* fix(agent): don't let a materialized default budget defeat context scaling
#1230 scales agent_input_token_budget to the model's context window unless
the user explicitly set a budget, detected via is_setting_overridden(). But
the settings-save path materializes every DEFAULT_SETTINGS key into
settings.json (load_settings merges defaults; handlers persist the merged
dict), so the persisted default 6000 reads as "overridden" and the budget
code takes the min(6000, ctx) branch — silently re-capping long-context
models at 6000 for anyone who has ever saved a setting. This reintroduces
the exact regression #1170/#1230 set out to fix.
Add is_setting_customized() (saved value != default) and gate the scaling
on it instead of mere presence. A persisted default is not a user choice.
is_setting_overridden has exactly one consumer (this budget path), so the
change is contained. Tests cover the materialized-default regression, a
deliberately-chosen budget still being honoured, and the absent-key case.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(agent): rework context-budget fix per review (#4122)
Address RaresKeY's review:
P2 (explicitness): is_setting_customized treated a saved value equal to the
default as "not explicit", which ALSO blocked a user from deliberately pinning
the default budget. Reframe the default value itself as the AUTO sentinel —
agent_input_token_budget == DEFAULT_BUDGET means "scale to the model's context
window", any other value is an explicit cap. A materialized default still reads
as auto (fixing the original regression), and any non-default value the user
chooses is now honoured. Drop the now-unused is_setting_customized helper.
P2 (fallback context): auto-scaling trusted get_context_length() even when it
returned only the bare DEFAULT_CONTEXT fallback (no endpoint-reported / known
window), over-allocating on self-hosted/proxy setups. Add get_context_length_known()
(also returns whether the window was actually discovered); the budget block
passes 0 when unknown so auto-scaling stays conservative instead of inflating to
an unproven window.
hard_max stays auto-only — a deliberate explicit budget wins (#1190); kept that
contract and answered the reviewer's question rather than silently reversing it.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test(agent): lock the materialized-default budget regression (review on #4121)
Per WGlynn's review on the issue: add an end-to-end regression that saves an
UNRELATED setting (which makes the settings-save path materialize the budget
default into settings.json) and asserts the budget still auto-scales rather than
re-reading as an explicit 6000 cap — locking the exact reopening shut.
To make the test bite the production decision (not just re-derive it), extract
`budget_is_explicit()` into src/context_budget.py and use it from the agent loop.
It keys off value-vs-default (the default is the auto sentinel), NOT settings
presence — which is the whole point, since the save path materializes defaults.
Note: after this PR's rework, is_setting_overridden has ZERO production callers,
so the merged-dict materialization smell can't reach any setting through a
presence check today (WGlynn's durability concern).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(agent): bind the budget context window to its own provenance (review #4122)
RaresKeY caught a correctness bug in the fallback-context guard: stream_agent_loop
kept only the `known` flag from get_context_length_known() and budgeted off the
passed-in `context_length`, which can come from a *different* lookup. Two failures:
- local endpoints are re-queried, so the passed value can be a stale DEFAULT_CONTEXT
fallback while the fresh probe proves the real (smaller) served context — we'd
scale off the stale value;
- callers that don't pass context_length (scheduled tasks, teacher escalation,
skill test runs, bg_monitor) were capped at 6000 even when a long window is
discoverable.
Extract budget_context_for_model() which returns the freshly-probed window when
known else 0, binding the flag to the value it proves; the agent loop uses it.
Regression tests cover the stale-fallback, no-arg-caller, and probe-error paths.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* docs(agent): fix stale budget comments + tighten to the contract (review #4122)
- settings.py: an explicit budget is clamped to the window only — hard_max is
auto-only (#1190); drop the incorrect "and to hard_max".
- is_setting_overridden docstring: drop the stale "adaptive budgets" example;
point value-sensitive callers at context_budget.budget_is_explicit.
- Tighten the budget-block comments to the contract (default = auto sentinel,
non-default = explicit cap, hard_max = auto-only ceiling).
