Files
AgapHost/ai/model_registry.py
alvis 9094d71e2f ai: migrate LLM backbone from Kimi CLI to Codex CLI
Retires the Moonshot/Kimi subscription in favour of the already-paid ChatGPT
plan. Both CLI wrappers now run `codex exec`; the kimi-agent container is gone.

adolf-llm + hindsight-llm:
- runKimi -> runCodex (`codex exec --json --skip-git-repo-check`), resume via
  `codex exec resume <thread_id>`.
- MCP moves from a per-session .mcp.json (a workaround for Kimi having no
  --mcp-config-file flag) to a $CODEX_HOME/config.toml generated once at
  startup from shared-mcp.json. Field translation is load-bearing:
  bearerTokenEnvVar -> bearer_token_env_var, enabledTools -> enabled_tools.
- approval_policy="never" + sandbox_mode required, or unattended turns block
  on an approval prompt nobody can answer.

kimi-agent removed. It was the ONLY large-tier deployment behind LiteLLM, so
deleting it outright would have silently degraded every large-tier request to
the local 4B model via the existing fallbacks. tier-large, the auto_router
complex-reasoning route and their fallbacks now point at the codex-backed
adolf-llm wrapper (model_name: codex-agent).

Three environment blockers fixed along the way:
- OpenAI geo-blocks this host (403 unsupported_country_region_territory).
  Both containers now egress via the host xray proxy, with NO_PROXY keeping
  MCP and *.alogins.net traffic off the tunnel.
- node:22-slim ships no system CA store; the Rust codex binary validates TLS
  against it, so every HTTPS call failed with a generic transport error while
  Node's own fetch worked. ca-certificates added to both images.
- `codex exec resume` rejects -C/--cd (plain `codex exec` accepts it), which
  broke follow-up turns while first turns succeeded.

Known regression: Kimi's managed-usage API has no Codex equivalent, so the
/usage route returns 501 and there is no quota probe for the codex model.
The two quota plugins degrade quietly to no output.

Also: stop tracking cognee.env (live LLM + JWT secrets) and gitignore it.
The secrets remain in earlier history and should be rotated.

Verified live: plain turn, SSE streaming, session resume, MCP tool call,
bearer-token MCP call, and completions through both LiteLLM routes.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014Y5QPagv4iun1ghpwM96Ff
2026-08-01 06:13:27 +00:00

302 lines
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Python
Executable File

