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63 Commits

Author SHA1 Message Date
nikakhalatiani 927b1f7ecf fix(llm): normalize OpenAI-compatible chat URLs 
Normalize OpenAI-compatible chat URL shapes so base /v1 endpoints route to /v1/chat/completions while already-full chat endpoints remain idempotent.

Preserve native local Ollama routing for bare localhost:11434 endpoints, keep localhost:11434/v1 as OpenAI-compatible, and add focused regression coverage for provider detection, chat target URLs, and model listing from /v1.

Part of #541.
 2026-06-28 15:30:15 +01:00
Rudra Sarker 090f4078d8 fix(llm-core): prevent cache-affinity fields from reaching Cerebras 
Recognize api.cerebras.ai as a Cerebras cloud provider so llama.cpp/LM Studio cache-affinity fields are not attached even when endpoint_kind is misconfigured as local. Add regression coverage for provider detection, self-hosted classification, and payload field exclusion.
 2026-06-27 18:07:12 +01:00
SINE 2dfc83ee22 fix(models): accept bare-list /models responses (Together AI) (#4761) 
* fix(api): handle varying response formats for model IDs from compatible providers

merge conflict for pr-2204 resolved

* fix(modal): keep body-portaled dropdowns above their tool modal at any stack depth (#4720) (#4724)

* fix(memory): keep the Brain memory item menu above the modal at any stack depth

The memory item "⋮" dropdown is portaled to <body> with a hardcoded
z-index of 10001. Tool modals, however, get a monotonically increasing
z-index from modalManager's bring-to-front counter (_modalTopZ), which
climbs unbounded as modals are opened/restored over a session. Once that
counter passes 10001, the Brain modal stacks above the body-portaled
dropdown, so the menu renders behind the panel — visible only where it
spills past the modal's edge (#4720).

Derive the dropdown's z-index from the owning modal's current z-index
(+1), keeping 10001 as a floor for the common low-counter case, so the
menu always sits just above its modal however high the counter has climbed.

Verified with document.elementFromPoint at the dropdown's location: with a
high modal z-index the old build returns the modal at every sampled point
(menu behind); the fixed build returns the dropdown (menu on top). The
default low-counter case is unchanged (z stays 10001).

* refactor(modal): route body-portaled dropdowns through a shared topPortalZ() helper

The hardcoded z-index:10001 the Brain memory menu used (#4720) is the same
literal shared by ~16 body-portaled dropdowns across calendar, cookbook,
cookbookServe, documentLibrary, emailLibrary, gallery, notes, emojiPicker and
memory — each renders behind its owning tool modal once modalManager's
bring-to-front counter climbs past the literal over a long session.

Promote the per-dropdown fix into a single topPortalZ() helper in
toolWindowZOrder.js — the existing source of truth for tool-window z, already
imported by modalManager's _bringToFront and notes.js — returning
max(topToolWindowZ(), dock-chip floor) + 1, so a portaled dropdown always sits
just above the live tool-window stack however high the counter has climbed.
Route all 16 sites through it. The slashCommands tour tooltips and the
cookbookServe VRAM dialog are intentionally left out (neither is a modal-owned
portaled dropdown).

Add tests/test_portal_dropdown_z_js.py covering the helper, including the #4720
scenario (modal counter at 99999 -> dropdown at 100000). Existing
test_notes_z_order_js.py stays green.

* fix(llm): detect mistral.ai provider and support reasoning_effort (#4698)

* fix(llm): detect mistral.ai provider and support reasoning_effort

Four coupled bugs broke Mistral thinking model support:

1. _detect_provider() had no mistral.ai host check, so all Mistral
   endpoints fell through to the generic 'openai' provider string.
   _provider_display_name() correctly identified them as 'Mistral',
   making any 'if provider == "Mistral"' check elsewhere dead code.

2. reasoning_effort parameter was never sent in the request payload,
   so Mistral never activated thinking mode even when the user
   configured a thinking-capable model (mistral-small-latest,
   mistral-medium-latest, magistral-*).

3. Mistral returns content as a typed array
   ([{"type":"thinking",...},{"type":"text",...}]) when
   reasoning is on, not as a plain string. Both the streaming and
   non-streaming parsers expected strings and silently dropped the
   thinking content.

4. _THINKING_MODEL_PATTERNS didn't include magistral or mistral-*
   model prefixes, so the frontend wouldn't tag reasoning output
   as thinking even after the above were fixed.

Fix:
- Add mistral.ai to _detect_provider() host checks
- Add a _normalize_mistral_content() helper that splits the typed
  array into (text, thinking) strings
- Inject payload["reasoning_effort"] = "high" when provider is
  Mistral and _supports_thinking(model) is true, in both stream_llm
  and llm_call_async payload construction
- Wire the normalizer into both response parsers
- Extend _THINKING_MODEL_PATTERNS to include magistral,
  mistral-small, mistral-medium, mistral-large

Tested on Docker install with mistral-small-latest +
reasoning_effort=high. Reasoning streams correctly into the
thinking panel after the fix.

Fixes #4678

* fix(llm): address review — lowercase provider id, configurable effort, tests

Addresses vdmkenny's review on PR #4698:

1. Removed duplicate 'if provider == "mistral"' block in stream_llm
   — two back-to-back copies, one was dead-redundant.

2. Dropped personal-context comment ('free-tier limits are generous
   for this user') and made reasoning_effort configurable via env var
   ODYSSEUS_MISTRAL_REASONING_EFFORT (high / medium / low / none).
   Default remains 'high' for backward compat with the tested behavior.

3. Recased provider id from 'Mistral' to 'mistral' to match the
   lowercase convention used by every other provider id in the file
   (openai, anthropic, ollama, copilot, ...). _provider_display_name()
   still returns the Title-Case 'Mistral' for UI labels — only the
   runtime id used in 'if provider == ...' checks was recased.

4. Added tests/test_llm_core_mistral_content.py with 13 tests pinning
   _normalize_mistral_content()'s contract: string passthrough, the
   Mistral array format (thinking + text blocks), and edge cases
   (empty, garbage, None, wrong types, missing fields, string-vs-array
   inner thinking field).

Also fixed a gap the review didn't catch: the non-streaming paths
(llm_call sync + llm_call_async) were missing the reasoning_effort
injection entirely. Added the same injection to both, so Deep Research
and agent tool calls also activate Mistral thinking.

All 13 new tests pass. Existing reasoning/streaming/ollama-thinking
tests still pass (38 tests, no regressions).

Fixes #4678

* fix: Images cannot be seen by model that is vision capable (#4726)

* fix: Images cannot be seen by model that is vision capable

* fix: skip http(s) image_url for Ollama (images[] is base64-only)

---------

Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>

* fix(chat): strip executed email tool fences from the live stream (#3993) (#4275)

* fix(chat): strip executed email tool fences from the live stream (#3993)

The backend strips every fenced tool block from persisted text (the regex in
src/tool_parsing.py is built from the full TOOL_TAGS set, which includes the
email tools), so a reloaded session renders cleanly. The live frontend path
uses a separate hardcoded EXEC_FENCE_RE in static/js/chatRenderer.js that only
listed web_search/read_file/write_file/create_document/edit_document/
update_document — so executed email tool fences (list_emails, etc.) lingered as
raw code blocks in the live assistant bubble until the user reloaded.

Add the nine email tool tags to EXEC_FENCE_RE so the live render settles into
the same clean layout as the history reload. bash/python stay excluded on
purpose: those are languages a user may legitimately have asked the model to
show as code, not tool invocations.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(chat): single-source live exec-fence tool list from TOOL_TAGS (#3993)

Per review: EXEC_FENCE_RE was a second, hand-maintained copy of the
executable-tool list, so any tool not in it — and every future tool added to
TOOL_TAGS — would leave its executed fence lingering in the live bubble until
reload (the original #3993 bug, recurring one tool at a time).

EXEC_FENCE_RE is now built from an explicit EXEC_TOOL_TAGS list that mirrors
TOOL_TAGS (src/agent_tools/__init__.py) minus bash/python, which stay excluded
as legitimate code-example languages. A new regression test
(test_exec_fence_re_covers_all_executable_tools) extracts both lists from
source and fails if they drift, so the whole class is caught in CI instead of
by a user — the "minimum acceptable middle ground" from the review, made exact
(set equality, not just coverage).

