Background macOS desktop control via cua-driver MCP — does NOT steal the
user's cursor or keyboard focus, works with any tool-capable model.
Replaces the Anthropic-native `computer_20251124` approach from the
abandoned #4562 with a generic OpenAI function-calling schema plus SOM
(set-of-mark) captures so Claude, GPT, Gemini, and open models can all
drive the desktop via numbered element indices.
- `tools/computer_use/` package — swappable ComputerUseBackend ABC +
CuaDriverBackend (stdio MCP client to trycua/cua's cua-driver binary).
- Universal `computer_use` tool with one schema for all providers.
Actions: capture (som/vision/ax), click, double_click, right_click,
middle_click, drag, scroll, type, key, wait, list_apps, focus_app.
- Multimodal tool-result envelope (`_multimodal=True`, OpenAI-style
`content: [text, image_url]` parts) that flows through
handle_function_call into the tool message. Anthropic adapter converts
into native `tool_result` image blocks; OpenAI-compatible providers
get the parts list directly.
- Image eviction in convert_messages_to_anthropic: only the 3 most
recent screenshots carry real image data; older ones become text
placeholders to cap per-turn token cost.
- Context compressor image pruning: old multimodal tool results have
their image parts stripped instead of being skipped.
- Image-aware token estimation: each image counts as a flat 1500 tokens
instead of its base64 char length (~1MB would have registered as
~250K tokens before).
- COMPUTER_USE_GUIDANCE system-prompt block — injected when the toolset
is active.
- Session DB persistence strips base64 from multimodal tool messages.
- Trajectory saver normalises multimodal messages to text-only.
- `hermes tools` post-setup installs cua-driver via the upstream script
and prints permission-grant instructions.
- CLI approval callback wired so destructive computer_use actions go
through the same prompt_toolkit approval dialog as terminal commands.
- Hard safety guards at the tool level: blocked type patterns
(curl|bash, sudo rm -rf, fork bomb), blocked key combos (empty trash,
force delete, lock screen, log out).
- Skill `apple/macos-computer-use/SKILL.md` — universal (model-agnostic)
workflow guide.
- Docs: `user-guide/features/computer-use.md` plus reference catalog
entries.
44 new tests in tests/tools/test_computer_use.py covering schema
shape (universal, not Anthropic-native), dispatch routing, safety
guards, multimodal envelope, Anthropic adapter conversion, screenshot
eviction, context compressor pruning, image-aware token estimation,
run_agent helpers, and universality guarantees.
469/469 pass across tests/tools/test_computer_use.py + the affected
agent/ test suites.
- `model_tools.py` provider-gating: the tool is available to every
provider. Providers without multi-part tool message support will see
text-only tool results (graceful degradation via `text_summary`).
- Anthropic server-side `clear_tool_uses_20250919` — deferred;
client-side eviction + compressor pruning cover the same cost ceiling
without a beta header.
- macOS only. cua-driver uses private SkyLight SPIs
(SLEventPostToPid, SLPSPostEventRecordTo,
_AXObserverAddNotificationAndCheckRemote) that can break on any macOS
update. Pin with HERMES_CUA_DRIVER_VERSION.
- Requires Accessibility + Screen Recording permissions — the post-setup
prints the Settings path.
Supersedes PR #4562 (pyautogui/Quartz foreground backend, Anthropic-
native schema). Credit @0xbyt4 for the original #3816 groundwork whose
context/eviction/token design is preserved here in generic form.
On AWS Bedrock (and Azure AI Foundry), Claude Opus 4.6/4.7 and Sonnet 4.6
are capped at 200K context unless the request carries the
`context-1m-2025-08-07` beta header. On native Anthropic (api.anthropic.com)
1M went GA so the header is a harmless no-op, but Bedrock/Azure still gate
it as beta as of 2026-04.
Hermes was advertising 1M in model_metadata.py (`claude-opus-4-7: 1000000`)
while silently sending a request without the beta — so Bedrock users saw
a 200K ceiling with no error message, and no config knob unblocked it.
Claude Code sends this header by default, which is why the same Bedrock
credentials worked there.
- Add `context-1m-2025-08-07` to `_COMMON_BETAS` (alongside interleaved
thinking and fine-grained tool streaming).
- Strip it in `_common_betas_for_base_url` for MiniMax bearer-auth
endpoints — they host their own models, not Claude, so Anthropic beta
headers are irrelevant and could risk rejection.
- Attach `_COMMON_BETAS` as `default_headers` on the AnthropicBedrock
client. Previously that constructor passed no betas at all, so native
Anthropic had the 1M unlock via default_headers but Bedrock didn't.
- Fast-mode per-request `extra_headers` already rebuilds from
`_common_betas_for_base_url`, so it picks up the 1M beta automatically.
Reported by user 'Rodmar' on Discord: Bedrock Opus 4.7 stuck at 200K while
same credentials worked in Claude Code.
A misconfigured auxiliary.compression.model is a user-fixable problem that silent recovery would hide. The previous retry-on-main logic transparently swallowed aux-model failures whenever the fallback succeeded, leaving the user's broken config in place and racking up future failures.
Track the aux-model failure on the compressor alongside the existing fallback-placeholder fields:
- _last_aux_model_failure_model: str | None
- _last_aux_model_failure_error: str | None
Both are set at the moment the aux model errors (captured before summary_model is cleared for retry), regardless of whether the retry succeeds. Cleared at compress() start and on on_session_reset() so a clean run doesn't leak stale warnings.
Surface at three places:
- gateway hygiene auto-compress: ℹ note to the platform adapter (thread_id preserved)
- gateway /compress command: ℹ line appended to the reply
- CLI via _emit_warning: deduped on (model, error) so repeat compactions don't spam
Distinct from the existing ⚠️ dropped-turns warning — different severity, different emoji, explicit 'context is intact' reassurance.
The existing retry-on-main path in _generate_summary only fires for errors that match the _is_model_not_found heuristic (404/503, 'model_not_found', 'does not exist', 'no available channel'). Other misconfiguration errors — 400s from aggregators, provider-specific 'no route' strings, opaque rejections — fall straight through to the transient-cooldown branch, which drops N turns of context and inserts a static placeholder.
Losing context is almost always worse than one extra summary attempt. Add a best-effort retry-on-main for the unknown-error branch, guarded by the same invariants as the existing fast-path retry: only when summary_model differs from main, and only once per compressor (_summary_model_fallen_back).
Tests cover: 404 fast-path fallback still works, unknown 400 now falls back, same-model aux skips retry (no infinite loop), and a double-failure (aux + main) stops at 2 calls.
When auxiliary compression's summary LLM call fails (e.g. model 404,
auxiliary model misconfigured), the compressor still drops the selected
turns and inserts a static fallback placeholder — the dropped context
is unrecoverable.
Previously the only signal of this was a WARNING in agent.log. Gateway
users (Telegram/Discord/etc.) had no way to know context was lost
because the existing _emit_warning path requires a status_callback,
and the gateway hygiene path uses a temporary _hyg_agent with
quiet_mode=True and no callback wired up.
Changes:
- ContextCompressor: track _last_summary_fallback_used and
_last_summary_dropped_count on each compress() call. Cleared at the
start of compress() and on session reset.
- gateway/run.py hygiene: after auto-compress, inspect the temp
agent's compressor; if fallback was used, send a visible ⚠️ warning
to the user via the platform adapter (TG/Discord/etc.) including
dropped count and the underlying error.
- gateway/run.py /compress: append the same warning to the manual
compress reply so users running /compress see the failure too.
Acceptance:
- Summary success: no user-visible warning (unchanged).
- Summary failure on gateway hygiene: user receives a TG/Discord
message with dropped count + error + remediation hint.
- Summary failure on /compress: warning appended to the command reply.
- CLI status_callback / _emit_warning path is untouched.
- Test coverage: two new tests verify the tracking fields are set on
failure and cleared on subsequent success.
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)
- config.py: remove dead ENV_VARS_BY_VERSION[17] entry (current _config_version
is 22, so all users are past version 17 and would never be prompted for
GMI_API_KEY on upgrade — consistent with how arcee was added)
- auxiliary_client.py: use google/gemini-3.1-flash-lite-preview as GMI aux
model instead of anthropic/claude-opus-4.6 (matches cheap fast-model pattern
used by all other providers: zai→glm-4.5-flash, kimi→kimi-k2-turbo-preview,
stepfun→step-3.5-flash, kilocode→google/gemini-3-flash-preview)
- test_gmi_provider.py: fix malformed write_text() call in doctor test
(was: write_text("GMI_API_KEY=*** encoding="utf-8") → missing closing quote,
wrote literal string 'GMI_API_KEY=*** encoding=' to .env file)
- test_gmi_provider.py + test_auxiliary_client.py: update aux model assertions
to match new cheaper default
- docs/integrations/providers.md: add 'gmi' to inline 'Supported providers'
fallback list (was only in the table, not the inline list at line ~1181)
- docs/reference/cli-commands.md: add 'gmi' to --provider choices list
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>
* 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.
Closes#15775.
Title generation swallowed exceptions at debug level and returned None,
so a depleted auxiliary provider (e.g. OpenRouter 402) silently left
sessions with NULL titles. Reporter observed 45 untitled sessions
accumulated over 19 days with no user-visible indication.
- agent/title_generator.py: accept optional failure_callback, bump log
to WARNING, invoke callback on call_llm exception (swallowing callback
errors so nothing can crash the fire-and-forget worker thread).
- cli.py, gateway/run.py: pass agent._emit_auxiliary_failure as the
callback so failures route through the existing user-visible warning
channel.
- tests: cover callback fires / errors are swallowed / no-callback
legacy behavior / maybe_auto_title forwards kwarg to worker.
raw_content from message["content"] can be a list that contains bare
strings, not only dicts. The previous `p.get("text", "")` call raised
AttributeError on string items, crashing context compression for any
session that had a message with mixed content.
Guard with isinstance checks: dict → .get("text"), str → len(p),
fallback → len(str(p)). Adds a regression test covering the bare-string
case that would have AttributeError'd on the pre-fix code.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
_find_tail_cut_by_tokens called len(content) to estimate message tokens.
When content is a list of blocks (multimodal: text + image_url), len()
returns block count (e.g. 2) rather than character count, so a message
with 500 chars of text was counted as ~10 tokens instead of ~135.
This caused the backward walk to exhaust all messages before hitting the
budget ceiling; the head_end safeguard then forced cut = n - min_tail,
shrinking the protected tail to the bare minimum and preventing effective
compression of long multimodal conversations.
Fix mirrors the existing pattern in _prune_old_tool_results (line 487):
sum(len(p.get("text", "")) for p in raw_content)
if isinstance(raw_content, list) else len(raw_content)
Tests: 3 new cases in TestTokenBudgetTailProtection — regression guard
(confirms the test fails with the bug), plain-string regression guard,
and image-only block edge case.
Fixes#16087.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two related fixes for OpenClaw-residue problems after an OpenClaw→Hermes
migration (especially migrations done via OpenClaw's own tool, which
doesn't archive the source directory).
1. optional-skills/migration/openclaw-migration/scripts/openclaw_to_hermes.py:
rebrand_text() was rewriting ~/.openclaw/config.yaml → ~/.Hermes/config.yaml
(capital H — a directory that doesn't exist). Now case-preserving:
"OpenClaw" → "Hermes" (prose), but "openclaw" → "hermes" (so filesystem
paths land on the real Hermes home). Regex logic unchanged — replacement
function now checks if the matched text was all-lowercase and emits the
replacement in the matching case.
2. agent/onboarding.py + cli.py: one-time startup banner the first time
Hermes launches and finds ~/.openclaw/. Tells the user to run
`hermes claw cleanup` to archive it, gated on the existing onboarding
seen-flag framework (onboarding.seen.openclaw_residue_cleanup in
config.yaml). Fires once per install; re-running requires wiping that
flag or running cleanup directly.
Tests:
- 4 new TestDetectOpenclawResidue tests (present / absent / file-instead-
of-dir / default-home smoke)
- 2 TestOpenclawResidueHint tests (content check)
- 2 TestOpenclawResidueSeenFlag tests (flag isolation + round-trip)
- test_rebrand_text_preserves_filesystem_path_casing regression test
with 4 scenarios including the exact ~/.openclaw/config.yaml case
- Existing test_rebrand_text_* tests updated to the new case-preserving
contract (lowercase input → lowercase output)
Co-authored-by: teknium1 <teknium@noreply.github.com>
`_resolve_effective_accept()` used `return bool(cfg_val)` for the
`hooks_auto_accept` config key. In Python, `bool("false")` is `True`,
so a user setting `hooks_auto_accept: "false"` (quoted YAML string)
in `config.yaml` would silently enable auto-approval of every shell
hook, bypassing the consent prompt entirely.
Replace the coercion with the same type-aware parsing already used for
the HERMES_ACCEPT_HOOKS env var three lines above: bool passthrough,
strings checked against {1,true,yes,on} case-insensitively, everything
else (including "false", None, 0, ints) rejected.
Add TestHooksAutoAcceptParsing guarding the regression across all four
value shapes (bool, string-truthy, string-falsy, missing/None).
Reported by @sprmn24 in #16244.
Enter while the agent is busy can now inject the typed text via /steer —
arriving at the agent after the next tool call — instead of interrupting
(current default) or queueing for the next turn.
Changes:
- cli.py: keybinding honors busy_input_mode='steer' by calling
agent.steer(text) on the UI thread (thread-safe), with automatic
fallback to 'queue' when the agent is missing, steer() is unavailable,
images are attached, or steer() rejects the payload. /busy accepts
'steer' as a fourth argument alongside queue/interrupt/status.
- gateway/run.py: busy-message handler and the PRIORITY running-agent
path both route through running_agent.steer() when the mode is 'steer',
with the same fallback-to-queue safety net. Ack wording tells users
their message was steered into the current run. Restart-drain queueing
now also activates for 'steer' so messages aren't lost across restarts.
- agent/onboarding.py: first-touch hint has a steer branch for both
CLI and gateway.
- hermes_cli/commands.py: /busy args_hint updated to include steer,
and 'steer' is registered as a subcommand (completions).
- hermes_cli/web_server.py: dashboard select widget offers steer.
- hermes_cli/config.py, cli-config.yaml.example, hermes_cli/tips.py:
inline docs updated.
- website/docs/user-guide/cli.md + messaging/index.md: documented.
- Tests: steer set/status path for /busy; onboarding hints;
_load_busy_input_mode accepts steer; busy-session ack exercises
steer success + two fallback-to-queue branches.
Requested on X by @CodingAcct.
Default is unchanged (interrupt).
PR #16046 added /busy and /verbose hints to the classic CLI and the
gateway runner but skipped the Ink TUI (and therefore the dashboard
/chat page, which embeds the TUI via PTY). This extends the same
latch to the TUI with TUI-native wording.
The TUI's busy-input model is not the /busy knob from the CLI —
single Enter while busy auto-queues, double Enter on an empty line
interrupts. The new busy-input hint teaches THAT gesture instead of
telling the user to flip a config that does not apply.
Changes:
- agent/onboarding.py — add busy_input_hint_tui() + tool_progress_hint_tui()
- tui_gateway/server.py — onboarding.claim JSON-RPC (Ink triggers busy
hint on enqueue) + _maybe_emit_onboarding_hint helper hooked into
_on_tool_complete for the 30s/tool_progress=all path. Same
config.yaml latch so each hint fires at most once per install across
CLI, gateway, and TUI combined.
- ui-tui/src/gatewayTypes.ts — OnboardingClaimResponse + onboarding.hint event
- ui-tui/src/app/createGatewayEventHandler.ts — render the hint event as sys()
- ui-tui/src/app/useSubmission.ts — claim busy_input_prompt on first
busy enqueue
- tests/agent/test_onboarding.py — +3 cases for TUI hint shape
- tests/tui_gateway/test_protocol.py — +4 cases for onboarding.claim
- website/docs/user-guide/tui.md — new 'Interrupting and queueing'
section explaining the TUI's double-Enter model and the hints
Validation:
scripts/run_tests.sh tests/agent/test_onboarding.py \
tests/tui_gateway/test_protocol.py \
tests/gateway/test_busy_session_ack.py
-> 66 passed
npm --prefix ui-tui run type-check -> clean
npm --prefix ui-tui run lint -> clean
npm --prefix ui-tui run build -> clean
Instead of a blocking first-run questionnaire, show a one-time hint the first
time the user hits each behavior fork:
1. First message while the agent is working — appends a hint to the busy-ack
explaining the /busy queue vs /busy interrupt knob, phrased to match the
mode that was just applied (don't tell a queue-mode user to switch to
queue).