Comment/docstring-only; no behaviour change.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* docs(agent): correct budget issue citations (#1190 → merged #1230/#1273)
The context-budget contract (auto-sentinel, explicit budgets honoured,
hard_max auto-only) merged via #1230 — #1190 was the earlier, closed,
superseded PR. Re-point the contract comments at #1230 (the live source,
already cited for the auto-sentinel two lines up in settings.py).
The configurable hard_max setting (`agent_input_token_hard_max`) was a
reviewer requirement first raised on #1190, omitted from the merged #1230,
and actually added in #1273 — credit #1273 for it and correct the test
comment's history (it previously implied this PR completed the requirement).
Comment/docstring-only; no behaviour change.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Make src.youtube_handler a compatibility wrapper around services.youtube.youtube_handler so transcript state, URL parsing, and timeout behavior no longer diverge.
Lock the API key encryption key file to owner-only permissions on creation and when reading existing keys, with regression coverage for permissions and encryption roundtrip.
load_settings() already catches PermissionError, but load_features() caught only
FileNotFoundError/JSONDecodeError/ValueError. An existing-but-unreadable
data/features.json (e.g. root-owned after a deploy) therefore raised instead of
falling back to DEFAULT_FEATURES, taking down GET /api/auth/features and anything
that reads feature flags. Add PermissionError to the except tuple to match
load_settings().
Adds tests/test_load_features_permission_error.py.
Co-authored-by: Alexandre Teixeira <111787685+alteixeira20@users.noreply.github.com>
* feat(agent): workspace confinement via context-local binding + get_workspace tool
Bind the per-turn workspace once in execute_tool_block; the shared path
resolvers (_resolve_tool_path / _resolve_search_root) and the subprocess cwd
helper (agent_cwd) read it, so file tools + bash/python are confined centrally
and a new tool that uses the shared helpers cannot accidentally bypass it.
Adds the admin-gated /api/workspace/browse picker, a workspace pill + directory
modal (reusing existing modal/button CSS), the /workspace slash command, and a
get_workspace tool (replaces a system-prompt block). Confinement is OS-agnostic
(realpath/normcase/commonpath) and docker-safe (container paths, no host
assumptions). Reopens#2023.
* ux(workspace): clarify workspace is not a sandbox
Picker modal note + pill tooltip + get_workspace tool/output wording now state
plainly: read_file/write_file/edit_file/grep/glob/ls are confined to the folder,
but bash/python only start there (cwd) and are not sandboxed. Modal note reuses
the existing .muted class.
* fix(agent): treat an active workspace as file-work intent
A vague low-signal message (e.g. "look at the local project") matches no
domain keywords, so tool retrieval is skipped and only always-available tools
are offered — leaving the agent with no file access even though a workspace is
set. When a workspace is active, include the file/code tools (incl.
get_workspace) on low-signal turns so the agent can act on the folder.
Also requires the tool index (ChromaDB) to be reachable for normal retrieval;
that is an environment dependency, not part of this change.
* ux(workspace): hide pill + overflow entry in chat mode
Workspace only scopes the agent's file/shell tools, so the pill and the
overflow 'Workspace' entry are agent-only now — hidden in chat mode like the
bash toggle. Mode read from the DOM in syncWorkspaceIndicator; applyMode() is
called from the agent/chat setMode handler.
* prompt(tools): steer bash/python to defer to the dedicated file tools
bash/python schema descriptions (what native-tool-calling models read) were
bare and gave no steer, so models would do file ops via the shell (e.g. writing
SVG/HTML, which then dumps raw markup into the tool preview). Tell bash/python
in the schema + tool-index + prompt section to prefer read_file/write_file/
edit_file/grep/glob/ls and only be used for what those do not cover.
* prompt(tools): keep bash/python deferral generic (no hardcoded tool names)
Reference 'a dedicated tool' rather than listing read_file/write_file/grep/etc.
by name, so the guidance does not go stale if those tools are renamed.