Verified: pytest tests/test_live_strip_email_tool_fences.py (5 passed);
node --check static/js/chatRenderer.js; and a node run of the built regex
confirms email/generate_image/manage_memory/ls fences strip while
bash/python/sh are preserved.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(chat): build live exec-fence list from /api/tools at runtime (#3993)

Make TOOL_TAGS the single source for live exec-fence stripping. chatRenderer.js
no longer hard-codes a tool list; it fetches the backend's authoritative set
once from GET /api/tools (sorted(TOOL_TAGS)) and builds EXEC_FENCE_RE from it at
load, minus bash/python. No second list to drift, and a future tool added to
TOOL_TAGS is covered automatically — without touching the streaming path.

Until the fetch resolves EXEC_FENCE_RE is null and exec fences aren't stripped
(a sub-second window before the first stream); the backend already strips
persisted history, so a reload always renders clean.

Drop test_exec_fence_re_covers_all_executable_tools (no hand-maintained list to
guard) and add source-level guards: the frontend keeps no hard-coded list and
fetches /api/tools, and the endpoint serves the full sorted(TOOL_TAGS).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CVCKth4g8pWh7pwFDVm4iL

* fix(chat): warn on /api/tools fetch failure instead of swallowing it (#3993)

A fresh-context review flagged that loadExecFenceRegex's catch silently
discarded errors: if the one-shot fetch fails, EXEC_FENCE_RE stays null for the
whole session and live exec fences go unstripped until reload, with zero signal.
console.warn it, and correct the comment to describe the failure mode honestly
(was understated as just a sub-second startup window).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CVCKth4g8pWh7pwFDVm4iL

---------

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(routes): log and cleanly 500 on unreadable HTML page (#4637)

* fix(routes): serve 404 instead of 500 when an HTML page file is missing

_serve_html_with_nonce opened the HTML file with no error handling, and
callers such as /backgrounds and /login pass their paths in with no
existence check, so a missing or unreadable file raised an unhandled
OSError that surfaced as a 500. Wrap the read and raise HTTPException(404)
instead; the normal render path (CSP-nonce substitution) is unchanged.

Fixes #4594

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(routes): distinguish missing page (404) from read failure (500)

The previous fix caught a broad OSError and returned 404 for every
failure, which masks real server-side problems (permission errors, I/O
failures) as "not found" and lets them slip past error alerting. Split
FileNotFoundError (genuine 404) from other OSError, which now logs the
exception and returns a generic 500 — without leaking the OS error
string or file path into the response body.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(routes): treat unreadable bundled HTML page as logged 500, not 404

Per PR #4637 review: every caller of the page-render helper serves a fixed,
server-owned template (index/login/backgrounds), never a client-supplied
path. So a missing or unreadable file is a server fault (broken deployment),
not a client "not found" — a 404 there mislabels a server error and hides a
missing core template from 5xx alerting, contradicting the OSError->500
rationale this PR is built on. Collapse both branches into a single logged,
leak-free 500.

Move the helper to src.app_helpers.serve_html_with_nonce so the behavior can
be unit-tested without importing the whole app (app.py is the slim
orchestrator; the test harness stubs src.database, so importing app in tests
is not viable). Add tests pinning missing/unreadable -> 500 (not 404) and
nonce injection on the happy path.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

* feat(catalog): add Gemma 4 12B/QAT entries and RTX 3050 bandwidth (#4728)

Add official Gemma 4 12B-it plus QAT-INT4/INT8 catalog entries (with their
GGUF sources), QAT quantization support across the quant tables and the
prequantized-prefix list, and the missing RTX 3050 / 3050 Ti memory
bandwidth so speed estimates stop falling back to the generic cuda value.

* fix debugging on windows (#4679)

* fix: Real-ESRGAN install + Cookbook deps-panel crash on the Python 3.14 image (#4694)

* fix(docker): make Real-ESRGAN installable on the Python 3.14 image

realesrgan's deps basicsr/gfpgan/facexlib (unmaintained since 2022) read
their version in setup.py via `exec(...); locals()['__version__']`, which
raises KeyError on Python 3.13+ — PEP 667 made locals() in a function an
independent snapshot that exec() can no longer mutate. That fails the
Cookbook "install realesrgan" sdist build on the python:3.14 base.

Add a `realesrgan-wheels` builder stage that fetches the pinned sdists,
patches get_version() to exec into an explicit namespace dict, and builds
wheels; the final stage installs them --no-deps so a later
`pip install realesrgan` resolves from wheels instead of rebuilding the
broken sdists. torch stays a runtime pull to keep the base image lean.

Also add the runtime libs opencv-python (cv2) needs — libgl1,
libglib2.0-0t64, libxcb1 — which the slim base omits; without them the
install succeeds but `import cv2` dies with
`libxcb.so.1: cannot open shared object file`.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(cookbook): don't let a package's sys.exit() on import hang the deps panel

The local optional-dependency probe imports each package in-process and
catches ImportError / Exception. But a package can call sys.exit() at
import time — e.g. rembg does `sys.exit(1)` when no onnxruntime backend
loads. SystemExit is a BaseException, not Exception, so it escaped the
probe, propagated out of the list_packages endpoint, and hung the whole
Dependencies panel / worker (the UI loads forever).

Catch (Exception, SystemExit) so one broken optional package is reported
as not-usable instead of taking down the panel.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

* fix(routes): 500 (not 404) when the app-shell index.html is missing (#4791)

Follow-up to #4637. serve_index — the handler for / and the SPA deep-link
routes (/notes, /calendar, /cookbook, /email, /memory, /gallery, /tasks,
/library) — pre-checked os.path.exists and raised its own
HTTPException(404, "index.html not found") when the bundle was missing. So a
missing core template returned 404 before serve_html_with_nonce's 500 could
fire, the one inconsistency left after #4637.

index.html is a fixed, app-bundled template; a missing one is a broken
deployment (server fault), not a client "not found", so it should surface as a
logged 500 in 5xx alerting rather than a 404. Keep the static->root fallback,
drop the redundant existence guard and the dead-end 404, and let the shared
helper handle the missing case.

Verified against the running app: / and /notes return 200 with the bundle
present and a logged 500 when index.html is absent.

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

* fix(setup): load .env so a pre-seeded admin password is honored on native installs (#4787)

setup.py read ODYSSEUS_ADMIN_USER / ODYSSEUS_ADMIN_PASSWORD via os.getenv()
but never loaded .env, so on native Linux/macOS installs a password
pre-seeded in .env (documented in docs/setup.md and .env.example) was
silently ignored and a random one generated, breaking the first login.
Docker was unaffected because compose passes the vars into the container env.

Call load_dotenv(BASE_DIR/.env, encoding="utf-8-sig") at the top of main(),
mirroring app.py (utf-8-sig tolerates a Notepad UTF-8 BOM). load_dotenv does
not override already-exported OS vars, so the existing precedence is kept.
python-dotenv is already a required dependency.

Adds a regression test that pre-seeds credentials only in .env (not the
shell) and asserts the stored bcrypt hash matches the pre-seeded password.

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix: email poller marks calendar extraction processed on LLM failure (#4622)

Move calendar processed-marker insert into the LLM success path (else branch).
Previously, the INSERT ran even after a transient LLM failure, causing the
poller to skip retrying calendar extraction on subsequent runs.

Minimal change: only touches the try/except/else control flow in
_auto_summarize_pass_single() — preserves existing formatting and line endings.

* feat(ui): add toggle for padding around chat area (#4691)

* feat: Allow admins to choose if they want to share defaults (#4752)

* First bare fix

* Adding the option toggle

* toggle function fix

* Final fix, added missing /auth/

* Extended toggle text & added tests

* Comments change

* Description toggle change

* br tag fix

* description change based on suggestion

* fix(agent): parse misfenced read_file calls (#4799)

* fix: use atomic write in APIKeyManager.save() to prevent credential data loss (#4591) (#4597)

* fix: use atomic write in APIKeyManager.save() to prevent data loss

Opening api_keys.json with 'w' truncates the file before writing, so a
crash, disk-full, or mid-write error leaves all stored provider API keys
corrupted. Switch to atomic write (temp file + fsync + os.replace) so
the original file is always intact on any failure.

Fixes #4591

* chore: trigger CI re-run

* chore: update PR description

* chore: fix how-to-test section for description check

---------

Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>

* feat(discovery): detect llama.cpp servers and label local providers (#4729)

* feat(discovery): detect llama.cpp servers and label local providers

Scan port 8080 (llama-server) and 11435 (APFEL) during discovery, fingerprint
llama.cpp via its native /props endpoint, and label well-known local serving
ports (8080 llama.cpp, 8000 vLLM, 1234 LM Studio, 11434 Ollama) consistently
in both the Python provider helper and the JS endpoint UI. Adds a llama.cpp
hint to the /setup slash command.

