metis library

Phase 85–86 flywheel producer for education (libs/metis/training-data/src): MetisTrainingDataPipeline turns eleven MetisTrainingKind signals — lecture-quality, assessment-effectiveness, learning-progression, diagram-quality, agent-trace, cost-quality, educational-experiment, and peers — into governance-gated (governanceGrantId) MetisTrainingRecords emitted through a pluggable MetisTrainingSink.

The adaptive-learning engine (@metis/adaptive, libs/metis/adaptive/src), organised into profiling/, adaptation/, and path/. It carries genuine knowledge-tracing algorithms, not heuristics: path/bkt.ts implements Corbett & Anderson (1995) two-state-HMM Bayesian Knowledge Tracing (with the Beck & Chang 2007 pS + pG < 1 identifiability bound), alongside fsrs.ts (FSRS-5 spaced repetition / DSR model), graph-knowledge-tracing.ts (Nakagawa-style GKT), and a unified adaptive-knowledge-engine.ts that composes BKT + GKT + FSRS. The profiling and adaptation modules add learning-style detection, difficulty adjustment, prerequisite checking, and pacing optimization, so the platform can select the next best content for a given learner.

The comprehensive multi-agent framework (@metis/agents, libs/metis/agents/src). core/ provides the infrastructure — agent registry, lifecycle with validated status transitions, a communication/message bus, events, priorities, retry policy, and monitoring — and agents/ holds the specialised educational agents: curriculum, content, assessment, evaluation, fact-checking, moderator, research, citation (with a citation-renderer), diagram, tutor, feedback, learning-style, scaffolding, an adaptive-sequencer, a misconception-graph, an arithmetic-equation-checker, and a media sub-crew (planner/coder/critic with a media-benchmark). An adversarial-eval/ module adds an evaluator and regression suite. This is the orchestration brain that turns a request into planned, generated, checked educational content.

A type-only library (@metis/agents-types, tagged type:types-adjacent under layer:domain) that defines the agent framework's shared shapes: base/ (agent config, state, checkpoints, conversation messages, token estimation, status-transition validation) and a ReAct framework module (thoughts/actions/observations, iteration traces, convergence detection, action-success metrics). It has no business logic of its own — it is the contract surface that metis-agents and other agent consumers build against so agent state and ReAct traces are defined once.

The typed REST/realtime SDK for the platform (@metis/api-client, libs/metis/api-client/src), its header noting it was "ported and enhanced from minerva/sdk/typescript/". It provides a middleware-pipeline HttpClient, JWT/API-key/Basic auth with auto-refresh (TokenStore/TokenProvider), error mapping into @oshun/errors, configurable exponential-backoff retry, in-memory LRU request caching, cursor and offset pagination iterators, a reconnecting WebSocket client, and request-mocking utilities for tests. It is the boundary external callers use to reach Metis without re-declaring its HTTP surface.

The psychometrics and assessment-generation library (@metis/assessment, libs/metis/assessment/src). It is split into irt-models/ and irt-calc/ (real item-response theory, including the 3PL model with item difficulty, discrimination, and guessing parameters), plus rubric/, adaptive/ (adaptive item selection), feedback/, grading/, analytics/, and a generation/ module re-exported under Gen…-prefixed names that builds question banks, Bloom-aligned stems, and quality-scored distractors (semantic, misconception, numerical, partial-truth, reversed-logic, overgeneralization). It exists so Metis can author, calibrate, and adaptively administer assessments with genuine measurement theory rather than ad-hoc scoring.

The shared primitive layer (@metis/core, libs/metis/core/src) that nearly every other Metis library imports. enums/ defines the platform's controlled vocabularies — content types/formats, course status/level/category, difficulty, Bloom taxonomy, cognitive-load, learning style, assessment/question types, grading strategies, pipeline stages, lint severity/category, quality scores, citation styles, credibility levels, accessibility levels — with helpers for enum validation, numeric mapping, ordering, and legal status transitions. It carries no engine logic; it is the bottom-of-the-graph type spine that keeps Bloom/difficulty/quality scales consistent across the whole pipeline.

Course authoring and lifecycle (@metis/course, libs/metis/course/src), composed of syllabus/, versioning/, bundle/, export/, analytics/, validation/, and operations/ modules. It takes the content the agents and generation libraries produce and assembles it into a structured, versioned, validated, exportable course — the packaging tier that sits between raw generated material and a publishable artifact.

