The three google-workspace scripts (setup.py, google_api.py, gws_bridge.py)
each had their own way of resolving HERMES_HOME:
- setup.py imported hermes_constants (crashes outside Hermes process)
- google_api.py used os.getenv inline (no strip, no empty handling)
- gws_bridge.py defined its own local get_hermes_home() (duplicate)
Extract the common logic into _hermes_home.py which:
- Delegates to hermes_constants when available (profile support, etc.)
- Falls back to os.getenv with .strip() + empty-as-unset handling
- Provides display_hermes_home() with ~/ shortening for profiles
All three scripts now import from _hermes_home instead of duplicating.
7 regression tests cover the fallback path: env var override, default
~/.hermes, empty env var, display shortening, profile paths, and
custom non-home paths.
Closes#12722
Extracts _needs_kimi_tool_reasoning() for symmetry with the existing
_needs_deepseek_tool_reasoning() helper, so _copy_reasoning_content_for_api
uses the same detection logic as _build_assistant_message. Future changes
to either provider's signals now only touch one function.
Adds tests/run_agent/test_deepseek_reasoning_content_echo.py covering:
- All 3 DeepSeek detection signals (provider, model, host)
- Poisoned history replay (empty string fallback)
- Plain assistant turns NOT padded
- Explicit reasoning_content preserved
- Reasoning field promoted to reasoning_content
- Existing Kimi/Moonshot detection intact
- Non-thinking providers left alone
21 tests, all pass.
DeepSeek V4 thinking mode requires reasoning_content on every
assistant message that includes tool_calls. When this field is
missing from persisted history, replaying the session causes
HTTP 400: 'The reasoning_content in the thinking mode must be
passed back to the API.'
Two-part fix (refs #15250):
1. _copy_reasoning_content_for_api: Merge the Kimi-only and
DeepSeek detection into a single needs_tool_reasoning_echo
check. This handles already-poisoned persisted sessions by
injecting an empty reasoning_content on replay.
2. _build_assistant_message: Store reasoning_content='' on new
DeepSeek tool-call messages at creation time, preventing
future session poisoning at the source.
Additional fix:
3. _handle_max_iterations: Add missing call to
_copy_reasoning_content_for_api in the max-iterations flush
path (previously only main loop and flush_memories had it).
Detection covers:
- provider == 'deepseek'
- model name containing 'deepseek' (case-insensitive)
- base URL matching api.deepseek.com (for custom provider)
``run_conversation`` was calling ``memory_manager.sync_all(
original_user_message, final_response)`` at the end of every turn
where both args were present. That gate didn't consider the
``interrupted`` local flag, so an external memory backend received
partial assistant output, aborted tool chains, or mid-stream resets as
durable conversational truth. Downstream recall then treated the
not-yet-real state as if the user had seen it complete, poisoning the
trust boundary between "what the user took away from the turn" and
"what Hermes was in the middle of producing when the interrupt hit".
Extracted the inline sync block into a new private method
``AIAgent._sync_external_memory_for_turn(original_user_message,
final_response, interrupted)`` so the interrupt guard is a single
visible check at the top of the method instead of hidden in a
boolean-and at the call site. That also gives tests a clean seam to
assert on — the pre-fix layout buried the logic inside the 3,000-line
``run_conversation`` function where no focused test could reach it.
The new method encodes three independent skip conditions:
1. ``interrupted`` → skip entirely (the #15218 fix). Applies even
when ``final_response`` and ``original_user_message`` happen to
be populated — an interrupt may have landed between a streamed
reply and the next tool call, so the strings on disk are not
actually the turn the user took away.
2. No memory manager / no final_response / no user message →
preserve existing skip behaviour (nothing new for providerless
sessions, system-initiated refreshes, tool-only turns that never
resolved, etc.).
3. Sync_all / queue_prefetch_all exceptions → swallow. External
memory providers are strictly best-effort; a misconfigured or
offline backend must never block the user from seeing their
response.
