When the self-improvement background review fires after a turn, it runs
in a bg thread and emits a ' 💾 <summary>' line to announce what it
saved to memory or skills. Two problems made this invisible to users
even when the review successfully modified a skill:
1. The print went through `_cprint` (prompt_toolkit's print_formatted_text)
on a bg thread while the CLI's PromptSession was live. Direct
print_formatted_text races with the input-area redraw and the line
can land behind/above the prompt, scrolled off without the user
seeing it.
2. The message said only '💾 Skill created.' / '💾 Memory updated'
with no indication that the self-improvement loop was the one doing
this. Users who did catch the line couldn't tell the background
review from some other agent action.
Fixes:
- `_cprint` now detects when it's called from a non-app thread with a
running prompt_toolkit Application, and routes through
`run_in_terminal` via `loop.call_soon_threadsafe`. That pauses the
input, prints the line above the prompt, and redraws — the normal
prompt_toolkit contract for bg-thread output. Direct-print fallback
preserved for the no-app / same-thread / import-error paths. Affects
every bg-thread emission, not just the review summary (curator
summaries and auxiliary failure prints benefit too).
- The summary now reads ' 💾 Self-improvement review: <summary>' in
both the CLI and the gateway `background_review_callback` path, so
the origin is unambiguous.
Tests:
- New `tests/cli/test_cprint_bg_thread.py` covers all five routing
branches (no app, app-not-running, cross-thread schedule, same-thread
direct, app-loop-attribute-error, import-error).
- New case in `tests/run_agent/test_background_review.py` asserts the
attributed prefix shows up in both `_safe_print` and
`background_review_callback`.
Live E2E: exercised _cprint from a bg thread inside a real Application
event loop; confirmed get_app_or_none() sees the app, call_soon_threadsafe
schedules run_in_terminal, and the inner _pt_print runs.
Builds on #16855 (@lsdsjy) which fixed DeepSeek v4 reasoning_content
replay via model_extra fallback + capturing tool_calls at method entry.
Kimi / Moonshot thinking mode enforces the same echo-back contract and
hits the same 400 when a tool-call turn is persisted without
reasoning_content.
- _build_assistant_message: pad branch now uses _needs_thinking_reasoning_pad()
(DeepSeek OR Kimi) instead of _needs_deepseek_tool_reasoning() alone.
- Extract _needs_thinking_reasoning_pad() and reuse it in
_copy_reasoning_content_for_api so both sites share one predicate.
- tests/run_agent/test_deepseek_reasoning_content_echo.py: add
TestBuildAssistantMessagePadsStrictProviders parametrized over DeepSeek
(attr=None, attr-absent), Kimi (attr=None), Moonshot (via base_url),
and an OpenRouter negative control that must NOT pad. Proven to fail
2/5 cases on Kimi/Moonshot without this change.
- scripts/release.py: add AUTHOR_MAP entries for lsdsjy and season179.
Refs #17400.
Co-authored-by: season179 <season.saw@gmail.com>
The _CODEX_AUX_MODEL constant had already rotated twice in 6 weeks
(gpt-5.3-codex -> gpt-5.2-codex -> now broken again at gpt-5.2-codex)
because ChatGPT-account Codex gates which models it accepts via an
undocumented, shifting allow-list that OpenAI publishes no changelog
for. Any pinned default will keep going stale. Issue #17533 reports
the current breakage: every ChatGPT-account auxiliary fallback fails
with HTTP 400 "model is not supported" and the 60s pause loop degrades
long sessions.
Rather than reset the clock with another stale pin (PR #17544 proposes
gpt-5.2-codex -> gpt-5.4), remove the hardcoded second-order Codex
fallback entirely:
- Delete `_CODEX_AUX_MODEL`.
- Drop `_try_codex` from `_get_provider_chain()` (the auto chain now
ends at api-key providers; 4 rungs instead of 5).
- Rename `_try_codex() -> _build_codex_client(model)` and require an
explicit model from the caller. No more guessing.
- `resolve_provider_client("openai-codex", model=None)` now warns and
returns (None, None) instead of silently guessing a stale model ID.
- Remove `_try_codex` from the `provider="custom"` fallback ladder
(same stale-constant trap).
- `_resolve_strict_vision_backend("openai-codex")` routes through
`resolve_provider_client` so the caller's explicit model is honored.
Codex-main users are unaffected: Step 1 of `_resolve_auto` already
uses `main_provider` + `main_model` directly and passes the user's
configured Codex model through `resolve_provider_client`, which never
touched `_CODEX_AUX_MODEL`. Per-task overrides (`auxiliary.<task>.provider/model`)
continue to work and are the supported way to route specific aux tasks
through Codex.
Users whose main provider fails with a payment/connection error and
who have ONLY ChatGPT-account Codex auth will now see the 60s pause
without a stale-model-rejection noise line in between -- same outcome,
cleaner failure.
Closes#17533. Supersedes #17544 (which resets the clock on the
same stale-constant problem).
feat(gateway): refine Platform._missing_ and platform-connected dispatch
Restricts plugin-name acceptance to bundled plugin scan + registry
(no arbitrary string -> enum-pollution), pulls per-platform connectivity
checks into a _PLATFORM_CONNECTED_CHECKERS lambda map with a clean
_is_platform_connected method, and adds tests covering the checker map,
plugin platform interface, and IRC setup wizard.
Fixes#6672
Memory providers now receive on_session_switch() whenever AIAgent.session_id
rotates mid-process — /resume, /branch, /reset, /new, and context
compression. Before this, providers that cached per-session state in
initialize() (Hindsight's _session_id, _document_id, accumulated
_session_turns, _turn_counter) kept writing into the old session's
record after the agent had moved on.
MemoryProvider ABC
------------------
- New optional hook on_session_switch(new_session_id, *,
parent_session_id='', reset=False, **kwargs) with no-op default for
backward compat. reset=True signals /reset or /new — providers should
flush accumulated per-session buffers. reset=False for /resume,
/branch, compression where the logical conversation continues.
MemoryManager
-------------
- on_session_switch() fans the hook out to every registered provider.
Isolated try/except per provider — one bad provider can't block others.
- Empty/None new_session_id is a no-op to avoid corrupting provider state
during shutdown paths.
run_agent.py
------------
- _sync_external_memory_for_turn now passes session_id=self.session_id
into sync_all() and queue_prefetch_all(). Providers with defensive
session_id updates in sync_turn (Hindsight already had this at
plugins/memory/hindsight/__init__.py:1199) now actually receive the
current id.
- Compression block at ~L8884 already notified the context engine of
the rollover; now also calls
_memory_manager.on_session_switch(reason='compression').
cli.py
------
- new_session() fires reset=True, reason='new_session' so providers
flush buffers.
- _handle_resume_command fires reset=False, reason='resume' with the
previous session as parent_session_id.
- _handle_branch_command fires reset=False, reason='branch' with the
parent session_id already captured for the DB parent link.
gateway/run.py
--------------
- _handle_resume_command now evicts the cached AIAgent, mirroring
/branch and /reset. The next message rebuilds a fresh agent whose
memory provider initialize() runs with the correct session_id —
matches the pattern the gateway already uses for provider state
cross-session transitions.
Hindsight reference implementation
----------------------------------
- plugins/memory/hindsight/__init__.py adds on_session_switch that:
updates _session_id, mints a fresh _document_id (prevents
vectorize-io/hindsight#1303 overwrite), and clears _session_turns /
_turn_counter / _turn_index so in-flight batches don't flush under
the new document id. parent_session_id only overwritten when provided
(avoids clobbering on a bare switch).
Tests
-----
- tests/agent/test_memory_session_switch.py: new dedicated file. ABC
default no-op, manager fan-out, failure isolation, empty-id no-op,
session_id propagation through sync_all/queue_prefetch_all, Hindsight
state transitions for every reset/non-reset case, parent preservation.
- tests/cli/test_branch_command.py: new test verifying /branch fires
the hook with correct parent_session_id + reset=False + reason.
- tests/gateway/test_resume_command.py: new test verifying /resume
evicts the cached agent.
- tests/run_agent/test_memory_sync_interrupted.py: updated existing
assertions to account for the session_id kwarg on sync_all and
queue_prefetch_all.
E2E verified (real imports, tmp HERMES_HOME):
- /resume: session_id updates, doc_id fresh, buffers cleared, parent set
- /branch: session_id forks, parent links to original
- /new: reset=True clears accumulated state
- compression: reason='compression' propagated, lineage preserved
- Empty id: no-op, state preserved
- Legacy provider without on_session_switch: no crash
Reported by @nicoloboschi (Hindsight maintainer); related scope-widening
comment by @kidonng extending coverage to compression.
MiniMax's /anthropic endpoint documents cache_control support (0.1x read
pricing, 5-min TTL) for MiniMax-M2.7, M2.5, M2.1, M2. PR #12846 gated
third-party Anthropic-wire caching on 'claude' in model name, which left
MiniMax's own model family re-paying full input tokens every turn.
Opt in explicitly via provider id (minimax / minimax-cn) or host match
(api.minimax.io / api.minimaxi.com). Narrow allowlist mirroring the
existing Qwen/Alibaba branch below; leaves room for a capability-based
surface (ProviderConfig.supports_anthropic_cache) if a third provider
needs it.
Closes#17332
The background skill-review prompts (_SKILL_REVIEW_PROMPT and the **Skills**
half of _COMBINED_REVIEW_PROMPT) steered the reviewer toward passive
behavior — most passes concluded 'Nothing to save.' even when the session
produced real lessons. User-preference corrections (style, format,
legibility, verbosity) were especially lost: they were read as memory
signals only, so skills never carried the fix.
This rewrite changes the stance:
- **Active-update bias.** The reviewer now treats inaction as a missed
learning opportunity. 'Nothing to save.' remains an explicit escape
but is no longer framed as the most-common outcome.
- **User-preference corrections are first-class skill signals.** Style,
tone, format, legibility, verbosity complaints — and the actual
phrasings users use ('stop doing X', 'this is too verbose', 'I hate
when you Y', 'remember this') — now warrant patching the skill that
governs the task, not just writing to memory.
- **Loaded-skill-first preference order.** When a skill was loaded via
/skill-name or skill_view during the session, the reviewer patches
THAT one first. It was in play; it's the right place.
- **Four-step ladder: patch-loaded → patch-umbrella → support-file →
create.** Support files are explicitly enumerated as three kinds:
* references/<topic>.md — session-specific detail OR condensed
knowledge banks (quoted research, API docs excerpts, domain notes)
* templates/<name>.<ext> — starter files to copy and modify
* scripts/<name>.<ext> — statically re-runnable actions
- **Name-veto for CREATE.** New skill names MUST be class-level — no PR
numbers, error strings, codenames, library-alone names, or session
artifacts ('fix-X / debug-Y / audit-Z-today'). If the proposed name
only fits today's task, fall back to one of the patch/support-file
options.
- **Memory scope clarified.** 'who the user is and what the current
situation and state of your operations are' — MEMORY.md is
situational/state, USER.md is identity/preferences.