* style(workspace): drop em-dashes from added code comments/strings
* ux(workspace): terser non-sandbox note in picker (no tool-name list)
* ux(workspace): mirror terse non-sandbox wording in pill tooltip
* chore: untrack local venv symlink (run-only, not part of the feature)
* prompt(workspace): keep get_workspace text generic (no hardcoded tool names)
* fix(agent): low-signal + workspace surfaces only read-only file tools
Intersect the files tool group with PLAN_MODE_READONLY_TOOLS so a vague message
in a workspace exposes read_file/grep/glob/ls/get_workspace for exploration, but
not write_file/edit_file/bash/python -- those wait for a request that actually
calls for them (RAG retrieval still adds them on a real ask).
* feat(workspace): cap browse listing at 500 dirs with a truncated hint
Mirror the filesystem_tools._CODENAV_MAX_HITS pattern with a module-local
_MAX_BROWSE_DIRS so a directory with thousands of children does not dump every
row into the picker; the response carries a truncated flag and the modal tells
the user to type a path to jump in.
* chore: untrack local venv symlink (run-only artifact)
* fix(workspace): vet the workspace root against the sensitive-path deny list at bind time
The in-workspace resolver deny-lists sensitive paths inside the workspace,
but the empty-path search root is the workspace itself, so a workspace of
~/.ssh could be listed via ls with no path. vet_workspace() (public, in
tool_execution next to the resolvers) rejects non-directories and sensitive
roots before the path is ever bound; chat_routes uses it instead of its
inline isdir check.
* fix(workspace): reject filesystem roots and stop showing rejected workspaces as active
Review findings from #3665:
P2: vet_workspace accepted / (and would accept drive/UNC roots), which makes
every absolute path 'inside' the workspace and collapses confinement into
host-wide file access. A root is its own dirname, so reject when
dirname(resolved) == resolved; the browse response now carries a selectable
flag and the picker disables 'Use this folder' on unselectable dirs.
P3: /workspace set stored any string client-side and the chat route silently
dropped rejected values, so the pill could claim a confinement that was not
in effect. New admin-gated /api/workspace/vet validates manual paths before
they persist (canonical path returned), and when a posted workspace is
rejected at send time the stream emits workspace_rejected so the client
clears the stored value and toasts instead of continuing silently.
* fix(workspace): check caller privilege before vetting the posted workspace
Review finding: /api/chat_stream called vet_workspace() on the posted value
for every caller and emitted workspace_rejected on failure, so a non-admin
who can chat but cannot use file/shell tools could distinguish existing
directories from missing/file/sensitive/root paths by whether the event
appeared. The resolution now lives in _resolve_request_workspace, which
drops the submitted value uniformly for non-admin callers, with no vetting
and no event, before the path ever touches the filesystem. Admin and
single-user behavior is unchanged. Test pins that valid and invalid paths
are indistinguishable for a non-admin and that vet_workspace is never
invoked for them.
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.
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.
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>
Found the reason yesterday's tool-retrieval drop wasn't taking effect:
in _build_agent_prompt, when relevant_tools was provided, it computed
tool_names = set(ALWAYS_AVAILABLE) | set(relevant_tools)
which silently re-added every tool get_tools_for_query had just
deliberately discarded. So when a 'save this for <person>' query
dropped manage_memory from the retrieved set, the prompt builder put
it right back, and the model saw both tools again.
Trust the relevant_tools set. get_tools_for_query already starts from
ALWAYS_AVAILABLE — any discard there is intentional and should
propagate. Only force-include ask_user and update_plan here as belt-
and-suspenders since the agent loop relies on those for its own
control flow.
Other callers (task_scheduler) already union ALWAYS_AVAILABLE or
ASSISTANT_ALWAYS_AVAILABLE into relevant_tools before passing it in,
so they're unaffected.
Description-level steering wasn't enough — even with the explicit 'DO
NOT use for info about another person' in manage_memory's description,
models kept choosing memory over manage_contact. They can't if memory
isn't in the toolset.