* fix(discovery): don't infer the serving tool from the port alone

Per review: vLLM, SGLang, llama.cpp and plain OpenAI-compatible servers all
share 8000/8080, so labeling by port mislabels real setups (a vLLM box on 8080
shown as llama.cpp). Drop the port->tool assertions from _provider_label and
providerLabel; the authoritative signal is the /props fingerprint done during
discovery, which is unchanged. Loopback now reads a neutral 'local endpoint' /
'Local'. Tests updated to assert the neutral labels.

* refactor(tools): migrate config/integration admin tools to the registry (#4742)

Part of #3629 (the `admin_tools.py` bullet). Moves the config/integration admin
tools off the legacy elif dispatch chain in tool_implementations.py onto the
agent_tools registry:

  manage_endpoints, manage_mcp, manage_webhooks, manage_tokens, manage_settings

The do_* implementations (and manage_mcp's command-allowlist / RCE guard:
_validate_mcp_command, _mcp_allowed_commands, and the _MCP_* constants) move
verbatim into the new src/agent_tools/admin_tools.py. They register through a
single ADMIN_TOOL_HANDLERS map that TOOL_HANDLERS.update()s, and the five elif
branches plus their imports are dropped from tool_execution.py, so these tools
now flow through _direct_fallback like the other migrated clusters. The names
are re-exported from src.agent_tools for back-compat.

Dedup:
  - _parse_tool_args was duplicated in tool_implementations.py and
    document_tools.py. It now lives once in src.tool_utils (which imports nothing
    from the project beyond src.constants, so this introduces no cycle) and both
    call sites import it from there. The orphaned `import json` in document_tools
    is removed with it.
  - The five tools share one _owner_adapter(fn) factory that threads ctx["owner"]
    into the owner-taking do_* signature, instead of five near-identical wrappers.

Tests: new tests/test_admin_tools_registry.py pins the registration, the
re-export back-compat, the owner-threading adapter, and the single-source
_parse_tool_args (across admin_tools and document_tools). Existing MCP /
settings / webhook suites are repointed at the new module.

* refactor(exceptions): dedupe src/exceptions via core re-export (#4785)

src/exceptions.py was a byte-for-byte duplicate of the canonical
core/exceptions.py. Replace its class bodies with a re-export shim
(mirroring the core/constants.py -> src/constants.py pattern) so the
exception classes are defined in exactly one place. Also fix the stale
"# src/exceptions.py" header comment in core/exceptions.py.

No behavior change: both import paths resolve to the same class objects
(verified by identity), so `except SessionNotFoundError` works regardless
of which module it was imported from. Ran py_compile and
pytest tests/test_app.py (12 passed).

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(tasks): normalize task endpoint URL to /chat/completions before model call (#4619)

Upstream bug (present in pewdiepie-archdaemon/odysseus main): the task
executor passes task.endpoint_url VERBATIM to the model HTTP call, unlike
the chat path which stores build_chat_url(normalize_base(base)) on the
session. A task carrying an explicit bare OpenAI-compatible base such as
"http://host:11434/v1" therefore POSTs to a 404 ("page not found"); the
agent loop swallows the empty body into "The model returned an empty
response" and marks the run success, so nothing surfaces the failure.

Tasks that omit an endpoint dodge this only because _resolve_defaults()
cribs an already-full URL from a recent chat session. The API/token path
(e.g. an external client that POSTs /api/tasks with endpoint_url=".../v1")
hits it every time.

Fix: route every resolved task endpoint through _normalize_chat_endpoint()
at the three resolution sites (_execute_llm_task, the persona/research
session path, and _execute_research_task). The helper is idempotent
(strips any existing chat suffix, re-appends the correct one) and leaves
native-Ollama (/api...) and already-concrete URLs untouched, so other
providers are unaffected. Proven via isolated repro: ".../v1" -> 404 ->
empty; ".../v1/chat/completions" -> 200 -> real gemma4:31b output.

Regression test asserts the bare-/v1 -> full-chat-URL mapping, idempotency,
and the native-Ollama/empty passthroughs.

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(model-routes): harden _probe_endpoint against malformed model-list responses (#4789)

* fix(model-routes): harden _probe_endpoint against malformed model-list responses

_probe_endpoint parsed model lists with data.get(...) at four sites without
checking that data is a dict, and built the list with a truthiness-only
filter. A /models (or /api/tags) endpoint returning HTTP 200 with valid but
non-dict JSON ([], "x", null, 123) made data.get(...) raise AttributeError,
and a non-string id like 123 passed the filter and then hit .startswith() /
.lower() in the Z.AI/Kimi curated merge and _is_chat_model(). Both errors are
swallowed by the broad except Exception, but the comprehension dies mid-list
so the ENTIRE probed model list is discarded and the endpoint silently
degrades — masking a misconfigured/non-compliant upstream as "no models".

- Guard each data.get(...) with isinstance(data, dict) so a non-dict body
  falls through the existing `or []` default.
- Restrict the OpenAI and Ollama model-list comprehensions to non-empty str
  values, protecting the .startswith() merges and both _is_chat_model calls.
- Add an isinstance guard at the top of _is_chat_model (defense in depth for
  all four call sites).

No behavior change for well-formed {"data":[...]} / {"models":[...]}
responses. Adds regression tests (non-dict body via caplog, mixed/all
non-string ids, _is_chat_model boundary) that fail before the fix and pass
after.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(model-routes): extract _openai_model_ids / _ollama_model_names helpers

Per review on #4789: the malformed-response guards were inlined four times in
_probe_endpoint (two OpenAI-id comprehensions, two Ollama-name comprehensions).
Pull each into a small, directly-testable helper so the security-relevant
parsing lives in one place and a future malformed-shape fix doesn't have to be
applied in four spots (CONTRIBUTING flags repeated logic for this reason).

Behavior is unchanged. Adds direct unit tests for both helpers (non-dict body,
non-string ids, non-dict entries, name>model precedence).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(cookbook): only block model launch on real port collisions (#4760)

* Fix #4507: only block model launch on real port collisions

Quick-run hardcoded port 8000 and never called _nextAvailablePort(), so
every launch collided. Both pre-launch guards (serve panel + quick-run)
were count-based and fired regardless of port.

- quick-run now auto-assigns a free port (8080 for llama.cpp)
- both guards parse the new port and only prompt on a real overlap,
  stopping only the colliding serve
- dialog reports the actual port instead of a hardcoded 8000

* refactor(cookbook): share _taskPort for port parsing; auto-assign llama.cpp port

Addresses review on #4760:
- _taskPort regex now matches --port= as well as --port (space)
- _nextAvailablePort and both launch guards reuse _taskPort instead of inline regex
- quick-run llama.cpp no longer pins 8080, so two can run concurrently

* fix(cookbook): _taskPort also parses -p; add port-parsing tests

Addresses review on #4760:
- _taskPort now matches -p <n> too, so it's the complete single reader
  (was missing the short flag that other readers already handle)
- add tests/test_cookbook_port_parsing_js.py covering the port forms,
  shared-reader reuse, and llama.cpp auto-assign

* test(cookbook): extract pure port helpers and test behavior

Addresses review on #4760: the prior tests only asserted source strings.
- extract portOf() and nextFreePort() into static/js/cookbookPorts.js
- cookbookRunning.js imports them; _taskPort and _nextAvailablePort delegate
- tests run the helpers via node and assert real behavior: all port forms
  (--port, --port=, -p, -p=), next-free-port skipping taken ports, and the
  same-port-clash / different-port-coexist outcome

---------

Co-authored-by: samy <samy@odysseus.boukouro.com>

* fix(ui): route tasks.js + skills.js dropdowns through topPortalZ() (#4768)

Fixes #4767. #4724 routed 16 body-portaled dropdowns through the shared
topPortalZ() helper so they always render just above the currently-raised tool
modal, but two were missed and still used a hardcoded z-index, so they hit the
same #4720 bug once a modal's bring-to-front counter climbed past the literal:

  - tasks.js _showTaskDropdown(): inline z-index:100000 on .task-dropdown
  - skills.js kebab menu (.skill-kebab-menu): z-index:100002 in style.css

Both now set zIndex from topPortalZ() after they are appended to the body,
matching the other migrated sites. The dead CSS z-index on .skill-kebab-menu is
removed (the inline value always wins). test_portal_dropdown_z_js.py gains a
source guard asserting both files use topPortalZ() and that no hardcoded
100000/100002 portal literal survives in either file or style.css.