2. First tool that runs for >= 30s in the noisiest progress mode
(tool_progress: all) — prints a hint about /verbose to cycle display
modes (all -> new -> off -> verbose). Gated on /verbose actually being
usable on the surface: always shown on CLI; on gateway only shown when
display.tool_progress_command is enabled.
Each hint is latched in config.yaml under onboarding.seen.<flag>, so it
fires exactly once per install across CLI, gateway, and cron, then never
again. Users can wipe the section to re-see hints.
New:
- agent/onboarding.py — is_seen / mark_seen / hint strings, shared by
both CLI and gateway.
- onboarding.seen in DEFAULT_CONFIG (hermes_cli/config.py) and in
load_cli_config defaults (cli.py). No _config_version bump — deep
merge handles new keys.
Wired:
- gateway/run.py: _handle_active_session_busy_message appends the hint
after building the ack. progress_callback tracks tool.completed
duration and queues the tool-progress hint into the progress bubble.
- cli.py: CLI input loop appends the busy-input hint on the first busy
Enter; _on_tool_progress appends the tool-progress hint on the first
>=30s tool completion. In-memory CLI_CONFIG is also updated so
subsequent fires in the same process are suppressed immediately.
All writes go through atomic_yaml_write and are wrapped in try/except
so onboarding can never break the input/busy-ack paths.
`_apply_model_switch_result` (the interactive `/model` picker's
confirmation path) printed `ModelInfo.context_window` straight from
models.dev, which reports the vendor-wide value (1.05M for gpt-5.5 on
openai). ChatGPT Codex OAuth caps the same slug at 272K, so the picker
showed 1M while the runtime (compressor, gateway `/model`, typed
`/model <name>`) correctly used 272K — the classic 'sometimes 1M,
sometimes 272K' mismatch on a single model.
Both display paths now go through `resolve_display_context_length()`,
matching the fix that `_handle_model_switch` received earlier.
Also bump the stale last-resort fallback in DEFAULT_CONTEXT_LENGTHS
(`gpt-5.5: 400000 -> 1050000`) to match the real OpenAI API value; the
272K Codex cap is already enforced via the Codex-OAuth branch, so the
fallback now reflects what every non-Codex probe-miss should see.
Tests: adds `test_apply_model_switch_result_context.py` with three
scenarios (Codex cap wins, OpenRouter shows 1.05M, resolver-empty falls
back to ModelInfo). Updates the existing non-Codex fallback test to
assert 1.05M (the correct value).
## Validation
| path | before | after |
|-------------------------------|-----------|-----------|
| picker -> gpt-5.5 on Codex | 1,050,000 | 272,000 |
| picker -> gpt-5.5 on OpenAI | 1,050,000 | 1,050,000 |
| picker -> gpt-5.5 on OpenRouter | 1,050,000 | 1,050,000 |
| typed /model gpt-5.5 on Codex | 272,000 | 272,000 |
#14934 added deepseek-v4-pro / deepseek-v4-flash to the DeepSeek native
provider but the context-window lookup still falls back to the existing
"deepseek" substring entry (128K). DeepSeek V4 ships with a 1M context
window, so any caller relying on get_model_context_length() for
pre-flight token budgeting (compression, context warnings) under-counts
by ~8x.
Add explicit lowercase entries for the four DeepSeek model ids that
ship 1M context:
- deepseek-v4-pro
- deepseek-v4-flash
- deepseek-chat (legacy alias, server-side maps to v4-flash non-thinking)
- deepseek-reasoner (legacy alias, server-side maps to v4-flash thinking)
Longest-key-first substring matching means these explicit entries also
cover the vendor-prefixed forms (deepseek/deepseek-v4-pro on OpenRouter
and Nous Portal) without regressing the existing 128K fallback for
older / unknown DeepSeek model ids on custom endpoints.
Source: https://api-docs.deepseek.com/zh-cn/quick_start/pricing
Nous Portal multiplexes multiple upstream providers (DeepSeek, Kimi,
MiMo, Hermes) behind one endpoint. Before this fix, any 429 on any of
those models recorded a cross-session file breaker that blocked EVERY
model on Nous for the cooldown window -- even though the caller's
own RPM/RPH/TPM/TPH buckets were healthy. Users hit a DeepSeek V4 Pro
capacity error, restarted, switched to Kimi 2.6, and still got
'Nous Portal rate limit active -- resets in 46m 53s'.
Nous already emits the full x-ratelimit-* header suite on every
response (captured by rate_limit_tracker into agent._rate_limit_state).
We now gate the breaker on that data: trip it only when either the
429's own headers or the last-known-good state show a bucket with
remaining == 0 AND a reset window >= 60s. Upstream-capacity 429s
(healthy buckets everywhere, but upstream out of capacity) fall
through to normal retry/fallback and the breaker is never written.
Note: the in-memory 'restart TUI/gateway to clear' workaround
circulated in Discord does NOT work -- the breaker is file-backed at
~/.hermes/rate_limits/nous.json. The workaround for users still
affected by a bad state file is to delete it.
Reported in Discord by CrazyDok1 and KYSIV (Apr 2026).
Fixes#15779. Custom-provider per-model context_length (`custom_providers[].models.<id>.context_length`) is now honored across every resolution path, not just agent startup. Also adds 256K as the top probe tier and default fallback.
## What changed
New helper `hermes_cli.config.get_custom_provider_context_length()` — single source of truth for the per-model override lookup, with trailing-slash-insensitive base-url matching.
`agent.model_metadata.get_model_context_length()` gains an optional `custom_providers=` kwarg (step 0b — runs after explicit `config_context_length` but before every other probe).
Wired through five call sites that previously either duplicated the lookup or ignored it entirely:
- `run_agent.py` startup — refactored to use the new helper (dedups legacy inline loop, keeps invalid-value warning)
- `AIAgent.switch_model()` — re-reads custom_providers from live config on every /model switch
- `hermes_cli.model_switch.resolve_display_context_length()` — new `custom_providers=` kwarg
- `gateway/run.py` /model confirmation (picker callback + text path)
- `gateway/run.py` `_format_session_info` (/info)
## Context probe tiers
`CONTEXT_PROBE_TIERS = [256_000, 128_000, 64_000, 32_000, 16_000, 8_000]` — was `[128_000, ...]`. `DEFAULT_FALLBACK_CONTEXT` follows tier[0], so unknown models now default to 256K. The stale `128000` literal in the OpenRouter metadata-miss path is replaced with `DEFAULT_FALLBACK_CONTEXT` for consistency.
## Repro (from #15779)
```yaml
custom_providers:
- name: my-custom-endpoint
base_url: https://example.invalid/v1
model: gpt-5.5
models:
gpt-5.5:
context_length: 1050000
```
`/model gpt-5.5 --provider custom:my-custom-endpoint` → previously "Context: 128,000", now "Context: 1,050,000".
## Tests
- `tests/hermes_cli/test_custom_provider_context_length.py` — new file, 19 tests covering the helper, step-0b integration, and the 256K tier invariants
- `tests/hermes_cli/test_model_switch_context_display.py` — added regression tests for #15779 through the display resolver
- `tests/gateway/test_session_info.py` — updated default-fallback assertion (128K → 256K)
- `tests/agent/test_model_metadata.py` — updated tier assertions for the new top tier
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.
Generalize the temperature-specific 400 retry that shipped in PR #15621 so
the same reactive strategy covers any provider that rejects an arbitrary
request parameter — — not just temperature.
- agent/auxiliary_client.py:
* New _is_unsupported_parameter_error(exc, param): matches the same six
phrasings the old temperature detector did plus 'unrecognized parameter'
and 'invalid parameter', against any named param.
* _is_unsupported_temperature_error is now a thin back-compat wrapper so
existing imports and tests keep working.
* The max_tokens → max_completion_tokens retry branch in call_llm and
async_call_llm now (a) gates on 'max_tokens is not None' so we do not
pop a key that was never set and silently substitute a None value on
the retry, and (b) also matches the generic helper in addition to the
legacy 'max_tokens' / 'unsupported_parameter' substring checks — picking
up phrasings like 'Unknown parameter: max_tokens' that previously slipped
through.
- tests/agent/test_unsupported_parameter_retry.py: 18 new tests covering
the generic detector across params, the back-compat wrapper, and the two
hardenings to the max_tokens retry branch (None gate + generic phrasing).
Credit: retry-generalization pattern from @nicholasrae's PR #15416. That PR
also proposed the reactive temperature retry which landed independently via
PR #15621 + #15623 (co-authored with @BlueBirdBack). This commit salvages
the remaining hardening ideas onto current main.
Universal reactive fix for 'HTTP 400: Unsupported parameter: temperature'
across all providers/models — not just Codex Responses.
The same backend can accept temperature for some models and reject it for
others (e.g. gpt-5.4 accepts but gpt-5.5 rejects on the same OpenAI
endpoint; similar patterns on Copilot, OpenRouter reasoning routes, and
Anthropic Opus 4.7+ via OAI-compat). An allow/deny-list by model name does
not scale.
call_llm / async_call_llm now detect the concrete 'unsupported parameter:
temperature' 400 and transparently retry once without temperature. Kimi's
server-managed omission and Opus 4.7+'s proactive strip stay in place —
this is the safety net for everything else.
Changes:
- agent/auxiliary_client.py: add _is_unsupported_temperature_error helper;
wire into both sync and async call_llm paths before the existing
max_tokens/payment/auth retry ladder
- tests/agent/test_unsupported_temperature_retry.py: 19 tests covering
detector phrasings, sync + async retry, no-retry-without-temperature,
and non-temperature 400s not triggering the retry
Builds on PR #15620 (codex_responses fallback) which stripped temperature
up front for that one api_mode. This PR closes the gap for every other
provider/model combo via reactive retry.
Credit: retry approach and detector originate from @BlueBirdBack's PR #15578.
Co-authored-by: BlueBirdBack <BlueBirdBack@users.noreply.github.com>
update_model() recalculated threshold_tokens but left tail_token_budget
and max_summary_tokens at their __init__ values. When switching from a
200K model to 32K, the tail budget stayed at ~20K tokens (62% of 32K)
instead of the intended ~10%.
Adds budget recalculation in update_model() and 2 regression tests.
## Problem
When a pooled HTTPS connection to the Bedrock runtime goes stale (NAT
timeout, VPN flap, server-side TCP RST, proxy idle cull), the next
Converse call surfaces as one of:
* botocore.exceptions.ConnectionClosedError / ReadTimeoutError /
EndpointConnectionError / ConnectTimeoutError
* urllib3.exceptions.ProtocolError
* A bare AssertionError raised from inside urllib3 or botocore
(internal connection-pool invariant check)
The agent loop retries the request 3x, but the cached boto3 client in
_bedrock_runtime_client_cache is reused across retries — so every
attempt hits the same dead connection pool and fails identically.
Only a process restart clears the cache and lets the user keep working.
The bare-AssertionError variant is particularly user-hostile because
str(AssertionError()) is an empty string, so the retry banner shows:
⚠️ API call failed: AssertionError
📝 Error:
with no hint of what went wrong.
## Fix
Add two helpers to agent/bedrock_adapter.py:
* is_stale_connection_error(exc) — classifies exceptions that
indicate dead-client/dead-socket state. Matches botocore
ConnectionError + HTTPClientError subtrees, urllib3
ProtocolError / NewConnectionError, and AssertionError
raised from a frame whose module name starts with urllib3.,
botocore., or boto3.. Application-level AssertionErrors are
intentionally excluded.
* invalidate_runtime_client(region) — per-region counterpart to
the existing reset_client_cache(). Evicts a single cached
client so the next call rebuilds it (and its connection pool).
Wire both into the Converse call sites:
* call_converse() / call_converse_stream() in
bedrock_adapter.py (defense-in-depth for any future caller)
* The two direct client.converse(**kwargs) /
client.converse_stream(**kwargs) call sites in run_agent.py
(the paths the agent loop actually uses)
On a stale-connection exception, the client is evicted and the
exception re-raised unchanged. The agent's existing retry loop then
builds a fresh client on the next attempt and recovers without
requiring a process restart.
## Tests
tests/agent/test_bedrock_adapter.py gets three new classes (14 tests):
* TestInvalidateRuntimeClient — per-region eviction correctness;
non-cached region returns False.
* TestIsStaleConnectionError — classifies botocore
ConnectionClosedError / EndpointConnectionError /
ReadTimeoutError, urllib3 ProtocolError, library-internal
AssertionError (both urllib3.* and botocore.* frames), and
correctly ignores application-level AssertionError and
unrelated exceptions (ValueError, KeyError).
* TestCallConverseInvalidatesOnStaleError — end-to-end: stale
error evicts the cached client, non-stale error (validation)
leaves it alone, successful call leaves it cached.
All 116 tests in test_bedrock_adapter.py pass.
Signed-off-by: Andre Kurait <andrekurait@gmail.com>
Bedrock's aws_sdk auth_type had no matching branch in
resolve_provider_client(), causing it to fall through to the
"unhandled auth_type" warning and return (None, None). This broke
all auxiliary tasks (compression, memory, summarization) for Bedrock
users — the main conversation loop worked fine, but background
context management silently failed.
Add an aws_sdk branch that creates an AnthropicAuxiliaryClient via
build_anthropic_bedrock_client(), using boto3's default credential
chain (IAM roles, SSO, env vars, instance metadata). Default
auxiliary model is Haiku for cost efficiency.
Closes#13919
## Problem
`get_model_context_length()` in `agent/model_metadata.py` had a resolution
order bug that caused every Bedrock model to fall back to the 128K default
context length instead of reaching the static Bedrock table (200K for
Claude, etc.).
The root cause: `bedrock-runtime.<region>.amazonaws.com` is not listed in
`_URL_TO_PROVIDER`, so `_is_known_provider_base_url()` returned False.
The resolution order then ran the custom-endpoint probe (step 2) *before*
the Bedrock branch (step 4b), which:
1. Treated Bedrock as a custom endpoint (via `_is_custom_endpoint`).
2. Called `fetch_endpoint_model_metadata()` → `GET /models` on the
bedrock-runtime URL (Bedrock doesn't serve this shape).
3. Fell through to `return DEFAULT_FALLBACK_CONTEXT` (128K) at the
"probe-down" branch — never reaching the Bedrock static table.
Result: users on Bedrock saw 128K context for Claude models that
actually support 200K on Bedrock, causing premature auto-compression.
## Fix
Promote the Bedrock branch from step 4b to step 1b, so it runs *before*
the custom-endpoint probe at step 2. The static table in
`bedrock_adapter.py::get_bedrock_context_length()` is the authoritative
source for Bedrock (the ListFoundationModels API doesn't expose context
window sizes), so there's no reason to probe `/models` first.
The original step 4b is replaced with a one-line breadcrumb comment
pointing to the new location, to make the resolution-order docstring
accurate.
## Changes
- `agent/model_metadata.py`
- Add step 1b: Bedrock static-table branch (unchanged predicate, moved).
- Remove dead step 4b block, replace with breadcrumb comment.
- Update resolution-order docstring to include step 1b.
- `tests/agent/test_model_metadata.py`
- New `TestBedrockContextResolution` class (3 tests):
- `test_bedrock_provider_returns_static_table_before_probe`:
confirms `provider="bedrock"` hits the static table and does NOT
call `fetch_endpoint_model_metadata` (regression guard).
- `test_bedrock_url_without_provider_hint`: confirms the
`bedrock-runtime.*.amazonaws.com` host match works without an
explicit `provider=` hint.
- `test_non_bedrock_url_still_probes`: confirms the probe still
fires for genuinely-custom endpoints (no over-reach).
## Testing
pytest tests/agent/test_model_metadata.py -q
# 83 passed in 1.95s (3 new + 80 existing)
## Risk
Very low.
- Predicate is identical to the original step 4b — no behaviour change
for non-Bedrock paths.
- Original step 4b was dead code for the user-facing case (always hit
the 128K fallback first), so removing it cannot regress behaviour.
- Bedrock path now short-circuits before any network I/O — faster too.
- `ImportError` fall-through preserved so users without `boto3`
installed are unaffected.
## Related
- This is a prerequisite for accurate context-window accounting on
Bedrock — the fix for #14710 (stale-connection client eviction)
depends on correct context sizing to know when to compress.
Signed-off-by: Andre Kurait <andrekurait@gmail.com>
Bedrock model IDs use dots as namespace separators (anthropic.claude-opus-4-7,
us.anthropic.claude-sonnet-4-5-v1:0), not version separators.
normalize_model_name() was unconditionally converting all dots to hyphens,
producing invalid IDs that Bedrock rejects with HTTP 400/404.
