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Changelog

All notable changes to this project are documented here. The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.9.0] - 2026-05-28

Added

  • Fallback model + single front OpenAI gateway ("fleet"). A new scaffold-based deployment runs two always-warm vLLM backends behind one stdlib gateway that model-gear manages as three containers (model-gear-gateway, model-gear-vllm-primary, model-gear-vllm-fallback). The gateway routes each request by its model field, defaults an unknown/missing name to the primary, and fails over to the other backend when the chosen one refuses the connection or returns a 5xx before the response body (4xx is returned verbatim; no mid-stream retry). SSE streams are relayed chunk-by-chunk. Default fallback: the MoE mmangkad/Qwen3.6-35B-A3B-NVFP4.
  • New gateway package model_gear/gateway/ — a pure-stdlib (http.server + http.client, no runtime deps) reverse proxy: _routing.py (pure name/alias/default routing + failover ordering), _config.py (env → routing table + server config), server.py (the handle_post failover seam, upstream client, and ThreadingHTTPServer handler), run as python -m model_gear.gateway.
  • model init --fleet scaffolds the fleet templates (docker-compose.yml + .env + Dockerfile.gateway) and pins MODEL_GEAR_VERSION to the running release; model fleet up | down | status drives the deployment (up/down dry-run by default, --apply to commit; status is read-only and reports all three containers + the gateway /health + /v1/models).
  • Docs: docs/gateway-fleet.md (topology, routing/failover, memory, verbs), docs/qwen3.6-35b-a3b-nvfp4.md (the MoE fallback), a README "fleet" section, and model explain fleet / model explain gateway entries.

Changed

  • model_gear/runtime/_compose.py gained a template registry (SINGLE_TEMPLATES / FLEET_TEMPLATES), a templates= argument on scaffold_plan / write_scaffold (single-model stays the default — existing callers unchanged), a compose_up_build helper, and FLEET_CONTAINERS.
  • The fleet .env mirrors VLLM_MODEL / VLLM_SERVED_NAME / VLLM_TOOL_CALL_PARSER (= the primary) so the read-only single-model verbs (status / whoami / doctor) stay coherent on a fleet deployment. model switch remains single-model only.

Fixed

[0.8.1] - 2026-05-27

Changed

  • SonarCloud cleanup (no behavior change). Split cmd_switch into _select_parser / _emit_dry_run / _apply_switch helpers to bring its cognitive complexity under the gate, and hoisted the repeated "(unset)" literal in model status into a _UNSET constant.

[0.8.0] - 2026-05-27

Added

  • Per-model tool-call parser auto-selection. New model_gear/runtime/_parser.py infer_parser() maps a model name to its parser (qwen3_coder for Qwen3-Coder / Qwen3.6, hermes for Qwen3 dense, unknown → leave untouched). model switch now picks the right parser automatically so tool calling keeps working across a switch without the caller remembering it; --tool-call-parser still overrides (issue #13).
  • Post-switch / post-start tool-calling probe. model switch --apply and model serve --apply now probe tool_choice:"auto" once the container is healthy and report PASS/FAIL (with the called tool names) — reusing the existing assess probe. --no-probe skips it; the probe never aborts the command (unreachable / HTTP 400 degrade to a FAIL result).
  • model status reports the active tool_call_parser (VLLM_TOOL_CALL_PARSER), so "which gear am I in" is complete without docker inspect.

Changed

  • lepenseur is retired; the deployed agent is now model-gear. The tool and the deployed agent share one identity. Updated culture.yaml (suffix: model-gear), the AGENTS.md system prompt, model whoami / learn / explain output, the posting nick (.claude/skills.local.yaml.example), the compose/.env templates, README.md, and CLAUDE.md (the former "two identities" section now describes one).

Fixed

[0.7.0] - 2026-05-27

Added

  • OpenAI tool/function calling on the served vLLM model. The packaged compose template (model_gear/templates/docker-compose.yml) now serves with --enable-auto-tool-choice and --tool-call-parser=${VLLM_TOOL_CALL_PARSER:-hermes}, so tool_choice:"auto" requests return a tool_calls array instead of HTTP 400. Additive — plain chat/reasoning is unaffected, no extra GPU/memory cost. Unblocks coder-agent harnesses that drive the model entirely through tool calls (issue #9).
  • VLLM_TOOL_CALL_PARSER env var (default hermes) + model switch --tool-call-parser — the parser is per-model: hermes fits Qwen3 dense (e.g. Qwen3-32B), while Qwen3-Coder / Qwen3.6 checkpoints emit the XML function format and need qwen3_coder. switch writes the var only when the flag is given, so retuning a model never clobbers its parser.
  • model assess --tools — an opt-in tool-calling probe that verifies a tool_choice:"auto" request returns a tool_calls array naming a finish function. Degrades gracefully (a FAIL row, no abort) against a server that lacks the flags.

Changed

Fixed

[0.6.0] - 2026-05-27

Added

  • devague workflow trio vendored under .claude/skills/ (cite-don't-import): think (idea→spec), spec-to-plan (spec→plan), and assign-to-workforce (plan→parallel implementation) — the operator chain for the deterministic devague CLI. Authored in agentculture/devague, vendored via guildmaster; each carries type: command (load-bearing on the culture/agex backend, where a SKILL.md without type: is silently skipped). They drive the devague CLI at runtime (uv tool install devague), resolved portably by the wrappers.
  • docs/skill-sources.md — provenance ledger recording the citation path and authoring origin of every vendored skill (the trio plus the six steward-sourced skills).