* do_list_models in ai_interaction.py dropped

---------

Co-authored-by: Max Hsu <maxmilian@users.noreply.github.com>
Co-authored-by: aubrey <kyuhex@gmail.com>
Co-authored-by: Michael <52305679+michaelxer@users.noreply.github.com>
Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Ahmed Dlshad <ahmed.dlshad.m@gmail.com>
Co-authored-by: Joel Alejandro Escareño Fernández <52678667+TheAlexz@users.noreply.github.com>
Co-authored-by: Kalin Stoyanov <kgs.void@gmail.com>
Co-authored-by: Pedro Barbosa <devpedrobarbosa@gmail.com>
Co-authored-by: Solanki Sumit <125974181+YAMRAJ13y@users.noreply.github.com>
Co-authored-by: Rudra Sarker <78224940+rudra496@users.noreply.github.com>
Co-authored-by: Skoh <101289702+SkohTV@users.noreply.github.com>
Co-authored-by: Jakub Grula <ramsters110@gmail.com>
Co-authored-by: Dividesbyzer0 <54127744+zoomdbz@users.noreply.github.com>
Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
Co-authored-by: Magiomakes <114195802+Magiomakes@users.noreply.github.com>
Co-authored-by: Samy <12219635+touzenesmy@users.noreply.github.com>
Co-authored-by: samy <samy@odysseus.boukouro.com>
 2026-06-27 16:25:15 +01:00
Kevin Fiddick 8888819d74 Isolate untrusted context from visible user prompts (#3584) 
Prevent untrusted source/context guard text from being merged into the current visible user request during provider message sanitization.

Changes:
- Detect untrusted context blocks during LLM message sanitization
- Insert a short assistant boundary before the current user request
- Keep the visible user prompt as its own user message
- Preserve normal consecutive user-message merging for non-untrusted cases
- Strengthen prompt-security wording to avoid mentioning guard wrappers
- Add regression coverage for untrusted context followed by a user prompt

Notes:
- Untrusted context remains role:user for safety
- This does not add prompt debug logging
- This does not change frontend draft persistence
 2026-06-27 13:50:04 +01:00
Joel Alejandro Escareño Fernández e0ccf250a4 feat(discovery): detect llama.cpp servers and label local providers (#4729) 
* feat(discovery): detect llama.cpp servers and label local providers

Scan port 8080 (llama-server) and 11435 (APFEL) during discovery, fingerprint
llama.cpp via its native /props endpoint, and label well-known local serving
ports (8080 llama.cpp, 8000 vLLM, 1234 LM Studio, 11434 Ollama) consistently
in both the Python provider helper and the JS endpoint UI. Adds a llama.cpp
hint to the /setup slash command.

* fix(discovery): don't infer the serving tool from the port alone

Per review: vLLM, SGLang, llama.cpp and plain OpenAI-compatible servers all
share 8000/8080, so labeling by port mislabels real setups (a vLLM box on 8080
shown as llama.cpp). Drop the port->tool assertions from _provider_label and
providerLabel; the authoritative signal is the /props fingerprint done during
discovery, which is unchanged. Loopback now reads a neutral 'local endpoint' /
'Local'. Tests updated to assert the neutral labels.
 2026-06-23 23:39:56 +02:00
Michael e8175c9535 fix: Images cannot be seen by model that is vision capable (#4726) 
* fix: Images cannot be seen by model that is vision capable

* fix: skip http(s) image_url for Ollama (images[] is base64-only)

---------

Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
 2026-06-23 10:32:57 +02:00
aubrey bd9149f79a fix(llm): detect mistral.ai provider and support reasoning_effort (#4698) 
* fix(llm): detect mistral.ai provider and support reasoning_effort

Four coupled bugs broke Mistral thinking model support:

1. _detect_provider() had no mistral.ai host check, so all Mistral
   endpoints fell through to the generic 'openai' provider string.
   _provider_display_name() correctly identified them as 'Mistral',
   making any 'if provider == "Mistral"' check elsewhere dead code.

2. reasoning_effort parameter was never sent in the request payload,
   so Mistral never activated thinking mode even when the user
   configured a thinking-capable model (mistral-small-latest,
   mistral-medium-latest, magistral-*).

3. Mistral returns content as a typed array
   ([{"type":"thinking",...},{"type":"text",...}]) when
   reasoning is on, not as a plain string. Both the streaming and
   non-streaming parsers expected strings and silently dropped the
   thinking content.

4. _THINKING_MODEL_PATTERNS didn't include magistral or mistral-*
   model prefixes, so the frontend wouldn't tag reasoning output
   as thinking even after the above were fixed.

Fix:
- Add mistral.ai to _detect_provider() host checks
- Add a _normalize_mistral_content() helper that splits the typed
  array into (text, thinking) strings
- Inject payload["reasoning_effort"] = "high" when provider is
  Mistral and _supports_thinking(model) is true, in both stream_llm
  and llm_call_async payload construction
- Wire the normalizer into both response parsers
- Extend _THINKING_MODEL_PATTERNS to include magistral,
  mistral-small, mistral-medium, mistral-large

Tested on Docker install with mistral-small-latest +
reasoning_effort=high. Reasoning streams correctly into the
thinking panel after the fix.

Fixes #4678

* fix(llm): address review — lowercase provider id, configurable effort, tests

Addresses vdmkenny's review on PR #4698:

1. Removed duplicate 'if provider == "mistral"' block in stream_llm
   — two back-to-back copies, one was dead-redundant.

2. Dropped personal-context comment ('free-tier limits are generous
   for this user') and made reasoning_effort configurable via env var
   ODYSSEUS_MISTRAL_REASONING_EFFORT (high / medium / low / none).
   Default remains 'high' for backward compat with the tested behavior.

3. Recased provider id from 'Mistral' to 'mistral' to match the
   lowercase convention used by every other provider id in the file
   (openai, anthropic, ollama, copilot, ...). _provider_display_name()
   still returns the Title-Case 'Mistral' for UI labels — only the
   runtime id used in 'if provider == ...' checks was recased.

4. Added tests/test_llm_core_mistral_content.py with 13 tests pinning
   _normalize_mistral_content()'s contract: string passthrough, the
   Mistral array format (thinking + text blocks), and edge cases
   (empty, garbage, None, wrong types, missing fields, string-vs-array
   inner thinking field).

Also fixed a gap the review didn't catch: the non-streaming paths
(llm_call sync + llm_call_async) were missing the reasoning_effort
injection entirely. Added the same injection to both, so Deep Research
and agent tool calls also activate Mistral thinking.

All 13 new tests pass. Existing reasoning/streaming/ollama-thinking
tests still pass (38 tests, no regressions).

Fixes #4678
 2026-06-23 10:28:17 +02:00
pewdiepie-archdaemon 92daf4e560 Cookbook launch and gallery upload fixes 2026-06-22 01:49:15 +00:00
pewdiepie-archdaemon 75f04bc088 Merge origin/dev into main 2026-06-21 11:08:50 +00:00
pewdiepie-archdaemon c504214925 Cookbook model workflow fixes 2026-06-21 11:02:35 +00:00
nubs 0bfc7750a2 fix(llm): route gpt-oss harmony commentary channel without leaking markers/tool-args (#4523) 
The harmony stream router only recognized the analysis and final channels, so
gpt-oss's standard `commentary` channel (tool-call preambles / function-arg
bodies) was unhandled: the literal `<|channel|>commentary` marker, the
`to=functions.*` recipient, and the commentary body all leaked into the
visible answer. Add commentary to the marker regex + the suffix-hold table, and
route its body to thinking (only `final` is user-facing). Adds a regression
test (split-chunk + recipient + body), verified to fail without the fix.
 2026-06-18 21:12:25 +02:00
Lucas Daniel f4e8990635 chore: add warnings to silent except Exception blocks (#3212) 
* log(app): add warnings to silent except Exception blocks

- Internal tool auth header failure now logs a warning instead of
  silently passing, making auth bypass easier to spot in logs.
- Token last_used_at update failure now logs at DEBUG (fire-and-forget,
  non-critical, but useful when debugging token tracking issues).
- Image ownership verification failure now logs a warning so unexpected
  access-check errors surface instead of silently allowing the request.

