omninode-intelligence 0.24.0

Intelligence, indexing, and pattern services as first-class Omninode nodes

omniintelligence

Intelligence, pattern learning, code analysis, and evaluation as first-class ONEX (OmniNode eXecution) nodes.

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What this repo is

OmniIntelligence is the intelligence platform for the ONEX ecosystem. It provides pattern learning, code quality analysis, evaluation, intent classification, document analysis, CI failure tracking, bloom evaluation, and Claude Code hook processing as 59 first-class ONEX nodes. All nodes follow the ONEX Four-Node Architecture (Effect / Compute / Reducer / Orchestrator) and delegate all business logic to handler modules.

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Who uses it

• omniclaude — publishes Claude Code hook events consumed here; subscribes to intelligence events for hook routing
• omnimemory — consumes intent-classified and pattern events for graph and vector storage
• omnidash — projects quality-assessment, bloom-eval, routing-feedback, and pattern events into read-model dashboards
• omnimarket — portable workflow packages invoke intelligence nodes via the ONEX node entry-point registry

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What this repo owns

• Pattern learning: extraction, ML learning pipeline, storage, promotion, demotion, lifecycle management
• Code quality scoring and ONEX compliance assessment
• Semantic analysis, AST extraction, and code entity bridging
• Intent classification from Claude Code hook events
• Intent drift detection, cost forecasting, and LLM routing decisions
• Document ingestion, parsing, retrieval, and staleness detection
• CI failure tracking, error classification, and fingerprinting
• Bloom evaluation orchestration and plan multi-model review
• Routing feedback processing and compliance evaluation
• Claude Code hook event processing (UserPromptSubmit, Stop, and others)
• REST API for pattern query by enforcement nodes (GET /api/v1/patterns)

For the full node list see docs/reference/NODE_INVENTORY.md.

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What this repo does not own

Concern	Canonical owner
ONEX kernel, node execution, contracts, validation	omnibase_core
Kafka, PostgreSQL, runtime host, registration	omnibase_infra
Protocol interfaces	omnibase_spi
Portable workflow packages and automation logic	omnimarket
Vector and graph storage (Qdrant, Memgraph)	omnimemory
Dashboard UI and read-model projection surface	omnidash
Claude Code hooks, invocation UX, skills	omniclaude

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Install

uv add omninode-intelligence

Or install from source alongside sibling repos (editable):

uv sync --group all

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Common workflows

# Full test suite (required before any PR)
uv run pytest tests/ -v

# Unit tests only (fast, no infrastructure)
uv run pytest tests/ -v -m unit

# Audit tests (AST purity enforcement)
uv run pytest tests/ -v -m audit

# Integration tests (requires Postgres + Kafka on .201)
uv run pytest tests/ -v -m integration

# Lint and format
uv run ruff format src/ tests/
uv run ruff check --fix src/ tests/

# Type check
uv run mypy src/

# Pre-commit (run before staging)
pre-commit run --all-files

# Review calibration CLI
uv run python -m omniintelligence.review_pairing \
  --file plan.md --ground-truth codex --challenger deepseek-r1

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Architecture summary

OmniIntelligence is built on the ONEX Four-Node Architecture. Nodes are thin shells that delegate all logic to handler modules. Contract YAML files declare event bus subscriptions, publish topics, handler routing, and dependencies — no hardcoded topic strings in Python.

Node types:

Type	Example nodes	I/O
Compute (35+)	NodeQualityScoringCompute, NodeIntentClassifierCompute	None — pure transforms
Effect (14+)	NodeClaudeHookEventEffect, NodePatternStorageEffect	Kafka, PostgreSQL, external APIs
Reducer (2)	NodeDocPromotionReducer, NodePolicyStateReducer	FSM state transitions
Orchestrator (2)	NodeBloomEvalOrchestrator, NodePatternAssemblerOrchestrator	Workflow coordination

Key pipelines:

• Claude Code Hook → intent classification → omnimemory graph
• Session end (Stop hook) → pattern learning → pattern storage → PostgreSQL
• Pattern promotion/demotion → lifecycle transition → audit trail
• Quality assessment command → scoring compute → quality-assessment-completed → omnidash

Dash integration boundary: omnidash never queries this repo's database directly. All data flows via Kafka topics projected into omnidash_analytics. See docs/reference/DASH_INTEGRATION_TRUTH_BOUNDARY.md for the live/dead/gap status of each topic.

For topology diagrams and full pipeline details see docs/architecture/ONEX_FOUR_NODE_ARCHITECTURE.md.

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Documentation map

Document	Purpose
docs/INDEX.md	Canonical docs entrypoint
docs/architecture/ONEX_FOUR_NODE_ARCHITECTURE.md	Node topology, data flow, pipeline diagrams
docs/reference/NODE_INVENTORY.md	Full node inventory sourced from pyproject.toml
docs/reference/EVENT_SURFACE.md	Produced, consumed, dashboard-visible, and deprecated topics
docs/reference/DASH_INTEGRATION_TRUTH_BOUNDARY.md	Omnidash integration truth boundary
CLAUDE.md	Developer context, invariants, quick reference
AGENT.md	LLM navigation guide

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Development and test commands

# Install (all groups including dev)
uv sync --group all

# Full test suite
uv run pytest tests/ -v

# Lint and format
uv run ruff format src/ tests/ && uv run ruff check --fix src/ tests/

# Type check
uv run mypy src/

# Pre-commit
pre-commit run --all-files

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Security, contributing, and license

• SECURITY.md
• CONTRIBUTING.md
• CODE_OF_CONDUCT.md
• LICENSE — MIT