matrix-agent-validated/config/models.json

{
  "_description": "Lakehouse model matrix — authoritative routing for all agent tiers. Local models do the heavy lifting; cloud models are consulted sparingly for overview, strategic, and gatekeeper decisions. Read by tests/multi-agent/scenario.ts and mcp-server/index.ts.",
  "version": 1,
  "updated": "2026-04-21",
  "providers": {
    "ollama_local": {
      "base_url": "http://localhost:11434",
      "key_env": null
    },
    "ollama_cloud": {
      "base_url": "https://ollama.com",
      "key_env": "OLLAMA_CLOUD_KEY",
      "key_source": "/root/llm_team_config.json → providers.ollama_cloud.api_key",
      "rate_budget": {
        "calls_per_hour": 200,
        "calls_per_day": 2000,
        "notes": "Paid tier — generous. Policy: keep overview calls ≤ 3/scenario, strategic ≤ 10/day, gatekeeper ≤ 5/day."
      }
    }
  },
  "tiers": {
    "t1_hot": {
      "purpose": "Per tool call — SQL generation, hybrid_search, sql(). Runs 50-200 times per scenario. Latency-sensitive.",
      "kind": "local_fast",
      "primary": { "model": "qwen3.5:latest", "provider": "ollama_local", "context_window": 262144 },
      "fallback": { "model": "qwen2.5:latest", "provider": "ollama_local", "context_window": 32768 },
      "max_tokens": 1000,
      "temperature": 0.3,
      "never_route_cloud": true,
      "context_budget": {
        "system_prompt_cap": 4000,
        "prior_context_cap": 6000,
        "tool_results_cap": 8000,
        "safety_margin": 2000,
        "overflow_policy": "summarize_oldest_tool_results_via_t3"
      },
      "rationale": "qwen3.5:latest is a 9.7B thinking model with 262K context that emits clean JSON (verified 2026-04-21 after mistral A/B wipeout). Thinking budget requires max_tokens ≥ 400; we hold 1000 to keep propose_done payloads intact. qwen2.5 stays as fallback."
    },
    "t2_review": {
      "purpose": "Per step consensus — executor ↔ reviewer loop critique. 5-14 calls per event.",
      "kind": "local_balanced",
      "primary": { "model": "qwen3:latest", "provider": "ollama_local", "context_window": 40960 },
      "fallback": { "model": "qwen2.5:latest", "provider": "ollama_local", "context_window": 32768 },
      "max_tokens": 800,
      "temperature": 0.3,
      "never_route_cloud": true,
      "context_budget": {
        "system_prompt_cap": 2000,
        "recent_turns_cap": 4000,
        "safety_margin": 1000
      },
      "rationale": "Reviewer only needs to detect schema violations and drift — a 7B model is sufficient."
    },
    "t3_overview": {
      "purpose": "Mid-day checkpoint after every misplacement + every Nth event, and cross-day lesson. 1-3 calls per scenario.",
      "kind": "thinking_cloud",
      "primary": { "model": "gpt-oss:120b", "provider": "ollama_cloud", "context_window": 131072 },
      "local_fallback": { "model": "gpt-oss:20b", "provider": "ollama_local", "context_window": 131072 },
      "max_tokens": 900,
      "temperature": 0.2,
      "cloud_budget_per_scenario": 5,
      "env_flag": "LH_OVERVIEW_CLOUD=1",
      "context_budget": {
        "event_digest_cap": 30000,
        "checkpoint_cap": 8000,
        "lesson_corpus_cap": 40000,
        "safety_margin": 8000,
        "overflow_policy": "chunk_lessons_via_cosine_topk"
      },
      "rationale": "Same prompt family as local 20b (gpt-oss series) — prompts port directly. 120b is faster via cloud than 20b local in practice, and lessons are noticeably more specific."
