# Local LLM router config
# All weights, thresholds, and provider settings live here so they can be
# tuned without touching code. Loaded/validated by config.py.

objective:
  # Quality is the objective. Cost is a constraint and a tiebreak. Eco is
  # logged per request but is NOT optimized here — that judgement is made
  # outside this router.
  #
  # This replaced a weighted blend (cost 0.4 / eco 0.2 / proficiency 0.4).
  # Measurement killed it: turning the cost weight from 0.4 to ZERO changed
  # the winner in only 2 of 6 categories, so the blend was never steering on
  # quality — while 60% of every decision adjudicated fractions of a cent
  # (all real traffic to date totals $0.07).

  # Proficiency differences smaller than this are treated as equal and the
  # cheaper model wins. This is measurement noise, not preference: scores
  # currently rest on 2-3 samples per category, so a 0.05 gap is
  # indistinguishable from sampling variation and paying for it buys noise.
  # Narrow it as samples accumulate.
  # Above this pace ratio, the dashboard raises an alarm.
  # 1.25 = burning 25% faster than the plan allows.
  plan_pace_warn_ratio: 1.25
  quality_tolerance: 0.1

  # Cost is priced per-request from catalog prices, NOT from a benchmark
  # sweep. A fixed 400-token reference task ranked glm-5.2-fast 3.2x cheaper
  # than deepseek-v4-flash; on a realistic 70k-token prompt deepseek is 5.0x
  # cheaper. Attribution inverts with prompt size, so a fixed-shape benchmark
  # cannot rank models for a workload of another shape. Catalog prices scaled
  # to the actual request agree with the live measurement, cost nothing, and
  # need no sweep.

  # Share of prompt tokens served from the provider's prefix cache. Agent
  # clients resend the whole conversation each turn, so most of it is a hit.
  #
  # Measured token-weighted across 50 sessions and 40.7M tokens (2026-08-23):
  # 91.7% overall, 92.6% on the sessions above 400k tokens, which are the ones
  # that carry the cost. The previous 0.84 came from 2.2M tokens of much
  # earlier traffic.
  #
  # Raising it changed NO winner in any of the nine categories at 60k context
  # — checked before editing. It is here because the number should be true,
  # not because the routing needed it.
  assumed_cache_rate: 0.917

  # Completion length assumed when pricing a request. Real sessions here median
  # around 200-400 completion tokens against enormous prompts.
  assumed_completion_tokens: 500

  # Per-request ceiling on measured ENERGY, in kWh. null disables it.
  #
  # Denominated in kWh rather than dollars. For scale: the reference task runs
  # ~5e-06 kWh on the cheapest model and ~2.2e-04 on the most expensive.
  # Overage is billed against the account's credit balance;
  # plan_kwh_per_period gates nothing.
  max_energy_per_request:

  # The subscription's kWh allowance per billing period, for reporting burn in
  # /health. This is a planning figure only: per-request traffic is never
  # refused for exceeding plan_kwh_per_period — it gates nothing. Set to match
  # your plan; null disables the report. NeuralWatt also returns
  # allowance_remaining_usd per request, which is logged for /metrics, but that
  # is a dollar figure while the plan is denominated in energy.
  plan_kwh_per_period: 6.25

  # Hours of recent balance history used to estimate the burn rate. The most
  # recent monotonically-decreasing segment of allowance_remaining_usd values
  # (segments split at each balance increase) is examined over this window.
  quota_burn_window_hours: 24
  # Projected hours of runway below which /metrics and dashboards emit a
  # low-warning boolean; only fires when a burn rate estimate exists and the
  # projected remainder is positive but short.
  quota_runway_warning_hours: 6
  # A burn estimate needs at least this many balance samples in the most recent
  # monotonically-decreasing segment (post-top-up resets the segment); below
  # which the estimate is None with an explanatory runway note.
  quota_burn_min_segment_samples: 3
  # ...and the segment must span at least this many hours, otherwise the
  # estimate is None with an explanatory note (never a wild extrapolation).
  quota_burn_min_segment_hours: 0.5

  # The day-of-month your NeuralWatt subscription billing cycle resets. Set
  # this to YOUR real billing day so the admin quota modal shows a genuine
  # next-reset date instead of a misleading rolling-window start. null disables
  # the feature and the modal shows "not configured". Valid range: 1-28 (skip
  # 29-31 to avoid shorter-month edge cases).
  billing_reset_day: 6

  # Rejection-rate detection over route_decisions rows that selected no model
  # (422 "no model satisfies the hard filters"). The signal is novelty or rate —
  # NEVER mere presence: this deployment routinely has ~3 rejections/hour of
  # ordinary over-large tier-3 requests that are behaving as designed (6 in the
  # last 24h, 17 all-time at 2026-09-05), so a count-only tripwire would be
  # permanently on.
  #
  # alert window (hours): rejections this recent are counted per
  # (task_tier, digit-normalized reason) group.
  rejection_warning_window_hours: 1
  # baseline window (hours) BEFORE the alert window: groups absent here are
  # "new". A new group warns from 2 occurrences — the 2026-09-04 vision
  # incident produced exactly 2 and no rate threshold can sit below routine
  # noise yet above that.
  rejection_warning_baseline_hours: 24
  # a group already present in the baseline warns only at this many
  # occurrences inside the alert window: 6 = 2x the observed routine hourly
  # peak, far below the dozens/hour a deprecation flare produces.
  rejection_warning_min_count: 6

  # How far back /metrics looks to decide which models the router has actually
  # picked. Long on purpose: this answers "has this model EVER been chosen",
  # and a model that only wins one category can go days between selections.
  # Feeds two very different reports -- see metrics.selection_coverage.
  selection_coverage_window_hours: 168

  # Prefix-cache rate measured from energy_observations, and the expiry check
  # on assumed_cache_rate above. That constant was measured ONCE, on
  # 2026-08-23, and it is the highest-leverage term in routing.estimated_cost
  # on a 100k-token prompt — a premise that large should not go unchecked just
  # because it was true when it was written.
  #
  # The comparison is a DIVERGENCE from assumed_cache_rate, not an absolute
  # floor. "Is the cache working" is the wrong question; a deployment whose
  # real rate is 0.60 is not broken, it is mispriced, and a floor would say
  # nothing about the number the cost model is actually built on.
  #
  # Trailing window, in hours. Long for the same reason
  # selection_coverage_window_hours is: only a fraction of rows carry a
  # reported cached count, so a 1h window is usually empty.
  cache_rate_window_hours: 168
  # Absolute divergence from assumed_cache_rate that warns. 0.10 sits well
  # outside ordinary session-mix drift (measured 2026-09-13: 0.879 neuralwatt
  # / 0.901 openrouter against the assumed 0.917) and well inside the 0.952 vs
  # 0.478 same-model/switched gap the waves plan is chasing.
  cache_rate_warn_margin: 0.10
  # Minimum reported-cache observations before either warning fires, per
  # aggregate and per (provider, model) group. Observations, not tokens: one
  # 200k-token prompt outweighs a hundred ordinary turns, so a token floor
  # would let a single request's luck read as a measurement.
  cache_rate_warn_min_observations: 25

