Overview tab:
- Show Intelligence (model name) with click-to-change dropdown
- Split "Total Tokens" into "Input Tokens" and "Output Tokens"
- Model can be switched for existing agents via dropdown
Backend:
- Add input_tokens/output_tokens columns to managed_agents
- Track prompt_tokens and completion_tokens separately in executor
- Disable Ollama thinking by default (think:false) to prevent
empty responses from token exhaustion
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add current_activity field to managed agents that the executor updates
at each phase of a tick (loading model, delivering messages, generating
response, retrying, finalizing). The frontend polls this every 2s and
displays the live status instead of static "Agent is thinking...".
Backend:
- Add current_activity column to managed_agents (migration)
- Add _set_activity helper to AgentExecutor
- Update activity at: start_tick, model load, message delivery,
generation, retry, finalize
- Clear activity on end_tick
Frontend:
- InteractTab polls both messages and agent status in parallel
- Shows current_activity text with pulsing indicator
- Falls back to "Agent is thinking..." if activity is empty
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Ollama's prompt_eval_count may exclude KV-cached tokens (system
prompt, prior conversation turns), under-counting the prompt size
used for cost/FLOPs/energy calculations and leaderboard submissions.
Engine changes:
- Add estimate_prompt_tokens() helper in engine/_base.py that
computes cache-agnostic token count from message content
- Ollama + OpenAI-compat engines now use max(reported, estimated)
to ensure system prompt and full context are always counted
Savings/leaderboard changes:
- Add TOKEN_COUNTING_VERSION=2 to savings.py so frontends can
tag Supabase submissions (old=v1, corrected=v2)
- Desktop + web frontends forward version in leaderboard submissions
- Add POST /v1/telemetry/reset endpoint to clear stale telemetry
Active users auto-correct via upsert on next session; no manual
Supabase migration required.
- Boot sequence: Phase 2 now pulls ALL Qwen3.5 models that fit in RAM
before the server starts. SetupScreen blocks until every model is
downloaded, showing per-model progress. No more background Phase 4.
- Default model: third-largest fitting model (qwen3.5:9b on 64GB).
- Download button: added pull_ollama_model and delete_ollama_model Tauri
commands. Frontend uses Tauri invoke in desktop mode, fixing the
"Load failed!" CORS/timeout issue with fetch().
* feat: model catalogue with download/delete and auto-pull Qwen3.5
- Desktop boot: start server immediately with fallback model (qwen3:0.6b),
then pull preferred model (qwen3.5:4b) and remaining Qwen3.5 variants
that fit in RAM in the background. No more broken "Select model" state.
- Backend: add POST /v1/models/pull and DELETE /v1/models/{name} endpoints
so the frontend can trigger model downloads and deletions via Ollama.
- Frontend: redesign CommandPalette (Cmd+K) with two tabs — "Installed"
shows pulled models with select/delete, "Download Models" shows a
catalogue of popular models plus a custom model input field.
- Fix ollama_has_model() to use exact tag matching instead of prefix
matching, preventing false positives.
* fix: streaming, model switching, second-largest default, and tests
- Streaming: use direct engine streaming for non-tool requests so tokens
arrive in real-time instead of being batched by the agent bridge.
Add error handling to _handle_stream so engine errors surface as
content chunks instead of silent failures.
- Model selection: pick the second-largest Qwen3.5 model that fits
(leaves headroom for OS/apps) instead of the absolute largest.
- Model switching: abort in-flight stream when the user changes models
mid-generation, preventing stale-model errors. Improve error messages
in catch blocks.
- Tests: add tests/server/test_model_management.py with 11 tests
covering model pull/delete endpoints, streaming error resilience,
direct-engine streaming bypass, and model listing. All 100 server
tests pass.
Three issues fixed:
1. The desktop-latest release accumulated stale artifacts across builds
because tauri-action adds new files without removing old ones. Users
could download an outdated DMG. Added a clean-release job that wipes
all existing assets before the build matrix runs.
2. The frontend hardcoded DESKTOP_API to port 8000 but the Tauri backend
starts the server on port 8222. Added a get_api_base Tauri command so
the frontend fetches the port from the Rust backend at startup,
keeping JARVIS_PORT as the single source of truth.
3. frontend/package.json version was 1.0.0 while desktop and pyproject
are 0.1.0, causing duplicate artifact names in the release.
getBase() reads settings.apiUrl from localStorage so the API URL
configured in the Settings page is actually used by api.ts and sse.ts.
Previously hardcoded to localhost:8000, causing "Failed to get response"
when the backend ran on a different port.
Co-authored-by: robbym-dev <robbym-dev@users.noreply.github.com>
