Replace hardcoded [:2000] and [:4000] slicing with the shared _truncate
helper from tool_utils, which uses MAX_OUTPUT_CHARS and adds an explicit
truncation indicator when content is cut.
Scoped down from the original PR: only agent/tool-output display
behavior, no integrations.py changes.
Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
Co-authored-by: Alexandre Teixeira <111787685+alteixeira20@users.noreply.github.com>
* fix(llm): stop sending llama.cpp slot-affinity fields to cloud providers
_apply_local_cache_affinity adds session_id + cache_prompt for llama.cpp
KV-cache slot affinity (#2927), gated on _is_self_hosted_openai_compatible,
which treated any unknown OpenAI-compatible host as self-hosted. Strict
cloud providers added as custom endpoints (Mistral at api.mistral.ai)
reject unknown body fields, so every request failed with 422
extra_forbidden. Self-hosted now also requires the endpoint to resolve as
local via model_context.is_local_endpoint: loopback/private/tailscale
host, or endpoint kind explicitly configured as "local" (the escape hatch
for tunneled self-hosted servers). is_local_endpoint is promoted to a
public name since llm_core now shares it.
Fixes#3793
* test(llm): sweep cloud OpenAI-compatible hosts in affinity gating
Parametrized cases adapted from #3839 (credit: Shabablinchikow): deepseek,
x.ai, together, fireworks, and the Gemini OpenAI-compat endpoint must all
stay free of the llama.cpp extras, not just the Mistral host from #3793.
* fix(llm): narrow the Tailscale range to 100.64.0.0/10 in is_local_endpoint
Review finding on #3945: _PRIVATE_PREFIXES carried a bare "100." prefix,
treating all of 100.0.0.0/8 as local while Tailscale only uses the CGNAT
block 100.64.0.0/10. Public 100.x hosts (e.g. AWS ranges outside the
block) were classified local and still received the llama.cpp extras
this PR exists to keep away from strict providers. Match the narrowed
classification routes/model_routes.py already uses, with boundary tests
just below, inside, and just above the range.
_search_fts ran the FTS MATCH query, then looked up each hit's full row with its
own db.query(...).filter(id == message_id).first() inside a loop, so a search
returning N hits issued N extra SELECTs. Fetch all hit rows in a single IN(...)
query via _fetch_messages_by_id and reassemble results in hit (relevance) order.
Adds tests/test_session_search_batch_fetch.py asserting a single batched query
(and no query for empty input). Existing session-search tests stay green.
raw.githubusercontent.com serves Markdown as text/plain, JSON APIs and raw
config files serve application/json, and a lot of code and tool documentation
lives in .md/.txt. fetch_webpage_content only handled PDF and HTML, so a
non-HTML body produced empty content and web_fetch reported 'no readable text
content'. Add a branch that returns the body verbatim for non-HTML text/*,
JSON (application/json and +json), and a .md/.txt/.text/.json URL-suffix
fallback for mislabeled octet-stream. HTML and PDF handling unchanged.
Fixes#3808
* fix: use correct element IDs for privilege-gated button hiding
The privilege-gated button hiding in initializeEventListeners() used
stale element IDs that no longer exist in the DOM:
- 'tool-bash-btn' -> 'bash-toggle-btn' (the actual shell button ID)
- 'tool-image-btn' -> 'set-imgEnabledToggle' (admin settings toggle,
since no standalone image button exists in the composer)
Without this fix, users without can_use_bash / can_generate_images
privileges still see buttons that appear to work but then fail.
* fix: remove incorrect image generation toggle targeting
The set-imgEnabledToggle is the global admin Image Generation master
switch, not a per-user composer control. Non-admins without
can_generate_images never render that toggle, so the lookup is null
and the branch no-ops. Admins without the privilege get the app-wide
toggle force-unchecked based on personal privilege, which is confusing.
There is no composer image button in the DOM, so nothing to hide here.
Drop the can_generate_images block entirely as vdmkenny requested.
---------
Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
* fix(email): keep FETCH attributes Gmail sends after the header literal
imaplib returns a UID FETCH response as an interleaved list of
(meta, literal) tuples plus bare bytes elements. Which attributes land
where is server-specific: Dovecot sends FLAGS before the RFC822.HEADER
literal (inside the tuple meta), Gmail sends them after it, as a bare
` FLAGS (\Seen))` element. The email list grouping loop and the search
loop only inspected tuples, so on Gmail every message lost its FLAGS and
the whole mailbox rendered as unread/unflagged, with mark-read appearing
to have no effect.