[0.5.0] - 2026-05-27

Redesigned the repo around running, assessing, and switching the local vLLM model. The model-ops logic that lived in the model-runner skill is now a first-class CLI. lepenseur is still the deployed agent that consumes the served model; model-gear is the tool that runs it.

Added

  • Model-ops verbs on the model CLI: switch <model>, serve (alias start) / stop, status, assess (correctness probes), benchmark (decode throughput + prefill), and init (scaffold a deployment dir). Write verbs (switch/serve/stop/init) are dry-run by default and require --apply (mutation-safety rule).
  • Scaffold-based deployment. docker-compose.yml + env.example ship as packaged templates under model_gear/templates/; model init materialises them into ~/.model-gear (default), a TARGET, or the local folder. Every model-ops verb resolves the deployment dir via --compose-dir → $MODEL_GEAR_DIR → ~/.model-gear.
  • Ported runtime modules (model_gear/runtime/ + model_gear/assess.py), stdlib-only (urllib, fixed-argv subprocess), with full unit tests.
  • model overview now folds in the currently-served model and the candidate-model list, filterable with --current / --list.

Changed

  • PyPI distribution renamed lepenseur → model-gear; binary lepenseur → model; Python package lepenseur → model_gear. Error class LepenseurError → ModelGearError. The lepenseur console script is removed.
  • Agent-first verbs reframed for the tool: whoami reports tool/machine/served model/container health/agent; learn teaches the model-ops surface; explain catalog rewritten (switch/assess/backend/models/…).
  • doctor is now real — checks docker availability, deployment scaffold, .env ↔ culture.yaml coherence, and /health reachability (a down model is a warning, not a failure).
  • The model-runner skill is now a thin shim that execs model; its _assess.py was removed (the logic lives in model_gear/assess.py).
  • AGENTS.md / culture.yaml clarified: they describe the deployed lepenseur agent, not the repo. README + CLAUDE.md reoriented around model-gear.

Fixed

  • BREAKING: the vLLM container is renamed lepenseur-vllm → model-gear-vllm. A box running the old container must docker compose down under the old name, then model init --apply + model serve --apply.

[0.4.0] - 2026-05-27

Added

  • model-runner skill (local, not vendored): switch the local vLLM runtime model and assess/benchmark it (stdlib _assess.py for correctness + throughput, host-side facts via the wrapper). Drives this repo's compose + .env; documented in CLAUDE.md and README. Mutating verbs (switch, down) are dry-run by default and require --apply (CLAUDE.md mutation-safety rule); --port defaults to .env's VLLM_PORT (then 8000).

Changed

  • docs/qwen3.6-27b-nvfp4.md: filled with the live load-test (DGX Spark/GB10, 2026-05-27). mmangkad/Qwen3.6-27B-NVFP4 loads and serves under our vLLM image (no --trust-remote-code); ~7.9–8.0 tok/s decode, ~70 GB reserved, 29 GB weights. It is a hybrid Mamba/linear-attention vision-language model and is slower on decode than the 32B here — recommendation: keep the 32B. All pre-flight caveats (SGLang-only, multimodal, ModelOpt rc) validated/resolved.

[0.3.0] - 2026-05-27

Added

  • docs/qwen3-32b-nvfp4.md: per-model doc for the current runtime model, with a live test on DGX Spark (GB10) — nvcr.io/nvidia/vllm:26.04-py3 (engine 0.19.0+...nv26.04), ~9.7 tok/s decode (batch=1), ~2,800 tok/s prefill, ~72 GB reserved at gpu-memory-utilization=0.6, correctness verified.
  • docs/qwen3.6-27b-nvfp4.md: per-model doc for candidate mmangkad/Qwen3.6-27B-NVFP4. Its Qwen3_5ForConditionalGeneration arch is registered in the current vLLM image (so the same compose can serve it); live load-test/benchmark tracked by issue #6.
  • README "Per-model notes" linking both docs.

Fixed

  • docker-compose.yml: corrected the --reasoning-parser=qwen3 comment — on the nv26.04 build the <think> trace is returned in the reasoning field, not reasoning_content.

[0.2.0] - 2026-05-27

Added

  • docker-compose.yml + .env.example: a local vLLM server (NGC nvcr.io/nvidia/vllm image) that serves the runtime model as an OpenAI-compatible API on :8000 for the acp backend, tuned for DGX Spark (GB10 Blackwell, 128 GB unified memory).
  • README "Running the model locally (vLLM)" section.

Changed

  • Switched lepenseur's runtime model from nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 to nvidia/Qwen3-32B-NVFP4 across culture.yaml, AGENTS.md, lepenseur/explain/catalog.py, README.md, and CLAUDE.md (32B dense NVFP4 reasoning model with a thinking mode).

[0.1.0] - 2026-05-22

Added

  • Initial CLI/PyPI sibling scaffold (copied and adapted from the lecodeur twin): top-level lepenseur package with the lepenseur console script.
  • Read-only verbs: whoami, learn, explain, overview, and a cli noun with cli overview.
  • doctor verb shipped as a rubric-shaped stub; real self-diagnosis semantics for a thinking ("non-doer") agent are deferred to a follow-up.
  • Runtime identity files: AGENTS.md and culture.yaml (acp backend, vllm-local/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4).
  • CI: tests.yml (test + lint + afi cli doctor . --strict gate + version-check) and publish.yml (PyPI/TestPyPI via Trusted Publishing).
  • Six vendored skills under .claude/skills/ (cicd, communicate, version-bump, run-tests, sonarclaude, doc-test-alignment), provenance: steward.