- **Curator handoff.** Reviewer flags overlap; the background curator
handles consolidation at scale. Single-session reviewer doesn't
attempt umbrella-rebalancing.
Tests: tests/run_agent/test_review_prompt_class_first.py upgraded to
assert the new behavioral contracts (active bias, user-correction
signals, loaded-skill-first, support-file kinds, name-veto, memory
framing, curator handoff). 17 tests, all pass.
Co-authored-by: teknium1 <teknium@users.noreply.github.com>
CopilotACPClient communicates via subprocess stdio and returns a plain
SimpleNamespace from _create_chat_completion(). The streaming path tries
to iterate this as a stream, crashing with:
TypeError: 'types.SimpleNamespace' object is not iterable
Mirror the existing ACP exclusion pattern (used for Responses API upgrade)
to disable streaming when provider is copilot-acp or base_url starts with
acp:// or acp+tcp://.
Based on PR #9428 by @ningfangbin and issue #16271 by @Joseph19820124.
Fixes#16271
Adds a pre-call sanitizer that detects assistant messages containing only
reasoning (reasoning / reasoning_content, no visible content, no
tool_calls) and drops them from the API copy. Adjacent user messages
left behind are merged so role alternation is preserved for the
provider.
Mirrors Claude Code's approach in src/utils/messages.ts
(filterOrphanedThinkingOnlyMessages + mergeAdjacentUserMessages). We
drop the whole turn rather than fabricate stub text (the '.' /
'(continued)' pattern from contributor PRs #11098, #13010, #16842 that
were rejected because they put words in the model's mouth).
The stored conversation history (self.messages) is never mutated — only
the per-call api_messages copy. Users still see the reasoning block in
the CLI/gateway transcript; only the wire copy is cleaned. Session
persistence keeps the full trace.
Two call sites covered:
- Main agent loop, after _sanitize_api_messages (catches every turn).
- Iteration-limit-summary fallback path.
Tests: tests/run_agent/test_thinking_only_sanitizer.py — 25 cases
covering detection (string/list content, whitespace-only, tool_calls,
reasoning_details list form), drop behavior, adjacent-user merge
(string+string, list+list, mixed), non-mutation of input dicts, and
system-message handling.
E2E live-tested against 5 providers with a poisoned history (empty
assistant message + reasoning_content): OpenRouter→Anthropic/OpenAI/
DeepSeek-R1/Qwen, native Gemini. All 5 accepted the cleaned request.
Happy-path regression (5/5) confirms the sanitizer is a noop when no
thinking-only turn exists.
Related: #16823 (wontfix — stub-text approach rejected).
Co-authored-by: teknium1 <teknium@users.noreply.github.com>
Follow-up to #15328's vision-unsupported retry branch in run_agent.py.
_strip_images_from_messages() previously deleted any message whose content
was entirely images. That's fine for synthetic user messages injected for
attachment delivery, but it breaks providers for tool-role messages — the
paired tool_call_id on the preceding assistant message ends up unmatched,
which OpenAI-compatible APIs reject with HTTP 400.
Fix: tool-role messages whose content becomes empty are replaced with a
plaintext placeholder that preserves the tool_call_id linkage. Only
non-tool messages are dropped. Added 10 tests covering the role-alternation
invariants + image-type coverage.
Image-rejection detector: expanded phrase list (image content not
supported / multimodal input / vision input / model does not support
image) and gated on 4xx status so transient 5xx errors never get
misinterpreted as 'server said no to images'. Detection is documented as
best-effort English phrase matching.
AUTHOR_MAP: mapped 3820588+ddupont808@users.noreply.github.com to
ddupont808 so release notes attribute the salvage correctly.
Streaming-only providers (glm, MiniMax, gpt-5.x via aigw, Anthropic via
openai-compat shims) emit reasoning through delta.reasoning_content
chunks that get accumulated into the local reasoning_text string — but
never land on the assistant message object as a top-level attribute. The
prior guard at _build_assistant_message only wrote reasoning_content
when the SDK exposed hasattr(msg, 'reasoning_content'), so these
providers persisted the chain-of-thought under the internal 'reasoning'
key and omitted the protocol-standard field.
The poison was silent until the user later switched to a DeepSeek-v4 or
Kimi thinking model, at which point replay failed with HTTP 400:
'The reasoning_content in the thinking mode must be passed back to the
API.' One reported session store accumulated 4,031 poisoned messages
across 1,101 files (#16844).
Fix: add an additive fallback that promotes the already-sanitized
reasoning_text to reasoning_content when no earlier branch wrote it AND
reasoning text was actually captured. Layered on top of the existing
SDK-attr branch and DeepSeek ''-pad (#15250) rather than replacing them,
so every existing behavior is preserved:
- SDK-exposed reasoning_content (OpenAI/Moonshot/DeepSeek SDK) still
wins.
- DeepSeek tool-call ''-pad still fires when the SDK exposes the attr
but the value is None.
- Non-thinking turns with no reasoning leave the field absent, so
_copy_reasoning_content_for_api's cross-provider leak guard (#15748),
promote-from-'reasoning' tier, and thinking-pad tier remain live at
replay time.
- No empty '' gets eagerly written on every assistant turn (which would
have bypassed the read-side ladder and triggered empty thinking-block
insertion in the Anthropic adapter).
Tests: three new TestBuildAssistantMessage cases covering the streaming
promotion path, SDK precedence, and field-absent-when-no-reasoning
invariant.
Credit @Sanjays2402 for the original diagnosis and patch in #16884;
this is a scoped rework that preserves the existing read-side
compensation code as defense in depth.
Refs #16844, #16884, #15250, #15353, #15748.
Same layering concern as the persisted-assistant scrub already removed:
_emit_interim_assistant_message and the final_response return path were
mutating model output broadly. Streaming scrubber covers real leaks
delta-by-delta; these post-stream scrubs were redundant.
Reviewer pushback on the original boundary-hardening commits — three
overreach points pulled plugin-specific policy into shared core paths:
1. gateway/run.py hardcoded a '## Honcho Context' literal split for
vision-LLM output. Plugin-format heading in framework code; could
truncate legitimate output naturally containing that header.
Drop the literal split; keep generic sanitize_context (the wrapper
strip is plugin-agnostic). Plugin-specific cleanup belongs at the
provider boundary, not the shared gateway path.
2. run_agent.run_conversation scrubbed user_message and
persist_user_message before the conversation loop. User text is
sacred — if a user types a literal <memory-context> tag we must
not silently delete it. The producer (build_memory_context_block)
is the only legitimate emitter; user input should never need the
reverse op.
3. _build_assistant_message scrubbed model output before persistence.
Same hazard: would silently mutate legitimate documentation/code
the model emits containing the literal markers. The streaming
scrubber catches real leaks delta-by-delta before content is
concatenated; persist-time scrub was redundant belt-and-suspenders.
4. _fire_stream_delta stripped leading newlines from every delta unless
a paragraph break flag was set. Mid-stream '\n' is legitimate
markdown — lists, code fences, paragraph breaks — and chunk
boundaries are arbitrary. Narrow lstrip to the very first delta
of the stream only (so stale provider preamble still gets cleaned
on turn start, but mid-stream formatting survives).
Plus: build_memory_context_block now logs a warning when its defensive
sanitize_context strips something — surfaces buggy providers returning
pre-wrapped text instead of silently double-fencing.
Net architectural change: scrub surface collapses from 8 sites to 3
(StreamingContextScrubber on output deltas, plugin→backend send,
build_memory_context_block input-validation). Plugin-specific strings
stay out of shared runtime paths. User input and persisted assistant
output are no longer mutated.
Tests: rescoped TestMemoryContextSanitization (helper-correctness only,
no source-inspection of removed call sites), updated vision tests to
drop '## Honcho Context' literal-split assertions, updated
_build_assistant_message persistence test to assert preservation.
Added: cross-turn scrubber reset, build_memory_context_block warn-on-
violation, mid-stream newline preservation (plain + code fence).
fixes#5719
The auxiliary vision LLM called by gateway._enrich_message_with_vision
can echo its injected Honcho system prompt back into the image
description. That description gets embedded verbatim into the enriched
user message, so recalled memory (personal facts, dialectic output)
surfaces into a user-visible bubble.
Strips both forms of leak before embedding:
- <memory-context>...</memory-context> fenced blocks (sanitize_context)
- trailing '## Honcho Context' sections (header + everything after)
Plus regression tests:
- tests/agent/test_streaming_context_scrubber.py — 13 tests on the
stateful scrubber (whole block, split tags, false-positive partial
tags, unterminated span, reset, case-insensitivity)
- tests/run_agent/test_run_agent_codex_responses.py — 2 new tests on
_fire_stream_delta covering the realistic 7-chunk leak scenario and
the cross-turn scrubber reset
- tests/gateway/test_vision_memory_leak.py — 4 tests covering the
vision auto-analysis boundary (clean pass-through, '## Honcho Context'
header, fenced block, both patterns together)
Thread a vision-request flag through auxiliary provider resolution so Copilot clients can include Copilot-Vision-Request only for vision tasks. This preserves normal text requests while ensuring Copilot vision payloads reach the vision-capable route.
Add regression coverage for Copilot vision routing and keep cached text and vision clients separate so a text client without the header is not reused for vision.
Co-authored-by: dhabibi <9087935+dhabibi@users.noreply.github.com>
PR #13734 fixed the concurrent-tool-executor vector (ThreadPoolExecutor
workers didn't inherit the CLI's TLS approval callback). Two vectors
remained that could still land in the deadlocking input() fallback:
1. _spawn_background_review spawns a raw threading.Thread with no
approval callback installed, so any dangerous-command guard the
review agent trips falls back to input() -> deadlock against the
parent's prompt_toolkit TUI (same class as delegate_task subagents,
fixed in 023b1bff1 / #15491). Install a _bg_review_auto_deny
callback at thread start, clear on finally.
2. prompt_dangerous_approval's fallback unconditionally spawned a
daemon thread calling input() when approval_callback was None.
That fallback can never succeed under prompt_toolkit because the
user's Enter goes to pt's raw-mode stdin capture. Detect an active
pt Application via get_app_or_none() and fail closed (deny + log)
instead, so future threads that forget to install a callback
degrade gracefully instead of hanging 60s invisibly.
Regression guards:
- tests/run_agent/test_background_review.py verifies the review
worker thread sees a callable auto-deny callback mid-run and that
the slot is cleared in the finally block.
- tests/tools/test_approval.py TestFailClosedUnderPromptToolkit
verifies prompt_dangerous_approval returns 'deny' fast under a
mocked pt Application, and that a real callback still wins over
the guard.
The background skill/memory review agent was created without toolset
restrictions, inheriting the full default tool set. This allowed it to
use terminal, send_message, delegate_task, and other tools outside its
intended scope, potentially performing unrelated side effects after
skill creation.