  # --- Report-only measurement series. Neither is read by routing. ---------
  #
  # Cost-estimator calibration: routing.estimated_cost's prediction against
  # the provider's own billed figure, per (provider, model), joined on
  # request_id. Measured 2026-09-12 at ~100% telemetry coverage, the estimator
  # is high by 1.6x-13x depending on the model.
  #
  # The scale error is NOT the finding — a uniform overestimate reorders
  # nothing, because the ranking is a comparison and every candidate moves
  # together. The SPREAD in that error is the finding: it priced
  # deepseek/deepseek-v4-flash below qwen3.6-35b while the bill said the
  # reverse. /metrics reports `spread` for exactly that reason.
  #
  # Nothing applies these factors. They are here so their stability can be
  # judged first — this project has already mistaken one moment of a moving
  # per-model quantity for a constant (see CLAUDE.md, "Attribution drifts
  # across hours, so sampling must too").
  #
  # Trailing window, in hours. Long for the same reason
  # cache_rate_window_hours is: only rows with both an estimate and a billed
  # figure qualify, and a short window is usually too thin to read.
  cost_calibration_window_hours: 168
  # Joined observations before a (provider, model) factor is marked
  # `sufficient` and allowed to set the reported spread. Groups below it are
  # still listed with their counts — the count is itself information — but a
  # ratio over three requests must not become the headline.
  cost_calibration_min_observations: 25

  # Router-observed latency: router_wall_seconds and router_ttft_seconds from
  # energy_observations, p50 and p95 per (provider, model). These are the
  # ROUTER's clock, not the provider's duration_seconds — which is why this
  # can see OpenRouter at all, since OpenRouter reports no duration.
  #
  # It found z-ai/glm-5.3-flash, a model with "flash" in its name, at a p50 of
  # 9.6s to first token against 1.6s for deepseek-v4-flash on NeuralWatt.
  # Reported, not scored: latency is not an objective here, and one reading of
  # a quantity that tracks pool load is not grounds to make it one.
  latency_window_hours: 168
  # Observations before a group's percentiles are marked `sufficient`. Applied
  # SEPARATELY to the wall and TTFT counts, because TTFT is streaming-only by
  # nature and a buffered deployment legitimately has fewer of them.
  latency_min_observations: 25

  # Gate: with this off, routing is byte-identical to today.
  # Flip on only after the Wave 1 post-restart baseline day;
  # see plans/token-waste-waves.md Wave 2 gate.
  incumbent_cache_pricing: false

  # Challenger cache-rate dial.
  # - null (default): neutral, follows assumed_cache_rate → today's ranking exactly
  #   including the incumbent; this is the off position for tuning.
  # - 0.0: challengers priced as fully cold prompts (maximum incumbent advantage).
  # - Any value between is a partial cache-penalty — the whole wave dials
  #   from off to full here, no revert needed.
  incumbent_challenger_cache_rate:

  # Seconds between refreshes of the measured per-(provider, model) cache
  # rates. The TTL is measured from the first call after the process started
  # (time.monotonic), so a brief post-restart cold period is expected.
  incumbent_rate_refresh_seconds: 300

  # Minimum reported-cache observations before a per-(provider, model)
  # cache rate is trusted for pricing decisions — the independent pricing
  # floor. Tuning this does NOT move the cache-rate warning floor
  # (cache_rate_warn_min_observations): the two knobs answer different
  # questions with different failure costs.
  #
  # Shipped with a documented default (25) so the code always reads a
  # concrete float when the penalty is engaged.
  incumbent_rate_min_observations: 25

  # Credit-aware routing attenuation (OFF BY DEFAULT).
  # When enabled, a provider configured with a balance_url (e.g. OpenRouter's
  # prepaid account balance) gets its *comparison cost* inflated inside the
  # quality-first ranker as its account balance nears zero. Quality bands still
  # win; this only shifts ties. Providers whose balance comes from per-request
  # energy telemetry allowance_remaining_usd (e.g. NeuralWatt's overage-billed
  # subscription) are ALWAYS multiplier 1.0 regardless of their reading, so a
  # low soft_floor_usd never biases routing toward an attenuated provider just
  # because a telemetry provider's allowance reads near zero under normal use.
  #
  # This knob is deliberately NOT exposed in the admin UI persisted-config
  # allowlist (_CONFIG_ALLOWLIST in admin.py) or _ProviderUpdateBody; enabling
  # or tuning it requires editing this file and restarting llm-router.service
  # (the dispatcher's module-level cfg binds at import).
  credit_attenuation:
    enabled: false
    soft_floor_usd: 5.0          # balance >= this -> multiplier 1.0
    zero_floor_usd: 0.0          # balance <= this -> max_multiplier
    max_multiplier: 5.0          # maximum cost-inflation at/below zero floor
    refresh_seconds: 300         # cache duration for resolved multipliers

context:
  safety_factor: 0.75          # fraction of advertised context treated as usable
  default_output_reserve_tokens: 4096
  # Ceiling on the output reserve, as a fraction of the usable window. A
  # provider's advertised max_output_tokens is normally a small per-request
  # cap, but OpenRouter reports max_completion_tokens -- "the most you may
  # ASK for", 0.8-0.9 of context on a dozen rows. Subtracting that whole
  # left 12 models at an effective context of 0, silently unroutable.
  # Only the catalog-derived reserve is capped; a per_model_overrides
  # reserve is a measurement and is used as written.
  max_output_reserve_fraction: 0.5
  # Per-model exceptions to the two settings above, for a row whose real
  # limits you have measured. The global factor has to hold for the whole
  # catalog, so it is deliberately pessimistic; a model you have actually
  # pushed to its limit deserves its own number.
  #
  # Both keys are optional and each falls back to the global on its own.
  # An override of 0 reserve tokens means zero, not "unset".
  #
  #   per_model_overrides:
  #     qwen3.6-35b:
  #       safety_factor: 0.85
  #       output_reserve_tokens: 8192
  #
  # Takes effect on the next `python poller.py` -- effective_context_window is
  # computed at poll time, not per request.
  per_model_overrides: {}

tiers:
  # Maps a tier number to a human label, purely for logging/dashboards.
  1: "cheap / simple"
  2: "mid / general"
  3: "frontier / high-stakes"

tiering:
  # Auto-tiering pass knobs. cheap_completion_max is the completion-cost
  # (per 1M tokens) ceiling below which a model is eligible for tier 1.
  # model_tiers overrides the heuristic per model_id and applies to ALL
  # providers (limitation vs a (model_id, provider) key).
  cheap_completion_max: 1.00