This affected both the main agent loop (partially mitigated by
_anthropic_preserve_dots in run_agent.py) and all auxiliary client calls
(compression, session_search, vision, etc.) which go through
_AnthropicCompletionsAdapter and never pass preserve_dots=True.
Fix: add _is_bedrock_model_id() to detect Bedrock namespace prefixes
(anthropic., us., eu., ap., jp., global.) and skip dot-to-hyphen
conversion for these IDs regardless of the preserve_dots flag.
Bug 3 — Stale OAuth token not detected in 'hermes model':
- _model_flow_anthropic used 'has_creds = bool(existing_key)' which treats
any non-empty token (including expired OAuth tokens) as valid.
- Added existing_is_stale_oauth check: if the only credential is an OAuth
token (sk-ant- prefix) with no valid cc_creds fallback, mark it stale
and force the re-auth menu instead of silently accepting a broken token.
Bug 4 — macOS Keychain credentials never read:
- Claude Code >=2.1.114 migrated from ~/.claude/.credentials.json to the
macOS Keychain under service 'Claude Code-credentials'.
- Added _read_claude_code_credentials_from_keychain() using the 'security'
CLI tool; read_claude_code_credentials() now tries Keychain first then
falls back to JSON file.
- Non-Darwin platforms return None from Keychain read immediately.
Tests:
- tests/agent/test_anthropic_keychain.py: 11 cases covering Darwin-only
guard, security command failures, JSON parsing, fallback priority.
- tests/hermes_cli/test_anthropic_model_flow_stale_oauth.py: 8 cases
covering stale OAuth detection, API key passthrough, cc_creds fallback.
Refs: #12905
Two small fixes triggered by a support report where the user saw a
cryptic 'HTTP 400 - Error 400 (Bad Request)!!1' (Google's GFE HTML
error page, not a real API error) on every gemini-2.5-pro request.
The underlying cause was an empty GOOGLE_API_KEY / GEMINI_API_KEY, but
nothing in our output made that diagnosable:
1. hermes_cli/dump.py: the api_keys section enumerated 23 providers but
omitted Google entirely, so users had no way to verify from 'hermes
dump' whether the key was set. Added GOOGLE_API_KEY and GEMINI_API_KEY
rows.
2. agent/gemini_native_adapter.py: GeminiNativeClient.__init__ accepted
an empty/whitespace api_key and stamped it into the x-goog-api-key
header, which made Google's frontend return a generic HTML 400 long
before the request reached the Generative Language backend. Now we
raise RuntimeError at construction with an actionable message
pointing at GOOGLE_API_KEY/GEMINI_API_KEY and aistudio.google.com.
Added a regression test that covers '', ' ', and None.
Concurrent Hermes processes (e.g. cron jobs) refreshing a Nous OAuth token
via resolve_nous_runtime_credentials() write the rotated tokens to auth.json.
The calling process's pool entry becomes stale, and the next refresh against
the already-rotated token triggers a 'refresh token reuse' revocation on
the Nous Portal.
_sync_nous_entry_from_auth_store() reads auth.json under the same lock used
by resolve_nous_runtime_credentials, and adopts the newer token pair before
refreshing the pool entry. This complements #15111 (which preserved the
obtained_at timestamps through seeding).
Partial salvage of #10160 by @konsisumer — only the agent/credential_pool.py
changes + the 3 Nous-specific regression tests. The PR also touched 10
unrelated files (Dockerfile, tips.py, various tool tests) which were
dropped as scope creep.
Regression tests:
- test_sync_nous_entry_from_auth_store_adopts_newer_tokens
- test_sync_nous_entry_noop_when_tokens_match
- test_nous_exhausted_entry_recovers_via_auth_store_sync
The least_used strategy selected entries via min(request_count) but
never incremented the counter. All entries stayed at count=0, so the
strategy degenerated to fill_first behavior with no actual load balancing.
Now increments request_count after each selection and persists the update.
Pass an explicit HOME into Copilot ACP child processes so delegated ACP runs do not fail when the ambient environment is missing HOME.
Prefer the per-profile subprocess home when available, then fall back to HOME, expanduser('~'), pwd.getpwuid(...), and /home/openclaw. Add regression tests for both profile-home preference and clean HOME fallback.
Refs #11068.
Two narrow fixes motivated by #15099.
1. _seed_from_singletons() was dropping obtained_at, agent_key_obtained_at,
expires_in, and friends when seeding device_code pool entries from the
providers.nous singleton. Fresh credentials showed up with
obtained_at=None, which broke downstream freshness-sensitive consumers
(self-heal hooks, pool pruning by age) — they treated just-minted
credentials as older than they actually were and evicted them.
2. When the Nous Portal OAuth 2.1 server returns invalid_grant with
'Refresh token reuse detected' in the error_description, rewrite the
message to explain the likely cause (an external process consumed the
rotated RT without persisting it back) and the mitigation. The generic
reuse message led users to report this as a Hermes persistence bug when
the actual trigger was typically a third-party monitoring script calling
/api/oauth/token directly. Non-reuse errors keep their original server
description untouched.
Closes#15099.
Regression tests:
- tests/agent/test_credential_pool.py::test_nous_seed_from_singletons_preserves_obtained_at_timestamps
- tests/hermes_cli/test_auth_nous_provider.py::test_refresh_token_reuse_detection_surfaces_actionable_message
- tests/hermes_cli/test_auth_nous_provider.py::test_refresh_non_reuse_error_keeps_original_description
Google AI Studio's free tier (<= 250 req/day for gemini-2.5-flash) is
exhausted in a handful of agent turns, so the setup wizard now refuses
to wire up Gemini when the supplied key is on the free tier, and the
runtime 429 handler appends actionable billing guidance.
Setup-time probe (hermes_cli/main.py):
- `_model_flow_api_key_provider` fires one minimal generateContent call
when provider_id == 'gemini' and classifies the response as
free/paid/unknown via x-ratelimit-limit-requests-per-day header or
429 body containing 'free_tier'.
- Free -> print block message, refuse to save the provider, return.
- Paid -> 'Tier check: paid' and proceed.
- Unknown (network/auth error) -> 'could not verify', proceed anyway.
Runtime 429 handler (agent/gemini_native_adapter.py):
- `gemini_http_error` appends billing guidance when the 429 error body
mentions 'free_tier', catching users who bypass setup by putting
GOOGLE_API_KEY directly in .env.
Tests: 21 unit tests for the probe + error path, 4 tests for the
setup-flow block. All 67 existing gemini tests still pass.
PR #14935 added a Codex-aware context resolver but only new lookups
hit the live /models probe. Users who had run Hermes on gpt-5.5 / 5.4
BEFORE that PR already had the wrong value (e.g. 1,050,000 from
models.dev) persisted in ~/.hermes/context_length_cache.yaml, and the
cache-first lookup in get_model_context_length() returns it forever.
Symptom (reported in the wild by Ludwig, min heo, Gaoge on current
main at 6051fba9d, which is AFTER #14935):
* Startup banner shows context usage against 1M
* Compression fires late and then OpenAI hard-rejects with
'context length will be reduced from 1,050,000 to 128,000'
around the real 272k boundary.
Fix: when the step-1 cache returns a value for an openai-codex lookup,
check whether it's >= 400k. Codex OAuth caps every slug at 272k (live
probe values) so anything at or above 400k is definitionally a
pre-#14935 leftover. Drop that entry from the on-disk cache and fall
through to step 5, which runs the live /models probe and repersists
the correct value (or 272k from the hardcoded fallback if the probe
fails). Non-Codex providers and legitimately-cached Codex entries at
272k are untouched.
Changes:
- agent/model_metadata.py:
* _invalidate_cached_context_length() — drop a single entry from
context_length_cache.yaml and rewrite the file.
* Step-1 cache check in get_model_context_length() now gates
provider=='openai-codex' entries >= 400k through invalidation
instead of returning them.
Tests (3 new in TestCodexOAuthContextLength):
- stale 1.05M Codex entry is dropped from disk AND re-resolved
through the live probe to 272k; unrelated cache entries survive.
- fresh 272k Codex entry is respected (no probe call, no invalidation).
- non-Codex 1M entries (e.g. anthropic/claude-opus-4.6 on OpenRouter)
are unaffected — the guard is strictly scoped to openai-codex.
Full tests/agent/test_model_metadata.py: 88 passed.
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.
Gemini's Schema validator requires every `enum` entry to be a string,
even when the parent `type` is integer/number/boolean. Discord's
`auto_archive_duration` parameter (`type: integer, enum: [60, 1440,
4320, 10080]`) tripped this on every request that shipped the full
tool catalog to generativelanguage.googleapis.com, surfacing as
`Gateway: Non-retryable client error: Gemini HTTP 400 (INVALID_ARGUMENT)
Invalid value ... (TYPE_STRING), 60` and aborting the turn.
Sanitize by dropping the `enum` key when the declared type is numeric
or boolean and any entry is non-string. The `type` and `description`
survive, so the model still knows the allowed values; the tool handler
keeps its own runtime validation. Other providers (OpenAI,
OpenRouter, Anthropic) are unaffected — the sanitizer only runs for
native Gemini / cloudcode adapters.
Reported by @selfhostedsoul on Discord with hermes debug share.
Keep auxiliary provider resolution aligned with the switch and persisted main-provider paths when models.dev returns github-copilot slugs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Auxiliary tasks (session_search, flush_memories, approvals, compression,
vision, etc.) that route to a named custom provider declared under
config.yaml 'providers:' with 'api_mode: anthropic_messages' were
silently building a plain OpenAI client and POSTing to
{base_url}/chat/completions, which returns 404 on Anthropic-compatible
gateways that only expose /v1/messages.
Two gaps caused this:
1. hermes_cli/runtime_provider.py::_get_named_custom_provider — the
providers-dict branch (new-style) returned only name/base_url/api_key/
model and dropped api_mode. The legacy custom_providers-list branch
already propagated it correctly. The dict branch now parses and
returns api_mode via _parse_api_mode() in both match paths.
2. agent/auxiliary_client.py::resolve_provider_client — the named
custom provider block at ~L1740 ignored custom_entry['api_mode']
and unconditionally built an OpenAI client (only wrapping for
Codex/Responses). It now mirrors _try_custom_endpoint()'s three-way
dispatch: anthropic_messages → AnthropicAuxiliaryClient (async wrapped
in AsyncAnthropicAuxiliaryClient), codex_responses → CodexAuxiliaryClient,
otherwise plain OpenAI. An explicit task-level api_mode override
still wins over the provider entry's declared api_mode.
Fixes#15033
Tests: tests/agent/test_auxiliary_named_custom_providers.py gains a
TestProvidersDictApiModeAnthropicMessages class covering
- providers-dict preserves valid api_mode
- invalid api_mode values are dropped
- missing api_mode leaves the entry unchanged (no regression)
- resolve_provider_client returns (Async)AnthropicAuxiliaryClient for
api_mode=anthropic_messages
- full chain via get_text_auxiliary_client / get_async_text_auxiliary_client
with an auxiliary.<task> override
- providers without api_mode still use the OpenAI-wire path
- hermes_cli/auth.py: add _default_verify() with macOS Homebrew certifi
fallback (mirrors weixin 3a0ec1d93). Extend env var chain to include
REQUESTS_CA_BUNDLE so one env var works across httpx + requests paths.
- agent/model_metadata.py: add _resolve_requests_verify() reading
HERMES_CA_BUNDLE / REQUESTS_CA_BUNDLE / SSL_CERT_FILE in priority
order. Apply explicit verify= to all 6 requests.get callsites.
- Tests: 18 new unit tests + autouse platform pin on existing
TestResolveVerifyFallback to keep its "returns True" assertions
platform-independent.
Empirically verified against self-signed HTTPS server: requests honors
REQUESTS_CA_BUNDLE only; httpx honors SSL_CERT_FILE only. Hermes now
honors all three everywhere.
Triggered by Discord reports — Nous OAuth SSL failure on macOS
Homebrew Python; custom provider self-signed cert ignored despite
REQUESTS_CA_BUNDLE set in env.
OpenRouter returns a 404 with the specific message
'No endpoints available matching your guardrail restrictions and data
policy. Configure: https://openrouter.ai/settings/privacy'
when a user's account-level privacy setting excludes the only endpoint
serving a model (e.g. DeepSeek V4 Pro, which today is hosted only by
DeepSeek's own endpoint that may log inputs).
Before this change we classified it as model_not_found, which was
misleading (the model exists) and triggered provider fallback (useless —
the same account setting applies to every OpenRouter call).
Now it classifies as a new FailoverReason.provider_policy_blocked with
retryable=False, should_fallback=False. The error body already contains
the fix URL, so the user still gets actionable guidance.
On ChatGPT Codex OAuth every gpt-5.x slug actually caps at 272,000 tokens,
but Hermes was resolving gpt-5.5 / gpt-5.4 to 1,050,000 (from models.dev)
because openai-codex aliases to the openai entry there. At 1.05M the
compressor never fires and requests hard-fail with 'context window
exceeded' around the real 272k boundary.
Verified live against chatgpt.com/backend-api/codex/models:
gpt-5.5, gpt-5.4, gpt-5.4-mini, gpt-5.3-codex, gpt-5.2-codex,
gpt-5.2, gpt-5.1-codex-max → context_window = 272000
Changes:
- agent/model_metadata.py:
* _fetch_codex_oauth_context_lengths() — probe the Codex /models
endpoint with the OAuth bearer token and read context_window per
slug (1h in-memory TTL).
* _resolve_codex_oauth_context_length() — prefer the live probe,
fall back to hardcoded _CODEX_OAUTH_CONTEXT_FALLBACK (all 272k).
* Wire into get_model_context_length() when provider=='openai-codex',
running BEFORE the models.dev lookup (which returns 1.05M). Result
persists via save_context_length() so subsequent lookups skip the
probe entirely.
* Fixed the now-wrong comment on the DEFAULT_CONTEXT_LENGTHS gpt-5.5
entry (400k was never right for Codex; it's the catch-all for
providers we can't probe live).
Tests (4 new in TestCodexOAuthContextLength):
- fallback table used when no token is available (no models.dev leakage)
- live probe overrides the fallback
- probe failure (non-200) falls back to hardcoded 272k
- non-codex providers (openrouter, direct openai) unaffected
Non-codex context resolution is unchanged — the Codex branch only fires
when provider=='openai-codex'.
Fixes a broader class of 'tools.function.parameters is not a valid
moonshot flavored json schema' errors on Nous / OpenRouter aggregators
routing to moonshotai/kimi-k2.6 with MCP tools loaded.
## Moonshot sanitizer (agent/moonshot_schema.py, new)
Model-name-routed (not base-URL-routed) so Nous / OpenRouter users are
covered alongside api.moonshot.ai. Applied in
ChatCompletionsTransport.build_kwargs when is_moonshot_model(model).
Two repairs:
1. Fill missing 'type' on every property / items / anyOf-child schema
node (structural walk — only schema-position dicts are touched, not
container maps like properties/$defs).
2. Strip 'type' at anyOf parents; Moonshot rejects it.
## MCP normalizer hardened (tools/mcp_tool.py)
Draft-07 $ref rewrite from PR #14802 now also does:
- coerce missing / null 'type' on object-shaped nodes (salvages #4897)
- prune 'required' arrays to names that exist in 'properties'
(salvages #4651; Gemini 400s on dangling required)
- apply recursively, not just top-level
These repairs are provider-agnostic so the same MCP schema is valid on
OpenAI, Anthropic, Gemini, and Moonshot in one pass.
## Crash fix: safe getattr for Tool.inputSchema
_convert_mcp_schema now uses getattr(t, 'inputSchema', None) so MCP
servers whose Tool objects omit the attribute entirely no longer abort
registration (salvages #3882).
## Validation
- tests/agent/test_moonshot_schema.py: 27 new tests (model detection,
missing-type fill, anyOf-parent strip, non-mutation, real-world MCP
shape)
- tests/tools/test_mcp_tool.py: 7 new tests (missing / null type,
required pruning, nested repair, safe getattr)
- tests/agent/transports/test_chat_completions.py: 2 new integration
tests (Moonshot route sanitizes, non-Moonshot route doesn't)
- Targeted suite: 49 passed
- E2E via execute_code with a realistic MCP tool carrying all three
Moonshot rejection modes + dangling required + draft-07 refs:
sanitizer produces a schema valid on Moonshot and Gemini
A test in tests/agent/test_credential_pool.py
(test_try_refresh_current_updates_only_current_entry) monkeypatched
refresh_codex_oauth_pure() to return the literal fixture strings
'access-new'/'refresh-new', then executed the real production code path
in agent/credential_pool.py::try_refresh_current which calls
_sync_device_code_entry_to_auth_store → _save_provider_state → writes
to `providers.openai-codex.tokens`. That writer resolves the target via
get_hermes_home()/auth.json. If the test ran with HERMES_HOME unset (direct
pytest invocation, IDE runner bypassing conftest discovery, or any other
sandbox escape), it would overwrite the real user's auth store with the
fixture strings.