Extract the grouping into _group_uid_fetch_records(), fold bare bytes
parts into the current message meta there, and reuse it in both the
batched list fetch and the per-UID search fetch. Covered by unit tests
with captured Gmail-shaped and Dovecot-shaped responses.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(email): use raw byte literals for IMAP backslash escapes
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
fire() and fire_and_forget() scheduled delivery with bare create_task()/
loop.create_task() and kept no reference. asyncio holds only a weak reference to
a task, so the GC could collect a delivery (or the fire() coroutine itself)
before it completed, silently dropping the webhook.
Track in-flight tasks in a set on the manager via a _spawn_tracked() helper that
holds a strong reference for the task's lifetime and discards it on completion
(add_done_callback), and route both schedule sites through it.
Adds tests/test_webhook_task_refs.py.
Starlette 1.2.0 prefers httpx2 in the test client and emits a
StarletteDeprecationWarning on TestClient import when only classic httpx
is installed. Adding httpx2 silences the suite-wide warning; runtime code
keeps importing httpx directly and is unaffected.
Fixes#3942
PATCH and DELETE /api/tokens/{id} both called require_admin but never
checked that the token belonged to the requesting admin. Any admin could
rename, re-scope, or delete another admin's token by ID.
create_token already stamps owner on every token — update and delete
just never read it. Fixed by comparing token.owner against
get_current_user(request) after the 404 guard, same pattern the rest of
the auth routes use. Check is skipped when current_user is falsy
(AUTH_ENABLED=false / single-user mode).
Fixes#3898
Anthropic removed the sampling parameters (temperature, top_p, top_k)
starting with Claude Opus 4.7 — sending temperature at all, even 0.0,
returns HTTP 400. _build_anthropic_payload sent it unconditionally, so
every native-Anthropic request to Opus 4.7/4.8 failed: the research probe
(ResearchHandler._probe_endpoint, temperature=0) aborted runs before they
started, and all DeepResearcher._llm calls 400'd.
Add _anthropic_rejects_temperature (version-gates opus-N-M >= (4,7)) and
omit temperature in the Anthropic builder for those models. Older Claude
models (Opus 4.6 and below, Sonnet/Haiku) keep temperature and the
existing [0,1] clamp.
The version gate is hardened against real-world model id shapes:
- a word-boundary anchor so a substring like `octopus-4-8` is not read
as Opus and stripped of temperature;
- a 1-2 digit minor cap so a dated id such as `claude-opus-4-20250514`
(Opus 4.0, listed in ANTHROPIC_MODELS) parses as major-only and keeps
temperature, while dated 4.7+ snapshots still match;
- a non-string guard so a non-string model can't raise AttributeError
(the previous builder never called .lower() on it).
Adds regression tests covering 4.7/4.8 omission, older/dated/legacy
retention, the substring overmatch, and non-string inputs.
Fixes#3065
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Find-GitBash accepted the Microsoft Store / WSL bash.exe alias and only probed <root>\Git, so it never detected per-user Git for Windows installs under %LocalAppData%\Programs\Git and could skip the launcher's "install Git Bash" note even when no usable Git Bash was present.
Reject the WSL stub (system32/sysnative/windowsapps) and also probe %LocalAppData%\Programs\Git, mirroring core/platform_compat.find_bash.
Refs #3740
find_bash() rejected the WindowsApps WSL stub and then probed only %LocalAppData%\Git, so per-user Git for Windows installs (winget / Inno Setup {userpf}) under %LocalAppData%\Programs\Git were never found and the Cookbook reported "needs Git Bash" despite Git being installed.
Add the Programs\Git subfolder to the LocalAppData fallback root.
Adding a new endpoint only auto-set the global default chat endpoint when
none was configured (`if not settings.get("default_endpoint_id")`). When the
existing default pointed at an endpoint the user had since disabled, it was
never reassigned, so features that read the raw `default_endpoint_id` setting
(notably Memory → Tidy) failed with "No default model configured — set one in
Settings" even though an enabled endpoint existed.
