Closes#132 — jarvis init now recommends a Qwen3 dense model based on
available memory using an explicit tier table:
≤8 GB → qwen3:1.7b (~1.1 GB)
8-16GB → qwen3:4b (~2.5 GB)
16-32GB → qwen3:8b (~5.2 GB)
32GB+ → qwen3:14b (~9.3 GB)
Adds qwen3:0.6b, 1.7b, 4b, 14b, 30b to the model catalog. Falls back
to scanning all compatible models if the tiered pick doesn't support
the selected engine. Shows the user why the model was chosen and how
to download later if declined.
Adds `codex/` prefixed model support using the OpenAI Responses API —
the same protocol used by zeroclaw and other Codex-compatible tools.
Live-tested against gpt-5-mini-2025-08-07 with:
- Generate (non-streaming): confirmed working
- System prompt → instructions mapping: confirmed working
- SSE streaming: confirmed working (9 chunks)
- End-to-end via `jarvis ask`: confirmed working
Implementation:
- Default endpoint: api.openai.com/v1/responses (standard API key)
- Override via OPENAI_CODEX_BASE_URL for ChatGPT OAuth tokens
(e.g. chatgpt.com/backend-api/codex)
- Auth via OPENAI_CODEX_API_KEY env var
- Responses API format: input array, instructions field, output_text extraction
- Handles reasoning+message output blocks correctly
- SSE streaming parses response.output_text.delta events
Models: codex/gpt-4o, codex/gpt-4o-mini, codex/o3-mini,
codex/gpt-5-mini, codex/gpt-5-mini-2025-08-07
Usage:
export OPENAI_CODEX_API_KEY="your-api-key-or-oauth-token"
jarvis ask "Hello" --model codex/gpt-5-mini-2025-08-07
Closes#134
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The model catalog listed non-existent Qwen3.5 sizes (3B, 8B, 14B) and
pointed to MLX community repos that don't exist, causing `jarvis init`
to recommend models that cannot be downloaded on Apple Silicon.
Replace with the actual Qwen3.5 model family sizes (0.8B, 2B, 9B, 27B)
and verified mlx-community repo URLs from HuggingFace.
Fixes#129
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fixes recommend_model() returning empty string on Apple Silicon
when MLX is the recommended engine. Also adds gguf_file and
mlx_repo download metadata, and estimated_download_gb helper.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat: add MiniMax as cloud inference provider
Add MiniMax M2.5 and M2.5-highspeed as a 5th cloud provider alongside
OpenAI, Anthropic, Google, and OpenRouter. Uses the OpenAI-compatible
API at api.minimax.io/v1 via the existing openai SDK dependency.
Changes:
- Add MiniMax client init, generate, and streaming in CloudEngine
- Add MiniMax models to model catalog with correct pricing
- Add MINIMAX_API_KEY environment variable support
- Add temperature clamping (0.01-1.0) per MiniMax API constraints
- Add 19 unit tests and 3 integration tests
- Update docs and README with MiniMax provider info
* feat: upgrade MiniMax default model to M2.7
- Add MiniMax-M2.7 and MiniMax-M2.7-highspeed to model list
- Set MiniMax-M2.7 as default model (first in list)
- Keep all previous models (M2.5, M2.5-highspeed) as alternatives
- Update pricing table with M2.7 entries
- Update model catalog with M2.7 specs
- Update docs to list all available MiniMax models
- Update unit tests (23 passing) and integration tests (3 passing)
---------
Co-authored-by: Octopus <octo-patch@users.noreply.github.com>
* 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>
