Sort the check_ssrf import alphabetically to fix ruff I001. Mock the
SSRF check in URL-fetching tests since it requires the Rust backend
which isn't available in CI. Add a dedicated test verifying SSRF
rejection.
- Engine discovery (#73): get_engine() now falls back to any healthy
engine when the explicitly-requested key fails, instead of returning
None. Fixes LM Studio (and other non-default engines) not being found
when deploying agents.
- FTS5 case sensitivity & scoring (#67): Add explicit unicode61 tokenizer
to the FTS5 virtual table for case-insensitive search. Replace ambiguous
`rank` column with explicit `bm25()` call with column weights (id=0,
content=1, source=0.5) to produce correct positive scores. Includes
auto-migration for existing databases.
- Interactive engine selection (#72): `jarvis init` now detects running
engines and presents an interactive picker. Also accepts `--engine`
flag to skip the prompt. Engine choice flows through to config
generation.
- Windows desktop support (#68): Add RAM detection via wmic, Windows
binary paths (LOCALAPPDATA, ProgramFiles, cargo), port cleanup via
netstat+taskkill, and USERPROFILE fallback for HOME env var.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
messages_to_dicts() serializes tool_call arguments as JSON strings,
but OpenAI-compatible servers (vLLM, SGLang, llama.cpp, etc.) expect
them as JSON objects. This caused 400 Bad Request errors on every
multi-turn tool-calling conversation, breaking GAIA, DeepPlanning,
and LifelongAgent benchmarks for all local models.
The fix adds _fix_tool_call_arguments() to _OpenAICompatibleEngine
which json.loads() any string-typed arguments back to dicts before
sending. Applied to both generate() and stream() methods. This is
the same fix as commit 45c0e44 (Ollama), now applied to all
OpenAI-compatible engines.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Previously, running `uv run jarvis start` would fail to start the process with error:
"No module named openjarvis.cli.__main__; 'openjarvis.cli' is a package and cannot be
directly executed" because the CLI package lacked an entry point.
Signed-off-by: Patrick Fung <patrick_fung@foxmail.com>
- Ollama engine: capture eval_count and prompt_eval_count from the
streaming final chunk (includes thinking/reasoning tokens).
- Routes: include usage data in the SSE finish chunk so the frontend
gets accurate token counts even in streaming mode.
- X-Ray footer: show estimated thinking tokens when completion_tokens
significantly exceeds visible output (e.g. "1695 generated · 11 prompt
· ~1680 thinking").
* feat: add interactive agent confirmation mode and fix tool loading
- Add `interactive` and `confirm_callback` params to ToolUsingAgent and
NativeReActAgent so CLI sessions can prompt user before tool execution
- Fix tool resolution in `ask` and `chat` commands to fall back to
config.tools.enabled when no --tools flag is provided
- Register shell_exec tool in tools/__init__.py auto-discovery
- Add dspy and gepa as optional learning extras (learning-dspy, learning-gepa)
- Fix openjarvis-rust lock version (1.0.0 → 0.1.0)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(cli): support configured tool defaults and confirmations
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
- MultiEngine: wraps multiple engines and routes by model name, so local
Ollama models and cloud models appear together in the model selector.
- CloudEngine: added OpenRouter support (openrouter/ prefix) with popular
models from OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, Qwen.
- Server auto-detects API keys at startup and enables cloud alongside
local models when keys are present. No config changes needed.
- Streaming works for all cloud providers.
Set env vars to enable: OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY,
OPENROUTER_API_KEY. Models appear automatically in the Cmd+K dropdown.
* 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.
The SecurityHeadersMiddleware ran before CORSMiddleware (Starlette
executes middleware in LIFO order) and added headers to OPTIONS
preflight requests. The Content-Security-Policy: default-src 'self'
header told the browser to reject cross-origin connections, so fetch()
from the Tauri webview (https://tauri.localhost) to the API server
(http://127.0.0.1) was blocked — causing "Failed to get response" on
every chat message in the desktop app.
Two fixes:
- Skip security headers on OPTIONS requests so CORS preflight works
- Remove Content-Security-Policy from API responses — it is a
document-level browser policy irrelevant to JSON API responses and
breaks any cross-origin API consumer
* chore: create learning subdirectory structure (routing, agents, intelligence)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: extract classify_query to routing/_utils.py
Move the classify_query() function and its regex patterns into a shared
utility module so multiple routing policies can import it without
depending on the full trace_policy module.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: move routing files to learning/routing/ subdirectory
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: create LearnedRouterPolicy merging trace-driven + SFT routing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add conditional Algolia DocSearch integration
Add Algolia DocSearch as an optional search upgrade — native lunr.js
search remains the default until credentials are configured. Includes
CDN assets, Jinja2 conditional config injection, init script with
graceful fallback, light/dark theme CSS, improved search tokenization
for snake_case/dotted identifiers, and search boosts for key pages.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: move agent_evolver and skill_discovery to learning/agents/
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: move learning/orchestrator to learning/intelligence/orchestrator
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: delete removed learning policies, rewrite __init__.py, clean up api_routes
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add SFT/GRPO/DSPy/GEPA config dataclasses, update LearningConfig
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add general-purpose SFT trainer (intelligence/sft_trainer.py)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: update stale imports in multi_model_router example
Update imports to use new learning/routing/ paths after the
subdirectory reorganization. Replace BanditRouterPolicy with
LearnedRouterPolicy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add general-purpose GRPO trainer (intelligence/grpo_trainer.py)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add DSPy agent optimizer (agents/dspy_optimizer.py)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add GEPA agent optimizer (agents/gepa_optimizer.py)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add learning-dspy and learning-gepa optional dependency extras
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: update integration test to check for learned policy instead of grpo
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: clean up stale APIs and unused params in examples
- deep_research: remove system_prompt and max_turns params not accepted
by Jarvis.ask(), inline system prompt into the query instead
- doc_qa: remove unused --top-k CLI arg that was never passed to the API
- multi_model_router: fix select_model() call to match single-arg signature
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: import SFT/GRPO trainers in intelligence/__init__.py for registry
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* chore: remove .md file changes from PR
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* chore: restore search boost frontmatter for key docs pages
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
OLLAMA_HOST env var was unconditionally overriding the host parameter
in OllamaEngine.__init__, even when explicitly set via config.toml.
Priority is now: config.toml > OLLAMA_HOST env var > hardcoded default.
