update across processes: write_credential_pool merged only
entries missing from the caller's snapshot; for entries present on
both sides the caller's in-memory copy won wholesale. A process
holding a snapshot taken before another process marked a key
exhausted would, on its next persist (e.g. a round-robin rotation),
write the key back as healthy — erasing the cooldown so every
process resumes hammering a rate-limited key. Merge status fields by
last_status_at recency: adopt the on-disk status only when it is
strictly newer AND still binding (DEAD, or EXHAUSTED with an
unexpired cooldown), and never onto re-authed (token-changed)
entries, so legitimate expiry-clears and fresh logins are preserved.
2. Wrong-key quarantine: when mark_exhausted_and_rotate received an
api_key_hint that matched no entry, it fell through to
current()/_select_unlocked() — on a freshly loaded pool that selects
the NEXT healthy key and benches it for the full cooldown TTL,
punishing an innocent credential. When a hint is provided but
unmatched, rotate without marking anything instead of guessing.
Includes regression tests.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
A 402/429/401 is an API-key–level failure (account out of balance,
rate-limited, or key rejected), but the same key can back more than one
pool entry — e.g. an explicit pool entry plus a `model_config` entry
auto-seeded from `model.api_key`, both carrying the identical
`runtime_api_key`.
`mark_exhausted_and_rotate(api_key_hint=...)` only marked the *first*
matching entry, leaving the sibling OK. `_select_unlocked()` then kept
handing back the same depleted key, so the billing-recovery `continue`
loop in the conversation retry path never converged: the request hung
until the client disconnected (~2.5min observed against DeepSeek),
emitting only `response.created` with no 402 ever surfaced to the user.
Mark every entry sharing the failed key so the pool can reach the
"no available entries" state and let the error propagate immediately.
Adds a regression test covering two entries backed by the same key.
The dedup-reset calls assumed a full AIAgent; gateway/loop test doubles
built via object.__new__ lack _clear_context_overflow_warn and crashed
in build_turn_context (caught by test_api_content_sidecar on CI slice 3).
getattr-guard all four call sites per the established test-double pitfall
pattern (AGENTS.md #17).
Follow-up fixes for the #62625 salvage:
- Dedup-reset gap (sweeper review): when the block clears while the
context is STILL over threshold, execution enters the compression
branch — the PR's 'else' reset never ran, so the warning stayed
suppressed forever after the first block. _clear_context_overflow_warn()
now fires on every automatic compression path: turn-context preflight,
conversation_loop pre-API gate, and the post-tool loop-compaction gate.
- should_compress_info on current main: main refactored should_compress
into _automatic_compression_blocked()/_locally(); the tuple variant now
derives its reason from the same in-memory state via
_compression_block_reason(), keeping cooldown:<s>/ineffective shapes.
- ContextEngine.should_compress_info ABC default now actually returns
(should_compress(tokens), None) — the PR's default had a docstring but
no return (returned None, would crash tuple-unpacking call sites).
- Below-threshold guard: the turn-context persisted-cooldown branch and
the conversation_loop pre-API cooldown branch no longer warn when the
estimate is under threshold (should_compress_info returns a None
reason; the preflight pre-check is not a threshold guarantee). The
pre-API guard also honors compression.max_attempts instead of a
hardcoded 3, and no longer fabricates a cooldown reason.
- Noise-filter survival (#69550 composition): warning text is now a
template constant (CONTEXT_OVERFLOW_BLOCKED_WARNING_TEMPLATE) marked
FAILURE-CLASS, pinned un-swallowed in VISIBLE_COMPRESSION_MESSAGES and
in new tests that execute the real _TELEGRAM_NOISY_STATUS_RE +
_prepare_gateway_status_message.
- Contributor mapping for stanislav@local -> sl4m3.
- ContextEngine.should_compress_info() default impl so plugin engines
(e.g. _StubEngine) don't raise AttributeError at the call site.
- Centralise warning/reset in AIAgent._warn_context_overflow_blocked /
_clear_context_overflow_warn so turn-context and conversation-loop guards
share identical dedup logic and reset on the real compression boundary.
- Cover conversation_loop.py pre-API (~L1007) and loop-compaction (~L4774)
guards, not just the turn-context preflight.
- _FakeAgent mirrors the two helpers; test suite green (219 passed).
Fixes#62708
Previously, when a session crossed the compression threshold but compression
was skipped (summary-LLM cooldown, #11529, or anti-thrashing, #40803), the
model kept accumulating context until it hit the hard provider token limit and
silently stopped answering — with no signal to the user about why.
Changes:
- context_compressor.should_compress_info() returns a (should_compress, reason)
tuple. reason is 'cooldown:<seconds>' or 'ineffective' when compression is
needed but blocked. should_compress() keeps its bool contract so existing
callers (conversation_loop.py) and regression #29335 are unaffected.
- turn_context.build_turn_context() emits a deduped _emit_warning when the
context is over threshold but compression is blocked, advising /new or
/compress. Dedup keys on the block *kind* (cooldown/ineffective), not the
ticking countdown, so a cooldown doesn't re-fire the warning every turn.
- Adds tests/agent/test_turn_context_overflow_warning.py covering the tuple
shape, both block kinds, dedup, and re-fire-after-clear.