Restrict the review agent to only memory and skills toolsets by passing
enabled_toolsets=['memory', 'skills'] during AIAgent construction.
Fixes#15204
* feat(image-input): native multimodal routing based on model vision capability
Attach user-sent images as OpenAI-style content parts on the user turn when
the active model supports native vision, so vision-capable models see real
pixels instead of a lossy text description from vision_analyze.
Routing decision (agent/image_routing.py::decide_image_input_mode):
agent.image_input_mode = auto | native | text (default: auto)
In auto mode:
- If auxiliary.vision.provider/model is explicitly configured, keep the
text pipeline (user paid for a dedicated vision backend).
- Else if models.dev reports supports_vision=True for the active
provider/model, attach natively.
- Else fall back to text (current behaviour).
Call sites updated: gateway/run.py (all messaging platforms), tui_gateway
(dashboard/Ink), cli.py (interactive /attach + drag-drop).
run_agent.py changes:
- _prepare_anthropic_messages_for_api now passes image parts through
unchanged when the model supports vision — the Anthropic adapter
translates them to native image blocks. Previous behaviour
(vision_analyze → text) only runs for non-vision Anthropic models.
- New _prepare_messages_for_non_vision_model mirrors the same contract
for chat.completions and codex_responses paths, so non-vision models
on any provider get text-fallback instead of failing at the provider.
- New _model_supports_vision() helper reads models.dev caps.
vision_analyze description rewritten: positions it as a tool for images
NOT already visible in the conversation (URLs, tool output, deeper
inspection). Prevents the model from redundantly calling it on images
already attached natively.
Config default: agent.image_input_mode = auto.
Tests: 35 new (test_image_routing.py + test_vision_aware_preprocessing.py),
all existing tests that reference _prepare_anthropic_messages_for_api
still pass (198 targeted + new tests green).
* feat(image-input): size-cap + resize oversized images, charge image tokens in compressor
Two follow-ups that make the native image routing safer for long / heavy
sessions:
1) Oversize handling in build_native_content_parts:
- 20 MB ceiling per image (matches vision_tools._MAX_BASE64_BYTES,
the most restrictive provider — Gemini inline data).
- Delegates to vision_tools._resize_image_for_vision (Pillow-based,
already battle-tested) to downscale to 5 MB first-try.
- If Pillow is missing or resize still overshoots, the image is
dropped and reported back in skipped[]; caller falls back to text
enrichment for that image.
2) Image-token accounting in context_compressor:
- New _IMAGE_TOKEN_ESTIMATE = 1600 (matches Claude Code's constant;
within the realistic range for Anthropic/GPT-4o/Gemini billing).
- _content_length_for_budget() helper: sums text-part lengths and
charges _IMAGE_CHAR_EQUIVALENT (1600 * 4 chars) per image/image_url/
input_image part. Base64 payload inside image_url is NOT counted
as chars — dimensions don't matter, only image-presence.
- Both tail-cut sites (_prune_old_tool_results L527 and
_find_tail_cut_by_tokens L1126) now call the helper so multi-image
conversations don't slip past compression budget.
Tests: 9 new in test_image_routing.py (oversize triggers resize,
resize-fails-returns-None, oversize-skipped-reported), 11 new in
test_compressor_image_tokens.py (flat charge per image, multiple images,
Responses-API / Anthropic-native / OpenAI-chat shapes, no-inflation on
raw base64, bounds-check on the constant, integration test that an
image-heavy tail actually gets trimmed).
* fix(image-input): replace blanket 20MB ceiling with empirically-verified per-provider limits
The previous commit imposed a hardcoded 20 MB base64 ceiling on all
providers, triggering auto-resize on anything larger. This was wrong in
both directions:
* Too loose for Anthropic — actual limit is 5 MB (returns HTTP 400
'image exceeds 5 MB maximum' above that).
* Too strict for OpenAI / Codex / OpenRouter — accept 49 MB+ without
complaint (empirically verified April 2026 with progressive PNG
sizes).
New behaviour:
* _PROVIDER_BASE64_CEILING table: only anthropic and bedrock have a
ceiling (5 MB, since bedrock-on-Claude shares Anthropic's decoder).
* Providers NOT in the table get no ceiling — images attach at native
size and we trust the provider to return its own error if it
disagrees. A provider-specific 400 message is clearer than us
guessing wrong and silently degrading image quality.
* build_native_content_parts() gains a keyword-only provider arg;
gateway/CLI/TUI pass the active provider so Anthropic users get
auto-resize protection while OpenAI users don't pay it.
* Resize target dropped from 5 MB to 4 MB to slide safely under
Anthropic's boundary with header overhead.
Empirical measurements (direct API, no Hermes in the loop):
image b64 anthropic openrouter/gpt5.5 codex-oauth/gpt5.5
0.19 MB ✓ ✓ ✓
12.37 MB ✗ 400 5MB ✓ ✓
23.85 MB ✗ 400 5MB ✓ ✓
49.46 MB ✗ 413 ✓ ✓
Tests: rewrote TestOversizeHandling (5 tests): no-ceiling pass-through,
Anthropic resize fires, Anthropic skip on resize-fail, build_native_parts
routes ceiling by provider, unknown provider gets no ceiling. All 52
targeted tests pass.
* refactor(image-input): attempt native, shrink-and-retry on provider reject
Replace proactive per-provider size ceilings with a reactive shrink path
on the provider's actual rejection. All providers now attempt native
full-size attachment first; if the provider returns an image-too-large
error, the agent silently shrinks and retries once.
Why the previous design was wrong: hardcoding provider ceilings
(anthropic=5MB, others=unlimited) meant OpenAI users on a 10MB image
paid no tax, but Anthropic users lost quality on anything >5MB even
though the empirical behaviour at provider-reject time is the same
(shrink + retry). Baking the table into the routing layer also
requires updating Hermes every time a provider's limit changes.
Reactive design:
- image_routing.py: _file_to_data_url encodes native size, no ceiling.
build_native_content_parts drops its provider kwarg.
- error_classifier.py: new FailoverReason.image_too_large + pattern
match ("image exceeds", "image too large", etc.) checked BEFORE
context_overflow so Anthropic's 5MB rejection lands in the right
bucket.
- run_agent.py: new _try_shrink_image_parts_in_messages walks api
messages in-place, re-encodes oversized data: URL image parts
through vision_tools._resize_image_for_vision to fit under 4MB,
handles both chat.completions (dict image_url) and Responses
(string image_url) shapes, ignores http URLs (provider-fetched).
New image_shrink_retry_attempted flag in the retry loop fires the
shrink exactly once per turn after credential-pool recovery but
before auth retries.
E2E verified live against Anthropic claude-sonnet-4-6:
- 17.9MB PNG (23.9MB b64) attached at native size
- Anthropic returns 400 "image exceeds 5 MB maximum"
- Agent logs '📐 Image(s) exceeded provider size limit — shrank and
retrying...'
- Retry succeeds, correct response delivered in 6.8s total.
Tests: 12 new (8 shrink-helper shapes + 4 classifier signals),
replaces 5 proactive-ceiling tests with 3 simpler 'native attach works'
tests. 181 targeted tests pass. test_enum_members_exist in
test_error_classifier.py updated for the new enum value.
On provider switches mid-session (e.g. MiniMax -> DeepSeek), the source
assistant turn carries a 'reasoning' field written by the prior provider
but no 'reasoning_content' key. _copy_reasoning_content_for_api would
promote that foreign 'reasoning' to 'reasoning_content' on the outbound
DeepSeek request, leaking a cross-provider chain of thought and in
practice causing HTTP 400.
DeepSeek's own _build_assistant_message always pins reasoning_content=''
at creation time for tool-call turns, so the shape (reasoning set,
reasoning_content absent, tool_calls present) is unreachable from
same-provider DeepSeek history — it can only come from a prior provider.
Pad with '' in that case instead of promoting.
Healthy same-provider 'reasoning' promotion (no tool_calls, or on
providers that do not require the empty-string pin) is unchanged.
When _compress_context rotates session_id (compression split), fire
on_session_start(new_sid, boundary_reason="compression",
old_session_id=<old>) on the active context engine. Plugin engines
(e.g. hermes-lcm) use this to preserve DAG lineage across the rollover
instead of re-initializing fresh per-session state.
Built-in ContextCompressor.on_session_start accepts **kwargs and ignores
them — no behavior change for default users.
Closes hermes-lcm#68 symptom: after Hermes compressed and minted a new
physical session, LCM was treating the split as a fresh /new and losing
continuity (compression_count: 1, store_messages: 0, dag_nodes: 0).
Credit: @Tosko4 (PR #13370) — minimized scope to the boundary_reason
signal only; the broader session-lifecycle refactor will be taken in
separate PRs if justified by concrete plugin need.
Background review fork now inherits session_id, credential_pool, and
status_callback from the parent (added in #16099 after this PR was
written). Extend the bare-agent helper so the regression test keeps
reaching the cleanup assertions instead of failing in the runtime
resolver.
Signed-off-by: Teknium <8425893+teknium1@users.noreply.github.com>
Temporary background review agents can initialize Hindsight-backed memory clients, but close() alone skips provider teardown. Shut the memory provider down before closing so aiohttp sessions do not leak at process exit.
Made-with: Cursor
The background skill-review prompt (spawned after N user turns) now instructs
the reviewer to SURVEY existing skills first, identify the CLASS of task, and
PREFER updating/generalizing an existing skill over creating a new narrow one.
This reduces near-duplicate skill accumulation at the source. Catches the
common failure mode where repeated tasks of the same class each spawn their
own specific skill ("fix-my-tauri-error", "fix-my-electron-error") instead
of a single class-level skill ("desktop-app-build-troubleshooting").
Applied to both _SKILL_REVIEW_PROMPT and the **Skills** half of
_COMBINED_REVIEW_PROMPT. Memory-only review prompt unchanged.
Groundwork for the Curator feature (issue #7816) — the creation-side fix.
Curator handles the retirement/consolidation side in a follow-up PR.
Tests assert the behavioral instructions are present (survey, class, update-
over-create, overlap-flagging, opt-out clause) rather than snapshotting the
full prompt text.
Azure OpenAI exposes an OpenAI-compatible endpoint at
`{resource}.openai.azure.com/openai/v1` that accepts the standard
`openai` Python client. Two issues prevented gpt-5.x models from working:
1. `_max_tokens_param()` only sent `max_completion_tokens` for
`api.openai.com` URLs. Azure also requires `max_completion_tokens`
for gpt-5.x models.
2. The `codex_responses` upgrade gate unconditionally upgraded gpt-5.x
to Responses API. Azure does NOT support the Responses API — it serves
gpt-5.x on the regular `/chat/completions` path, causing a 404.
Fix: add `_is_azure_openai_url()` that matches `openai.azure.com` URLs.
- `_max_tokens_param()` now returns `max_completion_tokens` for Azure.
- The `codex_responses` upgrade gate skips Azure so gpt-5.x stays on
`chat_completions` where Azure actually serves it.