  # Advertised context_window at or above which a model is NOT eligible for
  # tier 1, whatever it costs. Tier is a capability FLOOR (routing drops any
  # row with tier < required_tier), so tier 1 means "simple work only" — and
  # deciding that on price alone excluded deepseek-v4-flash from every tier-2
  # request purely for being $0.28/1M, despite a 1M window and 1.00 on all
  # three coding categories.
  #
  # 512000 sits in the empty band between the catalog's 256K class (262128)
  # and its 1M class (1048560) — a 2x margin either side, so it is not fitted
  # to any one model. gemma-4-31b (256K) stays tier 1; the 1M rows do not.
  tier1_context_max: 512000

  model_tiers:
    # This model lives on Ollama, not the NeuralWatt cloud catalog, so the
    # tiering heuristic never sees it -- src/tier.py OVERWRITES models.tier on
    # every run, so this pin is the ONLY thing that survives. If you swap the
    # local dispatch model, change the KEY here too or it silently loses its
    # tier. Pinned to tier 1 (cheap / simple) because it is targeted at
    # lightweight summarization and diff-checking — precisely the use-cases
    # tier 1 was designed for.
    qwen2.5-coder-router:14b: 1

proficiency:
  # Blending rule: leaderboard vs self-eval, once self-eval sample size
  # crosses the threshold below. Below threshold, leaderboard score alone
  # is used so thin self-eval data doesn't dominate.
  self_eval_min_samples: 10
  leaderboard_weight: 0.3
  self_eval_weight: 0.7
  # Prior strength (k) for the empirical-Bayes shrunken estimate: the peer
  # prior contributes k pseudo-observations, pulling each noisy per-model
  # score toward the global average. 20 pseudo-observations damps thin
  # self-eval data (n < 100 per model) without erasing the per-model signal.
  outcome_prior_strength: 20
  categories:
  - coding_general
  - coding_refactor
  - debugging
  - docs_writing
  - summarization
  # file_summarization + diff_checking are served by the local dispatch model
  # (see local_dispatch_models: section below).
  # They are kept adjacent to summarization since this model is targeted
  # at lightweight summarization and diff-checking tasks.
  - file_summarization
  - diff_checking
  - translation
  - reasoning_math
  - tool_use_agentic
  - general_chat

exploration:
  # Epsilon-greedy exploration: on the epsilon share of requests, picks the
  # hard-filter-eligible candidate with the fewest outcome samples (tie-break:
  # lowest cost) instead of the highest-score model. Deliberately ON by default
  # because the system is a ranking loop — without periodic exploration it
  # converges on whatever happens to be sampled most, creating exposure bias.
  # Adjust once real exploration traffic (was_exploration=True in
  # route_decisions) shows how often the exploration path picks differently
  # from the greedy path.
  enabled: true
  # Probability of exploration per request. 0.03 = ~3% of requests take an
  # exploratory path, giving ~97% exploit on known winners while still
  # occasionally sampling under-explored candidates.
  epsilon: 0.03
  # Cap: exploration candidate cost must be <= this multiple of the ranking
  # winner's cost. 4.0 gives headroom — an under-sampled model can be more
  # expensive than the greedy winner without eating the explore budget on
  # wildly off-target picks.
  max_cost_ratio: 4.0
  # Only tiers 1 and 2 models are eligible for exploration. Tier 3 (frontier)
  # is too expensive to spend on random sampling — save the explore budget for
  # models where being wrong costs less.
  max_tier: 2

escalation:
  enabled: true
  max_tier: 3

  # Bump the tier when the classifier is unsure of its own call. DEFAULT OFF:
  # this pays frontier prices on a hunch, before anything has gone wrong. The
  # iteration budget below spends after a check has actually failed, which is
  # strictly better on both mandates — the cheap attempt usually succeeds and
  # costs nothing extra, and when it fails you have evidence.
  preemptive_on_low_confidence: false
  min_confidence_before_bump: 0.6

iteration:
  # A tier is not only a capability floor, it is a budget for getting the
  # answer right. These are corrective attempts AFTER a verification failure,
  # not speculative retries.
  #
  # Retries are matched to the failure: a truncated answer gets a bigger token
  # budget on the SAME model (a different one would also run out), while a
  # malformed answer escalates to the next-best candidate (more tokens will
  # not make unparseable output parse).
  enabled: true
  attempts_by_tier:
    1: 0    # cheap/simple — one shot; iterating costs more than it is worth
    2: 1
    3: 2

  # Interactive requests are capped below their tier's budget regardless of
  # tier: every retry doubles time-to-answer, and in interactive use latency
  # IS a quality loss. Batch work does not care.
  max_attempts_interactive: 1