* log(chat_routes): add warnings to silent except Exception blocks

- clear_orphaned_session_endpoint: log before rollback so failures
  appear in traces when users see stale/deleted model options.
- _endpoint_has_model (JSON parse): log malformed cached_models instead
  of silently treating endpoint as valid.
- _has_any_visible_model (JSON parse): log malformed cached_models
  instead of silently returning empty list.
- timezone header parse: log failure so time-zone-related tool bugs
  (wrong scheduled times, calendar events) are traceable.
- attachments JSON parse: log failure so silently-dropped attachments
  are visible in server logs.

* log(email_routes): add warnings to silent except Exception blocks

- Email alias resolution failure now logs a warning instead of silently
  returning an empty list, making broken account configs diagnosable.

* log(document_routes): add warnings to silent except Exception blocks

- Export ZIP request body parse failure now logs a warning so empty
  exports caused by malformed requests are diagnosable.
- clear_active_document failure on detach now logs a warning to help
  trace doc re-injection bugs like #1160.

* log(agent_loop): add warnings to silent except Exception blocks

- builtin tool overrides load failure now logs a warning so misconfigured
  settings don't silently fall back to defaults without a trace.
- Timezone context injection failure now logs a warning to help debug
  incorrect scheduled times in agent-created tasks.
- PDF form-backed document detection failure now logs a warning so
  broken form-doc UI is traceable to the root cause.

* log(llm_core): add warnings to silent except Exception blocks

- Malformed URL in _is_ollama_native_url now logs a warning so bad
  endpoint configs are traceable instead of silently returning False.
- Model list fetch failure now logs a warning with the endpoint URL so
  endpoints that silently vanish from the model picker are diagnosable.

* log: pass exception via exc_info instead of string interpolation

* fix(logging): avoid logging raw URLs in llm_core error paths

Drop the raw url/base_chat_url from the Ollama-detection and
model-list-fetch warning logs added by this sweep, since these values
can contain private hostnames, internal IPs, credentials, or other
deployment details.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
 2026-06-15 17:49:27 +01:00
Josh Patra f5d3e5098a fix(llm): omit temperature for Kimi K2.5 and K2.6 (#3960) 2026-06-15 20:29:22 +09:00
KYDNO 955455b797 fix(kimi): resolve Kimi Code API 403 errors and User-Agent restrictions (#3549) 
* fix(kimi): resolve Kimi Code API 403 errors and User-Agent restrictions

Kimi Code subscription keys require a whitelisted coding-agent User-Agent to avoid access_terminated_error 403s. This adds User-Agent probing and caching for Kimi Code endpoints.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(kimi): omit temperature for kimi-for-coding API calls

Kimi Code rejects any non-default temperature with HTTP 400, which broke deep research probes and low-temp LLM rounds.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-06-15 15:56:54 +09:00
cirim 056d1fb960 fix(llm): make connect timeout configurable 
Use a configurable LLM_CONNECT_TIMEOUT for call and stream connect budgets instead of the previous hard-coded 3s default.
 2026-06-15 15:11:38 +09:00
Kenny Van de Maele 263d41c58a fix(llm): stop sending llama.cpp slot-affinity fields to cloud providers (#3945) 
* 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.
 2026-06-11 17:51:03 +02:00
George Lawton 4f48cfa9ae fix: omit temperature for Opus 4.7+ on native Anthropic path (#3117) 
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>
 2026-06-11 16:27:40 +03:00
Lucas Daniel 55ff22c6d5 fix(chat): stabilize system prompt, sequence memory extraction, and send stable session id to preserve KV cache (#3360) 
* 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>
 2026-06-09 22:46:54 +01:00
Michael 2e6fff2212 fix: preserve reasoning_content in sanitized messages for Moonshot/Kimi (#3152) 
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>
 2026-06-09 21:44:38 +01:00
Sid 9e74a327f8 fix(llm): remove max_output_tokens from ChatGPT Subscription payload (#3656) 
ChatGPT's Codex API rejects any request that includes max_output_tokens,
returning HTTP 400 "Unsupported parameter: max_output_tokens". This caused
Deep Research to always fail during the endpoint probe when a ChatGPT
Subscription model was selected.

Remove the conditional that set payload["max_output_tokens"] in
_build_chatgpt_responses_payload(). The parameter is simply not sent.

Also update the two affected tests:
- Rename test_chatgpt_subscription_payload_uses_max_output_tokens →
  test_chatgpt_subscription_payload_omits_max_output_tokens
- Rename test_chatgpt_subscription_payload_omits_empty_max_output_tokens →
  test_chatgpt_subscription_payload_omits_max_output_tokens_when_zero
- Assert max_output_tokens is absent rather than present

Fixes #3650
 2026-06-09 17:42:12 +02:00
Maruf Hasan c3fcaf15b7 feat(providers): add NVIDIA AI provider endpoint support (#3456) 
* feat: add NVIDIA as an AI provider (integrate.api.nvidia.com)

* feat: add NVIDIA option to provider settings dropdown and aliases

* test: add NVIDIA provider detection and endpoint tests

* Add NVIDIA to _HOST_TO_CURATED and expand non-chat model filtering

- nvidia.com -> 'nvidia' curated key for proper provider routing
- _NON_CHAT_PREFIXES: bge, snowflake/arctic-embed, nvidia/nv-embed
- _NON_CHAT_CONTAINS: content-safety, -safety, -reward, nvclip,
  kosmos, fuyu, deplot, vila, neva, gliner, riva, -parse,
  -embedqa, -nemoretriever

* Expand non-chat model filtering for NVIDIA embedding/guard/video models

Add _NON_CHAT_PREFIXES: embed, recurrent
Add _NON_CHAT_CONTAINS: topic-control, guard, calibration,
  ai-synthetic-video, cosmos-reason2

Catches remaining unfiltered non-chat models from NVIDIA catalog:
embedding (llama-nemotron-embed, embed-qa), guard (llama-guard,
nemoguard-topic-control), calibration (ising-calibration),
video (ai-synthetic-video-detector, cosmos-reason2),
recurrent (recurrentgemma-2b)

* Filter non-chat models in _probe_endpoint via _is_chat_model()

Previously _is_chat_model() was only used in the per-model probe
and _first_chat_model(), so non-chat models still appeared in the
model picker even though they were filtered in those specific paths.
Applying the filter at _probe_endpoint() return ensures non-chat
models (embeddings, safety guards, reward, calibration, video
detectors, CLIP, VLM, translation, parsing, recurrent, etc.) never
enter cached_models and never appear in the picker.

* Fix _NON_CHAT_CONTAINS to catch org-prefixed embedding models

Prefix checks (mid.startswith) miss models with org prefixes like
baai/bge-m3, nvidia/embed-qa-4, google/recurrentgemma-2b, etc.
Adding the same terms to _NON_CHAT_CONTAINS ensures they are caught
regardless of the org prefix.

Adds: embed, bge, recurrent, starcoder, gemma-2b

* fix(model-routes): drop collision-prone substrings from global non-chat filter

The NVIDIA PR added several substrings to the shared _NON_CHAT_PREFIXES
and _NON_CHAT_CONTAINS tuples. These are intended to filter out
embedding, retrieval, safety, and vision models from NVIDIA's catalog
that are not chat-completions-capable. However, four of the added
substrings collide with legitimate chat models served by other providers:

  - gemma-2b  matches google/gemma-2b-it (instruct chat model)
  - starcoder matches bigcode/starcoder2-15b (code completion model)
  - recurrent matches google/recurrentgemma-2b (language model)
  - guard     matches meta-llama/Llama-Guard-3-8B (safety classifier)

Removing these four from the global tuples keeps the NVIDIA-specific
filtering intact (safety, embedding, retrieval, and vision models are
still caught by other tokens such as content-safety, -safety, -reward,
embed, bge, -embedqa, -nemoretriever, nvclip, deplot, etc.) while
preventing false negatives for instruct/code models on other providers.

Tests added for gemma-2b-it, google/gemma-2b-it, and
bigcode/starcoder2-15b-instruct asserting they are recognized as chat
models.

Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>

* fix(nvidia): remove duplicate bge/embed tokens from _NON_CHAT_CONTAINS

Tokens already present in _NON_CHAT_PREFIXES, making the CONTAINS
entries redundant since the prefix check runs first.

Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>

* fix(nvidia): move bge to CONTAINS, add llama-guard, remove stray blanks

Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>

* style: fix indentation of groq and xai test cases in test_provider_endpoints.py

---------

Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
 2026-06-09 11:06:12 +02:00
onemorethan0 8ae2b5f58c fix(llm): suppress thinking mode for qwen3/gemma4 on Ollama /v1 endpoint (#3228) 
* fix(llm): suppress thinking for qwen3/gemma4 on Ollama /v1 compat endpoint

When using qwen3, QwQ, gemma4, or other thinking models via Ollama's
OpenAI-compatible /v1 endpoint, the model routes all output into its
<think>...</think> reasoning block. Since Odysseus strips thinking
content from round_response and only accumulates native tool_calls,
this produces a round with 0 chars, 0 native calls, 0 tool blocks —
the agent appears to silently do nothing.

Root cause: Odysseus classifies the /v1 endpoint as provider="openai"
(not "ollama"), so the payload is built as a standard OpenAI payload
without any Ollama-specific options. Ollama's /v1 endpoint accepts
"think": false as a top-level parameter to suppress extended thinking,
but this was never sent.

Fix:
- Add _is_ollama_openai_compat_url() to detect local Ollama /v1 URLs
- Inject "think": false in both stream_llm and llm_call_async for
  thinking models (qwen3, QwQ, gemma4, DeepSeek-R1, etc.) on this
  endpoint

Verified with qwen3:14b on Ollama 0.24: with think=False the model
correctly emits native tool_calls in a single streaming chunk and
the agent executes bash/file/web tools as expected.

* fix(llm): extend _is_ollama_openai_compat_url to match localhost on any port

Per reviewer feedback on PR #3228:

1. Generalize host detection to mirror _is_ollama_native_url: match any
   localhost/127.0.0.1/0.0.0.0/::1 host (not just port 11434) so that
   custom OLLAMA_HOST ports and container remaps are also covered.

2. Add tests/test_llm_core_ollama_thinking.py covering:
   - _is_ollama_openai_compat_url for all positive/negative URL cases
     including IPv6, non-default port, native /api path, and real OpenAI
   - Payload injection: think:false set for Ollama /v1 thinking model,
     not set for non-thinking model, not set for real OpenAI endpoint,
     and set for localhost on a non-default port (the new case)
 2026-06-09 07:35:15 +02:00
Ocean Bennett e7c1d75884 fix(models): query v1 models for llama-server endpoints (#3380) 
* fix(models): query v1 models for llama-server endpoints

* test(models): accept owner kwargs in llama-server regression
 2026-06-09 01:09:02 +02:00
stocky789 1e0d9b92af feat: add ChatGPT Subscription provider (#2876) 
* feat: Add ChatGPT Subscription support and related features

- Introduced a new provider option for ChatGPT Subscription in the endpoint selection UI.
- Implemented OAuth flow for ChatGPT Subscription sign-in, including polling for authorization status.
- Updated admin interface to handle ChatGPT Subscription, including disabling API key input and providing user guidance.
- Enhanced cost tracking logic to differentiate between subscription and non-subscription endpoints.
- Added new slash commands for managing skills, including listing, searching, and invoking skills.
- Implemented caching for skill catalog to optimize performance.
- Updated tests to cover new ChatGPT Subscription functionality and ensure proper endpoint probing.
- Refactored existing code to accommodate new features and improve maintainability.

* refactor: share provider device-flow setup

- reuse one device-flow backend for Copilot and ChatGPT Subscription
- add one frontend device-flow helper for Settings and /setup
- put GitHub Copilot back into Add Models, now as a dropdown option
- make provider selection just select; clicking Add starts sign-in
- stop ChatGPT Subscription setup from opening auth tabs automatically
- make /setup copilot and /setup chatgpt-subscription work from chat
- show ChatGPT Subscription in the /setup suggestions
- show the real error message when setup fails
- add focused tests for the shared flow and setup UI

* feat(chatgpt-subscription): harden credential lifecycle and streamline auth UX

Backend:
- Resolve runtime bearer for provider-auth endpoints at probe time via a
  shared _resolve_probe_key() that delegates to resolve_endpoint_runtime,
  applied across all probe/refresh call sites.
- Skip live completion probes and health pings for discovery-only providers
  (centralized behind _is_discovery_only_provider) — the Codex/Responses API
  has no such endpoints, so status is derived from cached models.
- Never persist the short lived ChatGPT bearer to the plaintext sessions
  table; proactively clear any stale bearer left by an earlier code path.
- Revoke orphaned ProviderAuthSession credentials when the last endpoint
  backing them is deleted (_delete_orphaned_provider_auth), surfaced via
  cleared_provider_auth in the delete response.

Frontend (admin.js):
- Auto-start the device-auth flow on provider selection so the authorization
  panel (code + Authorize) shows immediately instead of behind a "Sign in" click.
- Remove the redundant top button for device auth providers, move retry
  into the panel via an inline "Try again".
- Drop the self-evident hint text and add an execCommand clipboard fallback so
  Copy works in non-secure (HTTP/LAN) contexts.

* fix: harden chatgpt subscription provider

* chore: remove PR media from branch

* Fix chatgpt subscription recovery and token handling

---------

Co-authored-by: 5p00kyy <admin@5p00ky.dev>
 2026-06-08 10:19:18 +02:00
adabarbulescu a8859bb25c fix(llm): Properly detect remote Ollama bare URLs as native endpoints (fixes #3252) (#3343) 2026-06-07 21:19:19 +02:00
M57 12cb39cbd9 feat: add OpenCode Zen and Go as provider options (#26) 
- Add OpenCode Zen (https://opencode.ai/zen/v1) and Go (https://opencode.ai/zen/go/v1)
- Add provider detection via _host_match() in llm_core.py
- Add curated model list entries in model_routes.py
- Add webhook provider URLs
- Add provider icon (providers.js) and dropdown options (index.html)
- Add auto-detection patterns and setup URLs (slashCommands.js)
- Whitelist opencode.ai in URL validation (admin.js)
- Rebased on main to fix merge conflicts with _HOST_TO_CURATED refactor

Co-authored-by: M57 <hy4ri@users.noreply.github.com>
 2026-06-07 16:43:00 +02:00
Mohammed Riaz 6ccd4500d7 fix(chat): show requested and actual reply models 
Show requested and actual reply models in chat labels when fallback or provider routing changes the responding model.
 2026-06-06 04:30:16 -06:00
nubs 47a47bf71d fix(llm): guard against null arguments in streaming tool-call accumulator (#2923) 2026-06-05 20:57:36 +02:00
nubs 8354948a1c fix(llm): route harmony thinking streams (#2449) 2026-06-05 15:22:08 +02:00
Isaiah Gardner 134c608466 fix: degrade missing/None content key in system messages to empty string (#2570) 2026-06-05 00:10:11 +02:00
Kenny Van de Maele 1cd0aa2b8c feat(provider): add GitHub Copilot provider with device-flow auth (#1480) 
* feat(provider): add GitHub Copilot provider with device-flow auth

Adds GitHub Copilot as a model provider, so Copilot models (gpt-4o/4.1/5,
Claude, Gemini, …) work through the normal chat + agent loop, incl. native
tool calling and vision.

Auth is one-click via the GitHub OAuth device flow; the access token is stored
as the endpoint's (encrypted) api_key and sent directly as `Authorization:
Bearer` (no Copilot-token exchange, no refresh — matching how editors talk to
the Copilot API). Copilot is a normal ModelEndpoint detected by host; the only
provider-specific behaviour is a small set of required request headers,
injected centrally.

Sign-in is available from Settings → model endpoints ("Connect GitHub
Copilot") and from chat via `/setup copilot`.

- src/copilot.py (new), routes/copilot_routes.py (new): constants, header
  builders, device-flow start/poll, model discovery, owner-scoped endpoint
  provisioning.
- src/llm_core.py, src/endpoint_resolver.py: detect `copilot`, inject headers,
  per-request x-initiator/vision.
- src/agent_loop.py: allowlist api.githubcopilot.com for native tool schemas.
- src/model_context.py: known context windows for Copilot (no unauthenticated
  /models probe).
- static/, README, tests/test_copilot*.py.

* Tidy copilot_routes: clarify supports_tools, note _PENDING is per-process
 2026-06-04 21:13:14 +02:00
Giuseppe 6d511f6e66 fix(llm): auto-detect <think> in content stream for unregistered thinking models (#2588) 
* fix(llm): auto-detect <think> in content stream for unregistered thinking models

_THINKING_MODEL_PATTERNS only covers known model families by name. Qwen3-derived
models with non-standard names (e.g. Qwopus, custom QwQ forks) are not matched,
so their <think>...</think> content streams through as visible chat text instead
of being routed to the thinking display.