- The fallback-provider api_mode picker also recognises Azure and stays
on chat_completions.
- Tests cover max_tokens routing, api_mode behaviour, and URL detection.
gpt-4.x models on Azure are unaffected (already used chat_completions +
max_tokens, which Azure accepts for those models).
Salvage of PR #10086 — rewritten against current main where the
codex_responses upgrade gate gained copilot-acp / explicit-api_mode
exclusions.
Previously _copy_reasoning_content_for_api only padded reasoning_content
when the assistant message had tool_calls. DeepSeek V4 thinking mode
requires the field on every assistant turn, including plain text replies
without tool_calls.
- Remove the 'source_msg.get("tool_calls") and' guard
- Update test: plain assistant turns now get padded for DeepSeek/Kimi
Fixes#15213
The Codex Responses API rejects input_text inside assistant messages —
only output_text and refusal are valid content types for assistant role.
_chat_content_to_responses_parts() previously hardcoded all text content
to input_text regardless of the message role. When an assistant message
had list-format content (multimodal or structured), this produced invalid
input_text parts that the API rejected with:
Invalid value: 'input_text'. Supported values are: 'output_text' and 'refusal'.
Fix: add a role parameter to _chat_content_to_responses_parts() that
selects output_text for assistant messages and input_text for user
messages. Thread this through _chat_messages_to_responses_input() and
_preflight_codex_input_items().
Fixes#15687
When a user sends /stop during a streaming API call, the outer poll loop
detects _interrupt_requested and closes the HTTP connection. However, the
inner _call() thread catches the connection error and enters its retry
loop — opening a FRESH connection without checking the interrupt flag.
On slow providers like ollama-cloud, each retry attempt blocks for the
full stream-read timeout (120s+). With 3 retry attempts this caused
510+ second delays between /stop and actual response — the agent appeared
completely unresponsive despite the stop being acknowledged.
Fix: add an _interrupt_requested check at the top of the streaming retry
loop so the agent exits immediately instead of retrying.
Also fix log truncation: all session key logging in gateway/run.py used
[:20] or [:30] slices, which truncated 'agent:main:telegram:dm:5690190437'
(33 chars) to 'agent:main:telegram:' — losing the identifying chat type
and user ID. Replace with full keys to make logs debuggable.
Reported by user Sidharth Pulipaka via Telegram on ollama-cloud provider.
The AIAgent.flush_memories pre-compression save, the gateway
_flush_memories_for_session, and everything feeding them are
obsolete now that the background memory/skill review handles
persistent memory extraction.
Problems with flush_memories:
- Pre-dates the background review loop. It was the only memory-save
path when introduced; the background review now fires every 10 user
turns on CLI and gateway alike, which is far more frequent than
compression or session reset ever triggered flush.
- Blocking and synchronous. Pre-compression flush ran on the live agent
before compression, blocking the user-visible response.
- Cache-breaking. Flush built a temporary conversation prefix
(system prompt + memory-only tool list) that diverged from the live
conversation's cached prefix, invalidating prompt caching. The
gateway variant spawned a fresh AIAgent with its own clean prompt
for each finalized session — still cache-breaking, just in a
different process.
- Redundant. Background review runs in the live conversation's
session context, gets the same content, writes to the same memory
store, and doesn't break the cache. Everything flush_memories
claimed to preserve is already covered.
What this removes:
- AIAgent.flush_memories() method (~248 LOC in run_agent.py)
- Pre-compression flush call in _compress_context
- flush_memories call sites in cli.py (/new + exit)
- GatewayRunner._flush_memories_for_session + _async_flush_memories
(and the 3 call sites: session expiry watcher, /new, /resume)
- 'flush_memories' entry from DEFAULT_CONFIG auxiliary tasks,
hermes tools UI task list, auxiliary_client docstrings
- _memory_flush_min_turns config + init
- #15631's headroom-deduction math in
_check_compression_model_feasibility (headroom was only needed
because flush dragged the full main-agent system prompt along;
the compression summariser sends a single user-role prompt so
new_threshold = aux_context is safe again)
- The dedicated test files and assertions that exercised
flush-specific paths
What this renames (with read-time backcompat on sessions.json):
- SessionEntry.memory_flushed -> SessionEntry.expiry_finalized.
The session-expiry watcher still uses the flag to avoid re-running
finalize/eviction on the same expired session; the new name
reflects what it now actually gates. from_dict() reads
'expiry_finalized' first, falls back to the legacy 'memory_flushed'
key so existing sessions.json files upgrade seamlessly.
Supersedes #15631 and #15638.
Tested: 383 targeted tests pass across run_agent/, agent/, cli/,
and gateway/ session-boundary suites. No behavior regressions —
background memory review continues to handle persistent memory
extraction on both CLI and gateway.
_check_compression_model_feasibility calls get_model_context_length
without provider=, so Codex OAuth users get 1,050,000 (from models.dev
for 'openai') instead of the actual 272,000 limit. This happens because
_infer_provider_from_url maps chatgpt.com → 'openai' (not 'openai-codex'),
skipping the Codex-specific resolution branch entirely.
Result: compression threshold set at 85% of 1.05M = 892K — conversations
never trigger compression, the context grows unbounded, and when gateway
hygiene eventually forces compression, the Codex endpoint drops the
oversized streaming request ('peer closed connection without sending
complete message body').
Fix: forward self.provider to get_model_context_length so provider-
specific resolution branches (Codex OAuth 272K, Copilot live /models,
Nous suffix-match) fire correctly.
Reported by user on GPT 5.5 via Codex OAuth Pro (paste.rs/vsra3).
When the auxiliary compression model's context is smaller than the main
model's compression threshold, _check_compression_model_feasibility
auto-lowers the session threshold. Previously it set:
new_threshold = aux_context
This let the raw message list grow to exactly aux_context tokens. But
compression and flush_memories actually send system_prompt + tool_schemas
+ messages to the aux model. With 50+ tools that overhead is 25-30K
tokens, so the full request overflowed aux with HTTP 400.
Subtract a headroom estimate from aux_context before setting the new
threshold: the actual tool-schema token count (from
estimate_request_tokens_rough) plus a 12K allowance for the system
prompt (not yet built at __init__ time) and flush-instruction overhead.
Clamp to MINIMUM_CONTEXT_LENGTH so the session still starts even with
an unusually heavy tool schema.
This fixes the 'flush_memories overflow on busy toolsets' path that
Teknium flagged — where main and aux can be nominally the same model
but still 400 because the threshold left no room for the request
overhead. Same fix also protects the normal compression summarisation
request on the same binding aux.
Tests: two new regression tests cover the headroom reservation and the
MINIMUM_CONTEXT_LENGTH floor. Two existing tests updated for the new
(lower) threshold values now that empty-tools still produces a 12K
static headroom deduction.
The memory-flush fallback for api_mode='codex_responses' was unconditionally
adding `temperature` to codex_kwargs before calling _run_codex_stream. The
Responses API does not accept temperature on any supported backend:
- chatgpt.com/backend-api/codex rejects it outright
- api.openai.com + gpt-5/o-series reasoning models reject it
- Copilot Responses rejects it on reasoning models
The CodexAuxiliaryClient adapter and the codex_responses transport both
correctly omit temperature — the flush fallback was the only path putting
it back. On errors from the primary aux path (e.g. expired OAuth token),
users saw `⚠ Auxiliary memory flush failed: HTTP 400: Unsupported parameter:
temperature`.
Reported by Garik [NOUS] on GPT-5.5 via Codex OAuth Pro.
Extracts _needs_kimi_tool_reasoning() for symmetry with the existing
_needs_deepseek_tool_reasoning() helper, so _copy_reasoning_content_for_api
uses the same detection logic as _build_assistant_message. Future changes
to either provider's signals now only touch one function.
Adds tests/run_agent/test_deepseek_reasoning_content_echo.py covering:
- All 3 DeepSeek detection signals (provider, model, host)
- Poisoned history replay (empty string fallback)
- Plain assistant turns NOT padded
- Explicit reasoning_content preserved
- Reasoning field promoted to reasoning_content
- Existing Kimi/Moonshot detection intact
- Non-thinking providers left alone
21 tests, all pass.
``run_conversation`` was calling ``memory_manager.sync_all(
original_user_message, final_response)`` at the end of every turn
where both args were present. That gate didn't consider the
``interrupted`` local flag, so an external memory backend received
partial assistant output, aborted tool chains, or mid-stream resets as
durable conversational truth. Downstream recall then treated the
not-yet-real state as if the user had seen it complete, poisoning the
trust boundary between "what the user took away from the turn" and
"what Hermes was in the middle of producing when the interrupt hit".
Extracted the inline sync block into a new private method
``AIAgent._sync_external_memory_for_turn(original_user_message,
final_response, interrupted)`` so the interrupt guard is a single
visible check at the top of the method instead of hidden in a
boolean-and at the call site. That also gives tests a clean seam to
assert on — the pre-fix layout buried the logic inside the 3,000-line
``run_conversation`` function where no focused test could reach it.
The new method encodes three independent skip conditions:
1. ``interrupted`` → skip entirely (the #15218 fix). Applies even
when ``final_response`` and ``original_user_message`` happen to
be populated — an interrupt may have landed between a streamed
reply and the next tool call, so the strings on disk are not
actually the turn the user took away.
2. No memory manager / no final_response / no user message →
preserve existing skip behaviour (nothing new for providerless
sessions, system-initiated refreshes, tool-only turns that never
resolved, etc.).
3. Sync_all / queue_prefetch_all exceptions → swallow. External
memory providers are strictly best-effort; a misconfigured or
offline backend must never block the user from seeing their
response.
The prefetch side-effect is gated on the same interrupt flag: the
user's next message is almost certainly a retry of the same intent,
and a prefetch keyed on the interrupted turn would fire against stale
context.
### Tests (16 new, all passing on py3.11 venv)
``tests/run_agent/test_memory_sync_interrupted.py`` exercises the
helper directly on a bare ``AIAgent`` (``__new__`` pattern that the
interrupt-propagation tests already use). Coverage:
- Interrupted turn with full-looking response → no sync (the fix)
- Interrupted turn with long assistant output → no sync (the interrupt
could have landed mid-stream; strings-on-disk lie)
- Normal completed turn → sync_all + queue_prefetch_all both called
with the right args (regression guard for the positive path)
- No final_response / no user_message / no memory manager → existing
pre-fix skip paths still apply
- sync_all raises → exception swallowed, prefetch still attempted
- queue_prefetch_all raises → exception swallowed after sync succeeded
- 8-case parametrised matrix across (interrupted × final_response ×
original_user_message) asserts sync fires iff interrupted=False AND
both strings are non-empty
Closes#15218
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Extends _repair_tool_call_arguments() to cover the most common local-model
JSON corruption pattern: llama.cpp/Ollama backends emit literal tabs and
newlines inside JSON string values (memory save summaries, file contents,
etc.). Previously fell through to '{}' replacement, losing the call.