    },
    "t4_strategic": {
      "purpose": "Daily playbook board re-ranking, weekly gap audit, pattern discovery across accumulated playbooks. 1-10 calls per day.",
      "kind": "thinking_cloud_large",
      "primary": { "model": "kimi-k2.6", "provider": "ollama_cloud", "context_window": 200000 },
      "secondary": { "model": "qwen3.5:397b", "provider": "ollama_cloud", "context_window": 131072 },
      "fallback": { "model": "glm-4.7", "provider": "ollama_cloud", "context_window": 131072 },
      "local_fallback": { "model": "gpt-oss:20b", "provider": "ollama_local", "context_window": 131072 },
      "max_tokens": 2000,
      "temperature": 0.2,
      "cloud_budget_per_day": 10,
      "context_budget": {
        "playbook_corpus_cap": 80000,
        "pattern_history_cap": 20000,
        "safety_margin": 16000,
        "overflow_policy": "two_pass_map_reduce"
      },
      "rationale": "J named qwen3.5 specifically. GLM-4.7 is a promising alternate for debate phase. Runs after all scenarios complete for the day."
    },
    "t5_gatekeeper": {
      "purpose": "MUST route here: architecture changes, new client onboarding, schema migrations, playbook retirements, index rebuilds, autotune config changes.",
      "kind": "thinking_cloud_deepest",
      "primary": { "model": "kimi-k2-thinking", "provider": "ollama_cloud", "context_window": 200000 },
      "fallback": { "model": "deepseek-v3.1:671b", "provider": "ollama_cloud", "context_window": 131072 },
      "secondary_fallback": { "model": "qwen3.5:397b", "provider": "ollama_cloud", "context_window": 131072 },
      "local_fallback": { "model": "gpt-oss:20b", "provider": "ollama_local", "context_window": 131072 },
      "max_tokens": 4000,
      "temperature": 0.1,
      "cloud_budget_per_day": 5,
      "audit_log": true,
      "context_budget": {
        "decision_doc_cap": 50000,
        "evidence_bundle_cap": 100000,
        "prior_gatekeeper_decisions_cap": 20000,
        "safety_margin": 20000,
        "overflow_policy": "escalate_to_kimi_k2_1t_or_split_decision"
      },
      "rationale": "Highest-stakes decisions — reasoning depth matters more than latency. Audit log so J can always see what the gatekeeper was asked and what it answered. No human approval required today; escalate later if mis-decisions show up."
    }
  },

  "embeddings": {
    "_description": "Vector embedding models for hybrid search + playbook_memory similarity. Always local — embedding calls are too high-volume for cloud. qwen3-embedding is the new primary (2026-04-21); nomic-embed-text-v2-moe is the MoE alternative for corpus-scale embedding; original nomic-embed-text stays as legacy fallback while vectord indexes are being re-embedded.",
    "primary": { "model": "qwen3-embedding", "provider": "ollama_local", "dim": 1024, "context_window": 32768 },
    "alternate": { "model": "nomic-embed-text-v2-moe", "provider": "ollama_local", "dim": 768, "context_window": 2048, "notes": "MoE variant — better for long documents, slower per-call, roughly matches nomic-embed-text on short text." },
    "legacy": { "model": "nomic-embed-text:latest", "provider": "ollama_local", "dim": 768, "context_window": 8192, "notes": "Used by existing vectord indexes. Do NOT switch live until indexes are rebuilt, or hybrid search recall will crater." },
    "migration_plan": "Re-embed workers_500k (and all production indexes) with qwen3-embedding, keep the old index under a _v1 suffix for rollback, flip the profile pointer only after recall benchmarks match or exceed legacy. Touches vectord::agent + autotune."
  },

  "context_management": {
    "_description": "Rule zero: NEVER call a model with more tokens than its context_window minus safety_margin. Every call goes through the budget checker first. If over budget → chunk, summarize, or escalate. This is the stability floor.",
    "token_estimator": {
      "method": "chars_div_4",
      "note": "Rough, biased safe by ~15%. For production, swap to tiktoken or the provider's tokenizer endpoint."
    },
    "overflow_policies": {
      "summarize_oldest_tool_results_via_t3": {
        "when": "T1 conversation history + tool results exceed context_budget.tool_results_cap",
        "how": "Send oldest N tool results to T3 with prompt 'summarize these into 500 tokens that preserve what the executor needs to know'; replace them with the summary in the running conversation."
      },
      "chunk_lessons_via_cosine_topk": {
        "when": "lesson corpus in data/_playbook_lessons/*.json exceeds lesson_corpus_cap",
        "how": "Embed the current scenario spec, cosine-rank all lessons, take top-K until budget exhausted. Fall back to date-sorted if embeddings unavailable."