The prefetch side-effect is gated on the same interrupt flag: the
user's next message is almost certainly a retry of the same intent,
and a prefetch keyed on the interrupted turn would fire against stale
context.
### Tests (16 new, all passing on py3.11 venv)
``tests/run_agent/test_memory_sync_interrupted.py`` exercises the
helper directly on a bare ``AIAgent`` (``__new__`` pattern that the
interrupt-propagation tests already use). Coverage:
- Interrupted turn with full-looking response → no sync (the fix)
- Interrupted turn with long assistant output → no sync (the interrupt
could have landed mid-stream; strings-on-disk lie)
- Normal completed turn → sync_all + queue_prefetch_all both called
with the right args (regression guard for the positive path)
- No final_response / no user_message / no memory manager → existing
pre-fix skip paths still apply
- sync_all raises → exception swallowed, prefetch still attempted
- queue_prefetch_all raises → exception swallowed after sync succeeded
- 8-case parametrised matrix across (interrupted × final_response ×
original_user_message) asserts sync fires iff interrupted=False AND
both strings are non-empty
Closes#15218
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
_load_auth_store() caught all parse/read exceptions and silently
returned an empty store, making corruption look like a logout with
no diagnostic information and no way to recover the original file.
Now copies the corrupt file to auth.json.corrupt before resetting,
and logs a warning with the exception and backup path.
Extends PR #15171 to also cover the server-side cancellation path (aiohttp
shutdown, request-level timeout) — previously only ConnectionResetError
triggered the incomplete-snapshot write, so cancellations left the store
stuck at the in_progress snapshot written on response.created.
Factors the incomplete-snapshot build into a _persist_incomplete_if_needed()
helper called from both the ConnectionResetError and CancelledError
branches; the CancelledError handler re-raises so cooperative cancellation
semantics are preserved.
Adds two regression tests that drive _write_sse_responses directly (the
TestClient disconnect path races the server handler, which makes the
end-to-end assertion flaky).
_submit_anthropic_pkce() retrieved sess under _oauth_sessions_lock but
wrote back to sess["status"] and sess["error_message"] outside the lock.
A concurrent session GC or cancel could race with these writes, producing
inconsistent session state.
Wrap all 4 sess write sites in _oauth_sessions_lock:
- network exception path (Token exchange failed)
- missing access_token path
- credential save failure path
- success path (approved)
Before this, typing during /compress was accepted by the classic CLI
prompt and landed in the next prompt after compression finished,
effectively consuming a keystroke for a prompt that was about to be
replaced. Wrapping the body in self._busy_command('Compressing
context...') blocks input rendering for the duration, matching the
pattern /skills install and other slow commands already use.
Salvages the useful part of #10303 (@iRonin). The `_compressing` flag
added to run_agent.py in the original PR was dead code (set in 3 spots,
read nowhere — not by cli.py, not by run_agent.py, not by the Ink TUI
which doesn't use _busy_command at all) and was dropped.
When display.busy_input_mode is 'queue', the runner-level PRIORITY block
in _handle_message was still calling running_agent.interrupt() for every
text follow-up to an active session. The adapter-level busy handler
already honors queue mode (commit 9d147f7fd), but this runner-level path
was an unconditional interrupt regardless of config.
Adds a queue-mode branch that queues the follow-up via
_queue_or_replace_pending_event() and returns without interrupting.
Salvages the useful part of #12070 (@knockyai). The config fan-out to
per-platform extra was redundant — runner already loads busy_input_mode
directly via _load_busy_input_mode().
Follow-up to @iRonin's Ctrl+D EOF fix. If the input text is empty but
the user has pending attached images, do nothing rather than exiting —
otherwise a stray Ctrl+D silently discards the attachments.
skill_view response went to the model verbatim; duplicating the SKILL.md
body as raw_content on every tool call added token cost with no agent-facing
benefit. Remove the field and update tests to assert on content only.
