# Model registry — models are the scarce queued resource. # # Per DESIGN-a2a-agents.md v2.1 §2-3b (commit df2071d5), kanboard task #133 # (A2A-1). Version-controlled here; the "model plane" (§3) and the fabric's # workers/routers read this data — they do not duplicate it. Lifecycle a(t) # *probe mechanics* (QuotaProbe, GPUResidencyProbe, ...) live in # kanboard/bin/kb_worker.py; this registry supplies the *parameters* those # probes consume (commands, fields, thresholds, VRAM footprints). # # Scope constraint (alvis, §3a): NO METERED API BY DEFAULT. The workflow is # Claude Code (a flat-subscription runtime -> agent registry #134, not here) # + the Kimi wrapper + a local GPU embedder + a small weak local model. The # governor arbitrates quota and GPU, not money. Any metered model below is # `metered: true, opt_in_required: true` and carries no default route to it # (see routing.metered_opt_in: [] at the bottom — empty means unreachable). # # Read with model_registry.py (same directory): resolve(), preload_check(). # # ── Coverage vs litellm-config.yaml (kb#195, 2026-07-26 audit) ────────── # Every model_name litellm-config.yaml defines must appear either as a # `litellm_model_name` below or in this exclusion list. litellm_key_spec() # default-denies anything not reachable via routing.tiers, so an excluded # model stays ungoverned-but-inert until someone wires it up (add it here # and to routing.tiers first). # # GOVERNED (present below): # - ollama/gemma3:4b -> id: local-small (hot path: Hindsight LLM/ # consolidation/reflect all route here as of 2026-07-26) # - judge -> id: paid-fallback (metered; see kb#164 for the fact that # the no-metered-API constraint has no runtime enforcement yet) # - kimi-agent -> id: kimi-agent (own container, live; see below) # - bge-m3 -> id: bge-m3 (kb#164, 2026-07-26: wired into litellm-config # .yaml pointing at ollama on 11436, the real embedder/routing # classifier; litellm_model_name below updated from null to "bge-m3") # # INTENTIONAL EXCLUSIONS (not governed by this registry, by design): # - tip-generator (ollama/qwen2.5:1.5b), embedder (ollama/nomic-embed- # text): aliases consumed by the separate oO ml/serving project, not # the a2a fabric. Tracked in oO/CLAUDE.md, not duplicated here. # - Raw ollama/* passthrough exposures — ollama/qwen3.5:4b, # ollama/qwen3:8b, ollama/qwen2.5:1.5b, ollama/qwen2.5:0.5b, # ollama/gemma3:1b, ollama/nomic-embed-text — manual/dev-console # access to the ollama instances for ad-hoc testing. No agent or # fabric workflow is registered against them (grepped agent-registry # .yaml and openai/*.py: no hits). Not in routing.tiers, so # litellm_key_spec() grants no agent access to them either way. # If one of these becomes a real dependency (as ollama/gemma3:4b # did), give it its own registry entry at that point. # - The 12 OpenRouter `*:free` models (meta-llama/llama-3.3-70b- # instruct:free, meta-llama/llama-3.2-3b-instruct:free, deepseek/ # deepseek-r1:free, qwen/qwen3-4b:free, qwen/qwen3-coder:free, # google/gemma-3-27b-it:free, google/gemma-3-12b-it:free, mistralai/ # mistral-small-3.1-24b-instruct:free, nvidia/nemotron-3-super- # 120b-a12b:free, openai/gpt-oss-120b:free, minimax/minimax-m2.5:free, # nousresearch/hermes-3-llama-3.1-405b:free) — human-facing manual- # selection models (e.g. # via Open WebUI), outside the agent fabric's model plane. Not # referenced by any agent registry entry, not in routing.tiers, so # resolve()/litellm_key_spec() never route an agent to them. Free # tier, so this is not the kb#164 metered-enforcement gap — flag # for a proper entry only if an agent workflow starts depending on # one of these. schema_version: 1 models: # ── kimi — main reasoning ────────────────────────────────────────────── # Flat Moonshot/Kimi subscription via `kimi login`, wrapped by two # independent Kimi-CLI containers (own OAuth creds volume each). Not # behind LiteLLM today — callers hit the wrapper HTTP endpoints directly. - id: kimi role: "main reasoning (adolf-llm / hindsight-llm Kimi-CLI wrappers)" litellm_model_name: null endpoints: - name: adolf-llm purpose: "Adolf's conversational backbone" url: "http://adolf-llm:8010" usage_url: "http://localhost:8010/usage" - name: hindsight-llm purpose: "Hindsight's structured-extraction LLM (HINDSIGHT_API_LLM_MODEL)" url: "http://hindsight-llm:8012/v1" model_name: "openai/hindsight-llm" tier: large context_tokens: 200000 # Moonshot Kimi K2 context window; re-verify if the CLI's pinned model changes tool_use_quality: high lifecycle: quota-gated quota: probe_command: ["kimi-usage", "--compact"] windows: - name: 5h field: "window_5h.pct" # adolf-llm server.js normalizeKimiUsage() field name approx_limit: "~60 msgs/5h" - name: weekly field: "weekly.pct" approx_limit: "~300 msgs/wk" threshold_pct: 95 gpu_residency: null cost_class: subscription # flat-rate, not metered — quota is the constraint, not spend metered: false opt_in_required: false # ── kimi-agent — own container, oO-adjacent Kimi CLI wrapper ─────────── # Distinct from `kimi` above: this is a third Kimi-CLI container # (openai/kimi-agent/, own Moonshot/Kimi subscription via `kimi login`, # own docker-compose service `kimi-agent`) that IS routed through # LiteLLM today (litellm-config.yaml model_name: kimi-agent -> # openai/kimi-agent -> http://kimi-agent:8000/v1). Documented here per # kb#195 coverage audit; deliberately NOT added to routing.tiers in this # pass (that would change litellm_key_spec() grants, out of scope for a # docs-alignment task) — no agent is currently opted into it. - id: kimi-agent role: "Kimi-CLI wrapper, own container (openai/kimi-agent/) — purpose/consumer not yet documented outside this registry" litellm_model_name: "kimi-agent" # openai/litellm-config.yaml model_list entry endpoints: - name: kimi-agent url: "http://kimi-agent:8000/v1" tier: large context_tokens: 200000 # same Moonshot Kimi K2 CLI as `kimi`; re-verify if the CLI's pinned model changes tool_use_quality: high lifecycle: quota-gated quota: probe_command: null # not yet wired to a probe; own subscription, same caveat as `kimi` windows: [] threshold_pct: null gpu_residency: null cost_class: subscription metered: false opt_in_required: false # ── local-small — the cheap tier ─────────────────────────────────────── # ollama/gemma3:4b on the GPU ollama instance. Already the live model for # Hindsight consolidation/reflect (HINDSIGHT_API_CONSOLIDATION_LLM_MODEL / # HINDSIGHT_API_REFLECT_LLM_MODEL, kb#88) and exposed via LiteLLM. - id: local-small role: "cheap tier — ollama small/weak local model (background extraction, consolidation, reflect)" litellm_model_name: "ollama/gemma3:4b" # openai/litellm-config.yaml model_list entry endpoints: - name: ollama-direct url: "http://host.docker.internal:11436" - name: via-litellm url: "http://litellm:4000/v1" tier: small context_tokens: 8192 # gemma3:4b default ctx; re-verify with `ollama show gemma3:4b` if raised tool_use_quality: low lifecycle: always-on quota: null gpu_residency: vram_mb: 4000 # approx measured footprint, within the shared 8GB card (see gpu_residency_policy below) never_evict: false # evictable — a bigger model may push it out; that's a silent regression to catch, not prevent here co_residency_group: interactive-local cost_class: free metered: false opt_in_required: false # ── bge-m3 — embedder + routing classifier ───────────────────────────── # Never-evict: it's both Hindsight's recall embedder AND (design §3a) the # embedding model LiteLLM Auto Router's semantic-router classifier will # use for tier/complexity routing — losing it degrades both recall AND # routing at once. - id: bge-m3 role: "embedder — also the routing classifier (§3a, LiteLLM Auto Router / semantic-router)" litellm_model_name: "bge-m3" # kb#164, 2026-07-26: wired into litellm-config.yaml (ollama/bge-m3 @ 11436) -- was null (unwired gap) endpoints: - name: ollama-direct url: "http://host.docker.internal:11436" openai_compatible_path: "/v1/embeddings" tier: small context_tokens: 8192 tool_use_quality: "n/a" # embedder, not a chat/tool-use model lifecycle: always-on quota: null gpu_residency: vram_mb: 1200 