New logic in ToolIndex.get_tools_for_query: detect three contact-save
patterns and discard manage_memory from the returned set (overriding
ALWAYS_AVAILABLE):
1. 'save [up to 3 words] for/to <name>' where <name> isn't a timing /
pronoun stopword (later, tomorrow, me, you, future, etc.). Catches
the canonical 'save this for X' and the wider 'save this address
for X', 'save it for X'.
2. 'to/in/into (my) contacts' or 'address book'. Catches both 'add X
to my contacts' and 'put this in my address book for X'.
3. Possessive: 'save (his/her/their) (address/phone/email/...)'.
Stronger signal — also force-adds manage_contact to the set in
case the keyword fallback missed it.
Verified: 8 positive contact patterns all drop memory, 10 false-
positive 'save X for later/tomorrow/me/the next thing' all keep it.
Even with manage_contact in the retrieved tool set, models were still
defaulting to manage_memory when the user pasted an address + 'save for
<person>'. Both tools were in front of the model and it picked memory.
Tighten both descriptions to steer at decision-time:
- agent_loop.py manage_memory description: clarify scope is facts
about the USER, with an explicit 'DO NOT use for info about another
person' + a 'use manage_contact instead' line.
- tool_index.py manage_memory description: same in shorter form, so the
embedded retrieval signal is consistent with the prompt-time
description.
The literal phrase 'add to contacts' missed when there was a name
between 'add' and 'to', e.g. 'add Pat to my contacts'. Anchor on the
tail with 'to my contacts', 'to contacts', 'to address book' so word
boundaries fire regardless of what sits in front.
Closes the gap that pushed the agent into manage_memory when the user
pasted an address and said 'save this for X'. manage_contact now
accepts an optional address arg end-to-end:
- routes/contacts_routes.py:
- _normalize_contact carries an 'address' field
- _build_vcard emits ADR:;;<address>;;;; (street component of the
RFC-6350 7-part ADR), only when address is non-empty
- _parse_vcards reads ADR, joins non-empty components with ', '
- _create_contact and _update_contact thread address through;
update preserves existing address when caller passes empty
- src/tool_implementations.py do_manage_contact:
- add accepts address; require at least name+address or email
(was: email required) so address-only contacts are addable
- update accepts address; require name OR emails OR address
- src/tool_schemas.py: schema gets a single 'address' string field
- src/tool_index.py + src/agent_loop.py: descriptions get one
'address' arg mention and a 'use this for save-X-for-person /
address pastes / phone-with-name' steering line. Net: a few
bytes added, not a paragraph.
Also: removed a stray name from the schema's manage_contact example
strings ('save Jonathan's email…') — no real names in the codebase.
When the user dumps a postal address or phone number alongside a
person's name and says 'save this for X', the vector retriever was
missing manage_contact because its description only mentioned the
literal word 'contact'. The model defaulted to manage_memory (which is
in ALWAYS_AVAILABLE), so the saved fact ended up as un-named memory
that wouldn't surface on a later 'what's X's address?' search.
- Rewrite manage_contact's index description to anchor on the
semantics: 'save info about another person', including postal/
mailing address, ZIP, phone, etc. Now it embeds close to address-
paste queries.
- Extend the keyword intent-map with 'save this for', 'save it for',
'mailing address', 'postal code', 'their address', etc. — common
ways users say 'this belongs to a contact' without the literal word
'contact'.
Closes the auto-send hole that let earlier models invent signatures
(e.g. signing 'David' for a user named Felix) and SMTP them to real
recipients before the user could review.
New setting: agent_email_confirm (default True).
When on, the MCP send_email and reply_to_email tools no longer SMTP
directly — they write the composed email to scheduled_emails with a new
status 'agent_draft' (far-future send_at so the scheduled-send poller
ignores them) and return a {pending: true, pending_id, to, subject,
body, message: ...} payload. The model surfaces that to the user.
Backend endpoints to approve / cancel:
- GET /api/email/pending → list staged drafts for the owner
- POST /api/email/pending/{id}/approve → flip status to 'pending' +
backdate send_at so the
existing scheduled-send
poller delivers immediately
- DELETE /api/email/pending/{id} → status = 'cancelled'
UI:
- Settings / AI Defaults gets a new 'Email Safety' card with the
toggle, default on.