#!/usr/bin/env python3
"""model_registry — reads model-registry.yaml (kb#133, A2A-1).
Per DESIGN-a2a-agents.md v2.1 §2-3b: the model registry is data, not logic.
a(t) probe *mechanics* (QuotaProbe, GPUResidencyProbe, ...) already live in
kanboard/bin/kb_worker.py — this module does not reimplement them. It gives
callers two things instead:
* resolve(tier) — "an available model for tier X" without the
caller naming a model. Structural availability
(lifecycle, metered opt-in) is decided here from
registry data; live a(t) truth (is the quota
window open right now, is the GPU actually free)
is decided by an optional `probe_check` callback
the caller supplies (e.g. wired to kb_worker's
Probe classes via to_probe_config()).
* preload_check(...) — the §3b GPU pre-load check, expressed purely from
registry numbers (never-evict reservations +
candidate footprint) plus a headroom figure the
caller supplies. It does not shell nvidia-smi
itself — kb_worker.GPUResidencyProbe (or
`nvidia-smi` directly) is the live-read path;
this stays pure/testable.
Usage (library):
from model_registry import load_registry, resolve, to_probe_config, preload_check
reg = load_registry()
model = resolve(reg, tier="large") # -> the "codex" entry
cfg = to_probe_config(reg, "codex") # -> kb_worker probe config dict
ok, reason = preload_check(reg, "local-small", headroom_mb=1900)
Usage (CLI, for manual verification):
./model_registry.py resolve --tier large
./model_registry.py resolve --tier large --allow-metered --opted-in agent:torgash
./model_registry.py probe-config --id kimi
./model_registry.py preload-check --id local-small --headroom-mb 1900
./model_registry.py preload-check --id local-small --live # shells nvidia-smi
./model_registry.py list
"""
import argparse
import json
import os
import subprocess
import sys
import yaml
HERE = os.path.dirname(os.path.abspath(__file__))
DEFAULT_REGISTRY_PATH = os.path.join(HERE, "model-registry.yaml")
class RegistryError(Exception):
pass
def load_registry(path=None):
"""Load and lightly validate model-registry.yaml."""
path = path or DEFAULT_REGISTRY_PATH
with open(path) as f:
reg = yaml.safe_load(f)
if not reg or "models" not in reg:
raise RegistryError(f"{path}: missing top-level 'models' list")
ids = [m["id"] for m in reg["models"]]
if len(ids) != len(set(ids)):
raise RegistryError(f"{path}: duplicate model ids in {ids}")
return reg
def get_model(registry, model_id):
for m in registry["models"]:
if m["id"] == model_id:
return m
raise RegistryError(f"unknown model id: {model_id!r}")
# ---------------------------------------------------------------------------
# resolve — "an available model for tier X" without the caller naming one.
# ---------------------------------------------------------------------------
def resolve(registry, tier, allow_metered=False, opted_in_key=None, probe_check=None):
"""Return the first model in `tier`'s pool that is structurally usable,
and (if probe_check is given) currently available.
Structural filter (from registry data alone):
- candidate must be listed under routing.tiers[tier]
- a metered model is only a candidate at all when the CALLER passes
allow_metered=True AND opted_in_key appears in routing.metered_opt_in
(§3a: "no metered API by default" — an empty metered_opt_in list, the
shipped default, makes every metered model structurally unreachable
regardless of allow_metered).
Live filter (optional): probe_check(model_dict) -> bool. Wire this to
kb_worker's Probe.available() (via to_probe_config below) when the
caller wants real a(t) truth instead of just structural eligibility.
"""
pools = registry.get("routing", {}).get("tiers", {})
if tier not in pools:
raise RegistryError(f"unknown tier: {tier!r} (have: {sorted(pools)})")
opted_in = set(registry.get("routing", {}).get("metered_opt_in", []) or [])
candidates = []
for model_id in pools[tier]:
m = get_model(registry, model_id)
if m.get("metered"):
if not allow_metered:
continue
if not m.get("opt_in_required", True):
# Registry says this metered model doesn't need opt-in — treat
# as a data error rather than silently routing to it.
raise RegistryError(
f"model {model_id!r} is metered but opt_in_required=false; "
"fix the registry entry, this helper will not assume implicit access"
)
if opted_in_key is None or opted_in_key not in opted_in:
continue
candidates.append(m)
for m in candidates:
if probe_check is None or probe_check(m):
return m
raise RegistryError(
f"no available model for tier={tier!r} "
f"(allow_metered={allow_metered}, opted_in_key={opted_in_key!r}); "
f"checked candidates: {[m['id'] for m in candidates] or pools[tier]}"
)
# ---------------------------------------------------------------------------
# to_probe_config — bridges registry quota data into kb_worker's probe cfg
# shape (kanboard/bin/kb_worker.py PROBE_BUILDERS), so probes read registry
# numbers instead of the registry re-implementing probe logic.
# ---------------------------------------------------------------------------
def to_probe_config(registry, model_id):
"""Return a dict matching kb_worker.py's `build_probe(cfg)` input shape
for `model_id`'s lifecycle. Raises if the model has no probe-relevant
lifecycle (e.g. cost-gated with no probe_command wired yet)."""
m = get_model(registry, model_id)
lifecycle = m["lifecycle"]
if lifecycle == "always-on":
return {"type": "always_on"}
if lifecycle == "quota-gated":
q = m.get("quota") or {}
windows = q.get("windows") or []
if not windows:
raise RegistryError(f"{model_id}: quota-gated but no quota.windows configured")
# kb_worker's QuotaProbe checks one field; the tightest (first-to-hit)
# window in practice is the short one — default to the first entry,
# callers needing multi-window gating build one probe per window.
window = windows[0]