Reassign the default when the configured endpoint is missing/disabled, via a
new pure `_default_endpoint_needs_assignment` helper. Adds unit coverage for
the helper plus route-level regression tests for the disabled/enabled cases.
Fixes#3586
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(hwfit): tolerate non-numeric gpu_count in /api/hwfit/models
The route did `n = int(gpu_count)` with no guard, so a non-numeric query param
like `?gpu_count=abc` raised ValueError and returned HTTP 500. Parse it
defensively (mirroring the gpu_group guard a few lines above): a malformed value
is ignored, exactly like omitting the param, and valid values still apply.
Adds tests/test_hwfit_gpu_count_nonnumeric.py: a non-numeric gpu_count returns a
ranking instead of raising, and a numeric value is still accepted.
* test(hwfit): cover non-numeric manual_gpu_count too
Follow-up to the gpu_count guard: add a regression test for the sibling
manual_gpu_count query param (the hardware simulator in _apply_manual_hardware),
which dev already guards by defaulting to 1 on a non-numeric value. This pins
that behaviour so the endpoint's count parsing is fully covered and cannot
regress to a 500.
_warmup_endpoints called model_discovery.get_endpoints(), which does not exist
on ModelDiscovery. It raised AttributeError on every startup and on every 60s
keepalive tick, was swallowed by the outer except, and pinged nothing, so the
cold-start prevention the loop exists for never ran.
Add ModelDiscovery.warmup_ping_urls(), which resolves the /models probe URLs
from the real discover_models() output, and call it from the warmup loop via
asyncio.to_thread (discovery does a blocking port scan, so keep it off the event
loop).
Adds tests/test_warmup_ping_urls.py: resolves /models URLs from discovered
items, honors the limit, degrades to [] on discovery failure, and documents that
get_endpoints never existed.
* fix: handle batch events format in manage_calendar tool
Models like deepseek-v4-flash emit batch events array instead of individual create_event calls. The tool defaulted to list_events (no action key), so events were never created despite the model confirming success.
- Add batch normalization in do_manage_calendar
- Map start/end objects to flat dtstart/dtend strings
- Add tests for both object and flat string formats
* fix: surface partial batch failures in manage_calendar
Partial failures were silently dropped - batches with mixed success/failure would report only created count with no error visibility.
- Return non-zero exit code for any failures
- Surface both created and failed counts in response
- Include first error message for debugging
- Add test for partial failure case
* chore: strip trailing whitespace in batch normalization block
* chore: strip whitespace-only blank lines in batch events test
The classify_events task pulled user memories to give the LLM personal context,
but read `m.content`, which the Memory ORM does not have (the column is `text`).
That raised AttributeError on the first row; the surrounding except swallowed it
and logged at debug, so the personal-context block was silently always empty and
events were classified without it.
Extract the rendering into `_memory_context_lines` (reads `text`, robust via
getattr, keeps the 200-char and 40-line caps) and raise the swallowed-exception
log to warning so a future schema mismatch is visible.
Adds tests/test_classify_events_memory_text.py for the field, truncation, blank
skipping, missing-attr robustness, and the line cap.
The AI-message copy buttons copied dataset.raw, which is the full
accumulated model output — still containing the <think time="...">
reasoning block and any tool-call markup that the renderer strips for
display. Pasting therefore leaked the model's thinking, and the first
heading after </think> lost its markdown formatting because it was
glued to the closing tag.
Add chatRenderer.copyMessageText(), which mirrors the display pipeline
(stripToolBlocks then extractThinkingBlocks) and falls back to the raw
text when stripping leaves nothing (thinking-only turns), and route
both copy handlers — the message footer and the slash-reply footer —
through it. The interrupted-turn Continue flow intentionally keeps
reading dataset.raw.
Fixes#3722
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* Switch to ddgs
duckduckgo_search was deprecated, this is the recommended replacement
* Update test_service_search_provider_guards.py
According to review comment
import_vcf built `text = data.get("vcf") or data.get("text") or ""`, so a
non-string JSON value (a number, list, etc.) stayed in place and the following
`text.strip()` raised AttributeError, returning HTTP 500. Coerce vcf/text/csv
with str() so non-string input degrades to the existing structured "no data"
response, matching the file's convention elsewhere.