The slash/preload caller (agent/skill_commands.py) already falls back to
content when raw_content is absent, and it calls skill_view(preprocess=False)
anyway, so content is already unrendered on that path.
- update faq answer with new `backup` command in release 0.9.0
- move profile export section together with backup section so related information can be read more easily
- add table comparison between `profile export` and `backup` to assist users if understanding the nuances between both
Extends _repair_tool_call_arguments() to cover the most common local-model
JSON corruption pattern: llama.cpp/Ollama backends emit literal tabs and
newlines inside JSON string values (memory save summaries, file contents,
etc.). Previously fell through to '{}' replacement, losing the call.
Adds two repair passes:
- Pass 0: json.loads(strict=False) + re-serialise to canonical wire form
- Pass 4: escape 0x00-0x1F control chars inside string values, then retry
Ports the core utility from #12068 / PR #12093 without the larger plumbing
change (that PR also replaced json.loads at 8 call sites; current main's
_repair_tool_call_arguments is already the single chokepoint, so the
upgrade happens transparently for every existing caller).
Credit: @truenorth-lj for the original utility design.
4 new regression tests covering literal newlines, tabs, re-serialisation
to strict=True-valid output, and the trailing-comma + control-char
combination case.
When the streaming path (chat completions) assembled tool call deltas and
detected malformed JSON arguments, it set has_truncated_tool_args=True but
passed the broken args through unchanged. This triggered the truncation
handler which returned a partial result and killed the session (/new required).
_many_ malformations are repairable: trailing commas, unclosed brackets,
Python None, empty strings. _repair_tool_call_arguments() already existed
for the pre-API-request path but wasn't called during streaming assembly.
Now when JSON parsing fails during streaming assembly, we attempt repair
via _repair_tool_call_arguments() before flagging as truncated. If repair
succeeds (returns valid JSON), the tool call proceeds normally. Only truly
unrepairable args fall through to the truncation handler.
This prevents the most common session-killing failure mode for models like
GLM-5.1 that produce trailing commas or unclosed brackets.
Tests: 12 new streaming assembly repair tests, all 29 existing repair
tests still passing.
The web UI schema advertised 'block' as a busy_input_mode choice, but
no implementation ever existed — the gateway and CLI both silently
collapsed 'block' (and anything other than 'queue') to 'interrupt'.
Users who picked 'block' in the dashboard got interrupts anyway.
Drop 'block' from the select options. The two supported modes are
'interrupt' (default) and 'queue'.
When a session is split by context compression mid-tool-call, an assistant
message may end up with truncated/invalid JSON in tool_calls[*].function.arguments.
On the next turn this is replayed verbatim and providers reject the entire request
with HTTP 400 invalid_tool_call_format, bricking the conversation in a loop that
cannot recover without manual session quarantine.
This patch adds a defensive sanitizer that runs immediately before
client.chat.completions.create() in AIAgent.run_conversation():
- Validates each assistant tool_calls[*].function.arguments via json.loads
- Replaces invalid/empty arguments with '{}'
- Injects a synthetic tool response (or prepends a marker to the existing one)
so downstream messages keep valid tool_call_id pairing
- Logs each repair with session_id / message_index / preview for observability
Defense in depth: corruption can originate from compression splits, manual edits,
or plugin bugs. Sanitizing at the send chokepoint catches all sources.
Adds 7 unit tests covering: truncated JSON, empty string, None, non-string args,
existing matching tool response (no duplicate injection), non-assistant messages
ignored, multiple repairs.
Fixes#15236
gpt-5.x on the Codex Responses API sometimes degenerates and emits
Harmony-style `to=functions.<name> {json}` serialization as plain
assistant-message text instead of a structured `function_call` item.
The intent never makes it into `response.output` as a function_call,
so `tool_calls` is empty and `_normalize_codex_response()` returns
the leaked text as the final content. Downstream (e.g. delegate_task),
this surfaces as a confident-looking summary with `tool_trace: []`
because no tools actually ran — the Taiwan-embassy-email bug report.