Observed in the wild: Teknium's ~/.hermes/auth.json providers.openai-codex.tokens
held 'access-new'/'refresh-new' for five days. His CLI kept working because
the credential_pool entries still held real JWTs, but `hermes model`'s live
discovery path (which reads via resolve_codex_runtime_credentials →
_read_codex_tokens → providers.tokens) was silently 401-ing.
Fixes:
- Delete test_try_refresh_current_updates_only_current_entry. It was the
only test that exercised a writer hitting providers.openai-codex.tokens
with literal stub tokens. The entry-level rotation behavior it asserted
is still covered by test_mark_exhausted_and_rotate_persists_status above.
- Add a seat belt in hermes_cli.auth._auth_file_path(): if PYTEST_CURRENT_TEST
is set AND the resolved path equals the real ~/.hermes/auth.json, raise
with a clear message. In production (no PYTEST_CURRENT_TEST), a single
dict lookup. Any future test that forgets to monkeypatch HERMES_HOME
fails loudly instead of corrupting the user's credentials.
Validation:
- production (no PYTEST_CURRENT_TEST): returns real path, unchanged behavior
- pytest + HERMES_HOME unset (points at real home): raises with message
- pytest + HERMES_HOME=/tmp/...: returns tmp path, tests pass normally
Commit 43de1ca8 removed the _nr_to_assistant_message shim in favor of
duck-typed properties on the ToolCall dataclass. However, the
extra_content property (which carries the Gemini thought_signature) was
omitted from the ToolCall definition. This caused _build_assistant_message
to silently drop the signature via getattr(tc, 'extra_content', None)
returning None, leading to HTTP 400 errors on subsequent turns for all
Gemini 3 thinking models.
Add the extra_content property to ToolCall (matching the existing
call_id and response_item_id pattern) so the thought_signature round-trips
correctly through the transport → agent loop → API replay path.
Credit to @celttechie for identifying the root cause and providing the fix.
Closes#14488
## Merged
Adds MiMo v2.5-pro and v2.5 support to Xiaomi native provider, OpenCode Go, and setup wizard.
### Changes
- Context lengths: added v2.5-pro (1M) and v2.5 (1M), corrected existing MiMo entries to exact values (262144)
- Provider lists: xiaomi, opencode-go, setup wizard
- Vision: upgraded from mimo-v2-omni to mimo-v2.5 (omnimodal)
- Config description updated for XIAOMI_API_KEY
- Tests updated for new vision model preference
### Verification
- 4322 tests passed, 0 new regressions
- Live API tested on Xiaomi portal: basic, reasoning, tool calling, multi-tool, file ops, system prompt, vision — all pass
- Self-review found and fixed 2 issues (redundant vision check, stale HuggingFace context length)
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).
* feat(agent): add PLATFORM_HINTS for matrix, mattermost, and feishu
These platform adapters fully support media delivery (send_image,
send_document, send_voice, send_video) but were missing from
PLATFORM_HINTS, leaving agents unaware of their platform context,
markdown rendering, and MEDIA: tag support.
Salvaged from PR #7370 by Rutimka — wecom excluded since main already
has a more detailed version.
Co-Authored-By: Marco Rutsch <marco@rutimka.de>
* test: add missing Markdown assertion for feishu platform hint
---------
Co-authored-by: Marco Rutsch <marco@rutimka.de>
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#66664. The build_anthropic_kwargs call site
used 'max_tokens or _get_anthropic_max_output(model)', which correctly
falls back when max_tokens is 0 or None (falsy) but lets negative ints
(-1, -500), fractional floats (0.5, 8192.7), NaN, and infinity leak
through to the Anthropic API. Anthropic rejects these with HTTP 400
('max_tokens: must be greater than or equal to 1'), turning a local
config error into a surprise mid-conversation failure.
Add two resolver helpers matching OpenClaw's:
_resolve_positive_anthropic_max_tokens — returns int(value) only if
value is a finite positive number; excludes bools, strings, NaN,
infinity, sub-one positives (floor to 0).
_resolve_anthropic_messages_max_tokens — prefers a positive requested
value, else falls back to the model's output ceiling; raises
ValueError only if no positive budget can be resolved.
The context-window clamp at the call site (max_tokens > context_length)
is preserved unchanged — it handles oversized values; the new resolver
handles non-positive values. These concerns are now cleanly separated.
Tests: 17 new cases covering positive/zero/negative ints, fractional
floats (both >1 and <1), NaN, infinity, booleans, strings, None, and
integration via build_anthropic_kwargs.
Refs: openclaw/openclaw#66664
Mid-stream SSL alerts (bad_record_mac, tls_alert_internal_error, handshake
failures) previously fell through the classifier pipeline to the 'unknown'
bucket because:
- ssl.SSLError type names weren't in _TRANSPORT_ERROR_TYPES (the
isinstance(OSError) catch picks up some but not all SDK-wrapped forms)
- the message-pattern list had no SSL alert substrings
The 'unknown' bucket is still retryable, but: (a) logs tell the user
'unknown' instead of identifying the cause, (b) it bypasses the
transport-specific backoff/fallback logic, and (c) if the SSL error
happens on a large session with a generic 'connection closed' wrapper,
the existing disconnect-on-large-session heuristic would incorrectly
trigger context compression — expensive, and never fixes a transport
hiccup.
Changes:
- Add ssl.SSLError and its subclass type names to _TRANSPORT_ERROR_TYPES
- New _SSL_TRANSIENT_PATTERNS list (separate from _SERVER_DISCONNECT_PATTERNS
so SSL alerts route to timeout, not context_overflow+compress)
- New step 5 in the classifier pipeline: SSL pattern check runs BEFORE
the disconnect check to pre-empt the large-session-compress path
Patterns cover both space-separated ('ssl alert', 'bad record mac')
and underscore-separated ('ERR_SSL_SSL/TLS_ALERT_BAD_RECORD_MAC')
forms. This is load-bearing because OpenSSL 3.x changed the error-code
separator from underscore to slash (e.g. SSLV3_ALERT_BAD_RECORD_MAC →
SSL/TLS_ALERT_BAD_RECORD_MAC) and will likely churn again — matching on
stable alert reason substrings survives future format changes.
Tests (8 new):
- BAD_RECORD_MAC in Python ssl.c format
- OpenSSL 3.x underscore format
- TLSV1_ALERT_INTERNAL_ERROR
- ssl handshake failure
- [SSL: ...] prefix fallback
- Real ssl.SSLError instance
- REGRESSION GUARD: SSL on large session does NOT compress
- REGRESSION GUARD: plain disconnect on large session STILL compresses
Port from cline/cline#10266.
When OpenAI-compatible proxies (OpenRouter, Vercel AI Gateway, Cline)
route Claude models, they sometimes surface the Anthropic-native cache
counters (`cache_read_input_tokens`, `cache_creation_input_tokens`) at
the top level of the `usage` object instead of nesting them inside
`prompt_tokens_details`. Our chat-completions branch of
`normalize_usage()` only read the nested `prompt_tokens_details` fields,
so those responses:
- reported `cache_write_tokens = 0` even when the model actually did a
prompt-cache write,
- reported only some of the cache-read tokens when the proxy exposed them
top-level only,
- overstated `input_tokens` by the missed cache-write amount, which in
turn made cost estimation and the status-bar cache-hit percentage wrong
for Claude traffic going through these gateways.
Now the chat-completions branch tries the OpenAI-standard
`prompt_tokens_details` first and falls back to the top-level
Anthropic-shape fields only if the nested values are absent/zero. The
Anthropic and Codex Responses branches are unchanged.
Regression guards added for three shapes: top-level write + nested read,
top-level-only, and both-present (nested wins).
`is_local_endpoint()` leaned on `ipaddress.is_private`, which classifies
RFC-1918 ranges and link-local as private but deliberately excludes the
RFC 6598 CGNAT block (100.64.0.0/10) — the range Tailscale uses for its
mesh IPs. As a result, Ollama reached over Tailscale (e.g.
`http://100.77.243.5:11434`) was treated as remote and missed the
automatic stream-read / stale-stream timeout bumps, so cold model load
plus long prefill would trip the 300 s watchdog before the first token.
Add a module-level `_TAILSCALE_CGNAT = ipaddress.IPv4Network("100.64.0.0/10")`
(built once) and extend `is_local_endpoint()` to match the block both
via the parsed-`IPv4Address` path and the existing bare-string fallback
(for symmetry with the 10/172/192 checks). Also hoist the previously
function-local `import ipaddress` to module scope now that it's used by
the constant.
Extend `TestIsLocalEndpoint` with a CGNAT positive set (lower bound,
representative host, MagicDNS anchor, upper bound) and a near-miss
negative set (just below 100.64.0.0, just above 100.127.255.255, well
outside the block, and first-octet-wrong).
Anthropic's API can legitimately return content=[] with stop_reason="end_turn"
when the model has nothing more to add after a turn that already delivered the
user-facing text alongside a trivial tool call (e.g. memory write). The transport
validator was treating that as an invalid response, triggering 3 retries that
each returned the same valid-but-empty response, then failing the run with
"Invalid API response after 3 retries."
The downstream normalizer already handles empty content correctly (empty loop
over response.content, content=None, finish_reason="stop"), so the only fix
needed is at the validator boundary.
Tests:
- Empty content + stop_reason="end_turn" → valid (the fix)
- Empty content + stop_reason="tool_use" → still invalid (regression guard)
- Empty content without stop_reason → still invalid (existing behavior preserved)
The 404 branch in _classify_by_status had dead code: the generic
fallback below the _MODEL_NOT_FOUND_PATTERNS check returned the
exact same classification (model_not_found + should_fallback=True),
so every 404 — regardless of message — was treated as a missing model.
This bites local-endpoint users (llama.cpp, Ollama, vLLM) whose 404s
usually mean a wrong endpoint path, proxy routing glitch, or transient
backend issue — not a missing model. Claiming 'model not found' misleads
the next turn and silently falls back to another provider when the real
problem was a URL typo the user should see.
Fix: only classify 404 as model_not_found when the message actually
matches _MODEL_NOT_FOUND_PATTERNS ("invalid model", "model not found",
etc.). Otherwise fall through as unknown (retryable) so the real error
surfaces in the retry loop.
Test updated to match the new behavior. 103 error_classifier tests pass.
- Add configurable retain_tags / retain_source / retain_user_prefix /
retain_assistant_prefix knobs for native Hindsight.
- Thread gateway session identity (user_name, chat_id, chat_name,
chat_type, thread_id) through AIAgent and MemoryManager into
MemoryProvider.initialize kwargs so providers can scope and tag
retained memories.
- Hindsight attaches the new identity fields as retain metadata,
merges per-call tool tags with configured default tags, and uses
the configurable transcript labels for auto-retained turns.
Co-authored-by: Abner <abner.the.foreman@agentmail.to>
Adds a first-class 'stepfun' API-key provider surfaced as Step Plan:
- Support Step Plan setup for both International and China regions
- Discover Step Plan models live from /step_plan/v1/models, with a
small coding-focused fallback catalog when discovery is unavailable
- Thread StepFun through provider metadata, setup persistence, status
and doctor output, auxiliary routing, and model normalization
- Add tests for provider resolution, model validation, metadata
mapping, and StepFun region/model persistence
Based on #6005 by @hengm3467.
Co-authored-by: hengm3467 <100685635+hengm3467@users.noreply.github.com>
* feat(plugins): pluggable image_gen backends + OpenAI provider
Adds a ImageGenProvider ABC so image generation backends register as
bundled plugins under `plugins/image_gen/<name>/`. The plugin scanner
gains three primitives to make this work generically:
- `kind:` manifest field (`standalone` | `backend` | `exclusive`).
Bundled `kind: backend` plugins auto-load — no `plugins.enabled`
incantation. User-installed backends stay opt-in.
- Path-derived keys: `plugins/image_gen/openai/` gets key
`image_gen/openai`, so a future `tts/openai` cannot collide.
- Depth-2 recursion into category namespaces (parent dirs without a
`plugin.yaml` of their own).
Includes `OpenAIImageGenProvider` as the first consumer (gpt-image-1.5
default, plus gpt-image-1, gpt-image-1-mini, DALL-E 3/2). Base64
responses save to `$HERMES_HOME/cache/images/`; URL responses pass
through.
FAL stays in-tree for this PR — a follow-up ports it into
`plugins/image_gen/fal/` so the in-tree `image_generation_tool.py`
slims down. The dispatch shim in `_handle_image_generate` only fires
when `image_gen.provider` is explicitly set to a non-FAL value, so
existing FAL setups are untouched.
- 41 unit tests (scanner recursion, kind parsing, gate logic,
registry, OpenAI payload shapes)
- E2E smoke verified: bundled plugin autoloads, registers, and
`_handle_image_generate` routes to OpenAI when configured
* fix(image_gen/openai): don't send response_format to gpt-image-*
The live API rejects it: 'Unknown parameter: response_format'
(verified 2026-04-21 with gpt-image-1.5). gpt-image-* models return
b64_json unconditionally, so the parameter was both unnecessary and
actively broken.
* feat(image_gen/openai): gpt-image-2 only, drop legacy catalog
gpt-image-2 is the latest/best OpenAI image model (released 2026-04-21)
and there's no reason to expose the older gpt-image-1.5 / gpt-image-1 /
dall-e-3 / dall-e-2 alongside it — slower, lower quality, or awkward
(dall-e-2 squares only). Trim the catalog down to a single model.
Live-verified end-to-end: landscape 1536x1024 render of a Moog-style
synth matches prompt exactly, 2.4MB PNG saved to cache.
* feat(image_gen/openai): expose gpt-image-2 as three quality tiers
Users pick speed/fidelity via the normal model picker instead of a
hidden quality knob. All three tier IDs resolve to the single underlying
gpt-image-2 API model with a different quality parameter:
gpt-image-2-low ~15s fast iteration
gpt-image-2-medium ~40s default
gpt-image-2-high ~2min highest fidelity
Live-measured on OpenAI's API today: 15.4s / 40.8s / 116.9s for the
same 1024x1024 prompt.
Config:
image_gen.openai.model: gpt-image-2-high
# or
image_gen.model: gpt-image-2-low
# or env var for scripts/tests
OPENAI_IMAGE_MODEL=gpt-image-2-medium
Live-verified end-to-end with the low tier: 18.8s landscape render of a
golden retriever in wildflowers, vision-confirmed exact match.
* feat(tools_config): plugin image_gen providers inject themselves into picker
'hermes tools' → Image Generation now shows plugin-registered backends
alongside Nous Subscription and FAL.ai without tools_config.py needing
to know about them. OpenAI appears as a third option today; future
backends appear automatically as they're added.
Mechanism:
- ImageGenProvider gains an optional get_setup_schema() hook
(name, badge, tag, env_vars). Default derived from display_name.
- tools_config._plugin_image_gen_providers() pulls the schemas from
every registered non-FAL plugin provider.
- _visible_providers() appends those rows when rendering the Image
Generation category.
- _configure_provider() handles the new image_gen_plugin_name marker:
writes image_gen.provider and routes to the plugin's list_models()
catalog for the model picker.
- _toolset_needs_configuration_prompt('image_gen') stops demanding a
FAL key when any plugin provider reports is_available().
FAL is skipped in the plugin path because it already has hardcoded
TOOL_CATEGORIES rows — when it gets ported to a plugin in a follow-up
PR the hardcoded rows go away and it surfaces through the same path
as OpenAI.
Verified live: picker shows Nous Subscription / FAL.ai / OpenAI.
Picking OpenAI prompts for OPENAI_API_KEY, then shows the
gpt-image-2-low/medium/high model picker sourced from the plugin.
397 tests pass across plugins/, tools_config, registry, and picker.
* fix(image_gen): close final gaps for plugin-backend parity with FAL
Two small places that still hardcoded FAL:
- hermes_cli/setup.py status line: an OpenAI-only setup showed
'Image Generation: missing FAL_KEY'. Now probes plugin providers
and reports '(OpenAI)' when one is_available() — or falls back to
'missing FAL_KEY or OPENAI_API_KEY' if nothing is configured.