Adds tests/test_contacts_import_nonstring.py covering non-string vcf, non-string
csv, and an empty body.
get_status() called get_avg_duration() unconditionally, and that helper globs
and JSON-parses every file under the research data dir. The SSE status stream
polls get_status() roughly once a second, so with a few saved reports each poll
re-read and re-parsed all of them, including for sessions that are not active
(the disk branch never even used the value).
Compute avg_duration only for active sessions and memoize it on the task entry,
so a long stream computes it once instead of on every poll. Behaviour is
unchanged: active streams still report avg_duration.
Adds tests/test_research_status_avg_duration.py: an inactive session does no
avg scan, and an active session computes it once across many polls.
SKILL.md files written with mixed-case owner (e.g. 'owner: Alice') were
skipped because the regex had no IGNORECASE flag. _usage.json keys like
'Alice::skill-name' were missed by the startswith prefix check for the
same reason.
Both comparisons now match the same way the deep_research and memory
blocks do — case-insensitively against old_username.
Fixes#3611
Direct DbSession.owner == user becomes WHERE owner IS NULL when user is None
(auth disabled), hiding all sessions that carry an explicit owner. Same flaw
on the Document and GalleryImage sub-queries (active-doc and gallery badges).
Replace all three with owner_filter(), which is a no-op when user is falsy.
Fixes#3620
The _migrate_* startup helpers in core/database.py opened a raw
sqlite3.connect() inside a try and called conn.close() as the last
statement in that try. If any earlier statement raised (locked DB,
unexpected schema, a failed ALTER), close() was skipped and the bare
except only logged the error — leaking the connection (file handle +
lock) for the lifetime of the process. These migrations run on every
startup.
Wrap each in the conn = None + try/except/finally pattern already used
by _migrate_chat_messages_fts in this same file, so the connection is
closed on all exit paths. 25 functions; no change on the success path.
Helpers that already close safely are left untouched: _migrate_chat_messages_fts
and _migrate_backfill_task_folders (the latter uses SQLAlchemy's
engine.connect() context manager).
Same bug class as the previously merged DB-connection-leak fix (#64)
and the IMAP logout-on-all-paths fix (#1530).
* fix(ui): escaped SVG renders as raw markup during web_search tool label
The _toolLabels['web_search'] entry embedded an SVG HTML string
concatenated with label text. At render time the entire value was
passed through esc(), HTML-escaping <svg> tags so the icon
displayed as raw text instead of rendering visually.
Fix: separate icon from label text via a _toolIcons map. The SVG
is injected as raw innerHTML (unescaped) in .agent-thread-icon,
while the label text remains safely escaped.
* test: add behavioral test for web_search tool icon rendering
Co-authored-by: TheDragonTail <jakeoldfield2@gmail.com>
---------
Co-authored-by: TheDragonTail <jakeoldfield2@gmail.com>
* fix(security): don't grant tool access in the pre-setup window
owner_is_admin_or_single_user() returned True whenever auth was not
configured, which conflated two very different states:
- intentional single-user mode (operator set AUTH_ENABLED=false), and
- the pre-setup window (auth enabled, but no admin created yet).
In the second state, blocked_tools_for_owner() returned an empty set, so
server-execution tools (bash/python) and other admin-only tools were
ungated. The auth middleware already 401s /api/ requests pre-setup, but a
caller that bypasses it (trusted loopback / internal-tool path) could reach
those tools before setup completed.
Treat "not configured" as admin only when auth is intentionally disabled
(AUTH_ENABLED=false), mirroring the AUTH_ENABLED parsing in app.py and
core.middleware. Single-user mode is preserved; the pre-setup window is now
non-admin as defense-in-depth.
Adds regression tests for both states.
Fixes#3201
Supported by Claude Opus 4.8
* refactor(security): reuse _auth_disabled() instead of a duplicate helper
Addresses review on #3506: src/auth_helpers.py already has _auth_disabled()
with the identical AUTH_ENABLED parse. Drop the duplicate
_auth_intentionally_disabled() and call the existing helper via a lazy import
inside owner_is_admin_or_single_user (mirroring the lazy core.auth import) to
avoid any import cycle. Removes the now-unused `import os`. Behaviour and the
two regression tests are unchanged.