Detect the pattern, scrub the content, and return finish_reason=
'incomplete' so the existing Codex-incomplete continuation path
(run_agent.py:11331, 3 retries) gets a chance to re-elicit a proper
function_call item. Encrypted reasoning items are preserved so the
model keeps its chain-of-thought on the retry.
Regression tests: leaked text triggers incomplete, real tool calls
alongside leak-looking text are preserved, clean responses pass
through unchanged.
Reported on Discord (gpt-5.4 / openai-codex).
Covers the two bugs salvaged from PR #15161:
- test_batch_runner_checkpoint: TestFinalCheckpointNoDuplicates asserts
the final aggregated completed_prompts list has no duplicate indices,
and keeps a sanity anchor test documenting the pre-fix pattern so a
future refactor that re-introduces it is caught immediately.
- test_model_tools: TestCoerceNumberInfNan asserts _coerce_number
returns the original string for inf/-inf/nan/Infinity inputs and that
the result round-trips through strict (allow_nan=False) json.dumps.
batch_runner: completed_prompts_set is already fully populated by the
time the aggregation loop runs (incremental updates happen at result
collection time), so the subsequent extend() call re-added every
completed prompt index a second time. Removed the redundant variable
and extend, and write sorted(completed_prompts_set) directly to the
final checkpoint instead.
model_tools: _coerce_number returned Python float('inf')/float('nan')
for inf/nan strings rather than the original string. json.dumps raises
ValueError for these values, so any tool call where the model emitted
"inf" or "nan" for a numeric parameter would crash at serialization.
Changed the guard to return the original string, matching the
function's documented "returns original string on failure" contract.
Replaces gpt-5.4 / gpt-5.4-pro entries in the OpenRouter fallback snapshot
and the Nous Portal curated list. Other aggregators (Vercel AI Gateway)
and provider-native lists are unchanged.
Inline diff segments were anchored relative to assistant narration, but the
turn details pane still rendered after streamSegments. On completion that put
the diff before the tool telemetry that produced it. When a turn has anchored
diff segments, commit the accumulated thinking/tool trail as a pre-diff trail
message, then render the diff and final summary.
TUI auto-resolves `display.personality` at session init, unlike the base CLI.
If config contains `agent.personalities: null`, `_resolve_personality_prompt`
called `.get()` on None and failed before model/provider selection.
Normalize null personalities to `{}` and surface a targeted config warning.
Tolerating null top-level keys silently drops user settings (e.g.
`agent.system_prompt` next to a bare `agent:` line is gone). Probe at
session create, log via `logger.warning`, and surface in the boot info
under `config_warning` — rendered in the TUI feed alongside the existing
`credential_warning` banner.
YAML parses bare keys like `agent:` or `display:` as None. `dict.get(key, {})`
returns that None instead of the default (defaults only fire on missing keys),
so every `cfg.get("agent", {}).get(...)` chain in tui_gateway/server.py
crashed agent init with `'NoneType' object has no attribute 'get'`.
Guard all 21 sites with `(cfg.get(X) or {})`. Regression test covers the
null-section init path reported on Twitter against the new TUI.
Recovers the manual click on the details accordion: with #14968's new
SECTION_DEFAULTS (thinking/tools start `expanded`), every panel render
was OR-ing the local open toggle against `visible.X === 'expanded'`.
That pinned `open=true` for the default-expanded sections, so clicking
the chevron flipped the local state but the panel never collapsed.
Local toggle is now the sole source of truth at render time; the
useState init still seeds from the resolved visibility (so first paint
is correct) and the existing useEffect still re-syncs when the user
mutates visibility at runtime via `/details`.
Same OR-lock cleared inside SubagentAccordion (`showChildren ||
openX`) — pre-existing but the same shape, so expand-all on the
spawn tree no longer makes inner sections un-collapsible either.