      },
      "two_pass_map_reduce": {
        "when": "T4 playbook corpus exceeds playbook_corpus_cap",
        "how": "Pass 1: chunk playbooks into ≤30K token shards, run primary model on each shard to emit 'shard summary'. Pass 2: feed all summaries to primary model for global synthesis. Logged as two audit entries."
      },
      "escalate_to_kimi_k2_1t_or_split_decision": {
        "when": "T5 decision evidence exceeds decision_doc_cap + evidence_bundle_cap",
        "how": "Prefer kimi-k2:1t which has 1M context. If still over, split decision into sub-decisions (e.g. 'retire playbooks by city' instead of 'retire playbooks globally') and loop."
      }
    },
    "chunking_cache": {
      "_description": "Precomputed shards of the playbook corpus, indexed by (corpus_version, shard_id). Avoids re-chunking on every T4 run.",
      "location": "data/_chunk_cache/",
      "invalidation": "Key includes corpus hash. When playbook_memory changes, the hash changes, the cache misses, and chunks regenerate.",
      "implementation_status": "SPEC — next sprint."
    },
    "implementation_status": "context_window + context_budget fields WIRED in config. Enforcement helper NOT yet wired in agent.ts. Next: add estimateTokens() + budgetCheck() helpers, route all generate() calls through them, emit a metric when overflow policy fires."
  },
  "experimental_rotation": {
    "enabled": false,
    "purpose": "Sample newer models on a schedule to collect comparison data without rate-limit risk.",
    "candidates": [
      { "model": "minimax-m2.7",      "notes": "Newer minimax; unknown output stability" },
      { "model": "glm-5",             "notes": "GLM next-gen; larger context" },
      { "model": "glm-5.1",           "notes": "Incremental on GLM-5" },
      { "model": "qwen3-next:80b",    "notes": "Qwen's experimental successor; smaller than 3.5" },
      { "model": "qwen3-coder-next",  "notes": "Coder-optimized — good for SQL gen T1 experiments" },
      { "model": "deepseek-v3.2",     "notes": "Smaller deepseek; reasoning/coding" },
      { "model": "nemotron-3-super",  "notes": "NVIDIA 230B; general" },
      { "model": "cogito-2.1:671b",   "notes": "671B general" },
      { "model": "mistral-large-3:675b", "notes": "Mistral's flagship; good T3 candidate" }
    ],
    "rotation": "weekly",
    "sample_rate": 0.1,
    "apply_to_tier": "t4_strategic",
    "notes": "When enabled, T4 routes 10% of calls to a rotating experimental model. Log comparison in /data/_model_eval/ — if the experimental consistently beats primary across 3 rotations, promote it to primary."
  },
  "playbook_versioning": {
    "enabled": true,
    "purpose": "A playbook can work, then break when architecture changes. Versioning lets us pin which change retired which playbook.",
    "dataset": "playbook_memory",
    "schema_additions": {
      "version": "integer — auto-increment per operation",
      "parent_id": "string — previous version entry_id for same operation (null for v1)",
      "retired_at": "timestamp — set when success_rate drops or architecture changes",
      "retirement_reason": "string — e.g. 'schema_migration:workers_500k 2026-05-03'",
      "architecture_snapshot": "object — crate versions, index name, schema fingerprint at seed time"
    },
    "retire_triggers": [
      "success_rate < 0.3 over last 20 citations",
      "schema_fingerprint mismatch detected at retrieval time",
      "architecture change event emitted by ingestd/vectord",
      "T5 gatekeeper explicitly retires via /vectors/playbook_memory/retire"
    ],
    "read_back_policy": "retrieval returns only non-retired versions. History endpoint /vectors/playbook_memory/history/{operation} returns the full chain.",
    "ui_surface": "mcp-server to render a diff view: side-by-side of playbook versions with a timeline of what changed and when.",
    "implementation_status": "SPEC — not yet wired. Target: next sprint. Touches gateway + catalogd + mcp-server."
  },
  "matrix_index_hybrid_search_note": "Phase 22 candidate: elevate the hybrid_search T1 tool to consult T3 when a pool returns <3 matches OR when the same (role, city) combo has failed N times in 24h. Consult result is a reformulated sql_filter the executor retries with. Keeps T1 fast on the happy path, escalates to T3 only on low-recall signals."
}