- image_generate tool schema description: said 'using FAL.ai, default
FLUX 2 Klein 9B'. Rewrote provider-neutral — 'backend and model are
user-configured' — and notes the 'image' field can be a URL or an
absolute path, which the gateway delivers either way via
extract_local_files().
Kimi's /coding endpoint speaks the Anthropic Messages protocol but has
its own thinking semantics: when thinking.enabled is sent, Kimi validates
the history and requires every prior assistant tool-call message to carry
OpenAI-style reasoning_content. The Anthropic path never populates that
field, and convert_messages_to_anthropic strips Anthropic thinking blocks
on third-party endpoints — so after one tool-calling turn the next request
fails with:
HTTP 400: thinking is enabled but reasoning_content is missing in
assistant tool call message at index N
Kimi on chat_completions handles thinking via extra_body in
ChatCompletionsTransport (#13503). On the Anthropic route, drop the
parameter entirely and let Kimi drive reasoning server-side.
build_anthropic_kwargs now gates the reasoning_config -> thinking block
on not _is_kimi_coding_endpoint(base_url).
Tests: 8 new parametric tests cover /coding, /coding/v1, /coding/anthropic,
/coding/ (trailing slash), explicit disabled, other third-party endpoints
still getting thinking (MiniMax), native Anthropic unaffected, and the
non-/coding Kimi root route.
Fourth and final transport — completes the transport layer with all four
api_modes covered. Wraps agent/bedrock_adapter.py behind the ProviderTransport
ABC, handles both raw boto3 dicts and already-normalized SimpleNamespace.
Wires all transport methods to production paths in run_agent.py:
- build_kwargs: _build_api_kwargs bedrock branch
- validate_response: response validation, new bedrock_converse branch
- finish_reason: new bedrock_converse branch in finish_reason extraction
Based on PR #13467 by @kshitijk4poor, with one adjustment: the main normalize
loop does NOT add a bedrock_converse branch to invoke normalize_response on
the already-normalized response. Bedrock's normalize_converse_response runs
at the dispatch site (run_agent.py:5189), so the response already has the
OpenAI-compatible .choices[0].message shape by the time the main loop sees
it. Falling through to the chat_completions else branch is correct and
sidesteps a redundant NormalizedResponse rebuild.
Transport coverage — complete:
| api_mode | Transport | build_kwargs | normalize | validate |
|--------------------|--------------------------|:------------:|:---------:|:--------:|
| anthropic_messages | AnthropicTransport | ✅ | ✅ | ✅ |
| codex_responses | ResponsesApiTransport | ✅ | ✅ | ✅ |
| chat_completions | ChatCompletionsTransport | ✅ | ✅ | ✅ |
| bedrock_converse | BedrockTransport | ✅ | ✅ | ✅ |
17 new BedrockTransport tests pass. 117 transport tests total pass.
160 bedrock/converse tests across tests/agent/ pass. Full tests/run_agent/
targeted suite passes (885/885 + 15 skipped; the 1 remaining failure is the
pre-existing test_concurrent_interrupt flake on origin/main).
Third concrete transport — handles the default 'chat_completions' api_mode used
by ~16 OpenAI-compatible providers (OpenRouter, Nous, NVIDIA, Qwen, Ollama,
DeepSeek, xAI, Kimi, custom, etc.). Wires build_kwargs + validate_response to
production paths.
Based on PR #13447 by @kshitijk4poor, with fixes:
- Preserve tool_call.extra_content (Gemini thought_signature) via
ToolCall.provider_data — the original shim stripped it, causing 400 errors
on multi-turn Gemini 3 thinking requests.
- Preserve reasoning_content distinctly from reasoning (DeepSeek/Moonshot) so
the thinking-prefill retry check (_has_structured) still triggers.
- Port Kimi/Moonshot quirks (32000 max_tokens, top-level reasoning_effort,
extra_body.thinking) that landed on main after the original PR was opened.
- Keep _qwen_prepare_chat_messages_inplace alive and call it through the
transport when sanitization already deepcopied (avoids a second deepcopy).
- Skip the back-compat SimpleNamespace shim in the main normalize loop — for
chat_completions, response.choices[0].message is already the right shape
with .content/.tool_calls/.reasoning/.reasoning_content/.reasoning_details
and per-tool-call .extra_content from the OpenAI SDK.
run_agent.py: -239 lines in _build_api_kwargs default branch extracted to the
transport. build_kwargs now owns: codex-field sanitization, Qwen portal prep,
developer role swap, provider preferences, max_tokens resolution (ephemeral >
user > NVIDIA 16384 > Qwen 65536 > Kimi 32000 > anthropic_max_output), Kimi
reasoning_effort + extra_body.thinking, OpenRouter/Nous/GitHub reasoning,
Nous product attribution tags, Ollama num_ctx, custom-provider think=false,
Qwen vl_high_resolution_images, request_overrides.
39 new transport tests (8 build_kwargs, 5 Kimi, 4 validate, 4 normalize
including extra_content regression, 3 cache stats, 3 basic). Tests/run_agent/
targeted suite passes (885/885 + 15 skipped; the 1 remaining failure is the
test_concurrent_interrupt flake present on origin/main).
Wire the auxiliary client (compaction, vision, session search, web extract)
to the Nous Portal's curated recommended-models endpoint when running on
Nous Portal, with a TTL-cached fetch that mirrors how we pull /models for
pricing.
hermes_cli/models.py
- fetch_nous_recommended_models(portal_base_url, force_refresh=False)
10-minute TTL cache, keyed per portal URL (staging vs prod don't
collide). Public endpoint, no auth required. Returns {} on any
failure so callers always get a dict.
- get_nous_recommended_aux_model(vision, free_tier=None, ...)
Tier-aware pick from the payload:
- Paid tier → paidRecommended{Vision,Compaction}Model, falling back
to freeRecommended* when the paid field is null (common during
staged rollouts of new paid models).
- Free tier → freeRecommended* only, never leaks paid models.
When free_tier is None, auto-detects via the existing
check_nous_free_tier() helper (already cached 3 min against
/api/oauth/account). Detection errors default to paid so we never
silently downgrade a paying user.
agent/auxiliary_client.py — _try_nous()
- Replaces the hardcoded xiaomi/mimo free-tier branch with a single call
to get_nous_recommended_aux_model(vision=vision).
- Falls back to _NOUS_MODEL (google/gemini-3-flash-preview) when the
Portal is unreachable or returns a null recommendation.
- The Portal is now the source of truth for aux model selection; the
xiaomi allowlist we used to carry is effectively dead.
Tests (15 new)
- tests/hermes_cli/test_models.py::TestNousRecommendedModels
Fetch caching, per-portal keying, network failure, force_refresh;
paid-prefers-paid, paid-falls-to-free, free-never-leaks-paid,
auto-detect, detection-error → paid default, null/blank modelName
handling.
- tests/agent/test_auxiliary_client.py::TestNousAuxiliaryRefresh
_try_nous honors Portal recommendation for text + vision, falls
back to google/gemini-3-flash-preview on None or exception.
Behavior won't visibly change today — both tier recommendations currently
point at google/gemini-3-flash-preview — but the moment the Portal ships
a better paid recommendation, subscribers pick it up within 10 minutes
without a Hermes release.
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.
The CLI has no attachment channel — MEDIA:<path> tags are only
intercepted on messaging gateway platforms (Telegram, Discord,
Slack, WhatsApp, Signal, BlueBubbles, email, etc.). On the CLI
they render as literal text, which is confusing for users.
The CLI platform hint was the one PLATFORM_HINTS entry that said
nothing about file delivery, so models trained on the messaging
hints would default to MEDIA: tags on the CLI too. Tool schemas
(browser_tool, tts_tool, etc.) also recommend MEDIA: generically.
Extend the CLI hint to explicitly discourage MEDIA: tags and tell
the agent to reference files by plain absolute path instead.
Add a regression test asserting the CLI hint carries negative
guidance about MEDIA: while messaging hints keep positive guidance.
Adds role='leaf'|'orchestrator' to delegate_task. With max_spawn_depth>=2,
an orchestrator child retains the 'delegation' toolset and can spawn its
own workers; leaf children cannot delegate further (identical to today).
Default posture is flat — max_spawn_depth=1 means a depth-0 parent's
children land at the depth-1 floor and orchestrator role silently
degrades to leaf. Users opt into nested delegation by raising
max_spawn_depth to 2 or 3 in config.yaml.
Also threads acp_command/acp_args through the main agent loop's delegate
dispatch (previously silently dropped in the schema) via a new
_dispatch_delegate_task helper, and adds a DelegateEvent enum with
legacy-string back-compat for gateway/ACP/CLI progress consumers.
Config (hermes_cli/config.py defaults):
delegation.max_concurrent_children: 3 # floor-only, no upper cap
delegation.max_spawn_depth: 1 # 1=flat (default), 2-3 unlock nested
delegation.orchestrator_enabled: true # global kill switch
Salvaged from @pefontana's PR #11215. Overrides vs. the original PR:
concurrency stays at 3 (PR bumped to 5 + cap 8 — we keep the floor only,
no hard ceiling); max_spawn_depth defaults to 1 (PR defaulted to 2 which
silently enabled one level of orchestration for every user).
Co-authored-by: pefontana <fontana.pedro93@gmail.com>
file_safety now uses profile-aware get_hermes_home(), so the test
fixture must override HERMES_HOME too — otherwise it resolves to the
conftest's isolated tempdir and the hub-cache path doesn't match.
* feat(skills): inject absolute skill dir and expand ${HERMES_SKILL_DIR} templates
When a skill loads, the activation message now exposes the absolute
skill directory and substitutes ${HERMES_SKILL_DIR} /
${HERMES_SESSION_ID} tokens in the SKILL.md body, so skills with
bundled scripts can instruct the agent to run them by absolute path
without an extra skill_view round-trip.
Also adds opt-in inline-shell expansion: !`cmd` snippets in SKILL.md
are pre-executed (with the skill directory as CWD) and their stdout is
inlined into the message before the agent reads it. Off by default —
enable via skills.inline_shell in config.yaml — because any snippet
runs on the host without approval.
Changes:
- agent/skill_commands.py: template substitution, inline-shell
expansion, absolute skill-dir header, supporting-files list now
shows both relative and absolute forms.
- hermes_cli/config.py: new skills.template_vars,
skills.inline_shell, skills.inline_shell_timeout knobs.
- tests/agent/test_skill_commands.py: coverage for header, both
template tokens (present and missing session id), template_vars
disable, inline-shell default-off, enabled, CWD, and timeout.
- website/docs/developer-guide/creating-skills.md: documents the
template tokens, the absolute-path header, and the opt-in inline
shell with its security caveat.
Validation: tests/agent/ 1591 passed (includes 9 new tests).
E2E: loaded a real skill in an isolated HERMES_HOME; confirmed
${HERMES_SKILL_DIR} resolves to the absolute path, ${HERMES_SESSION_ID}
resolves to the passed task_id, !`date` runs when opt-in is set, and
stays literal when it isn't.
* feat(terminal): source ~/.bashrc (and user-listed init files) into session snapshot
bash login shells don't source ~/.bashrc, so tools that install themselves
there — nvm, asdf, pyenv, cargo, custom PATH exports — stay invisible to
the environment snapshot Hermes builds once per session. Under systemd
or any context with a minimal parent env, that surfaces as
'node: command not found' in the terminal tool even though the binary
is reachable from every interactive shell on the machine.
Changes:
- tools/environments/local.py: before the login-shell snapshot bootstrap
runs, prepend guarded 'source <file>' lines for each resolved init
file. Missing files are skipped, each source is wrapped with a
'[ -r ... ] && . ... || true' guard so a broken rc can't abort the
bootstrap.
- hermes_cli/config.py: new terminal.shell_init_files (explicit list,
supports ~ and ${VAR}) and terminal.auto_source_bashrc (default on)
knobs. When shell_init_files is set it takes precedence; when it's
empty and auto_source_bashrc is on, ~/.bashrc gets auto-sourced.
- tests/tools/test_local_shell_init.py: 10 tests covering the resolver
(auto-bashrc, missing file, explicit override, ~/${VAR} expansion,
opt-out) and the prelude builder (quoting, guarded sourcing), plus
a real-LocalEnvironment snapshot test that confirms exports in the
init file land in subsequent commands' environment.
- website/docs/reference/faq.md: documents the fix in Troubleshooting,
including the zsh-user pattern of sourcing ~/.zshrc or nvm.sh
directly via shell_init_files.
Validation: 10/10 new tests pass; tests/tools/test_local_*.py 40/40
pass; tests/agent/ 1591/1591 pass; tests/hermes_cli/test_config.py
50/50 pass. E2E in an isolated HERMES_HOME: confirmed that a fake
~/.bashrc setting a marker var and PATH addition shows up in a real
LocalEnvironment().execute() call, that auto_source_bashrc=false
suppresses it, that an explicit shell_init_files entry wins over the
auto default, and that a missing bashrc is silently skipped.
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).
Add agent/transports/types.py with three shared dataclasses:
- NormalizedResponse: content, tool_calls, finish_reason, reasoning, usage, provider_data
- ToolCall: id, name, arguments, provider_data (per-tool-call protocol metadata)
- Usage: prompt_tokens, completion_tokens, total_tokens, cached_tokens
Add normalize_anthropic_response_v2() to anthropic_adapter.py — wraps the
existing v1 function and maps its output to NormalizedResponse. One call site
in run_agent.py (the main normalize branch) uses v2 with a back-compat shim
to SimpleNamespace for downstream code.
No ABC, no registry, no streaming, no client lifecycle. Those land in PR 3
with the first concrete transport (AnthropicTransport).
46 new tests:
- test_types.py: dataclass construction, build_tool_call, map_finish_reason
- test_anthropic_normalize_v2.py: v1-vs-v2 regression tests (text, tools,
thinking, mixed, stop reasons, mcp prefix stripping, edge cases)
Part of the provider transport refactor (PR 2 of 9).
Users can declare shell scripts in config.yaml under a hooks: block that
fire on plugin-hook events (pre_tool_call, post_tool_call, pre_llm_call,
subagent_stop, etc). Scripts receive JSON on stdin, can return JSON on
stdout to block tool calls or inject context pre-LLM.
Key design:
- Registers closures on existing PluginManager._hooks dict — zero changes
to invoke_hook() call sites
- subprocess.run(shell=False) via shlex.split — no shell injection
- First-use consent per (event, command) pair, persisted to allowlist JSON
- Bypass via --accept-hooks, HERMES_ACCEPT_HOOKS=1, or hooks_auto_accept
- hermes hooks list/test/revoke/doctor CLI subcommands
- Adds subagent_stop hook event fired after delegate_task children exit
- Claude Code compatible response shapes accepted
Cherry-picked from PR #13143 by @pefontana.
Pass the user's configured api_key through local-server detection and
context-length probes (detect_local_server_type, _query_local_context_length,
query_ollama_num_ctx) and use LM Studio's native /api/v1/models endpoint in
fetch_endpoint_model_metadata when a loaded instance is present — so the
probed context length is the actual runtime value the user loaded the model
at, not just the model's theoretical max.
Helps local-LLM users whose auto-detected context length was wrong, causing
compression failures and context-overrun crashes.
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).
* feat(security): URL query param + userinfo + form body redaction
Port from nearai/ironclaw#2529.
Hermes already has broad value-shape coverage in agent/redact.py
(30+ vendor prefixes, JWTs, DB connstrs, etc.) but missed three
key-name-based patterns that catch opaque tokens without recognizable
prefixes:
1. URL query params - OAuth callback codes (?code=...),
access_token, refresh_token, signature, etc. These are opaque and
won't match any prefix regex. Now redacted by parameter NAME.
2. URL userinfo (https://user:pass@host) - for non-DB schemes. DB
schemes were already handled by _DB_CONNSTR_RE.
3. Form-urlencoded body (k=v pairs joined by ampersands) -
conservative, only triggers on clean pure-form inputs with no
other text.
Sensitive key allowlist matches ironclaw's (exact case-insensitive,
NOT substring - so token_count and session_id pass through).
Tests: +20 new test cases across 3 test classes. All 75 redact tests
pass; gateway/test_pii_redaction and tools/test_browser_secret_exfil
also green.
Known pre-existing limitation: _ENV_ASSIGN_RE greedy match swallows
whole all-caps ENV-style names + trailing text when followed by
another assignment. Left untouched here (out of scope); URL query
redaction handles the lowercase case.