return {
"type": "quota",
"command": q["probe_command"],
"field": window["field"],
"threshold_pct": q.get("threshold_pct", 95),
}
if lifecycle == "cost-gated":
q = m.get("quota") or {}
if not q.get("probe_command"):
raise RegistryError(
f"{model_id}: cost-gated but no budget probe wired yet "
"(opt-in path incomplete — see registry comment)"
)
return {
"type": "budget",
"command": q["probe_command"],
"field": q["field"],
"limit": q["limit"],
}
if lifecycle == "on-demand":
ep = (m.get("endpoints") or [{}])[0]
if not ep.get("health_url"):
raise RegistryError(f"{model_id}: on-demand but no endpoint.health_url configured")
return {"type": "on_demand", "url": ep["health_url"]}
raise RegistryError(f"{model_id}: unknown lifecycle {lifecycle!r}")
# ---------------------------------------------------------------------------
# preload_check — §3b GPU pre-load check, pure registry-data math. The live
# VRAM headroom READ is the caller's job (kb_worker.GPUResidencyProbe or
# nvidia-smi directly) — see live_headroom_mb() below for a thin convenience
# wrapper used only by this module's own CLI, not by the check itself.
# ---------------------------------------------------------------------------
def preload_check(registry, candidate_id, headroom_mb, resident_ids=None):
"""Would loading `candidate_id` fit, given `headroom_mb` free VRAM right
now (as reported by a live probe) and `resident_ids` already loaded?
Never-evict models are never subtracted from headroom by a caller in the
first place (their footprint is a standing reservation baked into any
correct live headroom read) — this function just re-asserts that policy
from registry data: if `candidate_id` itself is never-evict, it always
passes (it's not something a worker "loads speculatively" and might be
told to skip); anything else must fit in the reported headroom.
"""
policy = registry.get("gpu_residency_policy") or {}
m = get_model(registry, candidate_id)
gr = m.get("gpu_residency")
if not gr:
return True, f"{candidate_id} has no gpu_residency entry (not a GPU-resident model)"
if gr.get("never_evict"):
return True, f"{candidate_id} is in the never-evict set — always resident by policy"
required_mb = gr["vram_mb"]
never_evict_ids = set(policy.get("never_evict_ids", []))
resident_ids = set(resident_ids or [])
# Sanity: if the live headroom read already accounts for never-evict
# reservations (the expected contract — see kb_worker.GPUResidencyProbe's
# never_evict_reserved_mb param), this is just a straight comparison.
# If a caller passes raw total-minus-used instead, warn via the reason
# string rather than silently under/over-reserving.
reserved_hint = sum(
get_model(registry, mid)["gpu_residency"]["vram_mb"]
for mid in never_evict_ids
if mid not in resident_ids # already counted as "used" if resident_ids says so
)
ok = headroom_mb >= required_mb
reason = (
f"headroom={headroom_mb}MB required={required_mb}MB "
f"(never-evict reserve expected already netted out by the caller's probe: "
f"~{reserved_hint}MB across {sorted(never_evict_ids)})"
)
return ok, reason
def live_headroom_mb(never_evict_reserved_mb=0):
"""Convenience for manual CLI checks only — NOT used by preload_check()
itself. Shells nvidia-smi the same way kb_worker.GPUResidencyProbe does."""
out = subprocess.run(
["nvidia-smi", "--query-gpu=memory.used,memory.total", "--format=csv,noheader,nounits"],
capture_output=True, text=True, timeout=5, check=True,
).stdout.strip().splitlines()[0]
used, total = (int(x) for x in out.split(","))
return total - used - never_evict_reserved_mb
# ---------------------------------------------------------------------------
# CLI — manual verification only, not part of the library contract.
# ---------------------------------------------------------------------------
def main():
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--registry", default=None, help="path to model-registry.yaml (default: sibling file)")
sub = ap.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("resolve")
p.add_argument("--tier", required=True)
p.add_argument("--allow-metered", action="store_true")
p.add_argument("--opted-in", default=None, help="virtual key claimed to be opted in")
p = sub.add_parser("probe-config")
p.add_argument("--id", required=True)
p = sub.add_parser("preload-check")
p.add_argument("--id", required=True)
p.add_argument("--headroom-mb", type=float, default=None)
p.add_argument("--live", action="store_true", help="read live headroom via nvidia-smi instead of --headroom-mb")
p.add_argument("--resident", action="append", default=[], help="repeatable: id already resident")
sub.add_parser("list")
args = ap.parse_args()
reg = load_registry(args.registry)
try:
if args.cmd == "resolve":
m = resolve(reg, args.tier, allow_metered=args.allow_metered, opted_in_key=args.opted_in)
print(json.dumps(m, indent=2))
elif args.cmd == "probe-config":
print(json.dumps(to_probe_config(reg, args.id), indent=2))
elif args.cmd == "preload-check":
headroom = args.headroom_mb
if args.live:
policy = reg.get("gpu_residency_policy") or {}
reserved = sum(get_model(reg, mid)["gpu_residency"]["vram_mb"]
for mid in policy.get("never_evict_ids", []))
headroom = live_headroom_mb(never_evict_reserved_mb=reserved)
if headroom is None:
raise RegistryError("preload-check needs --headroom-mb or --live")
ok, reason = preload_check(reg, args.id, headroom, resident_ids=args.resident)
print(json.dumps({"ok": ok, "reason": reason}))
sys.exit(0 if ok else 1)
elif args.cmd == "list":
for m in reg["models"]:
print(f"{m['id']:16} tier={m['tier']:6} lifecycle={m['lifecycle']:13} "
f"metered={m['metered']} role={m['role']}")
except RegistryError as exc:
print(f"error: {exc}", file=sys.stderr)
sys.exit(2)
if __name__ == "__main__":
main()