Adds two repair passes:
- Pass 0: json.loads(strict=False) + re-serialise to canonical wire form
- Pass 4: escape 0x00-0x1F control chars inside string values, then retry
Ports the core utility from #12068 / PR #12093 without the larger plumbing
change (that PR also replaced json.loads at 8 call sites; current main's
_repair_tool_call_arguments is already the single chokepoint, so the
upgrade happens transparently for every existing caller).
Credit: @truenorth-lj for the original utility design.
4 new regression tests covering literal newlines, tabs, re-serialisation
to strict=True-valid output, and the trailing-comma + control-char
combination case.
When the streaming path (chat completions) assembled tool call deltas and
detected malformed JSON arguments, it set has_truncated_tool_args=True but
passed the broken args through unchanged. This triggered the truncation
handler which returned a partial result and killed the session (/new required).
_many_ malformations are repairable: trailing commas, unclosed brackets,
Python None, empty strings. _repair_tool_call_arguments() already existed
for the pre-API-request path but wasn't called during streaming assembly.
Now when JSON parsing fails during streaming assembly, we attempt repair
via _repair_tool_call_arguments() before flagging as truncated. If repair
succeeds (returns valid JSON), the tool call proceeds normally. Only truly
unrepairable args fall through to the truncation handler.
This prevents the most common session-killing failure mode for models like
GLM-5.1 that produce trailing commas or unclosed brackets.
Tests: 12 new streaming assembly repair tests, all 29 existing repair
tests still passing.
When a session is split by context compression mid-tool-call, an assistant
message may end up with truncated/invalid JSON in tool_calls[*].function.arguments.
On the next turn this is replayed verbatim and providers reject the entire request
with HTTP 400 invalid_tool_call_format, bricking the conversation in a loop that
cannot recover without manual session quarantine.
This patch adds a defensive sanitizer that runs immediately before
client.chat.completions.create() in AIAgent.run_conversation():
- Validates each assistant tool_calls[*].function.arguments via json.loads
- Replaces invalid/empty arguments with '{}'
- Injects a synthetic tool response (or prepends a marker to the existing one)
so downstream messages keep valid tool_call_id pairing
- Logs each repair with session_id / message_index / preview for observability
Defense in depth: corruption can originate from compression splits, manual edits,
or plugin bugs. Sanitizing at the send chokepoint catches all sources.
Adds 7 unit tests covering: truncated JSON, empty string, None, non-string args,
existing matching tool response (no duplicate injection), non-assistant messages
ignored, multiple repairs.
Fixes#15236
gpt-5.x on the Codex Responses API sometimes degenerates and emits
Harmony-style `to=functions.<name> {json}` serialization as plain
assistant-message text instead of a structured `function_call` item.
The intent never makes it into `response.output` as a function_call,
so `tool_calls` is empty and `_normalize_codex_response()` returns
the leaked text as the final content. Downstream (e.g. delegate_task),
this surfaces as a confident-looking summary with `tool_trace: []`
because no tools actually ran — the Taiwan-embassy-email bug report.
Detect the pattern, scrub the content, and return finish_reason=
'incomplete' so the existing Codex-incomplete continuation path
(run_agent.py:11331, 3 retries) gets a chance to re-elicit a proper
function_call item. Encrypted reasoning items are preserved so the
model keeps its chain-of-thought on the retry.
Regression tests: leaked text triggers incomplete, real tool calls
alongside leak-looking text are preserved, clean responses pass
through unchanged.
Reported on Discord (gpt-5.4 / openai-codex).
Claude-style and some Anthropic-tuned models occasionally emit tool
names as class-like identifiers: TodoTool_tool, Patch_tool,
BrowserClick_tool, PatchTool. These failed strict-dict lookup in
valid_tool_names and triggered the 'Unknown tool' self-correction
loop, wasting a full turn of iteration and tokens.
_repair_tool_call already handled lowercase / separator / fuzzy
matches but couldn't bridge the CamelCase-to-snake_case gap or the
trailing '_tool' suffix that Claude sometimes tacks on. Extend it
with two bounded normalization passes:
1. CamelCase -> snake_case (via regex lookbehind).
2. Strip trailing _tool / -tool / tool suffix (case-insensitive,
applied twice so TodoTool_tool reduces all the way: strip
_tool -> TodoTool, snake -> todo_tool, strip 'tool' -> todo).
Cheap fast-paths (lowercase / separator-normalized) still run first
so the common case stays zero-cost. Fuzzy match remains the last
resort unchanged.
Tests: tests/run_agent/test_repair_tool_call_name.py covers the
three original reports (TodoTool_tool, Patch_tool, BrowserClick_tool),
plus PatchTool, WriteFileTool, ReadFile_tool, write-file_Tool,
patch-tool, and edge cases (empty, None, '_tool' alone, genuinely
unknown names).
18 new tests + 17 existing arg-repair tests = 35/35 pass.
Closes#14784
Extracts pool-rotation-room logic into `_pool_may_recover_from_rate_limit`
so single-credential pools no longer block the eager-fallback path on 429.
The existing check `pool is not None and pool.has_available()` lets
fallback fire only after the pool marks every entry as exhausted. With
exactly one credential in the pool (the common shape for Gemini OAuth,
Vertex service accounts, and any personal-key setup), `has_available()`
flips back to True as soon as the cooldown expires — Hermes retries
against the same entry, hits the same daily-quota 429, and burns the
retry budget in a tight loop before ever reaching the configured
`fallback_model`. Observed in the wild as 4+ hours of 429 noise on a
single Gemini key instead of falling through to Vertex as configured.
Rotation is only meaningful with more than one credential — gate on
`len(pool.entries()) > 1`. Multi-credential pools keep the current
wait-for-rotation behaviour unchanged.
Fixes#11314. Related to #8947, #10210, #7230. Narrower scope than
open PRs #8023 (classifier change) and #11492 (503/529 credential-pool
bypass) — this addresses the single-credential 429 case specifically
and does not conflict with either.
Tests: 6 new unit tests in tests/run_agent/test_provider_fallback.py
covering (a) None pool, (b) single-cred available, (c) single-cred in
cooldown, (d) 2-cred available rotates, (e) multi-cred all cooling-down
falls back, (f) many-cred available rotates. All 18 tests in the file
pass.
When using GitHub Copilot as provider, HTTP 401 errors could cause
Hermes to silently fall back to the next model in the chain instead
of recovering. This adds a one-shot retry mechanism that:
1. Re-resolves the Copilot token via the standard priority chain
(COPILOT_GITHUB_TOKEN -> GH_TOKEN -> GITHUB_TOKEN -> gh auth token)
2. Rebuilds the OpenAI client with fresh credentials and Copilot headers
3. Retries the failed request before falling back
The fix handles the common case where the gho_* OAuth token remains
valid but the httpx client state becomes stale (e.g. after startup
race conditions or long-lived sessions).
Key design decisions:
- Always rebuild client even if token string unchanged (recovers stale state)
- Uses _apply_client_headers_for_base_url() for canonical header management
- One-shot flag guard prevents infinite 401 loops (matches existing pattern
used by Codex/Nous/Anthropic providers)
- No token exchange via /copilot_internal/v2/token (returns 404 for some
account types; direct gho_* auth works reliably)
Tests: 3 new test cases covering end-to-end 401->refresh->retry,
client rebuild verification, and same-token rebuild scenarios.
Docs: Updated providers.md with Copilot auth behavior section.
json.JSONDecodeError inherits from ValueError. The agent loop's
non-retryable classifier at run_agent.py ~L10782 treated any
ValueError/TypeError as a local programming bug and short-circuited
retry. Without a carve-out, a transient JSONDecodeError from a
provider that returned a malformed response body, a truncated stream,
or a router-layer corruption would fail the turn immediately.
Add JSONDecodeError to the existing UnicodeEncodeError exclusion
tuple so the classified-retry logic (which already handles 429/529/
context-overflow/etc.) gets to run on bad-JSON errors.
Tests (tests/run_agent/test_jsondecodeerror_retryable.py):
- JSONDecodeError: NOT local validation
- UnicodeEncodeError: NOT local validation (existing carve-out)
- bare ValueError: IS local validation (programming bug)
- bare TypeError: IS local validation (programming bug)
- source-level assertion that run_agent.py still carries the carve-out
(guards against accidental revert)
Closes#14782
Make the main-branch test suite pass again. Most failures were tests
still asserting old shapes after recent refactors; two were real source
bugs.
Source fixes:
- tools/mcp_tool.py: _kill_orphaned_mcp_children() slept 2s on every
shutdown even when no tracked PIDs existed, making test_shutdown_is_parallel
measure ~3s for 3 parallel 1s shutdowns. Early-return when pids is empty.
- hermes_cli/tips.py: tip 105 was 157 chars; corpus max is 150.
Test fixes (mostly stale mock targets / missing fixture fields):
- test_zombie_process_cleanup, test_agent_cache: patch run_agent.cleanup_vm
(the local name bound at import), not tools.terminal_tool.cleanup_vm.
- test_browser_camofox: patch tools.browser_camofox.load_config, not
hermes_cli.config.load_config (the source module, not the resolved one).
- test_flush_memories_codex._chat_response_with_memory_call: add
finish_reason, tool_call.id, tool_call.type so the chat_completions
transport normalizer doesn't AttributeError.
- test_concurrent_interrupt: polling_tool signature now accepts
messages= kwarg that _invoke_tool() passes through.
- test_minimax_provider: add _fallback_chain=[] to the __new__'d agent
so switch_model() doesn't AttributeError.
- test_skills_config: SKILLS_DIR MagicMock + .rglob stopped working
after the scanner switched to agent.skill_utils.iter_skill_index_files
(os.walk-based). Point SKILLS_DIR at a real tmp_path and patch
agent.skill_utils.get_external_skills_dirs.
- test_browser_cdp_tool: browser_cdp toolset was intentionally split into
'browser-cdp' (commit 96b0f3700) so its stricter check_fn doesn't gate
the whole browser toolset; test now expects 'browser-cdp'.
- test_registry: add tools.browser_dialog_tool to the expected
builtin-discovery set (PR #14540 added it).
- test_file_tools TestPatchHints: patch_tool surfaces hints as a '_hint'
key on the JSON payload, not inline '[Hint: ...' text.
- test_write_deny test_hermes_env: resolve .env via get_hermes_home() so
the path matches the profile-aware denylist under hermetic HERMES_HOME.
- test_checkpoint_manager test_falls_back_to_parent: guard the walk-up
so a stray /tmp/pyproject.toml on the host doesn't pick up /tmp as the
project root.
- test_quick_commands: set cli.session_id in the __new__'d CLI so the
alias-args path doesn't trip AttributeError when fuzzy-matching leaks
a skill command across xdist test distribution.
- Load prompt_caching.cache_ttl in AIAgent (5m default, 1h opt-in)
- Document DEFAULT_CONFIG and developer guide example
- Add unit tests for default, 1h, and invalid TTL fallback
Made-with: Cursor
Pin the behaviour added in the preceding commit — `_get_proxy_for_base_url()`
must return None for hosts covered by NO_PROXY and the HTTPS_PROXY otherwise,
and the full `_create_openai_client()` path must NOT mount HTTPProxy for a
NO_PROXY host.