pinch:
  # Relevance-based context pruning (ported from the MIT-licensed llmrouter's
  # "pinch"). This is an OPTIONAL, pre-dispatch stage: when a conversation
  # exceeds budget_tokens, the provider-bound messages are trimmed BEFORE any
  # paid token is sent upstream. User/assistant/system messages are always
  # kept verbatim; only old TOOL RESULTS are shortened or dropped, because
  # they carry the bulk of a long agent session's tokens and are least needed
  # in full by the time the next turn is answered.
  #
  # On by default. Tool results can only be dropped safely because tool
  # outputs are idempotent enough for a placeholder; a wrong guess here loses
  # context, so start conservative (large budget, small reduction) and watch
  # route_decisions / pinch stats on real traffic before widening it.
  enabled: true
  budget_tokens: 50000
  keep_last_turns: 4
  max_summarize_chars: 4000
  # keep_last_turns has no size limit inside it: an entire autonomous
  # tool-call loop with no new user message can be one protected turn, and
  # one outsized tool result inside it (a full verbose test run, a huge file
  # read) ships verbatim regardless of size. Measured live 2026-09-06: a
  # 324k-token conversation shrank only ~8% because nearly all of it sat
  # inside the protected window. This closes that gap: any tool result
  # inside the protected window over this many characters still gets the
  # same head/tail elision candidates get. Deliberately a much higher bar
  # than max_summarize_chars -- recent results are more likely to still
  # matter -- so it only catches true outliers. Set to null to disable.
  protected_max_chars: 20000
  # Prefix-stability probe (Wave 1 item 1.3 of plans/token-waste-waves.md).
  # The provider bills the longest byte-identical PREFIX of a prompt at the
  # cached rate, so rewriting an early message re-bills everything after it.
  # The uniform pruning path compresses a contiguous positional region, so an
  # append cannot disturb it; the relevance path below compresses a prefix of
  # a relevance-ORDERED list, so a growing deficit pulls in one more candidate
  # each turn at an arbitrary message POSITION. Offline that rewrites 75% of a
  # payload's tokens; the live cache rate on pruned turns is 0.924, which is
  # not what that should look like. This settles it on real traffic.
  #
  # When on, each decision row records prefix_divergence_index,
  # prefix_tokens_after_divergence and prefix_prev_message_count -- three
  # integers. HASHES ONLY, and not even those: the per-message digests live in
  # process memory for exactly one turn and never reach the database, so
  # nothing reversible is stored and a restart costs one comparison per
  # session. Measured on the live median payload (98k tokens in, 74k out):
  # ~1.0 ms per turn, against a request path whose floor is a provider
  # round-trip of 1.4-2.0 s, plus ~10 bytes per decision row.
  prefix_probe: true
  relevance:
    # Off by default, matching every other new-and-unproven knob in this
    # project — and specifically requires pinch.enabled too, since this has no
    # effect otherwise. Ship it, watch route_decisions / pinch stats on real
    # traffic, then decide the default.
    enabled: true
    # An EMBEDDING model, not a chat model — this must not point at
    # classifier.model or verification.model. Pull one on the same Ollama:
    #   ollama pull nomic-embed-text
    model: "nomic-embed-text"
    base_url: "http://localhost:11434/v1"
    timeout_seconds: 10
    # Below this many trim-eligible candidates, skip the embedding call
    # entirely and fall back to uniform compression — a network round trip
    # to rank one candidate decides nothing.
    min_candidates: 2

session_cache:
  # In-memory per-session classification cache. Remembers the last
  # task_category / task_tier decision for each session for a few minutes, so
  # a long agent session skips the ~1-2s classifier round-trip on every turn.
  # Capability flags (tools / images / json) are NEVER cached — they are read
  # fresh from each request. Fallback classifications are NEVER cached. No
  # persistence: a restart just reclassifies each session once.
  #
  # Off by default, matching every other new-and-unproven knob in this
  # project: ship it, watch route_decisions.source="cached" on real traffic,
  # then decide the right default.
  enabled: true
  staleness_seconds: 1200

circuit_breaker:
  # Passive availability circuit breaker, on by default. When enabled, a model
  # that returns 5xx is temporarily excluded from routing with exponential
  # backoff; recovery is passive (the next real request becomes the probe once
  # the cooldown passes). It has a low-risk failure mode even when wrong.
  enabled: true
  initial_cooldown_seconds: 30
  max_cooldown_seconds: 600
  backoff_multiplier: 2.0

routing:
  # Access gating is prose-only in the NeuralWatt catalog ("Private preview
  # (grant-gated)", "(Canary)"), so the poller parses it into access_level and
  # routing excludes anything not listed here. Add 'preview'/'canary' only if
  # the account actually holds the grant — otherwise dispatch earns a 403.
  allowed_access_levels:
  - public

  # '-flex' rows are held server-side during peak until a capacity gap opens.
  # That's correct for overnight/batch agent work and wrong for anything
  # interactive, so a request has to opt in via latency_tolerance.
  default_latency_tolerance: interactive   # 'interactive' | 'batch'

  # Operator's default stance on routing to '-flex' serving-class rows for
  # requests that do not state one explicitly. A 4-position scale:
  #   no-flex      never route to a flex row
  #   auto         decide per request (the default; keeps existing behavior)
  #   prefer-flex  flex first, standard as fallback
  #   force-flex   flex only
  # 'force-flex' is the dangerous global default: it bypasses the
  # latency_tolerance: interactive hard filter for EVERY request, so even a
  # request that opted into interactive routing would admit rows that are held
  # server-side during peak. Use it only when you are certain the caller can
  # tolerate flex latency globally.
  default_flex_preference: auto

  # Bare `auto` resolves to this profile. This is the profile name the router
  # uses when the client does not specify one explicitly. It must name a
  # built-in profile or a config-defined profile; the value is validated at
  # load and will be editable from the admin Controls page in a later wave.
  default_profile: "default"

  # Minimum tool_use_agentic proficiency required of a model when the REQUEST
  # carries tool definitions. A filter, not a weight, because it is a
  # capability requirement rather than a preference.
  #
  # It does NOT ask whether the task is agentic — it asks whether the model
  # can be trusted with tools that are on the table. The measured failure is
  # the second one: deepseek-v4-flash scores 0.33 here, and the recorded case
  # is a NON-agentic prompt ("it is 1:20pm, my meeting is at 3pm, how many
  # minutes?") where it called two tools instead of subtracting. A model that
  # over-reaches is a hazard wherever tools exist, not only where a classifier
  # would say "agentic" — which it cannot do anyway: asked to identify six
  # unambiguous tool-use prompts, the local models managed 2/6 and 1/6.
  # Whether tools are present is stated in the request body. Read it.
  #
  # 0.5 sits in the empty band between the only two values the catalog
  # currently holds (0.33 and 1.00), so it is not fitted to either. A model
  # with NO measured tool score is unproven rather than proven bad, and is not
  # dropped.
  #
  # CURRENTLY DISABLED (null) pending experimentation. The trade being
  # measured: opencode sends `tools` on essentially every request, so with the
  # filter on, deepseek-v4-flash is excluded from ordinary agent traffic and
  # the ~7x cost advantage on coding routes goes unused. With it off, that
  # advantage applies — and a model measured at 0.33 on tool use handles
  # requests where tools are on the table.
  #
  # What would settle it is outcome data, not another benchmark: run with it
  # off, let POST /outcome report real pass/fail, and compare
  # tool_use_agentic proficiency for deepseek before and after. That is the
  # one signal here that knows whether the work actually worked.
  min_tool_proficiency:
  tool_use_category: tool_use_agentic

  # Request-side capability gates. These read the request body (image parts,
  # response_format) and hard-restrict to models whose catalog row declares the
  # capability. Unlike min_tool_proficiency these are NOT proficiency gates —
  # a wrong guess is a guaranteed provider 400, so they gate by default and
  # fail closed when the catalog flag is unknown.
  require_vision: true
  require_json_mode: true