The unified cross-domain search surface (@metis/discovery, libs/metis/discovery/src). It is the thinnest Metis library — a single index.ts plus its test — but it is fully implemented, not a scaffold: it declares the DiscoveryDomain set (Tara, Arete, Veritas, Nyx, Nisaba, Metis, Oshun), the full SearchObjectClass taxonomy and its class→domain mapping, a SearchableObject shape with accessibility, evidence-grounding, freshness, entitlement and rights metadata, and a real SearchableObjectCatalog with a lexical inverted index, facet vocabulary, embedding-class tracking, and domain/class consistency enforcement on upsert. It defines how Metis content becomes discoverable alongside other Oshun domains in one catalog.

The concept-graph engine (@metis/knowledge-graph, libs/metis/knowledge-graph/src), the most module-rich library in the area with construction/, operations/, retrieval/, embeddings/, gnn/, temporal/, persistence/, and applications/ directories. construction/ does concept and entity/relation extraction, entity resolution with conflict handling, graph deltas, versioning, and validation (cycles, orphans, severity-graded issues); the further modules add embeddings, graph-neural-network support, temporal versioning, and persistence. It builds and maintains the prerequisite/concept graph that adaptive sequencing, research, and assessment all reason over.

The learner-modeling and personalization library (@metis/learning, libs/metis/learning/src), spanning learning-paths, learner-profiles, mastery, progress-tracking, adaptation, content-selection, recommendations, personalization, learning-analytics, and subject-taxonomy. It models paths (with step typing, prerequisite ordering, circular-dependency detection, completion/duration computation), mastery levels, and per-learner progress. Where metis-adaptive owns the knowledge-tracing math, metis-learning owns the broader learner profile, path construction, and recommendation surface around it.

A type-only library (@metis/linters-types, libs/metis/linters-types/src) that defines the vocabulary of content-linting findings: finding severity/type/ category, section-type mappings and auto-fix flags, severity ordering and weights, and a large family of constructors for specific finding kinds (consistency, content, quiz-richness, lesson-richness, learning-objective, fact-claim, code-verification, multimodal-consistency, terminology, bias, citation) plus grouping/filtering helpers. It is the shared type spine the metis-quality linters emit against.

The educational LLM orchestration client (@metis/llm-client, libs/metis/llm-client/src). provider-adapter/ registers multiple provider adapters and routes each educational task by strategy (round-robin, capability-match, cost-aware, latency-aware, quality-weighted, task-specific), scores providers on compatibility/cost/latency/quality, supports fallback chains, and tracks per-provider performance — over a provider enum that includes ANTHROPIC, OPENAI, and Google. Further modules cover content generation, response parsing into typed content blocks, educational sessions, an understanding-classifier, and a quality-evaluator whose default judge is anthropic/claude-haiku-3.5 (with OpenAI and Google alternatives). The actual network call is an injectable producer seam rather than a vendored SDK, so the routing/scoring logic is the real, testable substance here.

The domain-entity model library (@metis/models, libs/metis/models/src) — distinct from metis-core's primitives in that it carries the richer educational entities and the action/diff vocabulary used to edit them. Its modules include actions (a large family of course/topic diffs: add-section, add-lesson, add-quiz, add-concept-node/edge, merge-elements, link-to-concept, etc., with validators), plans, learning-objectives, assessment/assessment-evidence, citations, findings, graph, rich-media, persona, session-memory, study-aids, and academic-integrity. It is the shared shape of "what a course/topic/lesson is and how it may be mutated" that the agents and course libraries operate on.

The media-generation pipeline (@metis/multimedia, libs/metis/multimedia/src), the largest module set in the area: audio/, video/, image/, diagram/, animation/, presentation/, lecture-generation/, avatar-teacher/, interactive/, accessibility/, and multilingual/. The audio layer is concretely real — a TTSEngine with Chatterbox and ElevenLabs variants and SSML generation/validation, plus an EBU-R128 loudness module (loudness-r128.ts) that does genuine K-weighting, integrated-LUFS and true-peak measurement, and WAV/PCM normalization, and a NarrationPlanner with adaptive pacing. It exists to turn lesson text into narrated, captioned, illustrated, accessible multimedia.

The content quality and compliance gate (@metis/quality, libs/metis/quality/src), organised into infrastructure/ (the linter registry, severity model, quality dimensions, grade thresholds, weighted penalty scoring), content/, technical/, and compliance/ linter families. It runs registered linters over generated content, scores it across weighted quality dimensions into a letter grade, and produces a LintReport. Paired with metis-linters-types (the finding vocabulary) and metis-verification (correctness), it is the style/compliance half of Metis's publish gate.

The research and evidence library (@metis/research, libs/metis/research/src), with knowledge-graph, corpus, search, embeddings, citations, credibility, and analytics modules. It maintains a research-oriented knowledge graph (entities/relationships with confidence levels, validation status, cross-domain links, orphan detection, basic metrics), gathers and searches a corpus, scores source credibility, and produces citations. It is the evidence-gathering substrate behind the fact-checking, citation, and research agents.