Refs: #14966
Manual /compress crashed with 'LCMEngine' object has no attribute
'_align_boundary_forward' when any context-engine plugin was active.
The gateway handler reached into _align_boundary_forward and
_find_tail_cut_by_tokens on tmp_agent.context_compressor, but those
are ContextCompressor-specific — not part of the generic ContextEngine
ABC — so every plugin engine (LCM, etc.) raised AttributeError.
- Add optional has_content_to_compress(messages) to ContextEngine ABC
with a safe default of True (always attempt).
- Override it in the built-in ContextCompressor using the existing
private helpers — preserves exact prior behavior for 'compressor'.
- Rewrite gateway /compress preflight to call the ABC method, deleting
the private-helper reach-in.
- Add focus_topic to the ABC compress() signature. Make _compress_context
retry without focus_topic on TypeError so older strict-sig plugins
don't crash on manual /compress <focus>.
- Regression test with a fake ContextEngine subclass that only
implements the ABC (mirrors LCM's surface).
Reported by @selfhostedsoul (Discord, Apr 22).
Closes#11616.
The agent's API retry loop hardcoded max_retries = 3, so users with
fallback providers on flaky primaries burned through ~3 × provider
timeout (e.g. 3 × 180s = 9 minutes) before their fallback chain got a
chance to kick in.
Expose a new config key:
agent:
api_max_retries: 3 # default unchanged
Set it to 1 for fast failover when you have fallback providers, or
raise it if you prefer longer tolerance on a single provider. Values
< 1 are clamped to 1 (single attempt, no retry); non-integer values
fall back to the default.
This wraps the Hermes-level retry loop only — the OpenAI SDK's own
low-level retries (max_retries=2 default) still run beneath this for
transient network errors.
Changes:
- hermes_cli/config.py: add agent.api_max_retries default 3 with comment.
- run_agent.py: read self._api_max_retries in AIAgent.__init__; replace
hardcoded max_retries = 3 in the retry loop with self._api_max_retries.
- cli-config.yaml.example: documented example entry.
- hermes_cli/tips.py: discoverable tip line.
- tests/run_agent/test_api_max_retries_config.py: 4 tests covering
default, override, clamp-to-one, and invalid-value fallback.
NormalizedResponse and ToolCall now have backward-compat properties
so the agent loop can read them directly without the shim:
ToolCall: .type, .function (returns self), .call_id, .response_item_id
NormalizedResponse: .reasoning_content, .reasoning_details,
.codex_reasoning_items
This eliminates the 35-line shim and its 4 call sites in run_agent.py.
Also changes flush_memories guard from hasattr(response, 'choices')
to self.api_mode in ('chat_completions', 'bedrock_converse') so it
works with raw boto3 dicts too.
WS1 items 3+4 of Cycle 2 (#14418).
3-layer chain (transport → v2 → v1) was collapsed to 2-layer in PR 7.
This collapses the remaining 2-layer (transport → v1 → NR mapping in
transport) to 1-layer: v1 now returns NormalizedResponse directly.
Before: adapter returns (SimpleNamespace, finish_reason) tuple,
transport unpacks and maps to NormalizedResponse (22 lines).
After: adapter returns NormalizedResponse, transport is a
1-line passthrough.
Also updates ToolCall construction — adapter now creates ToolCall
dataclass directly instead of SimpleNamespace(id, type, function).
WS1 item 1 of Cycle 2 (#14418).
Consolidate 4 per-transport lazy singleton helpers (_get_anthropic_transport,
_get_codex_transport, _get_chat_completions_transport, _get_bedrock_transport)
into one generic _get_transport(api_mode) with a shared dict cache.
Collapse the 65-line main normalize block (3 api_mode branches, each with
its own SimpleNamespace shim) into 7 lines: one _get_transport() call +
one _nr_to_assistant_message() shared shim. The shim extracts provider_data
fields (codex_reasoning_items, reasoning_details, call_id, response_item_id)
into the SimpleNamespace shape downstream code expects.
Wire chat_completions and bedrock_converse normalize through their transports
for the first time — these were previously falling into the raw
response.choices[0].message else branch.
Remove 8 dead codex adapter imports that have zero callers after PRs 1-6.
Transport lifecycle improvements:
- Eagerly warm transport cache at __init__ (surfaces import errors early)
- Invalidate transport cache on api_mode change (switch_model, fallback
activation, fallback restore, transport recovery) — prevents stale
transport after mid-session provider switch
run_agent.py: -32 net lines (11,988 -> 11,956).
PR 7 of the provider transport refactor.
Port from openclaw/openclaw#67318. Some open models (notably Gemma
variants served via OpenRouter) emit tool calls as XML blocks inside
assistant content instead of via the structured tool_calls field:
<function name="read_file"><parameter name="path">/tmp/x</parameter></function>
<tool_call>{"name":"x"}</tool_call>
<function_calls>[{...}]</function_calls>
Left unstripped, this raw XML leaked to gateway users (Discord, Telegram,
Matrix, Feishu, Signal, WhatsApp, etc.) and the CLI, since hermes-agent's
existing reasoning-tag stripper handled only <think>/<thinking>/<thought>
variants.
Extend _strip_think_blocks (run_agent.py) and _strip_reasoning_tags
(cli.py) to cover:
* <tool_call>, <tool_calls>, <tool_result>
* <function_call>, <function_calls>
* <function name="..."> ... </function> (Gemma-style)
The <function> variant is boundary-gated (only strips when the tag sits
at start-of-line or after sentence punctuation AND carries a name="..."
attribute) so prose mentions like 'Use <function> declarations in JS'
are preserved. Dangling <function name="..."> with no close is
intentionally left visible — matches OpenClaw's asymmetry so a truncated
streaming tail still reaches the user.
Tests: 9 new cases in TestStripThinkBlocks (run_agent) + 9 in new file
tests/run_agent/test_strip_reasoning_tags_cli.py. Covers Qwen-style
<tool_call>, Gemma-style <function name="...">, multi-line payloads,
prose preservation, stray close tags, dangling open tags, and mixed
reasoning+tool_call content.
Note: this port covers the post-streaming final-text path, which is what
gateway adapters and CLI display consume. Extending the per-delta stream
filter in gateway/stream_consumer.py to hide these tags live as they
stream is a separate follow-up; for now users may see raw XML briefly
during a stream before the final cleaned text replaces it.
Refs: openclaw/openclaw#67318
When the streaming connection dropped AFTER user-visible text was
delivered but a tool call was in flight, we stubbed the turn with a
'⚠ Stream stalled mid tool-call; Ask me to retry' warning — costing
an iteration and breaking the flow. Users report this happening
increasingly often on long SSE streams through flaky provider routes.
Fix: in the existing inner stream-retry loop, relax the
deltas_were_sent short-circuit. If a tool call was in flight
(partial_tool_names populated) AND the error is a transient connection
error (timeout, RemoteProtocolError, SSE 'connection lost', etc.),
silently retry instead of bailing out. Fire a brief 'Connection
dropped mid tool-call; reconnecting…' marker so the user understands
the preamble is about to be re-streamed.
Researched how Claude Code (tombstone + non-streaming fallback),
OpenCode (blind Effect.retry wrapping whole stream), and Clawdbot
(4-way gate: stopReason==error + output==0 + !hadPotentialSideEffects)
handle this. Chose the narrow Clawdbot-style gate: retry only when
(a) a tool call was actually in flight (otherwise the existing
stub-with-recovered-text is correct for pure-text stalls) and
(b) the error is transient. Side-effect safety is automatic — no
tool has been dispatched within this single API call yet.
UX trade-off: user sees preamble text twice on retry (OpenCode-style).
Strictly better than a lost action with a 'retry manually' message.
If retries exhaust, falls through to the existing stub-with-warning
path so the user isn't left with zero signal.
Tests: 3 new tests in TestSilentRetryMidToolCall covering
(1) silent retry recovers tool call; (2) exhausted retries fall back
to stub; (3) text-only stalls don't trigger retry. 30/30 pass.
Add ResponsesApiTransport wrapping codex_responses_adapter.py behind the
ProviderTransport ABC. Auto-registered via _discover_transports().
Wire ALL Codex transport methods to production paths in run_agent.py:
- build_kwargs: main _build_api_kwargs codex branch (50 lines extracted)
- normalize_response: main loop + flush + summary + retry (4 sites)
- convert_tools: memory flush tool override
- convert_messages: called internally via build_kwargs
- validate_response: response validation gate
- preflight_kwargs: request sanitization (2 sites)
Remove 7 dead legacy wrappers from AIAgent (_responses_tools,
_chat_messages_to_responses_input, _normalize_codex_response,
_preflight_codex_api_kwargs, _preflight_codex_input_items,
_extract_responses_message_text, _extract_responses_reasoning_text).
Keep 3 ID manipulation methods still used by _build_assistant_message.
Update 18 test call sites across 3 test files to call adapter functions
directly instead of through deleted AIAgent wrappers.
24 new tests. 343 codex/responses/transport tests pass (0 failures).
PR 4 of the provider transport refactor.
Reported during the TUI v2 blitz test: switching from openrouter to
anthropic via `/model <name> --provider anthropic` appeared to succeed,
but the next turn kept hitting openrouter — the provider the user was
deliberately moving away from.
Two gaps caused this:
1. `Agent.switch_model` reset `_fallback_activated` / `_fallback_index`
but left `_fallback_chain` intact. The chain was seeded from
`fallback_providers:` at agent init for the *original* primary, so
when the new primary returned 401 (invalid/expired Anthropic key),
`_try_activate_fallback()` picked the old provider back up without
informing the user. Prune entries matching either the old primary
(user is moving away) or the new primary (redundant) whenever the
primary provider actually changes.
2. `_apply_model_switch` persisted `HERMES_MODEL` but never updated
`HERMES_INFERENCE_PROVIDER`. Any ambient re-resolution of the runtime
(credential pool refresh, compressor rebuild, aux clients) falls
through to that env var in `resolve_requested_provider`, so it kept
reporting the original provider even after an in-memory switch.
Adds three regression tests: fallback-chain prune on primary change,
no-op on same-provider model swap, and env-var sync on explicit switch.
Qwen models on OpenCode, OpenCode Go, and direct DashScope accept
Anthropic-style cache_control markers on OpenAI-wire chat completions,
but hermes only injected markers for Claude-named models. Result: zero
cache hits on every turn, full prompt re-billed — a community user
reported burning through their OpenCode Go subscription on Qwen3.6.
Extend _anthropic_prompt_cache_policy to return (True, False) — envelope
layout, not native — for the Alibaba provider family when the model name
contains 'qwen'. Envelope layout places markers on inner content blocks
(matching pi-mono's 'alibaba' cacheControlFormat) and correctly skips
top-level markers on tool-role messages (which OpenCode rejects).