  # There is deliberately no flex discount knob. Whether a flex row is usable
  # at all is a hard filter above (latency_tolerance), not a price adjustment
  # -- being held server-side during peak is a latency property, and the
  # catalog advertises flex and standard at the same token price anyway.

local_vision:
  # Local Ollama vision fallback. Used ONLY when a request carries image
  # parts and routing finds NO cloud model that supports_vision — the cloud
  # catalog currently excludes the cost leader (deepseek) on vision, so a
  # fallback is what keeps image requests working instead of 422ing.
  # It speaks the OpenAI-compatible /v1 surface, so this is an Ollama endpoint
  # and the model must be pulled (`ollama pull qwen3-vl:4b`) on that host.
  enabled: true
  base_url: "http://localhost:11434/v1"
  api_key_env:
  # A Modelfile-tagged variant of qwen3-vl:4b, not the base library tag.
  # Measured live: the base tag comes up at Ollama's own default num_ctx
  # (32768) and costs 9.4GB loaded — resident alongside the classifier's
  # pre-fix 14GB, that left 1.9GB free on a 24GB card. num_ctx cannot be set
  # per-request here: verified live that Ollama's OpenAI-compatible endpoint
  # (0.22.0) silently ignores it under every field shape tried, so it has to
  # be baked into the model tag itself:
  #   printf 'FROM qwen3-vl:4b\nPARAMETER num_ctx 8192\n' > Modelfile.vision
  #   ollama create qwen3-vl-router:4b -f Modelfile.vision
  #
  # Reduced 16384 -> 8192 on 2026-09-03 to buy back KV cache. Most of this
  # model's footprint is KV, not weights: 3.3GB on disk but 6.9GB resident at
  # 16384, and 5.6GB at 8192. That 1.3GB is what lets the dispatch model run
  # at num_ctx 32768 (17GB) and still leave vision RESIDENT -- 23.2GB of 24GB
  # together. Without it, loading vision EVICTS the dispatch model entirely
  # and the next classification pays a ~6s cold reload on the latency floor.
  #
  # This is the one path that still has no measured ceiling: it gets the RAW
  # message list (dispatcher.py calls it BEFORE pinch pruning), so 8192 is a
  # judgement call, not a derived minimum. If image requests start failing on
  # context, raise this FIRST and drop the dispatch model to num_ctx 16384 to
  # pay for it. Budget guards (max_images, max_image_bytes) bound the image
  # side but not the conversation text around it.
  #
  # Do NOT swap this for a 1B-class model to save memory. Tested 2026-09-03:
  # moondream (1B, 2048 ctx) answered a real dashboard screenshot with "a
  # spreadsheet ... possibly related to business decisions or financial
  # analysis" -- it read no title, no column, no value, and confabulated a
  # plausible description instead. qwen3-vl:4b read the page title, quoted the
  # subtitle verbatim and recovered the row count. Screenshots are the actual
  # workload here, and dense small text is exactly where tiny VLMs fail.
  model: "qwen3-vl-router:4b"
  timeout_seconds: 60
  max_images: 4
  max_image_bytes: 9437184

local_dispatch_models:
  # Local Ollama models that can serve dispatch (not just classification/vision).
  # Each entry is a model that the router can route traffic to — the same path
  # that picks cloud models from the NeuralWatt catalog, but calling a local
  # OpenAI-compatible endpoint instead.
  #
  # These models MUST ALSO appear under proficiency.categories in eligible_categories
  # and MUST have a tier pin under tiering.model_tiers, because src/tier.py
  # OVERWRITES models.tier on every run (the config override is the only pin).
  #
  # To add a model: copy this block and change the values. Keep the comment
  # describing how to build the model tag (Modelfile recipe), because the num_ctx
  # baked into the tag is what Ollama's /v1 endpoint actually uses — setting it
  # per-request through the OpenAI API is not supported by Ollama.
  - # DEFAULT. Measured on a 24GB Quadro RTX 6000 (Turing, compute 7.5), and a
    # sound starting point for any 24-32GB card -- the numbers below are real,
    # not aspirational. It is a default rather than a recommendation only in
    # the sense that YOUR hardware and YOUR taste in models should win:
    # docs/local-models.md documents the measurement method so you can swap in
    # whatever you actually want to run and defend the choice with your own
    # numbers instead of inheriting these.
    #
    # Dormancy under the DEFAULT profile (measured 2026-09-02/03): quality-gated
    # ranking — the local row must score within objective.quality_tolerance
    # (0.10) of the cloud leader before its price advantage is even consulted.
    # Today it does not: 0.767 vs 0.95 on file_summarization (gap 0.183), so
    # this row is dormant under the default profile. That is by design, not a
    # bug. Do NOT widen quality_tolerance. It fires as a fallback when the cloud
    # is unavailable; see docs/routing.md § Local dispatch branch.
    #
    # qwen2.5-coder-router:14b serves BOTH local dispatch and classification,
    # so only one model stays resident. Selected 2026-09-02/03 by measurement:
    #
    #   file_summarization (n=6, judge-scored -- treat +-0.15 as a tie):
    #     deepseek-v4-flash (cloud) 0.95   <- local costs ~0.18 of quality
    #     qwen2.5-coder:32b         0.833  (22GB: evicts vision, 3.4s classify)
    #     qwen2.5-coder:14b Q8_0    0.817  (19GB, 1.5x slower, gain within noise)
    #     qwen2.5-coder:14b Q4_K_M  0.767  <- chosen
    #     nemotron-mini:4b          0.25   (the plan's original pick; FABRICATED
    #                                       on 3 of 6, and detected 0/4 bugs)
    #
    #   classification was 11/14 with 0 hard failures for EVERY variant above --
    #   quantization and context size changed only speed, never accuracy.
    #   Q4_K_M @ 32k: 1.14s mean. Q8_0 @ 32k: 3.79s, because it needs 24GB and
    #   Ollama spills 17% to CPU (watch the "17%/83%" column in `ollama ps`).
    #
    # num_ctx 32768 costs ~4GB of KV cache over 16384 (13GB -> 17GB resident).
    # That is affordable here ONLY because the vision model was retagged to
    # num_ctx 8192; together they are 23.2GB of 24GB, with ~800MB spare. On a
    # smaller card, drop this to 16384 first -- KV cache is the cheapest GB to
    # reclaim, and the classifier never needs it (classifier.max_input_chars
    # clamps to 8000 chars, ~2.7k tokens).
    #
    # Modelfile recipe (run on an Ollama host):
    #   ollama pull qwen2.5-coder:14b
    #   printf 'FROM qwen2.5-coder:14b\nPARAMETER num_ctx 32768\n' > Modelfile.local-dispatch
    #   ollama create qwen2.5-coder-router:14b -f Modelfile.local-dispatch
    model_id: "qwen2.5-coder-router:14b"
    base_url: "http://localhost:11434/v1"
    api_key_env:
    timeout_seconds: 180
    context_window: 32768
    max_output_tokens: 2048
    tier: 1
    # Restricted to file_summarization and diff_checking. The router's hard
    # filter (routing.py) will exclude this model for any other category. Do
    # not widen this without measuring the new category first -- this model
    # scored 0/4 at detecting bugs before the task set was fixed, and a
    # confidently wrong answer is worse than an honest error.
    eligible_categories:
    - file_summarization
    - diff_checking