never_evict: true co_residency_group: interactive-local cost_class: free metered: false opt_in_required: false # ── tei-reranker — interactive-critical, never-evict ─────────────────── # Not an LLM (cross-encoder rerank sidecar for Hindsight recall, kb#87) # but carries the same GPU-residency stakes as bge-m3, so it's tracked # here rather than invented as a separate registry class. - id: tei-reranker role: "cross-encoder reranker sidecar for Hindsight recall (interactive-critical)" litellm_model_name: null # TEI-compatible /rerank API; not routed through LiteLLM endpoints: - name: tei-reranker url: "http://tei-reranker:80" # host-published :8014 tier: small context_tokens: null tool_use_quality: "n/a" lifecycle: always-on quota: null gpu_residency: vram_mb: 1000 never_evict: true co_residency_group: interactive-local cost_class: free metered: false opt_in_required: false # ── paid-fallback — optional, opt-in only ────────────────────────────── # §3a: "Any paid deployment in the LiteLLM config must be explicitly # enabled per agent via its virtual key; nothing routes to a metered # model implicitly." routing.metered_opt_in below is the enforcement # point: empty list = no caller has opted in = unreachable by resolve(). - id: paid-fallback role: "optional metered fallback (e.g. Haiku) — disabled by default" litellm_model_name: "judge" # litellm-config.yaml's existing entry (anthropic/claude-haiku-4-5-20251001) endpoints: [] tier: large context_tokens: 200000 tool_use_quality: high lifecycle: cost-gated quota: probe_command: null # wire to a LiteLLM virtual-key budget probe (kb_worker.py BudgetProbe) once a caller opts in windows: [] threshold_pct: null gpu_residency: null cost_class: metered metered: true opt_in_required: true # ── GPU residency policy (§3b) ────────────────────────────────────────── # "a local model's a(t) is not 1": a(t) = f(VRAM headroom). Never-evict # models are excluded from eviction math entirely — their VRAM is a fixed # reservation. Everything else in a co-residency group must fit in what's # left. preload_check semantics documented here; implemented generically # in model_registry.py so it reads this data instead of hardcoding numbers. gpu_residency_policy: card: "GTX 1070, 8192 MB (single GPU today; §8 — more GPUs become a placement problem, same policy, more slots)" total_vram_mb: 8192 # Measured 2026-07-21: bge-m3 + gemma3:4b + tei-reranker ~= 6.2/8 GB. # Loading something bigger than local-small's footprint on top evicts # tei-reranker (LRU-ish ollama/torch behavior) -> silent recall-latency # regression. This is the regression the pre-load check exists to catch. measured_baseline_mb: 6200 never_evict_ids: [bge-m3, tei-reranker] co_residency_groups: interactive-local: [bge-m3, tei-reranker, local-small] preload_check: description: > Before a worker pulls a candidate model onto the GPU it must pass this check (see model_registry.py:preload_check): reserve every never_evict model's vram_mb unconditionally, subtract whatever else is currently resident, and require the candidate's own vram_mb to fit in what's left of total_vram_mb. A failing check means "park, don't load" — never silently evict a never-evict model. # ── routing ─────────────────────────────────────────────────────────────── # Tier pools a caller can ask for without naming a model (design §2: "target # = constraint-set"). metered_opt_in lists the virtual keys that have # explicitly opted into paid-fallback; empty = no metered model is reachable # by anyone, satisfying the "no metered API by default" acceptance bar. routing: tiers: small: [local-small] # paid-fallback listed as a large-tier candidate AFTER kimi so resolve() # can fail over to it when kimi's a(t)=0 (quota parked) — but only for a # caller that both passes allow_metered=True AND appears in # metered_opt_in below. With metered_opt_in empty (the shipped default) # resolve() skips it unconditionally, so it stays unreachable. large: [kimi, paid-fallback] metered_opt_in: [] # e.g. ["agent:torgash"] once a human explicitly opts a specific virtual key in