- Tool descriptions for send_email and reply_to_email now include the
pending behavior + an explicit 'DO NOT invent a signature, do not
type a person's name' guardrail.
Pass 2 (next): inline chat card with Send / Discard buttons so the user
doesn't have to type a confirmation reply. Today's prompt + the listing
endpoint give the model a clean path to surface drafts.
_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.
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.
Wire the existing built-in PERSONAS catalog through to scheduled tasks
the same way I wired it to reminder synthesis. Repurposes the
dormant scheduled_tasks.character_id column.
UI (static/js/tasks.js)
- New 'Persona' select in the LLM / Research task form, with the five
built-in characters (socrates/razor/nietzsche/spark/odysseus) plus a
default 'no persona' option. Pre-populates from existing.character_id
on edit. Non-llm/research types explicitly clear it on save.
API (routes/task_routes.py)
- TaskCreate + TaskUpdate gain character_id: Optional[str].
- _task_to_dict echoes character_id back so the form can hydrate on
edit. Update endpoint stores '' as None to allow clearing.
Runner (src/task_scheduler.py)
- When task.character_id is set and matches a built-in persona, prepend
the persona prompt to the task system prompt so the model speaks in
that voice while still knowing it's running a scheduled task.
- crew_member.personality still wins as the base; character_id stacks
on top.
* 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>
Two months of iteration on the Settings panel, integration forms, and
small visual nudges across the app. Highlights:
Settings restructure
- Add Models: split into separate Local + API cards (no more in-card
tabs); each fuses Type/Provider with the URL input.
- Added Models: new dedicated sidebar tab, with Probe + Clear-offline
pulled into its header; Local/API sub-section icons accent-tinted.
- Search: Web Search and a new Deep Research card (Model + tuning),
with a cross-link to AI Defaults. Provider hints use real clickable
anchors; Web Search Test button shows a whirlpool spinner.
- AI Defaults: Image Generation card returns; Research Model card
carries only Endpoint+Model with a cross-link to Search; Vision /
Default / Utility fallbacks unified under one numbered-row design
matching Search's chain.
- API Permissions (was 'API Tokens'): per-row rename, inline
Permissions toggle that expands the scope-edit panel, in-field
copy icons (icon→check on success). Empty state accent-tinted.
- Integrations: + Add Integration drops a type-picker menu directly
under the button (drop-up on tight viewports); each integration
form (API, CalDAV, CardDAV, Email, Codex/Claude, Vault, MCP) uses
the same accent-outlined Save/Test/Cancel buttons right-aligned.
- Danger Zone: Wipe→Delete with trash icons; new 'Delete everything'
row at the bottom that loops every category.
AI Synthesis (Reminders)
- Persona dropdown sourced from PROMPT_TEMPLATES + custom preset.
- src/reminder_personas.py mirrors the five built-ins for the
server-side synthesis path.
- dispatch_reminder() reads reminder_llm_persona and uses the
persona's system prompt; empty/unknown falls back to warm-neutral.
Esc handling
- Kebab menus and the provider picker intercept Esc in capture phase
so dismissing a popup no longer closes the whole Settings modal.
Accent tinting
- Scoped CSS rule across data-settings-panel=ai/services/added-models/
search/integrations/reminders for card h2 icons + the Added Models
sub-section icons.
Codex/Claude integration form
- No more auto-creation on form open — explicit Create token button.
- New tokens start with every scope granted; existing tokens move out
of the integration form into the API Permissions card.
- Setup reveal: copy buttons inline inside the token + setup code
blocks; shorter subtitle wording.
Misc visual polish
- Save/Test/Cancel uniformly accent-outlined and right-aligned on
every integration form.
- Provider logos render inline next to the search fallback selects
and the Deep Research Search dropdown.
- Trash icons in fallback rows bumped to 20x20 so they fill the 32px
button.
- Image generation default flipped to off.
* 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>
Abhishek Kumbhar