verification:
  # Structural checks (parse the code, never run it) are free, pure Python and
  # always on — they need no model and run anywhere, down to an RPi.
  # This section governs the LOCAL LLM check, which is not free.
  #
  # Set local_llm_enabled: false on a host with no usable local inference.
  # Structural checking and POST /outcome both keep working; only the
  # refusal/incoherence class of failure stops being caught.
  local_llm_enabled: true

  # This check speaks Ollama's NATIVE API (/api/chat with think=False), which
  # no cloud provider offers, so it is configured SEPARATELY from the
  # classifier rather than derived from it. Deriving it meant that pointing
  # classification anywhere else sent these requests to <that host>/api/chat.
  #
  # Point it at the SAME Ollama as the classifier when that is across a VPN;
  # leave the model null in that case, since the hosts then match.
  base_url: "http://localhost:11434"
  # null means "whatever classifier.model is", correct only while both run on
  # the same local Ollama — config load REFUSES the null once the hosts differ,
  # because the fallback would name a model this Ollama has never heard of and
  # the verifier would fail silently. Stated explicitly here anyway, matching
  # classifier.model exactly (see that field's comment) so both calls hit the
  # SAME resident Ollama instance, loaded once at its Modelfile-tagged
  # context size rather than two separately-sized copies.
  model: "qwen2.5-coder-router:14b"

  # Only check answers this large. Measured on real traffic: a local check
  # costs ~15% of a median 193-token answer, so it would only pay if such
  # answers failed more than ~15% of the time. At 1,500 completion tokens the
  # break-even failure rate drops to ~1.9%, which is plausible. Below the
  # threshold a check costs more than the risk it removes.
  min_completion_tokens: 600

  # The check runs AFTER the response has gone back to the client, so it never
  # adds its ~6s to anyone's latency. It exists to learn which models fail on
  # real work, not to gate answers.
  timeout_seconds: 60
  max_output_tokens: 1024

  # How far back /outcome looks when a report carries no request_id and no
  # matching directory. A test run follows the completion that caused it within
  # seconds, so this is deliberately short: a wide window sweeps in sessions
  # that finished long ago and makes every report look ambiguous. If more than
  # one conversation was active inside it, the report is refused rather than
  # guessed at.
  outcome_attribution_window_seconds: 120

freshness:
  stale_after_days: 3
  # Router refuses to route to a model whose row is stale/deprecated,
  # regardless of how good its score would otherwise be.
  exclude_stale: true
  exclude_deprecated: true
  # Allowlisting a model does nothing until a poll ingests it, and the timer
  # runs every 2 hours -- so a model added through /admin sat invisible until
  # the next tick, looking like the allowlist had not worked.
  #
  # Debounce, not delay: each further edit restarts the clock, so adding five
  # models one at a time costs one poll, not five. 0 disables the trigger and
  # leaves the timer as the only path.
  repoll_after_allowlist_change_seconds: 5.0

database:
  path: "router.db"

classifier:
  # The local LLM that classifies each task before a cloud model answers it.
  # "Local" means your hardware, not necessarily this machine — the box with
  # the GPU is usually not the laptop you are typing on. It speaks plain
  # OpenAI-compatible chat completions, so point it wherever Ollama lives:
  #
  #   same machine    base_url: http://localhost:11434/v1
  #   over a VPN      base_url: http://<vpn-ip>:11434/v1
  #                   (serving host needs deploy/ollama-over-vpn.conf; Ollama
  #                    binds loopback-only by default and will refuse)
  #
  # Any OpenAI-compatible endpoint works, so a cloud model can classify too —
  # set api_key_env for one that checks a key. Worth knowing before assuming
  # local is the cheap option: on five prompts NeuralWatt's deepseek-v4-flash
  # classified in 1.02s mean against qwen3.5's 11.58s on an RTX 6000, agreed
  # with the label 5/5 against 2/4, and cost $0.093 per thousand calls. Local
  # inference is not free, it is unbilled.
  provider: "ollama"
  base_url: "http://localhost:11434/v1"
  # Unset means unauthenticated, which is the Ollama case. Name the env var
  # holding the key when the endpoint actually checks one.
  api_key_env:
  # A Modelfile-tagged variant of mistral-nemo:12b, not the base library tag
  # — must match a model `ollama list` reports. Ollama loads a model at its
  # library Modelfile's default context unless told otherwise, and the base
  # tag never was: measured live, mistral-nemo:12b came up at num_ctx=32768
  # (14GB of a 24GB card) though max_input_chars + the system prompt +
  # max_output_tokens need well under a quarter of that. num_ctx cannot be
  # set per-request here: verified live that Ollama's OpenAI-compatible
  # endpoint (0.22.0) silently ignores it under every field shape tried (a
  # 200 comes back, the loaded context never changes), so it has to be baked
  # into the tag itself:
  #   printf 'FROM mistral-nemo:12b\nPARAMETER num_ctx 8192\n' > Modelfile.router
  #   ollama create mistral-nemo-router:12b -f Modelfile.router
  # 8192 leaves 2x headroom over the real requirement. verification.model
  # points at this same tag (see its own comment) so classify and verify
  # share one resident instance at one context size, rather than risking a
  # reload thrash from two differently-sized copies of the same base model.
  model: "qwen2.5-coder-router:14b"
  # A cold Ollama took 43s to answer the first classification, which blew the
  # old 30s ceiling and returned 503 to the client. Warm it is ~5s. The
  # ceiling is for a cold model load, not the steady state.
  timeout_seconds: 120
  # Classification must be reproducible: at the default temperature the same
  # task was classified tier 2 then tier 1 on consecutive calls, which routed
  # it to two different models. Routing that changes under an identical
  # prompt is untraceable.
  temperature: 0
  # Hard cap on the classifier's generation, to bound a failure mode that
  # cascades on REASONING models: they emit a long chain of thought, blow past
  # timeout_seconds, and Ollama keeps generating after the client gives up AND
  # serializes per model, so one runaway request queues every later one behind
  # it. Measured on qwen3.5, which failed this way on 4 of 19 calls — each one
  # a 15s wait ending in a silent fallback.
  #
  # The current default (mistral-nemo) does not reason, so this does not bind
  # for it. Kept anyway: it costs nothing when unused and is the only thing
  # standing between a swapped-in reasoning model and that cascade. If you do
  # swap one in, note 256 was too tight — the trace consumed the whole budget
  # and the model was truncated before emitting any JSON.
  max_output_tokens: 1024