When the first content delta opens with <think> and the model was not already
identified as a thinking model, dynamically flag the stream as a thinking model
for the remainder of the response. This enables the existing </think> repair path
(line below) and ensures the frontend receives the full <think>...</think> wrapper
it needs to split thinking from the final answer.

The check is restricted to the very first content delta (_first_content_sent is
False) to avoid misidentifying models that happen to write "<think>" mid-answer.

Fixes #2225
Related: #2420 (covered by separate PR from @AmmarS-Analyst), #2224 (@RaresKeY)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(llm): replace inert _thinking_model flag with _in_think_tag state machine

The original auto-detect set _thinking_model=True on the first <think> chunk
but still emitted it as a regular delta and set _first_content_sent=True
immediately, so no subsequent chunk could enter the repair path.

Replace with _in_think_tag bool: enter thinking mode when first content starts
with <think>, route all chunks to the thinking channel until </think> is found,
then the tail becomes the first regular delta. Adds three regression tests.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(llm): replace _first_content_sent guard with _think_open_stripped

Opening-tag stripping used `not _first_content_sent` as the guard, but
_first_content_sent stays False throughout the entire think block (it only
flips when regular content is emitted). So `find(">")` ran on every
reasoning chunk — not just the first — and silently truncated everything
before the first ">" in any reasoning text containing comparisons, arrows,
or code.

Fix: add `_think_open_stripped = False` alongside `_in_think_tag`. Use it
as the strip guard in both the "still inside <think>" path and the
"</think> found in same chunk" split path. Set it True once the opening
tag is consumed so all subsequent chunks reach the thinking channel
unmolested.

Add regression test: 3-chunk stream where the middle chunk contains
"c > d" — confirms "more c " is not dropped.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
 2026-06-04 20:18:19 +02:00
Giuseppe 531f426557 fix: KeyError on missing 'content' key in system messages (#2362) 
A system message that arrives without a 'content' key — possible via
malformed tool results — raised a KeyError in the hot path of llm_call,
llm_call_async, and stream_llm. Replace m["content"] with
m.get("content") or "" in all three functions so a missing key degrades
to an empty string instead of crashing.

Also removes a redundant .rstrip() after .strip() in _model_activity_key.

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
 2026-06-04 19:38:45 +02:00
Giuseppe ff8f9f2188 fix: llm_call_async does not retry on HTTP 429/502/503/504 (#2364) 
The retry loop raised immediately for any non-success HTTP response
regardless of attempt count. For transient upstream errors (rate limit,
bad gateway, gateway timeout) the function should back off and retry
within the existing attempt budget.

Also lets ConnectError / ConnectTimeout retry when the host has not been
cooled and attempts remain, instead of always raising on the first
connect failure.

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
 2026-06-04 19:35:55 +02:00
Giuseppe bc9104efe2 fix: SSE stream parser crashes with NoneType on providers sending null choice/usage/tc entries (#2389) 
* fix: SSE parser crashes with NoneType on MiniMax-M3 (and any provider sending null choice/usage/tc)

Three guards added in stream_llm:

1. choices[0] null check — MiniMax (and some other providers) send a
   choices entry as None. `_choices[0].get("delta")` raised
   AttributeError. Now checks `_choices[0] is not None` before calling
   .get().

2. usage null guard — j["usage"] can arrive as None (not a dict) on
   some providers. Added `or {}` so subsequent .get() calls don't crash.

3. tool_calls null entry skip — individual entries in the tool_calls
   array can be None. Added `if tc is None: continue` before
   tc.get("function").

All three match the `or {}` / null-guard pattern used elsewhere in the
same block. Safe for all OpenAI-compatible providers.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: guard null choice in elif-choices SSE branch

The usage-chunk path already guarded _choices[0] is not None, but the
elif "choices" branch that processes content/tool-call deltas did not.
A chunk like {"choices": [null]} or {"choices": [null], "usage": null}
reaches j["choices"][0].get("delta") and crashes with:

    'NoneType' object has no attribute 'get'

Fix: extract choices[0] into _c0 and continue to the next chunk when
it is None, matching the guard already applied in the usage path.

Adds three focused regressions covering the paths the maintainer flagged:
- {"choices": [null]}
- {"choices": [null], "usage": null}
- tool_calls array containing a null entry alongside a valid call

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
 2026-06-04 13:53:10 +01:00
tanmayraut45 f59edee611 Support extra CA bundle for private-CA LLM providers (#769) 
Adding GigaChat (Sber) or an on-premise enterprise LLM gateway as a
model endpoint fails on first probe with

    CERTIFICATE_VERIFY_FAILED: self-signed certificate in certificate
    chain (_ssl.c:1000)

because their TLS chain is signed by a private root CA (Russian Trusted
Root CA for GigaChat; corporate CA for on-prem) that isn't part of the
default system / certifi trust store. The endpoint shows offline in
the picker even though the URL and API key are correct (issue #722).

The right fix is to extend the trust store, not to weaken verification.
This change:

- src/tls_overrides.py: new module that resolves an opt-in env var
  LLM_CA_BUNDLE at import time, builds a shared SSLContext via
  ssl.create_default_context() (so the system / certifi bundle is
  loaded first) and layers the operator's PEM on top with
  load_verify_locations(). Exposes llm_verify() returning a value
  suitable for httpx `verify=`. Defaults to True (httpx built-in
  trust) when the env var is unset, when the file is missing, or
  when the PEM fails to load — verification is never silently
  disabled, the warning is logged and we fall back to the safe path.

- src/llm_core.py: thread llm_verify() into the shared AsyncClient
  used by stream_llm / streaming completions.

- routes/model_routes.py: thread llm_verify() into the five httpx.get
  call sites in _probe_endpoint / _ping_endpoint so adding a
  private-CA endpoint goes green on the very first probe and the
  picker stops showing it offline.

- .env.example: document LLM_CA_BUNDLE with the GigaChat case as the
  concrete example.

Deliberately NOT included: a verify=False knob (global or per-host).
Disabling verification exposes the affected endpoint to MITM, and the
operator-supplied bundle is the correct fix for legitimate private-CA
providers — so the only switch in this PR is the safe one.

Closes #722.
 2026-06-04 13:18:50 +01:00
Yuri a2e691da2b fix(models): stabilize proxy endpoint refresh behavior 
* fix: support large proxy model endpoint refresh

Large OpenAI-compatible proxy endpoints can expose hundreds of models and make /v1/models slow. Treating those endpoints like local model servers caused model picker opens and background probes to repeatedly hit /models, producing timeouts and making otherwise usable endpoints appear offline.

Make model endpoint discovery cached-first for normal UI usage, add explicit proxy/API classification and refresh policy fields, exclude proxy/API endpoints from aggressive local probing, and preserve cached models when refresh fails.

Manual Test/Add/Refresh actions still fetch the full model list with longer timeouts so users can intentionally import large proxy model lists without blocking normal model picker usage.

* fix: preserve endpoint ping status semantics
 2026-06-04 04:56:11 +01:00
danielroytel 39848a168b fix: recognize Gemma 4 as a thinking model and add context entry (#1642) 
Gemma 4 returns reasoning_content in streaming responses via
llama-server, but the model wasn't listed in _THINKING_MODEL_PATTERNS,
causing reasoning tokens to be mishandled. Add "gemma" to the pattern
list and register Gemma 4's 128K context window in KNOWN_CONTEXT_WINDOWS
so the agent loop budgets context correctly.

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
 2026-06-03 14:23:18 +09:00
Afonso Coutinho 19e62208d2 fix: streaming drops providers that emit SSE data lines with no space (#1701) 2026-06-03 13:37:14 +09:00
Afonso Coutinho 3da4edb442 fix: token usage dropped when it rides on a non-empty finish delta (#1703) 2026-06-03 13:36:57 +09:00
lekt8 126e91e8b9 Don't attempt the same (url, model) route twice in the fallback chains (#1733) 
The fallback helpers (llm_call_with_fallback, llm_call_async_with_fallback,
stream_llm_with_fallback) build their candidate list as the primary target
followed by the configured fallbacks. Callers prepend the session's live
(url, model) to default_model_fallbacks, so if the user also lists their current
model among the fallbacks — a common misconfiguration — the chain re-attempts
the very route that just failed: a wasted round-trip (and, for the streaming
path, a spurious 'fallback' notice for a switch that didn't actually happen).

Add a small _dedupe_candidates() helper that filters malformed entries and drops
a later repeat of an already-seen (url, model), preserving order (first wins,
keeping its headers). Apply it in all three fallback chains.