* feat: replace kimi-k2.5 with kimi-k2.6 on OpenRouter and Nous Portal
Update model catalogs for OpenRouter (fallback snapshot), Nous Portal,
and NVIDIA NIM to reference moonshotai/kimi-k2.6. Add kimi-k2.6 to
the fixed-temperature frozenset in auxiliary_client.py so the 0.6
contract is enforced on aggregator routings.
Native Moonshot provider lists (kimi-coding, kimi-coding-cn, moonshot,
opencode-zen, opencode-go) are unchanged — those use Moonshot's own
model IDs which are unaffected.
Adds regression tests for list-typed, int-typed, and None-typed message
fields on top of the dict-typed coverage from #11496. Guards against
other provider quirks beyond the original Pydantic validation case.
Credit to @elmatadorgh (#11264) for the broader type coverage idea.
When API providers return Pydantic-style validation errors where
body['message'] or body['error']['message'] is a dict (e.g.
{"detail": [...]}), the error classifier was crashing with
AttributeError: 'dict' object has no attribute 'lower'.
The 'or ""' fallback only handles None/falsy values. A non-empty
dict is truthy and passes through to .lower(), which fails.
Fix: Wrap all 5 call sites with str() before calling .lower().
This is a no-op for strings and safely converts dicts to their
repr for pattern matching (no false positives on classification
patterns like 'rate limit', 'context length', etc.).
Closes#11233
The streaming translator in agent/gemini_cloudcode_adapter.py keyed OpenAI
tool-call indices by function name, so when the model emitted multiple
parallel functionCall parts with the same name in a single turn (e.g.
three read_file calls in one response), they all collapsed onto index 0.
Downstream aggregators that key chunks by index would overwrite or drop
all but the first call.
Replace the name-keyed dict with a per-stream counter that persists across
SSE events. Each functionCall part now gets a fresh, unique index,
matching the non-streaming path which already uses enumerate(parts).
Add TestTranslateStreamEvent covering parallel-same-name calls, index
persistence across events, and finish-reason promotion to tool_calls.
When the model omits old_text on memory replace/remove, the tool preview
rendered as '~memory: ""' / '-memory: ""', which obscured what went wrong.
Render '<missing old_text>' in that case so the failure mode is legible
in the activity feed.
Narrow salvage from #12456 / #12831 — only the display-layer fix, not the
schema/API changes.
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>
Bedrock rejects ``global-anthropic-claude-opus-4-7`` with ``HTTP 400:
The provided model identifier is invalid`` because its inference
profile IDs embed structural dots
(``global.anthropic.claude-opus-4-7``) that ``normalize_model_name``
was converting to hyphens. ``AIAgent._anthropic_preserve_dots`` did
not include ``bedrock`` in its provider allowlist, so every Claude-on-
Bedrock request through the AnthropicBedrock SDK path shipped with
the mangled model ID and failed.
Root cause
----------
``run_agent.py:_anthropic_preserve_dots`` (previously line 6589)
controls whether ``agent.anthropic_adapter.normalize_model_name``
converts dots to hyphens. The function listed Alibaba, MiniMax,
OpenCode Go/Zen and ZAI but not Bedrock, so when a user set
``provider: bedrock`` with a dotted inference-profile model the flag
returned False and ``normalize_model_name`` mangled every dot in the
ID. All four call sites in run_agent.py
(``build_anthropic_kwargs`` + three fallback / review / summary paths
at lines 6707, 7343, 8408, 8440) read from this same helper.
The bug shape matches #5211 for opencode-go, which was fixed in commit
f77be22c by extending this same allowlist.
Fix
---
* Add ``"bedrock"`` to the provider allowlist.
* Add ``"bedrock-runtime."`` to the base-URL heuristic as
defense-in-depth, so a custom-provider-shaped config with
``base_url: https://bedrock-runtime.<region>.amazonaws.com`` also
takes the preserve-dots path even if ``provider`` isn't explicitly
set to ``"bedrock"``. This mirrors how the code downstream at
run_agent.py:759 already treats either signal as "this is Bedrock".
Bedrock model ID shapes covered
-------------------------------
| Shape | Preserved |
| --- | --- |
| ``global.anthropic.claude-opus-4-7`` (reporter's exact ID) | ✓ |
| ``us.anthropic.claude-sonnet-4-5-20250929-v1:0`` | ✓ |
| ``apac.anthropic.claude-haiku-4-5`` | ✓ |
| ``anthropic.claude-3-5-sonnet-20241022-v2:0`` (foundation) | ✓ |
| ``eu.anthropic.claude-3-5-sonnet`` (regional inference profile) | ✓ |
Non-Claude Bedrock models (Nova, Llama, DeepSeek) take the
``bedrock_converse`` / boto3 path which does not call
``normalize_model_name``, so they were never affected by this bug
and remain unaffected by the fix.
Narrow scope — explicitly not changed
-------------------------------------
* ``bedrock_converse`` path (non-Claude Bedrock models) — already
correct; no ``normalize_model_name`` in that pipeline.
* Provider aliases (``aws``, ``aws-bedrock``, ``amazon``,
``amazon-bedrock``) — if a user bypasses the alias-normalization
pipeline and passes ``provider="aws"`` directly, the base-URL
heuristic still catches it because Bedrock always uses a
``bedrock-runtime.`` endpoint. Adding the aliases themselves to the
provider set is cheap but would be scope creep for this fix.
* No other places in ``agent/anthropic_adapter.py`` mangle dots, so
the fix is confined to ``_anthropic_preserve_dots``.
Regression coverage
-------------------
``tests/agent/test_bedrock_integration.py`` gains three new classes:
* ``TestBedrockPreserveDotsFlag`` (5 tests): flag returns True for
``provider="bedrock"`` and for Bedrock runtime URLs (us-east-1 and
ap-northeast-2 — the reporter's region); returns False for non-
Bedrock AWS URLs like ``s3.us-east-1.amazonaws.com``; canary that
Anthropic-native still returns False.
* ``TestBedrockModelNameNormalization`` (5 tests): every documented
Bedrock model-ID shape survives ``normalize_model_name`` with the
flag on; inverse canary pins that ``preserve_dots=False`` still
mangles (so a future refactor can't decouple the flag from its
effect).
* ``TestBedrockBuildAnthropicKwargsEndToEnd`` (2 tests): integration
through ``build_anthropic_kwargs`` shows the reporter's exact model
ID ends up unmangled in the outgoing kwargs.
Three of the new flag tests fail on unpatched ``origin/main`` with
``assert False is True`` (preserve-dots returning False for Bedrock),
confirming the regression is caught.
Validation
----------
``source venv/bin/activate && python -m pytest
tests/agent/test_bedrock_integration.py tests/agent/test_minimax_provider.py
-q`` -> 84 passed (40 new bedrock tests + 44 pre-existing, including
the minimax canaries that pin the pattern this fix mirrors).
CI-aligned broad suite: 12827 passed, 39 skipped, 19 pre-existing
baseline failures (all reproduce on clean ``origin/main``; none in
the touched code path).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Follow-up to #12144. That PR standardized the kimi-k2.* temperature lock
against the Coding Plan endpoint (api.kimi.com/coding/v1) docs, where
non-thinking models require 0.6. Verified empirically against Moonshot
(April 2026) that the public chat endpoint (api.moonshot.ai/v1) has a
different contract for kimi-k2.5: it only accepts temperature=1, and rejects
0.6 with:
HTTP 400 "invalid temperature: only 1 is allowed for this model"
Users hit the public endpoint when KIMI_API_KEY is a legacy sk-* key (the
sk-kimi-* prefix routes to Coding Plan — see hermes_cli/auth.py). So for
Coding Plan subscribers the fix from #12144 is correct, but for public-API
users it reintroduces the exact 400 reported in #9125.
Reproduction on api.moonshot.ai/v1 + kimi-k2.5:
temperature=1.0 → 200 OK
temperature=0.6 → 400 "only 1 is allowed" ← #12144 default
temperature=None → 200 OK
Other kimi-k2.* models are unaffected empirically — turbo-preview accepts
0.6 and thinking-turbo accepts 1.0 on both endpoints — so only kimi-k2.5
diverges.
Fix: thread the client's actual base_url through _build_call_kwargs (the
parameter already existed but callers passed config-level resolved_base_url;
for auto-detected routes that was often empty). _fixed_temperature_for_model
now checks api.moonshot.ai first via an explicit _KIMI_PUBLIC_API_OVERRIDES
map, then falls back to the Coding Plan defaults. Tests parametrize over
endpoint + model to lock both contracts.
Closes#9125.
Smart model routing (auto-routing short/simple turns to a cheap model
across providers) was opt-in and disabled by default. This removes the
feature wholesale: the routing module, its config keys, docs, tests, and
the orchestration scaffolding it required in cli.py / gateway/run.py /
cron/scheduler.py.
The /fast (Priority Processing / Anthropic fast mode) feature kept its
hooks into _resolve_turn_agent_config — those still build a route dict
and attach request_overrides when the model supports it; the route now
just always uses the session's primary model/provider rather than
running prompts through choose_cheap_model_route() first.
Also removed:
- DEFAULT_CONFIG['smart_model_routing'] block and matching commented-out
example sections in hermes_cli/config.py and cli-config.yaml.example
- _load_smart_model_routing() / self._smart_model_routing on GatewayRunner
- self._smart_model_routing / self._active_agent_route_signature on
HermesCLI (signature kept; just no longer initialised through the
smart-routing pipeline)
- route_label parameter on HermesCLI._init_agent (only set by smart
routing; never read elsewhere)
- 'Smart Model Routing' section in website/docs/integrations/providers.md
- tip in hermes_cli/tips.py
- entries in hermes_cli/dump.py + hermes_cli/web_server.py
- row in skills/autonomous-ai-agents/hermes-agent/SKILL.md
Tests:
- Deleted tests/agent/test_smart_model_routing.py
- Rewrote tests/agent/test_credential_pool_routing.py to target the
simplified _resolve_turn_agent_config directly (preserves credential
pool propagation + 429 rotation coverage)
- Dropped 'cheap model' test from test_cli_provider_resolution.py
- Dropped resolve_turn_route patches from cli + gateway test_fast_command
— they now exercise the real method end-to-end
- Removed _smart_model_routing stub assignments from gateway/cron test
helpers
Targeted suites: 74/74 in the directly affected test files;
tests/agent + tests/cron + tests/cli pass except 5 failures that
already exist on main (cron silent-delivery + alias quick-command).
- only use the native adapter for the canonical Gemini native endpoint
- keep custom and /openai base URLs on the OpenAI-compatible path
- preserve Hermes keepalive transport injection for native Gemini clients
- stabilize streaming tool-call replay across repeated SSE events
- add follow-up tests for base_url precedence, async streaming, and duplicate tool-call chunks
- add a native Gemini adapter over generateContent/streamGenerateContent
- switch the built-in gemini provider off the OpenAI-compatible endpoint
- preserve thought signatures and native functionResponse replay
- route auxiliary Gemini clients through the same adapter
- add focused unit coverage plus native-provider integration checks
The cherry-picked salvage (admin28980's commit) added codex headers only on the
primary chat client path, with two inaccuracies:
- originator was 'hermes-agent' — Cloudflare whitelists codex_cli_rs,
codex_vscode, codex_sdk_ts, and Codex* prefixes. 'hermes-agent' isn't on
the list, so the header had no mitigating effect on the 403 (the
account-id header alone may have been carrying the fix).
- account-id header was 'ChatGPT-Account-Id' — upstream codex-rs auth.rs
uses canonical 'ChatGPT-Account-ID' (PascalCase, trailing -ID).
Also, the auxiliary client (_try_codex + resolve_provider_client raw_codex
branch) constructs OpenAI clients against the same chatgpt.com endpoint with
no default headers at all — so compression, title generation, vision, session
search, and web_extract all still 403 from VPS IPs.
Consolidate the header set into _codex_cloudflare_headers() in
agent/auxiliary_client.py (natural home next to _read_codex_access_token and
the existing JWT decode logic) and call it from all four insertion points:
- run_agent.py: AIAgent.__init__ (initial construction)
- run_agent.py: _apply_client_headers_for_base_url (credential rotation)
- agent/auxiliary_client.py: _try_codex (aux client)
- agent/auxiliary_client.py: resolve_provider_client raw_codex branch
Net: -36/+55 lines, -25 lines of duplicated inline JWT decode replaced by a
single helper. User-Agent switched to 'codex_cli_rs/0.0.0 (Hermes Agent)' to
match the codex-rs shape while keeping product attribution.
Tests in tests/agent/test_codex_cloudflare_headers.py cover:
- originator value, User-Agent shape, canonical header casing
- account-ID extraction from a real JWT fixture
- graceful handling of malformed / non-string / claim-missing tokens
- wiring at all four insertion points (primary init, rotation, both aux paths)
- non-chatgpt base URLs (openrouter) do NOT get codex headers
- switching away from chatgpt.com drops the headers
Several correctness and cost-safety fixes to the Honcho dialectic path
after a multi-turn investigation surfaced a chain of silent failures:
- dialecticCadence default flipped 3 → 1. PR #10619 changed this from 1 to
3 for cost, but existing installs with no explicit config silently went
from per-turn dialectic to every-3-turns on upgrade. Restores pre-#10619
behavior; 3+ remains available for cost-conscious setups. Docs + wizard
+ status output updated to match.
- Session-start prewarm now consumed. Previously fired a .chat() on init
whose result landed in HonchoSessionManager._dialectic_cache and was
never read — pop_dialectic_result had zero call sites. Turn 1 paid for
a duplicate synchronous dialectic. Prewarm now writes directly to the
plugin's _prefetch_result via _prefetch_lock so turn 1 consumes it with
no extra call.
- Prewarm is now dialecticDepth-aware. A single-pass prewarm can return
weak output on cold peers; the multi-pass audit/reconcile cycle is
exactly the case dialecticDepth was built for. Prewarm now runs the
full configured depth in the background.
- Silent dialectic failure no longer burns the cadence window.
_last_dialectic_turn now advances only when the result is non-empty.
Empty result → next eligible turn retries immediately instead of
waiting the full cadence gap.
- Thread pile-up guard. queue_prefetch skips when a prior dialectic
thread is still in-flight, preventing stacked races on _prefetch_result.
- First-turn sync timeout is recoverable. Previously on timeout the
background thread's result was stored in a dead local list. Now the
thread writes into _prefetch_result under lock so the next turn
picks it up.
- Cadence gate applies uniformly. At cadence=1 the old "cadence > 1"
guard let first-turn sync + same-turn queue_prefetch both fire.
Gate now always applies.
- Restored query-length reasoning-level scaling, dropped in 9a0ab34c.
Scales dialecticReasoningLevel up on longer queries (+1 at ≥120 chars,
+2 at ≥400), clamped at reasoningLevelCap. Two new config keys:
`reasoningHeuristic` (bool, default true) and `reasoningLevelCap`
(string, default "high"; previously parsed but never enforced).
Respects dialecticDepthLevels and proportional lighter-early passes.
- Restored short-prompt skip, dropped in ef7f3156. One-word
acknowledgements ("ok", "y", "thanks") and slash commands bypass
both injection and dialectic fire.
- Purged dead code in session.py: prefetch_dialectic, _dialectic_cache,
set_dialectic_result, pop_dialectic_result — all unused after prewarm
refactor.
Tests: 542 passed across honcho_plugin/, agent/test_memory_provider.py,
and run_agent/test_run_agent.py. New coverage:
- TestTrivialPromptHeuristic (classifier + prefetch/queue skip)
- TestDialecticCadenceAdvancesOnSuccess (empty-result retry, pile-up guard)
- TestSessionStartDialecticPrewarm (prewarm consumed, sync fallback)
- TestReasoningHeuristic (length bumps, cap clamp, interaction with depth)
- TestDialecticLifecycleSmoke (end-to-end 8-turn session walk)
Pass 3 of `_prune_old_tool_results` previously shrunk long `function.arguments`
blobs by slicing the raw JSON string at byte 200 and appending the literal
text `...[truncated]`. That routinely produced payloads like::
{"path": "/foo.md", "content": "# Long markdown
...[truncated]
— an unterminated string with no closing brace. Strict providers (observed
on MiniMax) reject this as `invalid function arguments json string` with a
non-retryable 400. Because the broken call survives in the session history,
every subsequent turn re-sends the same malformed payload and gets the same
400, locking the session into a re-send loop until the call falls out of
the window.
Fix: parse the arguments first, shrink long string leaves inside the parsed
structure, and re-serialise. Non-string values (paths, ints, booleans, lists)
pass through intact. Arguments that are not valid JSON to begin with (rare,
some backends use non-JSON tool args) are returned unchanged rather than
replaced with something neither we nor the provider can parse.