  # Where routing lands when the classifier times out, errors, or returns
  # something unparseable. A local model being slow should degrade routing,
  # not refuse the request — the caller is a coding agent that would rather
  # have a mid-tier answer than a 502.
  # Ceiling on the text handed to the classifier (head + tail, middle elided).
  # It decides a category and a tier; it does not need the document, and
  # feeding it one is harmful rather than merely wasteful. Measured on a ~20k
  # token prompt: qwen3.5 spent 28.7s and returned empty (budget consumed by
  # its reasoning trace), mistral-nemo spent 41.8s echoing the input back
  # inside its JSON. Both land on source: "fallback" — the same answer an
  # instant failure gives, after 30-40s of local inference.
  #
  # Nothing is lost: chat_completions measures the real conversation with
  # estimate_prompt_tokens and takes the larger value, so required_context
  # never depends on what the classifier saw. 0 disables clamping.
  max_input_chars: 8000

  # When the chat path supplies the previous turn as context (see the pinch /
  # context notes), frame the classifier input as "Context: <prev> / Message:
  # <current>" so a short follow-up inherits the prior turn's complexity
  # instead of being classified in isolation as trivial.
  context_framing: true

  fallback_tier: 2
  fallback_category: general_chat

  # --- which implementation is PRIMARY -----------------------------------
  # This is a peer concept to the fallback cascade below, not a replacement
  # for it: whichever mode is primary, a failure still walks the SAME
  # cascade (stale session -> session history -> cloud_fallback -> the
  # fallback_tier/fallback_category above).
  #
  #   local_llm      (default) the model/base_url above, unchanged.
  #   cloud_llm      a cloud model answers PRIMARY, not just as a fallback
  #                  after local fails. Requires exactly one of
  #                  cloud_primary or cloud_primary_auto below.
  #   local_encoder  a small, non-generative classifier (see the encoder:
  #                  block below) that cannot exhibit the runaway-reasoning
  #                  failure mode documented above, at the cost of only
  #                  producing task_category -- task_tier falls back to
  #                  fallback_tier for this mode. Zero-shot, not trained on
  #                  your traffic: this router never stores raw task text.
  mode: local_llm

  # Read only when mode: cloud_llm. Exactly one of these two:
  # cloud_primary:
  #   base_url: https://api.neuralwatt.com/v1
  #   model: deepseek-v4-flash
  #   api_key_env: NEURALWATT_API_KEY
  #   timeout_seconds: 2
  #   max_output_tokens: 1024
  # cloud_primary_auto: true   # resolve the cheapest routable model, live

  # Read only when mode: local_encoder. Every field has a default, so
  # `encoder: {}` is enough to opt in.
  # encoder:
  #   model: facebook/bart-large-mnli
  #   device: cpu
  #   confidence_threshold: 0.5

  # --- fallback cascade ---------------------------------------------------
  # When the local classifier fails, the router walks: stale session cache ->
  # this session's history in route_decisions -> the optional cloud
  # classifier below -> fallback_tier/fallback_category above. The first two
  # steps are free and local.
  #
  # Global backoff after a classifier failure. This is what bounds cloud
  # spend during a sustained outage: at most one cloud attempt per window
  # across ALL requests, not one per request. It is also reused as the window
  # in which an account-level provider refusal suppresses the cloud step,
  # since an out-of-credit account makes that call a guaranteed waste.
  cooldown_seconds: 30
  # Degradation warning on /metrics. Silent below degraded_warn_min
  # decisions in 24h, because a share computed over a handful of requests is
  # noise and a flapping warning is one nobody reads.
  degraded_warn_min: 20
  degraded_warn_threshold: 0.5
  # Optional. ABSENT BY DEFAULT, which is what makes the cascade cost
  # nothing: with no cloud_fallback block the router degrades straight to the
  # static guess. Uncomment and point it at any OpenAI-compatible endpoint to
  # trade a little money for a real classification during a local outage.
  # A configured api_key_env whose variable is missing degrades to the static
  # guess rather than failing the request -- unlike the primary classifier,
  # which raises, because this one is only reached when things already broke.
  # cloud_fallback:
  #   base_url: https://api.neuralwatt.com/v1
  #   model: deepseek-v4-flash
  #   api_key_env: NEURALWATT_API_KEY
  #   timeout_seconds: 2
  #   max_output_tokens: 1024
  response_format: "json"       # ask Ollama to constrain output to valid JSON
  # The labels a classifier may return. A SUBSET of proficiency.categories,
  # validated as one at config load, and deliberately not the same list:
  # proficiency.categories is the scoring axis (what a model is good at),
  # this is the set a classifier is asked to choose between.
  #
  # tool_use_agentic is excluded. It describes what a turn mechanically DOES
  # rather than what it is for, and an agent turn is always both -- measured
  # 2026-09-14 on live traffic with the session cache off, 31 of 31
  # consecutive turns classified tool_use_agentic and every one routed to the
  # slowest model in the catalog (14.8s TTFT, p95 40s). Per-turn
  # classification became accurate and routing got worse. The signal it was
  # standing in for is already read exactly and for free from the request's
  # own `tools` array by routing.min_tool_proficiency.
  #
  # WHAT THIS COSTS, so the next person does not discover it: with the
  # classifier unable to emit tool_use_agentic, no new POST /outcome report
  # attributes to that category, so its proficiency scores FREEZE at their
  # current values (e.g. qwen3.6-35b at 0.902 over 154 samples). The tool
  # filter keeps reading them; they simply stop accumulating. Accepted for
  # now.
  #
  # Considered and REJECTED as the fix: attributing outcomes to
  # tool_use_agentic whenever the request carried a `tools` array. opencode
  # sends `tools` on essentially every request, so the score would converge
  # on each model's overall pass rate and stop discriminating the exact thing
  # the category exists to detect -- a model that OVER-reaches for tools on
  # work that did not need them. A frozen honest score beats a live
  # meaningless one.
  #
  # Omit this key entirely to mean "every proficiency category".
  candidate_categories:
  - coding_general
  - coding_refactor
  - debugging
  - docs_writing
  - summarization
  - file_summarization
  - diff_checking
  - translation
  - reasoning_math
  - general_chat
  # The dispatcher appends the authoritative category list from
  # classifier.candidate_categories (above) to this prompt at call time. Do
  # not enumerate the categories here as well — a hand-copied list drifts, and
  # a category the model invents joins against nothing in the proficiency
  # table.
  system_prompt: |
    You are a task router. Given a task description and any attached context,
    respond with ONLY a JSON object with these fields:
    {
      "task_category": one of the allowed categories listed below,
      "task_tier": integer 1-3, where 1 is cheap/simple, 2 is mid/general,
                    and 3 is frontier/high-stakes,
      "required_context_tokens": integer estimate of prompt+context token count,
      "confidence": float 0-1
    }