Non-Qwen models on these providers (GLM, Kimi) keep their existing
behaviour — they have automatic server-side caching and don't need
client markers.
Upstream reference: pi-mono #3392 / #3393 documented this contract for
opencode-go Qwen models.
Adds 7 regression tests covering Qwen3.5/3.6/coder on each affected
provider plus negative cases for GLM/Kimi/OpenRouter-Qwen.
Kimi/Moonshot endpoints require explicit parameters that Hermes was not
sending, causing 'Response truncated due to output length limit' errors
and inconsistent reasoning behavior.
Root cause analysis against Kimi CLI source (MoonshotAI/kimi-cli,
packages/kosong/src/kosong/chat_provider/kimi.py):
1. max_tokens: Kimi's API defaults to a very low value when omitted.
Reasoning tokens share the output budget — the model exhausts it on
thinking alone. Send 32000, matching Kimi CLI's generate() default.
2. reasoning_effort: Kimi CLI sends this as a top-level parameter (not
inside extra_body). Hermes was not sending it at all because
_supports_reasoning_extra_body() returns False for non-OpenRouter
endpoints.
3. extra_body.thinking: Kimi CLI uses with_thinking() which sets
extra_body.thinking={"type":"enabled"} alongside reasoning_effort.
This is a separate control from the OpenAI-style reasoning extra_body
that Hermes sends for OpenRouter/GitHub. Without it, the Kimi gateway
may not activate reasoning mode correctly.
Covers api.kimi.com (Kimi Code) and api.moonshot.ai/cn (Moonshot).
Tests: 6 new test cases for max_tokens, reasoning_effort, and
extra_body.thinking under various configs.
Catalog snapshots, config version literals, and enumeration counts are data
that changes as designed. Tests that assert on those values add no
behavioral coverage — they just break CI on every routine update and cost
engineering time to 'fix.'
Replace with invariants where one exists, delete where none does.
Deleted (pure snapshots):
- TestMinimaxModelCatalog (3 tests): 'MiniMax-M2.7 in models' et al
- TestGeminiModelCatalog: 'gemini-2.5-pro in models', 'gemini-3.x in models'
- test_browser_camofox_state::test_config_version_matches_current_schema
(docstring literally said it would break on unrelated bumps)
Relaxed (keep plumbing check, drop snapshot):
- Xiaomi / Arcee / Kimi moonshot / Kimi coding / HuggingFace static lists:
now assert 'provider exists and has >= 1 entry' instead of specific names
- HuggingFace main/models.py consistency test: drop 'len >= 6' floor
Dynamicized (follow source, not a literal):
- 3x test_config.py migration tests: raw['_config_version'] ==
DEFAULT_CONFIG['_config_version'] instead of hardcoded 21
Fixed stale tests against intentional behavior changes:
- test_insights::test_gateway_format_hides_cost: name matches new behavior
(no dollar figures); remove contradicting '$' in text assertion
- test_config::prefers_api_then_url_then_base_url: flipped per PR #9332;
rename + update to base_url > url > api
- test_anthropic_adapter: relax assert_called_once() (xdist-flaky) to
assert called — contract is 'credential flowed through'
- test_interrupt_propagation: add provider/model/_base_url to bare-agent
fixture so the stale-timeout code path resolves
Fixed stale integration tests against opt-in plugin gate:
- transform_tool_result + transform_terminal_output: write plugins.enabled
allow-list to config.yaml and reset the plugin manager singleton
Source fix (real consistency invariant):
- agent/model_metadata.py: add moonshotai/Kimi-K2.6 context length
(262144, same as K2.5). test_model_metadata_has_context_lengths was
correctly catching the gap.
Policy:
- AGENTS.md Testing section: new subsection 'Don't write change-detector
tests' with do/don't examples. Reviewers should reject catalog-snapshot
assertions in new tests.
Covers every test that failed on the last completed main CI run
(24703345583) except test_modal_sandbox_fixes::test_terminal_tool_present
+ test_terminal_and_file_toolsets_resolve_all_tools, which now pass both
alone and with the full tests/tools/ directory (xdist ordering flake that
resolved itself).
The mid-run steer marker was '[USER STEER (injected mid-run, not tool
output): <text>]'. Replaced with a plain two-newline-prefixed
'User guidance: <text>' suffix.
Rationale: the marker lives inside the tool result's content string
regardless of whether the tool returned JSON, plain text, an MCP
result, or a plugin result. The bracketed tag read like structured
metadata that some tools (terminal, execute_code) could confuse with
their own output formatting. A plain labelled suffix works uniformly
across every content shape we produce.
Behavior unchanged:
- Still injected into the last tool-role message's content.
- Still preserves multimodal (Anthropic) content-block lists by
appending a text block.
- Still drained at both sites added in #12959 and #13205 — per-tool
drain between individual calls, and pre-API-call drain at the top
of each main-loop iteration.
Checked Codex's equivalent (pending_input / inject_user_message_without_turn
in codex-rs/core): they record mid-turn user input as a real role:user
message via record_user_prompt_and_emit_turn_item(). That's cleaner for
their Responses-API model but not portable to Chat Completions where
role alternation after tool_calls is strict. Embedding the guidance in
the last tool result remains the correct placement for us.
Validation: all 21 tests in tests/run_agent/test_steer.py pass.
Requests through Vercel AI Gateway now carry referrerUrl / appName /
User-Agent attribution so traffic shows up in the gateway's analytics.
Adds _AI_GATEWAY_HEADERS in auxiliary_client and a new
ai-gateway.vercel.sh branch in _apply_client_headers_for_base_url.
When /steer is sent during an API call (model thinking), the steer text
sits in _pending_steer until after the next tool batch — which may never
come if the model returns a final response. In that case the steer is
only delivered as a post-run follow-up, defeating the purpose.
Add a pre-API-call drain at the top of the main loop: before building
api_messages, check _pending_steer and inject into the last tool result
in the messages list. This ensures steers sent during model thinking are
visible on the very next API call.
If no tool result exists yet (first iteration), the steer is restashed
for the post-tool drain to pick up — injecting into a user message would
break role alternation.
Three new tests cover the pre-API-call drain: injection into last tool
result, restash when no tool message exists, and backward scan past
non-tool messages.
Kimi's gateway selects the correct temperature server-side based on the
active mode (thinking -> 1.0, non-thinking -> 0.6). Sending any
temperature value — even the previously "correct" one — conflicts with
gateway-managed defaults.
Replaces the old approach of forcing specific temperature values (0.6
for non-thinking, 1.0 for thinking) with an OMIT_TEMPERATURE sentinel
that tells all call sites to strip the temperature key from API kwargs
entirely.
Changes:
- agent/auxiliary_client.py: OMIT_TEMPERATURE sentinel, _is_kimi_model()
prefix check (covers all kimi-* models), _fixed_temperature_for_model()
returns sentinel for kimi models. _build_call_kwargs() strips temp.
- run_agent.py: _build_api_kwargs, flush_memories, and summary generation
paths all handle the sentinel by popping/omitting temperature.
- trajectory_compressor.py: _effective_temperature_for_model returns None
for kimi (sentinel mapped), direct client calls use kwargs dict to
conditionally include temperature.
- mini_swe_runner.py: same sentinel handling via wrapper function.
- 6 test files updated: all 'forces temperature X' assertions replaced
with 'temperature not in kwargs' assertions.
Net: -76 lines (171 added, 247 removed).
Inspired by PR #13137 (@kshitijk4poor).
Follow-up for PR #12252 salvage:
- Extract 75-line inline repair block to _repair_tool_call_arguments()
module-level helper for testability and readability
- Remove redundant 'import re as _re' (re already imported at line 33)
- Bound the while-True excess-delimiter removal loop to 50 iterations
- Add 17 tests covering all 6 repair stages
- Add sirEven to AUTHOR_MAP in release.py
Cherry-picked from PR #12481 by @Sanjays2402.
Reasoning models (GLM-5.1, QwQ, DeepSeek R1) inflate completion_tokens
with internal thinking tokens. The compression trigger summed
prompt_tokens + completion_tokens, causing premature compression at ~42%
actual context usage instead of the configured 50% threshold.
Now uses only prompt_tokens — completion tokens don't consume context
window space for the next API call.
- 3 new regression tests
- Added AUTHOR_MAP entry for @Sanjays2402
Closes#12026
OpenAI-compatible clients (Open WebUI, LobeChat, etc.) can now send vision
requests to the API server. Both endpoints accept the canonical OpenAI
multimodal shape:
Chat Completions: {type: text|image_url, image_url: {url, detail?}}
Responses: {type: input_text|input_image, image_url: <str>, detail?}
The server validates and converts both into a single internal shape that the
existing agent pipeline already handles (Anthropic adapter converts,
OpenAI-wire providers pass through). Remote http(s) URLs and data:image/*
URLs are supported.
Uploaded files (file, input_file, file_id) and non-image data: URLs are
rejected with 400 unsupported_content_type.
Changes:
- gateway/platforms/api_server.py
- _normalize_multimodal_content(): validates + normalizes both Chat and
Responses content shapes. Returns a plain string for text-only content
(preserves prompt-cache behavior on existing callers) or a canonical
[{type:text|image_url,...}] list when images are present.
- _content_has_visible_payload(): replaces the bare truthy check so a
user turn with only an image no longer rejects as 'No user message'.
- _handle_chat_completions and _handle_responses both call the new helper
for user/assistant content; system messages continue to flatten to text.
- Codex conversation_history, input[], and inline history paths all share
the same validator. No duplicated normalizers.
- run_agent.py
- _summarize_user_message_for_log(): produces a short string summary
('[1 image] describe this') from list content for logging, spinner
previews, and trajectory writes. Fixes AttributeError when list
user_message hit user_message[:80] + '...' / .replace().
- _chat_content_to_responses_parts(): module-level helper that converts
chat-style multimodal content to Responses 'input_text'/'input_image'
parts. Used in _chat_messages_to_responses_input for Codex routing.
- _preflight_codex_input_items() now validates and passes through list
content parts for user/assistant messages instead of stringifying.
- tests/gateway/test_api_server_multimodal.py (new, 38 tests)
- Unit coverage for _normalize_multimodal_content, including both part
formats, data URL gating, and all reject paths.
- Real aiohttp HTTP integration on /v1/chat/completions and /v1/responses
verifying multimodal payloads reach _run_agent intact.
- 400 coverage for file / input_file / non-image data URL.
- tests/run_agent/test_run_agent_multimodal_prologue.py (new)
- Regression coverage for the prologue no-crash contract.
- _chat_content_to_responses_parts round-trip coverage.
- website/docs/user-guide/features/api-server.md
- Inline image examples for both endpoints.
- Updated Limitations: files still unsupported, images now supported.
Validated live against openrouter/anthropic/claude-opus-4.6:
POST /v1/chat/completions → 200, vision-accurate description
POST /v1/responses → 200, same image, clean output_text
POST /v1/chat/completions [file] → 400 unsupported_content_type
POST /v1/responses [input_file] → 400 unsupported_content_type
POST /v1/responses [non-image data URL] → 400 unsupported_content_type
Closes#5621, #8253, #4046, #6632.