Supported by Claude Opus 4.8
---------
Co-authored-by: SurprisedDuck <288741682+SurprisedDuck@users.noreply.github.com>
* fix(chat): stabilize system prompt, sequence memory extraction, send stable session id to preserve KV cache
Fixes#2927. As diagnosed in the issue, three things in Odysseus's request
pattern actively destroyed local backends' (llama.cpp / LM Studio) KV-cache
continuity, forcing a full prompt re-evaluation (15-30s+) on every turn:
1. Dynamic content folded into the system prompt every turn. Both the chat
preface (ChatProcessor.build_context_preface) and the agent system prompt
(_build_system_prompt) injected current_datetime_prompt() — text that
changes every minute — directly into system-role messages, which llm_core
then concatenates into the single system message sent as the cached
prefix. Any byte difference there invalidates the entire cache. Moved this
to a new current_datetime_context_message() helper that returns a
standalone user-role message, inserted near the end of the array (right
before the latest user turn) instead of mixed into the system prompt. The
static system prefix (preset prompt + safety policy + agent base prompt)
now stays byte-identical across turns of the same session.
2. Memory/skill extraction side-requests competed with the main completion.
run_post_response_tasks fired extract_and_store / maybe_extract_skill via
asyncio.create_task — fire-and-forget coroutines that could overlap the
next turn's main request and steal llama.cpp's limited processing slots,
evicting the cached checkpoint. They're now queued through a new
_run_extraction_jobs_sequentially helper that waits for the session's
stream to go idle and runs the jobs strictly one at a time.
3. No stable session identifier was sent to local backends, so llama.cpp
assigned a new processing slot via LRU every turn ("session_id=<empty>
server-selected (LCP/LRU)"), losing slot affinity. Added
_apply_local_cache_affinity() in llm_core, which sets session_id and
cache_prompt: true on outgoing payloads — gated to self-hosted
OpenAI-compatible endpoints only (never api.openai.com or other cloud
providers, which reject unrecognized request fields with a 400). Threaded
session_id through stream_llm / llm_call_async / stream_agent_loop from
the existing Odysseus session id.
Tests in tests/test_kv_cache_invalidation_2927.py exercise the real payload-
assembly and scheduling code paths: byte-identical system prefix across two
turns of the same session (with a regression check that genuinely changed
instructions DO still change it), the dynamic time block landing as a
user-role message, extraction jobs waiting for the stream to go idle and
running sequentially, and the outgoing payload carrying a stable session_id
(same across turns of one session, different across sessions) only for
self-hosted endpoints. Updated tests/test_user_time.py for the new message
placement.
* fix(tests): accept owner= kwarg in normalize_model_id monkeypatch
The upstream normalize_model_id signature now takes an owner= keyword
argument, and chat_helpers.py passes owner=getattr(sess, "owner", None)
at the call site. Update the test stub lambda to **kwargs so it handles
the new argument without breaking, and update chat_helpers.py to forward
the owner parameter consistently.
---------
Co-authored-by: Alexandre Teixeira <111787685+alteixeira20@users.noreply.github.com>
* fix(integrations): truncate api_call JSON lists with sentinel instead of mid-string cut
* fix(integrations): avoid mutating response dict in-place on truncation
* fix(integrations): truncate dict responses and bound list sentinel overhead
- Dict path now walks keys in insertion order, adding them one at a time
while checking that the accumulated dict + _truncated marker fits within
the 12 000-char limit. Previously the marker was appended without removing
any content, so large dicts were not actually truncated.
- List path now subtracts the sentinel's serialised size (+ element-separator
padding) from the budget before binary-searching, so the final array
including the sentinel stays at or under the limit.
- Add regression tests: large-dict actually-truncated, small-dict pass-through,
and list-with-sentinel respects the size bound.
---------
Co-authored-by: Alexandre Teixeira <111787685+alteixeira20@users.noreply.github.com>
Providers like Moonshot (Kimi K2.5/K2.6) require the reasoning_content
field to be present on assistant tool-call messages in multi-turn
conversations. The sanitizer's allow-list was missing this field,
causing HTTP 400: 'thinking is enabled but reasoning_content is missing
in assistant tool call message at index N'.
Add reasoning_content to the allowed field set in
_sanitize_llm_messages and cover with regression tests.
Fixes#3118
Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
Co-authored-by: Alexandre Teixeira <111787685+alteixeira20@users.noreply.github.com>
Alexandre Teixeira