dispatch_providers:
  neuralwatt:
    base_url: "https://api.neuralwatt.com/v1"
    api_key_env: "NEURALWATT_API_KEY"
    has_energy_telemetry: true
    # false: NeuralWatt reports its bill in a top-level `cost` block (and as a
    # `: cost {...}` SSE comment when streaming), so no request-side opt-in is
    # needed and none is sent. See the openrouter entry below for what the
    # flag does when it is on.
    reports_cost_in_usage: false
    enabled: true

  openrouter:
    base_url: "https://openrouter.ai/api/v1"
    api_key_env: "OPENROUTER_API_KEY"
    # Account-level balance poll; OpenRouter reports per-completion energy via
    # a separate allowance_remaining field, so this URL is for the prepaid pool.
    balance_url: "https://openrouter.ai/api/v1/credits"
    has_energy_telemetry: false
    # true: send OpenRouter's own `usage: {"include": true}` opt-in on every
    # upstream request. `stream_options.include_usage` is the OpenAI spelling
    # and gets token counts; this is the separate OpenRouter one, and without
    # it the accounting block is simply absent -- no billed `usage.cost` and
    # no `usage.prompt_tokens_details.cached_tokens`. Both ride on this one
    # opt-in, so the two coverage figures move together.
    #
    # It does NOT bring generation timing: OpenRouter serves that only from
    # its separate /api/v1/generation endpoint, so `duration_seconds` stays
    # NULL on OpenRouter rows. Turn this off only for a provider that returns
    # accounting unasked, or one that rejects the key.
    reports_cost_in_usage: true
    enabled: true
    require_allowlist: true

local_compute:
  # "Gaming mode", inverted: true means the router may use local hardware.
  # Set it to false when you stop Ollama to give the GPU back to something
  # else, and the router SKIPS every local call instead of discovering the
  # outage one 120s timeout at a time: classifier, /health probe, local
  # verification, the local-vision fallback, and local dispatch rows (which
  # drop out of the candidate set rather than being picked and then failing).
  #
  # ONE flag that the call sites read, not a macro that rewrites
  # verification.local_llm_enabled / local_vision.enabled / local_energy.enabled.
  # Those keep their own meanings; this is an outer AND over all of them, so
  # turning it back on restores exactly the state you left.
  #
  # Config load REFUSES false unless classifier.cloud_fallback is configured.
  # Skipping the local classifier does not make classification remote, it makes
  # it a static guess -- and that guess is recorded as general_chat, a fully
  # scored category, so it looks like real classification afterwards.
  # Uncommenting the cloud_fallback example above is the intended setup path.
  enabled: true

local_energy:
  # Local energy metering for the router's own Ollama calls (classifier,
  # verifier, local-vision fallback). Cloud providers expose per-request energy,
  # but these calls run on your own hardware and their electricity is real even
  # though it never shows up on the NeuralWatt bill.
  #
  # OFF by default. Enable only after setting a real per-kWh rate below.
  # Config load REFUSES enabled: true with a null tariff because a null rate
  # would record cost_usd = 0 instead of "unknown".
  enabled: false

  # Power sampler. Only "nvidia_smi" is supported today. It shells out rather
  # than importing pynvml so the router has no new dependency and degrades
  # cleanly on non-NVIDIA hosts.
  meter: "nvidia_smi"

  # How often to sample GPU power while a local call is running. 0.25s was the
  # original llmrouter pinch sample rate and is fine as a starting point.
  sample_interval_seconds: 0.25

  # YOUR real per-kWh electricity rate. Set this before enabling metering.
  #
  # Example (do not use this number unless it matches your bill):
  #   tariff_usd_per_kwh: 0.12
  #
  # Set 2026-09-02 from the user's Great Lakes Energy bill. This is the
  # MARGINAL rate -- what one more kWh actually costs -- not the all-in
  # effective rate of 0.183 ($410.15 / 2,244 kWh). The difference is the
  # $52.30/month of fixed charges, which are paid whether or not the GPU
  # runs, so loading them onto compute would overstate local cost by ~15%
  # and bias routing toward the cloud for a reason that is not real.
  #   14.742c energy + 0.188c PSCR + 0.316c EO = 15.246c, x 1.04 MI sales
  #   tax = 15.856c/kWh.
  tariff_usd_per_kwh:

  # YOUR grid carbon intensity in grams of CO2 equivalent per kWh. Optional:
  # null disables the carbon estimate but still records energy and cost.
  #
  # Example (do not use this number unless it matches your grid):
  #   grid_intensity_g_per_kwh: 475
  grid_intensity_g_per_kwh:

logging:
  # Whether to write a row to energy_observations for every completion. Off
  # means no cost accounting, no reference sweep and no /outcome attribution,
  # so leave it on unless you are debugging.
  #
  # There is no log_path. Nothing ever wrote a file -- the dispatcher prints to
  # stderr and the systemd unit hands that to the journal (`journalctl --user
  # -u llm-router -f`), so the setting named a destination that did not exist.
  log_energy_observations: true

  # Whether to write a row to route_decisions for every routing decision
  # (route | dispatch | chat | passthrough | local-vision). Off leaves the
  # monitoring TUI's decision history empty; it does not change what routes.
  log_route_decisions: true

  # debug | info | warning | error.
  #
  # info gives one line per request: what it was classified as, which model won,
  # what it cost, how long each stage took. debug adds why -- every candidate
  # that was dropped and by which filter, the ranking with scores, the
  # classifier's raw reply. It logs no conversation text at any level; prompts
  # here run 60k-150k tokens and the journal is on disk.
  #
  # LLM_ROUTER_LOG_LEVEL overrides this, so a running service can be turned up
  # without editing a tracked file:
  #
  #   systemctl --user edit llm-router      # Environment="LLM_ROUTER_LOG_LEVEL=debug"
  #   systemctl --user restart llm-router
  level: info