Co-authored-by: Paul Bergeron <paul@gamma.app>
Co-authored-by: zhangxicen <zhangxicen@example.com>
Co-authored-by: Manuel Schipper <manuelschipper@users.noreply.github.com>
Co-authored-by: pradeep7127 <pradeep7127@users.noreply.github.com>
Context compression silently failed when the auxiliary compression model's
context window was smaller than the main model's compression threshold
(e.g. GLM-4.5-air at 131k paired with a 150k threshold). The feasibility
check warned but the session kept running and compression attempts errored
out mid-conversation.
Two changes in _check_compression_model_feasibility():
1. Hard floor: if detected aux context < MINIMUM_CONTEXT_LENGTH (64k),
raise ValueError so the session refuses to start. Mirrors the existing
main-model rejection at AIAgent.__init__ line 1600. A compression model
below 64k cannot summarise a full threshold-sized window.
2. Auto-correct: when aux context is >= 64k but below the computed
threshold, lower the live compressor's threshold_tokens to aux_context
(and update threshold_percent to match so later update_model() calls
stay in sync). Warning reworded to say what was done and how to
persist the fix in config.yaml.
Only ValueError re-raises; other exceptions in the check remain swallowed
as non-fatal.
Third-party gateways that speak the native Anthropic protocol (MiniMax,
Zhipu GLM, Alibaba DashScope, Kimi, LiteLLM proxies) now work end-to-end
with the same feature set as direct api.anthropic.com callers. Synthesizes
eight stale community PRs into one consolidated change.
Five fixes:
- URL detection: consolidate three inline `endswith("/anthropic")`
checks in runtime_provider.py into the shared _detect_api_mode_for_url
helper. Third-party /anthropic endpoints now auto-resolve to
api_mode=anthropic_messages via one code path instead of three.
- OAuth leak-guard: all five sites that assign `_is_anthropic_oauth`
(__init__, switch_model, _try_refresh_anthropic_client_credentials,
_swap_credential, _try_activate_fallback) now gate on
`provider == "anthropic"` so a stale ANTHROPIC_TOKEN never trips
Claude-Code identity injection on third-party endpoints. Previously
only 2 of 5 sites were guarded.
- Prompt caching: new method `_anthropic_prompt_cache_policy()` returns
`(should_cache, use_native_layout)` per endpoint. Replaces three
inline conditions and the `native_anthropic=(api_mode=='anthropic_messages')`
call-site flag. Native Anthropic and third-party Anthropic gateways
both get the native cache_control layout; OpenRouter gets envelope
layout. Layout is persisted in `_primary_runtime` so fallback
restoration preserves the per-endpoint choice.
- Auxiliary client: `_try_custom_endpoint` honors
`api_mode=anthropic_messages` and builds `AnthropicAuxiliaryClient`
instead of silently downgrading to an OpenAI-wire client. Degrades
gracefully to OpenAI-wire when the anthropic SDK isn't installed.
- Config hygiene: `_update_config_for_provider` (hermes_cli/auth.py)
clears stale `api_key`/`api_mode` when switching to a built-in
provider, so a previous MiniMax custom endpoint's credentials can't
leak into a later OpenRouter session.
- Truncation continuation: length-continuation and tool-call-truncation
retry now cover `anthropic_messages` in addition to `chat_completions`
and `bedrock_converse`. Reuses the existing `_build_assistant_message`
path via `normalize_anthropic_response()` so the interim message
shape is byte-identical to the non-truncated path.
Tests: 6 new files, 42 test cases. Targeted run + tests/run_agent,
tests/agent, tests/hermes_cli all pass (4554 passed).
Synthesized from (credits preserved via Co-authored-by trailers):
#7410 @nocoo — URL detection helper
#7393 @keyuyuan — OAuth 5-site guard
#7367 @n-WN — OAuth guard (narrower cousin, kept comment)
#8636 @sgaofen — caching helper + native-vs-proxy layout split
#10954 @Only-Code-A — caching on anthropic_messages+Claude
#7648 @zhongyueming1121 — aux client anthropic_messages branch
#6096 @hansnow — /model switch clears stale api_mode
#9691 @TroyMitchell911 — anthropic_messages truncation continuation
Closes: #7366, #8294 (third-party Anthropic identity + caching).
Supersedes: #7410, #7367, #7393, #8636, #10954, #7648, #6096, #9691.
Rejects: #9621 (OpenAI-wire caching with incomplete blocklist — risky),
#7242 (superseded by #9691, stale branch),
#8321 (targets smart_model_routing which was removed in #12732).
Co-authored-by: nocoo <nocoo@users.noreply.github.com>
Co-authored-by: Keyu Yuan <leoyuan0099@gmail.com>
Co-authored-by: Zoee <30841158+n-WN@users.noreply.github.com>
Co-authored-by: sgaofen <135070653+sgaofen@users.noreply.github.com>
Co-authored-by: Only-Code-A <bxzt2006@163.com>
Co-authored-by: zhongyueming <mygamez@163.com>
Co-authored-by: Xiaohan Li <hansnow@users.noreply.github.com>
Co-authored-by: Troy Mitchell <i@troy-y.org>
CI on main had 7 failing tests. Five were stale test fixtures; one (agent
cache spillover timeout) was covering up a real perf regression in
AIAgent construction.
The perf bug: every AIAgent.__init__ calls _check_compression_model_feasibility
→ resolve_provider_client('auto') → _resolve_api_key_provider which
iterates PROVIDER_REGISTRY. When it hits 'zai', it unconditionally calls
resolve_api_key_provider_credentials → _resolve_zai_base_url → probes 8
Z.AI endpoints with an empty Bearer token (all 401s), ~2s of pure latency
per agent, even when the user has never touched Z.AI. Landed in
9e844160 (PR for credential-pool Z.AI auto-detect) — the short-circuit
when api_key is empty was missing. _resolve_kimi_base_url had the same
shape; fixed too.
Test fixes:
- tests/gateway/test_voice_command.py: _make_adapter helpers were missing
self._voice_locks (added in PR #12644, 7 call sites — all updated).
- tests/test_toolsets.py: test_hermes_platforms_share_core_tools asserted
equality, but hermes-discord has discord_server (DISCORD_BOT_TOKEN-gated,
discord-only by design). Switched to subset check.
- tests/run_agent/test_streaming.py: test_tool_name_not_duplicated_when_resent_per_chunk
missing api_key/base_url — classic pitfall (PR #11619 fixed 16 of
these; this one slipped through on a later commit).
- tests/tools/test_discord_tool.py: TestConfigAllowlist caplog assertions
fail in parallel runs because AIAgent(quiet_mode=True) globally sets
logging.getLogger('tools').setLevel(ERROR) and xdist workers are
persistent. Autouse fixture resets the 'tools' and
'tools.discord_tool' levels per test.
Validation:
tests/cron + voice + agent_cache + streaming + toolsets + command_guards
+ discord_tool: 550/550 pass
tests/hermes_cli + tests/gateway: 5713/5713 pass
AIAgent construction without Z.AI creds: 2.2s → 0.24s (9x)
Follow up salvaged PR #12668 by threading base_url through the
remaining direct-call sites so kimi-k2.5 uses temperature=1.0 on
api.moonshot.ai and keeps 0.6 on api.kimi.com/coding. Add focused
regression tests for run_agent, trajectory_compressor, and
mini_swe_runner.
One source fix (web_server category merge) + five test updates that
didn't travel with their feature PRs. All 13 failures on the 04-19
CI run on main are now accounted for (5 already self-healed on main;
8 fixed here).
Changes
- web_server.py: add code_execution → agent to _CATEGORY_MERGE (new
singleton section from #11971 broke no-single-field-category invariant).
- test_browser_camofox_state: bump hardcoded _config_version 18 → 19
(also from #11971).
- test_registry: add browser_cdp_tool (#12369) and discord_tool (#4753)
to the expected built-in tool set.
- test_run_agent::test_tool_call_accumulation: rewrite fragment chunks
— #0f778f77 switched streaming name-accumulation from += to = to
fix MiniMax/NIM duplication; the test still encoded the old
fragment-per-chunk premise.
- test_concurrent_interrupt::_Stub: no-op
_apply_pending_steer_to_tool_results — #12116 added this call after
concurrent tool batches; the hand-rolled stub was missing it.
- test_codex_cli_model_picker: drop the two obsolete tests that
asserted auto-import from ~/.codex/auth.json into the Hermes auth
store. #12360 explicitly removed that behavior (refresh-token reuse
races with Codex CLI / VS Code); adoption is now explicit via
`hermes auth openai-codex`. Remaining 3 tests in the file (normal
path, Claude Code fallback, negative case) still cover the picker.
Validation
- scripts/run_tests.sh across all 6 affected files + surrounding tests
(54 tests total) all green locally.
Adds a regression guard for the #11277 → proxy-bypass regression fixed in
42b394c3. With HTTPS_PROXY / HTTP_PROXY / ALL_PROXY set, the custom httpx
transport used for TCP keepalives must still route requests through an
HTTPProxy pool; without proxy env, no HTTPProxy mount should exist.
Also maps zrc <zhurongcheng@rcrai.com> → heykb in scripts/release.py
AUTHOR_MAP so the salvage PR passes the author-attribution CI check.
Follow-up on top of mvanhorn's cherry-picked commit. Original PR only
wired request_timeout_seconds into the explicit-creds OpenAI branch at
run_agent.py init; router-based implicit auth, native Anthropic, and the
fallback chain were still hardcoded to SDK defaults.
- agent/anthropic_adapter.py: build_anthropic_client() accepts an optional
timeout kwarg (default 900s preserved when unset/invalid).
- run_agent.py: resolve per-provider/per-model timeout once at init; apply
to Anthropic native init + post-refresh rebuild + stale/interrupt
rebuilds + switch_model + _restore_primary_runtime + the OpenAI
implicit-auth path + _try_activate_fallback (with immediate client
rebuild so the first fallback request carries the configured timeout).
- tests: cover anthropic adapter kwarg honoring; widen mock signatures
to accept the new timeout kwarg.
- docs/example: clarify that the knob now applies to every transport,
the fallback chain, and rebuilds after credential rotation.
Commit 4a9c3565 added a reference to `self.config` in
`_check_compression_model_feasibility()` to pass the user-configured
`auxiliary.compression.context_length` to `get_model_context_length()`.
However, `AIAgent` never stores the loaded config dict as an instance
attribute — the config is loaded into a local variable `_agent_cfg` in
`__init__()` and discarded after init.
This causes an `AttributeError: 'AIAgent' object has no attribute
'config'` on every session start when compression is enabled, caught by
the try/except and logged as a non-fatal DEBUG message.
Fix: store the loaded config as `self._config` in `__init__()` and
update the reference in the feasibility check to use `self._config`.