Observed in the wild: a `write_file` with ~800 chars of markdown `content`
triggered this on a real session against MiniMax-M2.7; every turn after
compression got rejected until the session was manually reset.
Tests:
- 7 direct tests of `_truncate_tool_call_args_json` covering valid-JSON
output, non-JSON pass-through, nested structures, non-string leaves,
scalar JSON, and Unicode preservation
- 1 end-to-end test through `_prune_old_tool_results` Pass 3 that
reproduces the exact failure payload shape from the incident
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(kimi): force fixed temperature on kimi-k2.* models (k2.5, thinking, turbo)
The prior override only matched the literal model name "kimi-for-coding",
but Moonshot's coding endpoint is hit with real model IDs such as
`kimi-k2.5`, `kimi-k2-turbo-preview`, `kimi-k2-thinking`, etc. Those
requests bypassed the override and kept the caller's temperature, so
Moonshot returns HTTP 400 "invalid temperature: only 0.6 is allowed for
this model" (or 1.0 for thinking variants).
Match the whole kimi-k2.* family:
* kimi-k2-thinking / kimi-k2-thinking-turbo -> 1.0 (thinking mode)
* all other kimi-k2.* -> 0.6 (non-thinking / instant mode)
Also accept an optional vendor prefix (e.g. `moonshotai/kimi-k2.5`) so
aggregator routings are covered.
* refactor(kimi): whitelist-match kimi coding models instead of prefix
Addresses review feedback on PR #12144.
- Replace `startswith("kimi-k2")` with explicit frozensets sourced from
Moonshot's kimi-for-coding model list. The prefix match would have also
clamped `kimi-k2-instruct` / `kimi-k2-instruct-0905`, which are the
separate non-coding K2 family with variable temperature (recommended 0.6
but not enforced — see huggingface.co/moonshotai/Kimi-K2-Instruct).
- Confirmed via platform.kimi.ai docs that all five coding models
(k2.5, k2-turbo-preview, k2-0905-preview, k2-thinking, k2-thinking-turbo)
share the fixed-temperature lock, so the preview-model mapping is no
longer an assumption.
- Drop the fragile `"thinking" in bare` substring test for a set lookup.
- Log a debug line on each override so operators can see when Hermes
silently rewrites temperature.
- Update class docstring. Extend the negative test to parametrize over
kimi-k2-instruct, Kimi-K2-Instruct-0905, and a hypothetical future
kimi-k2-experimental name — all must keep the caller's temperature.
Seven test files were asserting against older function signatures and
behaviors. CI has been red on main because of accumulated test debt
from other PRs; this catches the tests up.
- tests/agent/test_subagent_progress.py: _build_child_progress_callback
now takes (task_index, goal, parent_agent, task_count=1); update all
call sites and rewrite tests that assumed the old 'batch-only' relay
semantics (now relays per-tool AND flushes a summary at BATCH_SIZE).
Renamed test_thinking_not_relayed_to_gateway → test_thinking_relayed_to_gateway
since thinking IS now relayed as subagent.thinking.
- tests/tools/test_delegate.py: _build_child_agent now requires
task_count; add task_count=1 to all 8 call sites.
- tests/cli/test_reasoning_command.py: AIAgent gained _stream_callback;
stub it on the two test agent helpers that use spec=AIAgent / __new__.
- tests/hermes_cli/test_cmd_update.py: cmd_update now runs npm install
in repo root + ui-tui/ + web/ and 'npm run build' in web/; assert
all four subprocess calls in the expected order.
- tests/hermes_cli/test_model_validation.py: dissimilar unknown models
now return accepted=False (previously True with warning); update
both affected tests.
- tests/tools/test_registry.py: include feishu_doc_tool and
feishu_drive_tool in the expected builtin tool set.
- tests/gateway/test_voice_command.py: missing-voice-deps message now
suggests 'pip install PyNaCl' not 'hermes-agent[messaging]'.
411/411 pass locally across these 7 files.
Before: aggregator users (OpenRouter / Nous Portal) running 'auto'
routing for auxiliary tasks — compression, vision, web extraction,
session search, etc. — got routed to a cheap provider-side default
model (Gemini Flash). Non-aggregator users already got their main
model. Behavior was inconsistent and surprising — users picked
Claude / GPT / their preferred model, but side tasks ran on
Gemini Flash.
After: 'auto' means "use my main chat model" for every user,
regardless of provider type. Only when the main provider has no
working client does the fallback chain run (OpenRouter → Nous →
custom → Codex → API-key providers). Explicit per-task overrides
in config.yaml (auxiliary.<task>.provider / .model) still win —
they are a hard constraint, not subject to the auto policy.
Vision auto-detection follows the same policy: try main provider +
main model first (with _PROVIDER_VISION_MODELS overrides preserved
for providers like xiaomi and zai that ship a dedicated multimodal
model distinct from their chat model). Aggregator strict vision
backends are fallbacks, not the primary path.
Changes:
- agent/auxiliary_client.py: _resolve_auto() drops the
`_AGGREGATOR_PROVIDERS` guard. resolve_vision_provider_client()
auto branch unifies aggregator and exotic-provider paths —
everyone goes through resolve_provider_client() with main_model.
Dead _AGGREGATOR_PROVIDERS constant removed (was only used by
the guard we just removed).
- hermes_cli/main.py: aux config menu copy updated to reflect
the new semantics ("'auto' means 'use my main model'").
- tests/agent/test_auxiliary_main_first.py: 12 regression tests
covering OpenRouter/Nous/DeepSeek main paths, runtime-override
wins, explicit-config wins, vision override preservation for
exotic providers, and fallback-chain activation when the main
provider has no working client.
Co-authored-by: teknium1 <teknium@nousresearch.com>
Google-side 429 Code Assist errors now flow through Hermes' normal rate-limit
path (status_code on the exception, Retry-After preserved via error.response)
instead of being opaque RuntimeErrors. User sees a one-line capacity message
instead of a 500-char JSON dump.
Changes
- CodeAssistError grows status_code / response / retry_after / details attrs.
_extract_status_code in error_classifier picks up status_code and classifies
429 as FailoverReason.rate_limit, so fallback_providers triggers the same
way it does for SDK errors. run_agent.py line ~10428 already walks
error.response.headers for Retry-After — preserving the response means that
path just works.
- _gemini_http_error parses the Google error envelope (error.status +
error.details[].reason from google.rpc.ErrorInfo, retryDelay from
google.rpc.RetryInfo). MODEL_CAPACITY_EXHAUSTED / RESOURCE_EXHAUSTED / 404
model-not-found each produce a human-readable message; unknown shapes fall
back to the previous raw-body format.
- Drop gemma-4-26b-it from hermes_cli/models.py, hermes_cli/setup.py, and
agent/model_metadata.py — Google returned 404 for it today in local repro.
Kept gemma-4-31b-it (capacity-constrained but not retired).
Validation
| | Before | After |
|---------------------------|--------------------------------|-------------------------------------------|
| Error message | 'Code Assist returned HTTP 429: {500 chars JSON}' | 'Gemini capacity exhausted for gemini-2.5-pro (Google-side throttle...)' |
| status_code on error | None (opaque RuntimeError) | 429 |
| Classifier reason | unknown (string-match fallback) | FailoverReason.rate_limit |
| Retry-After honored | ignored | extracted from RetryInfo or header |
| gemma-4-26b-it picker | advertised (404s on Google) | removed |
Unit + E2E tests cover non-streaming 429, streaming 429, 404 model-not-found,
Retry-After header fallback, malformed body, and classifier integration.
Targeted suites: tests/agent/test_gemini_cloudcode.py (81 tests), full
tests/hermes_cli (2203 tests) green.
Co-authored-by: teknium1 <teknium@nousresearch.com>
First pass of test-suite reduction to address flaky CI and bloat.
Removed tests that fall into these change-detector patterns:
1. Source-grep tests (tests/gateway/test_feishu.py, test_email.py): tests
that call inspect.getsource() on production modules and grep for string
literals. Break on any refactor/rename even when behavior is correct.
2. Platform enum tautologies (every gateway/test_X.py): assertions like
`Platform.X.value == 'x'` duplicated across ~9 adapter test files.
3. Toolset/PLATFORM_HINTS/setup-wizard registry-presence checks: tests that
only verify a key exists in a dict. Data-layout tests, not behavior.
4. Argparse wiring tests (test_argparse_flag_propagation, test_subparser_routing
_fallback): tests that do parser.parse_args([...]) then assert args.field.
Tests Python's argparse, not our code.
5. Pure dispatch tests (test_plugins_cmd.TestPluginsCommandDispatch): patch
cmd_X, call plugins_command with matching action, assert mock called.
Tests the if/elif chain, not behavior.
6. Kwarg-to-mock verification (test_auxiliary_client ~45 tests,
test_web_tools_config, test_gemini_cloudcode, test_retaindb_plugin): tests
that mock the external API client, call our function, and assert exact
kwargs. Break on refactor even when behavior is preserved.
7. Schedule-internal "function-was-called" tests (acp/test_server scheduling
tests): tests that patch own helper method, then assert it was called.
Kept behavioral tests throughout: error paths (pytest.raises), security
tests (path traversal, SSRF, redaction), message alternation invariants,
provider API format conversion, streaming logic, memory contract, real
config load/merge tests.
Net reduction: 169 tests removed. 38 empty classes cleaned up.
Collected before: 12,522 tests
Collected after: 12,353 tests
The cache-read, cache-write, and total estimated-cost values shown in
/insights (and the per-model Cost column) were unreliable. Hide them from
both terminal and gateway renderings.
The underlying data pipeline is untouched — sessions still store
cache_read_tokens, cache_write_tokens, and estimated_cost_usd; the web
server, /usage command, and status bar are unaffected. Only the
InsightsEngine display layer is trimmed.
Changes:
- format_terminal: drop 'Cache read / Cache write' line, drop 'Est. cost'
from the Total tokens row, drop per-model 'Cost' column, drop the
'* Cost N/A for custom/self-hosted' footnote.
- format_gateway: drop cache breakdown from Tokens line, drop 'Est. cost'
line, drop per-model cost suffix.
- Tests updated to assert these strings are now absent.
Replace the HERMES_ENABLE_NOUS_MANAGED_TOOLS env-var feature flag with
subscription-based detection. The Tool Gateway is now available to any
paid Nous subscriber without needing a hidden env var.
Core changes:
- managed_nous_tools_enabled() checks get_nous_auth_status() +
check_nous_free_tier() instead of an env var
- New use_gateway config flag per tool section (web, tts, browser,
image_gen) records explicit user opt-in and overrides direct API
keys at runtime
- New prefers_gateway(section) shared helper in tool_backend_helpers.py
used by all 4 tool runtimes (web, tts, image gen, browser)
UX flow:
- hermes model: after Nous login/model selection, shows a curses
prompt listing all gateway-eligible tools with current status.
User chooses to enable all, enable only unconfigured tools, or skip.
Defaults to Enable for new users, Skip when direct keys exist.
- hermes tools: provider selection now manages use_gateway flag —
selecting Nous Subscription sets it, selecting any other provider
clears it
- hermes status: renamed section to Nous Tool Gateway, added
free-tier upgrade nudge for logged-in free users
- curses_radiolist: new description parameter for multi-line context
that survives the screen clear
Runtime behavior:
- Each tool runtime (web_tools, tts_tool, image_generation_tool,
browser_use) checks prefers_gateway() before falling back to
direct env-var credentials
- get_nous_subscription_features() respects use_gateway flags,
suppressing direct credential detection when the user opted in
Removed:
- HERMES_ENABLE_NOUS_MANAGED_TOOLS env var and all references
- apply_nous_provider_defaults() silent TTS auto-set
- get_nous_subscription_explainer_lines() static text
- Override env var warnings (use_gateway handles this properly now)
Regression from #11161 (Claude Opus 4.7 migration, commit 0517ac3e).
The Opus 4.7 migration changed `ADAPTIVE_EFFORT_MAP["xhigh"]` from "max"
(the pre-migration alias) to "xhigh" to preserve the new 4.7 effort level
as distinct from max. This is correct for 4.7, but Opus/Sonnet 4.6 only
expose 4 levels (low/medium/high/max) — sending "xhigh" there now 400s:
BadRequestError [HTTP 400]: This model does not support effort
level 'xhigh'. Supported levels: high, low, max, medium.
Users who set reasoning_effort=xhigh as their default (xhigh is the
recommended default for coding/agentic on 4.7 per the Anthropic migration
guide) now 400 every request the moment they switch back to a 4.6 model
via `/model` or config. Verified live against the Anthropic API on
`anthropic==0.94.0`.
Fix: make the mapping model-aware. Add `_supports_xhigh_effort()`
predicate (matches 4-7/4.7 substrings, mirroring the existing
`_supports_adaptive_thinking` / `_forbids_sampling_params` pattern).
On pre-4.7 adaptive models, downgrade xhigh→max (the strongest effort
those models accept, restoring pre-migration behavior). On 4.7+, keep
xhigh as a distinct level.
Per Anthropic's migration guide, xhigh is 4.7-only:
https://platform.claude.com/docs/en/about-claude/models/migration-guide
> Opus 4.7 effort levels: max, xhigh (new), high, medium, low.
> Opus 4.6 effort levels: max, high, medium, low.
SDK typing confirms: `anthropic.types.OutputConfigParam.effort: Literal[
"low", "medium", "high", "max"]` (v0.94.0 not yet updated for xhigh).
## Test plan
Verified live on macOS 15.5 / anthropic==0.94.0:
claude-opus-4-6 + effort=xhigh → output_config.effort=max → 200 OK
claude-opus-4-7 + effort=xhigh → output_config.effort=xhigh → 200 OK
claude-opus-4-6 + effort=max → output_config.effort=max → 200 OK
claude-opus-4-7 + effort=max → output_config.effort=max → 200 OK
`tests/agent/test_anthropic_adapter.py` — 120 pass (replaced 1 bugged
test that asserted the broken behavior, added 1 for 4.7 preservation).
Full adapter suite: 120 passed in 1.05s.
Broader suite (agent + run_agent + cli/gateway reasoning): 2140 passed
(2 pre-existing failures on clean upstream/main, unrelated).
## Platforms
Tested on macOS 15.5. No platform-specific code paths touched.
Claude Opus 4.7 introduced several breaking API changes that the current
codebase partially handled but not completely. This patch finishes the
migration per the official migration guide at
https://platform.claude.com/docs/en/about-claude/models/migration-guideFixesNousResearch/hermes-agent#11137
Breaking-change coverage:
1. Adaptive thinking + output_config.effort — 4.7 is now recognized by
_supports_adaptive_thinking() (extends previous 4.6-only gate).
2. Sampling parameter stripping — 4.7 returns 400 for any non-default
temperature / top_p / top_k. build_anthropic_kwargs drops them as a
safety net; the OpenAI-protocol auxiliary path (_build_call_kwargs)
and AnthropicCompletionsAdapter.create() both early-exit before
setting temperature for 4.7+ models. This keeps flush_memories and
structured-JSON aux paths that hardcode temperature from 400ing
when the aux model is flipped to 4.7.
3. thinking.display = "summarized" — 4.7 defaults display to "omitted",
which silently hides reasoning text from Hermes's CLI activity feed
during long tool runs. Restoring "summarized" preserves 4.6 UX.
4. Effort level mapping — xhigh now maps to xhigh (was xhigh→max, which
silently over-efforted every coding/agentic request). max is now a
distinct ceiling per Anthropic's 5-level effort model.
5. New stop_reason values — refusal and model_context_window_exceeded
were silently collapsed to "stop" (end_turn) by the adapter's
stop_reason_map. Now mapped to "content_filter" and "length"
respectively, matching upstream finish-reason handling already in
bedrock_adapter.
6. Model catalogs — claude-opus-4-7 added to the Anthropic provider
list, anthropic/claude-opus-4.7 added at top of OpenRouter fallback
catalog (recommended), claude-opus-4-7 added to model_metadata
DEFAULT_CONTEXT_LENGTHS (1M, matching 4.6 per migration guide).
7. Prefill docstrings — run_agent.AIAgent and BatchRunner now document
that Anthropic Sonnet/Opus 4.6+ reject a trailing assistant-role
prefill (400).
8. Tests — 4 new tests in test_anthropic_adapter covering display
default, xhigh preservation, max on 4.7, refusal / context-overflow
stop_reason mapping, plus the sampling-param predicate. test_model_metadata
accepts 4.7 at 1M context.
Tested on macOS 15.5 (darwin). 119 tests pass in
tests/agent/test_anthropic_adapter.py, 1320 pass in tests/agent/.
resolve_vision_provider_client() was receiving the raw call_llm
parameters instead of the resolved provider/model/key/url from
_resolve_task_provider_model(). This caused config overrides
(auxiliary.vision.provider, etc.) to be silently discarded.