The live-tutoring session engine (@metis/tutoring, libs/metis/tutoring/src), spanning session/, conversation/, hints/, scaffolding/, learning-style/, persona/, loop/, and analytics/. The session/ module is a full state machine — start/pause/resume/complete/expire/abandon transitions, event logging, difficulty updates, hint/error counters, idle/expiry tracking, and computed accuracy/hint-ratio/completion metrics. It powers a one-on-one tutoring interaction with adaptive hints, scaffolding, and persona, distinct from the batch content-generation agents.

The educational-correctness verification layer (@metis/verification, libs/metis/verification/src). It reuses @oshun/content-quality-judge for generic judge/calibration/grounding machinery and adds education-specific verifiers — factuality, faithfulness (faithfulness/trace-scorer), STEM/process step verification, claim extraction and verification with span-linking, contradiction detection, citation-sufficiency, pedagogy and judge-reliability checks — composed through a fail-loud VerificationGate. It also ships a generate→verify→refine loop (gate/generate-verify-refine.ts), conformal selective calibration, an eval harness with a gold fixture set, and hardening modules (budget ceiling, verification drift). It is the correctness gate every piece of generated content must clear before it ships, and it refuses to fabricate a verdict when its model boundary is unconfigured.

Experiment platform for the Metis pipeline (@metis/ab-testing, libs/metis/ab-testing/src). It is built from five real modules behind the barrel — experiment-manager, variant-assigner, metric-collector, statistical-analyzer, and result-reporter — with branded ID types and a full status/type/metric enum surface in src/types.ts. It exists so content and model changes can be rolled out as controlled experiments (deterministic variant assignment, metric collection, significance testing, and report generation) rather than shipped blind. Factory variants such as createStrictExperimentManager and createRapidExperimentManager preset the statistical rigor.

LLM spend governance (@metis/cost-tracking, libs/metis/cost-tracking/src), built from budget-manager, cost-calculator, usage-predictor, optimization-advisor, and reporting-engine modules over a rich branded-ID and enum surface in src/types.ts (budget periods/scopes, pricing tiers, cost categories, model providers, carryover policies, optimization strategies). It exists to attribute token cost to budgets and scopes, alert on overruns, predict usage, recommend cheaper routing, and produce grouped cost reports — the accounting layer behind metis-llm-client and metis-model-registry.

A type-only library (@metis/database-types, tagged type:types) that models a database-agnostic schema and query surface. schema/ defines collections, fields, indexes, relations, and validation rules with constructor and accessor helpers; query-builder/ defines filter operators, sorts, joins, aggregates, pagination, and result shapes with immutable addFilter/addSort/withPagination builders. It is pure type-and-helper scaffolding for persistence — no live driver — so Metis services can describe storage shapes uniformly.

Outcomes emission over real ed-tech interoperability standards (@metis/gradebook, libs/metis/gradebook/src). Beyond its emission core it ships concrete adapters for LTI-AGS (lti-ags.ts), xAPI (xapi.ts), cmi5 (cmi5.ts), IMS Caliper (caliper.ts), and file export (file-export.ts). It exists so learner grades and activity statements leave Metis in the formats institutional LMSs and learning-record stores actually expect, rather than a bespoke shape.

The cross-domain wiring layer (@metis/integrations, libs/metis/integrations/src). Its barrel pulls in canonical-wiring and integration modules for Lilith, Hathor, Isis, Iris, Psyche, Aja, Sophia, Themis, Yemaya, and Kalika, plus domain-integrations, cross-domain-flows, shared-libraries, and byom/ and standards/ (including institutional-delivery). It is how Metis plugs into the wider Oshun monorepo — the seam that connects the educational platform to conversation, worldbuilding, engine, fitness, and shared-infrastructure domains (its header references Phase-25 tasks).

The model catalog and task router (@metis/model-registry, libs/metis/model-registry/src), built from model-catalog, task-router, capability-matcher, performance-tracker, and selection-optimizer modules. It maintains catalog entries (provider, capabilities, tier, benchmarks), matches a task profile to a model by capability and confidence, records performance, and optimizes selection against objectives (cost, latency, quality). It is the decision layer metis-llm-client consults to pick which model serves a given educational task.

The prompt library and pipeline (@metis/prompt-management, libs/metis/prompt-management/src), with curriculum-prompt, pedagogical-prompt, assessment-prompt, few-shot-library, and prompt-pipeline modules over branded prompt IDs and pedagogy enums (Bloom level, pedagogical approach, prompt tone, audience, output format, rubric criteria/levels). It centralises the platform's educational prompts and few-shot exemplars so generation calls draw from versioned, typed templates rather than inline strings.