* 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.
get_context_length() cached the resolved context window by model id alone,
so two different remote endpoints serving the same model id (e.g. a capped
proxy at 8k vs. the full provider at 200k) collided: the first to resolve
won process-wide and the other endpoint was served the wrong window. That
silently over-trims conversations on the larger-window endpoint (it feeds
context_compactor) or overflows the smaller one (provider 400s).
Key the cache on (endpoint_url, model). Local endpoints already always
re-query, so they are unaffected.
Fixes#2603
* 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
* 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
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>
KNOWN_CONTEXT_WINDOWS lists 'o1' (200k) before 'o1-mini' (128k), and
_lookup_known returned on the first substring hit — so "o1-mini" matched
'o1' and reported 200000 instead of 128000. Track the longest matching
key instead, so the most specific entry wins regardless of table order.
When running Odysseus in Docker and connecting to a local LLM on the host machine (e.g. `llama.cpp` or `Ollama`), the standard endpoint `http://host.docker.internal` is used to breach the container network.
Because `host.docker.internal` was missing from `_LOCAL_HOSTS`, Odysseus incorrectly treated local self-hosted models as cloud APIs. This triggered the fallback behavior where actual API-reported context limits were being ignored and overridden by hardcoded fallbacks in `KNOWN_CONTEXT_WINDOWS`.
**Changes**
- Added `"host.docker.internal"` to the `_LOCAL_HOSTS` whitelist in `src/model_context.py` so that Dockerized deployments correctly trust and respect the context limits of locally hosted models.
**Checks Ran**
- [x] Syntax check (`python -m py_compile src/model_context.py`)
- [x] Tested manually in Docker (`docker compose up -d --build`) on a Windows host using `llama-server`. The correct API context length is now correctly reported in the UI instead of falling back to the 131k hardcode.
Kenny Van de Maele