Cherry-picked from #10901 by @lrawnsley.
Salvaged from PR #10643 by kshitijk4poor, updated for current main.
Root causes fixed:
1. Telegram xdist mock pollution — new tests/gateway/conftest.py with shared
mock that runs at collection time (prevents ChatType=None caching)
2. VIRTUAL_ENV env var leak — monkeypatch.delenv in _detect_venv_dir tests
3. Copilot base_url missing — add fallback in _resolve_runtime_from_pool_entry
4. Stale vision model assertion — zai now uses glm-5v-turbo
5. Reasoning item id intentionally stripped — assert 'id' not in (store=False)
6. Context length warning unreachable — pass base_url to AIAgent in test
7. Kimi provider label updated — 'Kimi / Kimi Coding Plan' matches models.py
8. Google Workspace calendar tests — rewritten for current production code,
properly mock subprocess on api_module, removed stale +agenda assertions
9. Credential pool auto-seeding — mock _select_pool_entry / _resolve_auto /
_import_codex_cli_tokens to prevent real credentials from leaking into tests
When Nous returns a 429, the retry amplification chain burns up to 9
API requests per conversation turn (3 SDK retries × 3 Hermes retries),
each counting against RPH and deepening the rate limit. With multiple
concurrent sessions (cron + gateway + auxiliary), this creates a spiral
where retries keep the limit tapped indefinitely.
New module: agent/nous_rate_guard.py
- Shared file-based rate limit state (~/.hermes/rate_limits/nous.json)
- Parses reset time from x-ratelimit-reset-requests-1h, x-ratelimit-
reset-requests, retry-after headers, or error context
- Falls back to 5-minute default cooldown if no header data
- Atomic writes (tempfile + rename) for cross-process safety
- Auto-cleanup of expired state files
run_agent.py changes:
- Top-of-retry-loop guard: when another session already recorded Nous
as rate-limited, skip the API call entirely. Try fallback provider
first, then return a clear message with the reset time.
- On 429 from Nous: record rate limit state and skip further retries
(sets retry_count = max_retries to trigger fallback path)
- On success from Nous: clear the rate limit state so other sessions
know they can resume
auxiliary_client.py changes:
- _try_nous() checks rate guard before attempting Nous in the auxiliary
fallback chain. When rate-limited, returns (None, None) so the chain
skips to the next provider instead of piling more requests onto Nous.
This eliminates three sources of amplification:
1. Hermes-level retries (saves 6 of 9 calls per turn)
2. Cross-session retries (cron + gateway all skip Nous)
3. Auxiliary fallback to Nous (compression/session_search skip too)
Includes 24 tests covering the rate guard module, header parsing,
state lifecycle, and auxiliary client integration.
When proxy env vars (HTTP_PROXY, HTTPS_PROXY, ALL_PROXY) contain
malformed URLs — e.g. 'http://127.0.0.1:6153export' from a broken
shell config — the OpenAI/httpx client throws a cryptic 'Invalid port'
error that doesn't identify the offending variable.
Add _validate_proxy_env_urls() and _validate_base_url() in
auxiliary_client.py, called from resolve_provider_client() and
_create_openai_client() to fail fast with a clear, actionable error
message naming the broken env var or URL.
Closes#6360
Co-authored-by: MestreY0d4-Uninter <MestreY0d4-Uninter@users.noreply.github.com>
Found via trace data audit: JWT tokens (eyJ...) and Discord snowflake
mentions (<@ID>) were passing through unredacted.
JWT pattern: matches 1/2/3-part tokens starting with eyJ (base64 for '{').
Zero false-positive risk — no normal text matches eyJ + 10+ base64url chars.
Discord pattern: matches <@digits> and <@!digits> with 17-20 digit snowflake
IDs. Syntactically unique to Discord's mention format.
Both patterns follow the same structural-uniqueness standard as existing
prefix patterns (sk-, ghp_, AKIA, etc.).
Memory provider discovery (discover_memory_providers, load_memory_provider)
only scanned the bundled plugins/memory/ directory. User-installed providers
at $HERMES_HOME/plugins/<name>/ were invisible, forcing users to symlink
into the repo source tree — which broke on hermes update and created a
dual-registration path causing duplicate tool names (400 errors on strict
providers like Xiaomi MiMo).
Changes:
- Add _get_user_plugins_dir(), _is_memory_provider_dir(), _iter_provider_dirs(),
and find_provider_dir() helpers to plugins/memory/__init__.py
- discover_memory_providers() now scans both bundled and user dirs
- load_memory_provider() uses find_provider_dir() (bundled-first)
- discover_plugin_cli_commands() uses find_provider_dir()
- _install_dependencies() in memory_setup.py uses find_provider_dir()
- User plugins use _hermes_user_memory namespace to avoid sys.modules collisions
- Non-memory user plugins filtered via source text heuristic
- Bundled providers always take precedence on name collisions
Fixes#4956, #9099. Supersedes #4987, #9123, #9130, #9132, #9982.
Memory provider plugins (e.g. Mnemosyne) can register tools via two paths:
1. Plugin system (ctx.register_tool) → tool registry → get_tool_definitions()
2. Memory manager → get_all_tool_schemas() → direct append in AIAgent.__init__
Path 2 blindly appended without checking if path 1 already added the same
tool names. This created duplicate function names in the tools array sent
to the API. Most providers silently handle duplicates, but Xiaomi MiMo
(via Nous Portal) strictly rejects them with a 400 Bad Request.
Fix: build a set of existing tool names before memory manager injection
and skip any tool whose name is already present.
Confirmed via live testing against Nous Portal:
- Unique tool names → 200 OK
- Duplicate tool names → 400 'Provider returned error'
The on_memory_write bridge that notifies external memory providers
(ClawMem, retaindb, supermemory, etc.) of built-in memory writes was
only present in the concurrent tool execution path (_invoke_tool).
The sequential path (_execute_tool_calls_sequential) — which handles
all single tool calls, the common case — was missing it entirely.
This meant external memory providers silently missed every single-call
memory write, which is the vast majority of memory operations.
Fix: add the identical bridge block to the sequential path, right
after the memory_tool call returns.
Closes#10174
OV transparently handles message history across /new and /compress: old
messages stay in the same session and extraction is idempotent, so there's
no need to rebind providers to a new session_id. The only thing the
session boundary actually needs is to trigger extraction.
- MemoryProvider / MemoryManager: remove on_session_reset hook
- OpenViking: remove on_session_reset override (nothing to do)
- AIAgent: replace rotate_memory_session with commit_memory_session
(just calls on_session_end, no rebind)
- cli.py / run_agent.py: single commit_memory_session call at the
session boundary before session_id rotates
- tests: replace on_session_reset coverage with routing tests for
MemoryManager.on_session_end
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace hasattr-forked OpenViking-specific paths with a proper base-class
hook. Collapse the two agent wrappers into a single rotate_memory_session
so callers don't orchestrate commit + rebind themselves.
- MemoryProvider: add on_session_reset(new_session_id) as a default no-op
- MemoryManager: on_session_reset fans out unconditionally (no hasattr,
no builtin skip — base no-op covers it)
- OpenViking: rename reset_session -> on_session_reset; drop the explicit
POST /api/v1/sessions (OV auto-creates on first message) and the two
debug raise_for_status wrappers
- AIAgent: collapse commit_memory_session + reinitialize_memory_session
into rotate_memory_session(new_sid, messages)
- cli.py / run_agent.py: replace hasattr blocks and the split calls with
a single unconditional rotate_memory_session call; compression path
now passes the real messages list instead of []
- tests: align with on_session_reset, assert reset does NOT POST /sessions
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The OpenViking memory provider extracts memories when its session is
committed (POST /api/v1/sessions/{id}/commit). Before this fix, the
CLI had two code paths that changed the active session_id without ever
committing the outgoing OpenViking session:
1. /new (new_session() in cli.py) — called flush_memories() to write
MEMORY.md, then immediately discarded the old session_id. The
accumulated OpenViking session was never committed, so all context
from that session was lost before extraction could run.
2. /compress and auto-compress (_compress_context() in run_agent.py) —
split the SQLite session (new session_id) but left the OpenViking
provider pointing at the old session_id with no commit, meaning all
messages synced to OpenViking were silently orphaned.
The gateway already handles session commit on /new and /reset via
shutdown_memory_provider() on the cached agent; the CLI path did not.
Fix: introduce a lightweight session-transition lifecycle alongside
the existing full shutdown path:
- OpenVikingMemoryProvider.reset_session(new_session_id): waits for
in-flight background threads, resets per-session counters, and
creates the new OV session via POST /api/v1/sessions — without
tearing down the HTTP client (avoids connection overhead on /new).
- MemoryManager.restart_session(new_session_id): calls reset_session()
on providers that implement it; falls back to initialize() for
providers that do not. Skips the builtin provider (no per-session
state).
- AIAgent.commit_memory_session(messages): wraps
memory_manager.on_session_end() without shutdown — commits OV session
for extraction but leaves the provider alive for the next session.
- AIAgent.reinitialize_memory_session(new_session_id): wraps
memory_manager.restart_session() — transitions all external providers
to the new session after session_id has been assigned.
Call sites:
- cli.py new_session(): commit BEFORE session_id changes, reinitialize
AFTER — ensuring OV extraction runs on the correct session and the
new session is immediately ready for the next turn.
- run_agent._compress_context(): same pattern, inside the
if self._session_db: block where the session_id split happens.
/compress and auto-compress are functionally identical at this layer:
both call _compress_context(), so both are fixed by the same change.
Tests added to tests/agent/test_memory_provider.py:
- TestMemoryManagerRestartSession: reset_session() routing, builtin
skip, initialize() fallback, failure tolerance, empty-manager noop.
- TestOpenVikingResetSession: session_id update, per-session state
clear, POST /api/v1/sessions call, API failure tolerance, no-client
noop.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Expose skill usage in analytics so the dashboard and insights output can
show which skills the agent loads and manages over time.
This adds skill aggregation to the InsightsEngine by extracting
`skill_view` and `skill_manage` calls from assistant tool_calls,
computing per-skill totals, and including the results in both terminal
and gateway insights formatting. It also extends the dashboard analytics
API and Analytics page to render a Top Skills table.
Terminology is aligned with the skills docs:
- Agent Loaded = `skill_view` events
- Agent Managed = `skill_manage` actions
Architecture:
- agent/insights.py collects and aggregates per-skill usage
- hermes_cli/web_server.py exposes `skills` on `/api/analytics/usage`
- web/src/lib/api.ts adds analytics skill response types
- web/src/pages/AnalyticsPage.tsx renders the Top Skills table
- web/src/i18n/{en,zh}.ts updates user-facing labels
Tests:
- tests/agent/test_insights.py covers skill aggregation and formatting
- tests/hermes_cli/test_web_server.py covers analytics API contract
including the `skills` payload
- verified with `cd web && npm run build`
Files changed:
- agent/insights.py
- hermes_cli/web_server.py
- tests/agent/test_insights.py
- tests/hermes_cli/test_web_server.py
- web/src/i18n/en.ts
- web/src/i18n/types.ts
- web/src/i18n/zh.ts
- web/src/lib/api.ts
- web/src/pages/AnalyticsPage.tsx
Seed qwen-oauth credentials from resolve_qwen_runtime_credentials() in
_seed_from_singletons(). Users who authenticate via 'qwen auth qwen-oauth'
store tokens in ~/.qwen/oauth_creds.json which the runtime resolver reads
but the credential pool couldn't detect — same gap pattern as copilot.
Uses refresh_if_expiring=False to avoid network calls during discovery.
Seed copilot credentials from resolve_copilot_token() in the credential
pool's _seed_from_singletons(), alongside the existing anthropic and
openai-codex seeding logic. This makes copilot appear in the /model
provider picker when the user authenticates solely through gh auth token.
Cherry-picked from PR #9767 by Marvae.
Production fixes:
- Add clear_session_context() to hermes_logging.py (fixes 48 teardown errors)
- Add clear_session() to tools/approval.py (fixes 9 setup errors)
- Add SyncError M_UNKNOWN_TOKEN check to Matrix _sync_loop (bug fix)
- Fall back to inline api_key in named custom providers when key_env
is absent (runtime_provider.py)
Test fixes:
- test_memory_user_id: use builtin+external provider pair, fix honcho
peer_name override test to match production behavior
- test_display_config: remove TestHelpers for non-existent functions
- test_auxiliary_client: fix OAuth tokens to match _is_oauth_token
patterns, replace get_vision_auxiliary_client with resolve_vision_provider_client
- test_cli_interrupt_subagent: add missing _execution_thread_id attr
- test_compress_focus: add model/provider/api_key/base_url/api_mode
to mock compressor
- test_auth_provider_gate: add autouse fixture to clean Anthropic env
vars that leak from CI secrets
- test_opencode_go_in_model_list: accept both 'built-in' and 'hermes'
source (models.dev API unavailable in CI)
- test_email: verify email Platform enum membership instead of source
inspection (build_channel_directory now uses dynamic enum loop)
- test_feishu: add bot_added/bot_deleted handler mocks to _Builder
- test_ws_auth_retry: add AsyncMock for sync_store.get_next_batch,
add _pending_megolm and _joined_rooms to Matrix adapter mocks
- test_restart_drain: monkeypatch-delete INVOCATION_ID (systemd sets
this in CI, changing the restart call signature)
- test_session_hygiene: add user_id to SessionSource
- test_session_env: use relative baseline for contextvar clear check
(pytest-xdist workers share context)
Port two improvements inspired by Kilo-Org/kilocode analysis:
1. Error classifier: add context overflow patterns for vLLM, Ollama,
and llama.cpp/llama-server. These local inference servers return
different error formats than cloud providers (e.g., 'exceeds the
max_model_len', 'context length exceeded', 'slot context'). Without
these patterns, context overflow errors from local servers are
misclassified as format errors, causing infinite retries instead
of triggering compression.
2. MCP initial connection retry: previously, if the very first
connection attempt to an MCP server failed (e.g., transient DNS
blip at startup), the server was permanently marked as failed with
no retry. Post-connect reconnection had 5 retries with exponential
backoff, but initial connection had zero. Now initial connections
retry up to 3 times with backoff before giving up, matching the
resilience of post-connect reconnection.
(Inspired by Kilo Code's MCP server disappearing fix in v1.3.3)
Tests: 6 new error classifier tests, 4 new MCP retry tests, 1
updated existing test. All 276 affected tests pass.
resolve_vision_provider_client() computed resolved_api_mode from config
but never passed it to downstream resolve_provider_client() or
_get_cached_client() calls, causing custom providers with
api_mode: anthropic_messages to crash when used for vision tasks.
Also remove the for_vision special case in _normalize_aux_provider()
that incorrectly discarded named custom provider identifiers.
Fixes#8857
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove the backward-compat code paths that read compression provider/model
settings from legacy config keys and env vars, which caused silent failures
when auto-detection resolved to incompatible backends.
What changed:
- Remove compression.summary_model, summary_provider, summary_base_url from
DEFAULT_CONFIG and cli.py defaults
- Remove backward-compat block in _resolve_task_provider_model() that read
from the legacy compression section
- Remove _get_auxiliary_provider() and _get_auxiliary_env_override() helper
functions (AUXILIARY_*/CONTEXT_* env var readers)
- Remove env var fallback chain for per-task overrides
- Update hermes config show to read from auxiliary.compression
- Add config migration (v16→17) that moves non-empty legacy values to
auxiliary.compression and strips the old keys
- Update example config and openclaw migration script
- Remove/update tests for deleted code paths
Compression model/provider is now configured exclusively via:
auxiliary.compression.provider / auxiliary.compression.model
Closes#8923
_query_local_context_length was checking model_info.context_length
(the GGUF training max) before num_ctx (the Modelfile runtime override),
inverse to query_ollama_num_ctx. The two helpers therefore disagreed on
the same model:
hermes-brain:qwen3-14b-ctx32k # Modelfile: num_ctx 32768
underlying qwen3:14b GGUF # qwen3.context_length: 40960
query_ollama_num_ctx correctly returned 32768 (the value Ollama will
actually allocate KV cache for). _query_local_context_length returned
40960, which let ContextCompressor grow conversations past 32768 before
triggering compression — at which point Ollama silently truncated the
prefix, corrupting context.
Swap the order so num_ctx is checked first, matching query_ollama_num_ctx.
Adds a parametrized test that seeds both values and asserts num_ctx wins.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