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
 2026-06-03 13:33:50 +09:00
Ethan b9c382006e Clamp Anthropic temperature to [0.0, 1.0] in _build_anthropic_payload (#1737) 
Anthropic's Messages API rejects temperature > 1.0 with HTTP 400, but
_build_anthropic_payload forwarded it verbatim. The shipped "Nietzsche" preset
uses temperature 1.2 and the UI slider allows up to 2.0, so every Claude request
under such a preset hard-broke. Clamp into [0.0, 1.0] in the Anthropic builder
only (OpenAI keeps its wider 0.0-2.0 range). Covers all three Anthropic call
paths, which build through this one function. None is passed through unchanged.

Fixes #1615

Co-authored-by: Ethan <23321960+0xLeathery@users.noreply.github.com>
 2026-06-03 13:29:36 +09:00
Shreyas S Joshi 7504fedb17 fix: surface reasoning_content when content is empty (thinking models) (#1233) 
Thinking models served via llama.cpp without --reasoning-format none
(e.g. Qwen3, DeepSeek-R1) route all tokens into reasoning_content and
return content="". Two call paths were silently broken:

- llm_call / llm_call_async (non-streaming): hard-keyed
  data["choices"][0]["message"]["content"] raises KeyError or returns
  empty string, discarding the entire response.

- stream_agent_loop end-of-round fallback: when full_response is empty
  but round_reasoning has content, the existing code replaced the
  response with the generic empty-response error message, discarding
  all reasoning tokens that were correctly accumulated during streaming.

Fix: in both non-streaming paths use msg.get("content") or
msg.get("reasoning_content") or "". In the streaming fallback, surface
round_reasoning as the answer before falling through to the error path.
 2026-06-03 01:41:24 +09:00
Afonso Coutinho 65751186bd fix: merging consecutive user messages corrupts multimodal (image) content (#1277) 
* fix: preserve multimodal content blocks when merging consecutive user messages

* test: consecutive user-message merge keeps multimodal image blocks
 2026-06-03 01:21:57 +09:00
Afonso Coutinho a04553013d fix: Anthropic responses with multiple text blocks lose all but the first (#1255) 
* fix: concatenate all Anthropic text blocks, not just the first

* test: Anthropic response parsing concatenates text blocks
 2026-06-03 00:57:20 +09:00
pewdiepie-archdaemon ff93a6c63b Polish email and cookbook flows 2026-06-02 22:42:07 +09:00
SurprisedDuck 934bca9e48 Providers: omit temperature for OpenAI reasoning models 
* fix: omit temperature for OpenAI reasoning models (o1/o3/o4/gpt-5)

These models only accept the default temperature; sending any explicit
value (even 0.0) returns HTTP 400 "Only the default (1) value is
supported". This broke two paths:

- Endpoint probing in _probe_single_model hardcodes temperature: 0.0, so
  a perfectly valid o3/gpt-5 endpoint is reported as failing in the
  Model Endpoints health check.
- Chat/stream payloads send temperature unconditionally, so a non-default
  temperature preset 400s on these models.

The code already special-cases the same model family for
max_completion_tokens, so this adds a sibling _restricts_temperature()
helper and omits the field for those models, letting the API use its
required default. gpt-4.5 is intentionally excluded (not a reasoning
model; accepts temperature normally).

Adds tests/test_llm_core_temperature.py covering the predicate and the
synchronous payload builder.

* fix: also omit temperature for reasoning models on the direct-POST paths

The first commit only covered llm_call/llm_call_async/stream_llm and the
endpoint probe. Email auto-summary, urgency-less spam classification, the
email reply-summary endpoint, and gallery vision tagging build their
OpenAI payloads inline and POST them directly (requests/httpx), bypassing
llm_core — so a reasoning model configured there would still 400 on the
temperature field. These sites already branch on _uses_max_completion_tokens,
so they're the same class; added the matching _restricts_temperature guard.

gallery_routes also gains the max_completion_tokens branch it was missing,
so gpt-5 vision tagging works end to end.

Note: email_pollers urgency scoring goes through llm_call_async and was
already covered.
 2026-06-02 20:58:33 +09:00
Leo 6c15dc7d33 Chat metrics: surface backend generation speed 
* Chat metrics: show backend's true generation t/s, not tokens÷wall-clock

The per-message tokens/sec read low and felt wrong because it was computed as
output_tokens / total_duration, where total_duration is wall-clock including
prefill, tool calls, and network — not pure decode time. llama.cpp already
reports the correct gen speed in its stream (timings.predicted_per_second), but
it was being dropped.

- llm_core.py: when parsing the OpenAI-compatible usage chunk, also read the
  sibling `timings` block llama.cpp includes — pass predicted_per_second through
  as gen_tps and prompt_per_second as prefill_tps on the usage event.
- agent_loop.py: capture backend_gen_tps/backend_prefill_tps from usage events;
  in _compute_final_metrics prefer backend_gen_tps over the wall-clock division
  when present (fall back to computed for cloud APIs that omit timings). Tag the
  result with tps_source ("backend" vs "computed") and surface prefill_tps.

Result: the displayed t/s now matches the model's real decode speed and is
stable regardless of prompt length (a long prefill no longer deflates it).

Checks: py_compile passes; verified extraction against a real llama.cpp final
chunk (gen 79 t/s surfaced vs the deflated wall-clock figure shown before).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* Chat metrics: surface true t/s on the direct-chat path too

Follow-up to the gen-tps work: the non-agent direct-chat stream path in
chat_routes turned the raw `usage` event straight into a metrics event but only
copied token counts — it never set tokens_per_second or response_time. So simple
(non-tool) replies showed "Speed: n/a" / "Time: undefineds" and the chip fell
back to a bare token count ("27 tok") instead of t/s.

Map the usage event's gen_tps (llama.cpp timings.predicted_per_second, added in
the prior commit) into tokens_per_second here too, tag tps_source=backend, and
set response_time from wall-clock for the stats popup.

Checks: py_compile passes; verified llama.cpp emits usage+timings on the final
stream chunk (gen ~90 t/s) that this path consumes.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* Tests: backend gen/prefill t/s passthrough and preference

Cover the two pieces of the true-t/s metric so it can be reviewed on its own:
- stream_llm surfaces llama.cpp's timings.predicted_per_second /
  prompt_per_second as gen_tps / prefill_tps on the usage event (captured
  llama.cpp final-chunk fixture), and omits them when the backend reports no
  timings.
- _compute_final_metrics prefers backend_gen_tps over output/wall-clock,
  tags tps_source ("backend" vs "computed"), and surfaces prefill_tps.

Reuses the fake-client stream harness from test_llm_core_streaming.py.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
 2026-06-02 20:52:08 +09:00
Tatlatat e084dc993e Chat: merge consecutive user messages for strict providers 
After a non-native tool round, the agent appends tool results as a {role:
'user'} message next to the user's original 'user' prompt, producing two
consecutive 'user' messages. Strict provider APIs (Anthropic/Claude) reject
consecutive same-role messages, so the follow-up generation request fails
silently — search returns sources, then nothing is generated.

_sanitize_llm_messages now merges consecutive 'user' messages (joining their
content). Only user/user is merged; normal chat and agent/tool turns already
alternate and are untouched.

Scoped down per maintainer review: the agent_loop 'output' source-extraction
change is already on main (#898/#901) and the broad-mocking web-sources test
was dropped. Added a focused test that runs consecutive-user messages through
the real _build_anthropic_payload and asserts the payload alternates correctly.
 2026-06-02 20:44:13 +09:00
Ernest Hysa a8a34bd22a Ollama: pass discovered num_ctx in chat requests 
_build_ollama_payload sends options.temperature and options.num_predict
to /api/chat, but never options.num_ctx. Ollama defaults num_ctx to 2048
when the option is omitted, so prompts going to any Ollama backend are
silently truncated there regardless of the model's actual capability.

Thread the discovered context length through the three call sites
(llm_call, llm_call_async, stream_llm) and emit options.num_ctx when it
is known and positive. The builder filters out the DEFAULT_CONTEXT
fallback (128000) so we don't lie to Ollama about models whose window
we couldn't actually discover. The issue's literal 'when > 2048'
heuristic is dropped: a model with a real context smaller than 2048
would OOM if Ollama used its default, so we pass the real value
regardless of size. Matches how src/context_compactor.py uses the
same helper.

Sister fix to PR #753 — that PR teaches the compactor the right budget,
this one tells Ollama to actually use that budget on the way in.
 2026-06-02 20:27:24 +09:00