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altimate-code

The open-source data engineering harness.

The intelligence layer for data engineering AI — 99+ deterministic tools for SQL analysis, column-level lineage, dbt, FinOps, and warehouse connectivity across every major cloud platform.

Run standalone in your terminal, embed underneath Claude Code or Codex, or integrate into CI pipelines and orchestration DAGs. Precision data tooling for any LLM.

npm npm npm downloads License: MIT CI Slack Docs

Install

# npm (recommended)
npm install -g @altimateai/altimate-code

# Homebrew
brew install AltimateAI/tap/altimate-code

Then — in order:

Step 1: Configure your LLM provider (required before anything works):

altimate        # Launch the TUI
/connect        # Interactive setup — choose your provider and enter your API key

No API key? Select Codex in the /connect menu — it's built-in and requires no setup.

Or set an environment variable directly:

export ANTHROPIC_API_KEY=your_key   # Anthropic Claude
export OPENAI_API_KEY=your_key      # OpenAI

Step 2 (optional): Auto-detect your data stack (read-only, safe for production connections):

altimate /discover

/discover auto-detects dbt projects, warehouse connections (from ~/.dbt/profiles.yml, Docker, environment variables), and installed tools (dbt, sqlfluff, airflow, dagster, and more). Skip this and start building — you can always run it later.

Zero Python setup required. On first run, the CLI automatically downloads uv, creates an isolated Python environment, and installs the data engine with all warehouse drivers. No pip install, no virtualenv management.

Why a specialized harness?

General AI coding agents can edit SQL files. They cannot understand your data stack. altimate gives any LLM a deterministic data engineering intelligence layer — no hallucinated SQL advice, no guessing at schema, no missed PII.

Capability General coding agents altimate
SQL anti-pattern detection None 19 rules, confidence-scored
Column-level lineage None Automatic from SQL, any dialect
Schema-aware autocomplete None Live-indexed warehouse metadata
Cross-dialect SQL translation None Snowflake ↔ BigQuery ↔ Databricks ↔ Redshift
FinOps & cost analysis None Credits, expensive queries, right-sizing
PII detection None 30+ regex patterns, 15 categories
dbt integration Basic file editing Manifest parsing, test gen, model scaffolding, lineage
Data visualization None Auto-generated charts from SQL results
Observability None Local-first tracing of AI sessions and tool calls

Benchmarked precision: 100% F1 on SQL anti-pattern detection (1,077 queries, 19 rules, 0 false positives). 100% edge-match on column-level lineage (500 queries, 13 categories). See methodology →

What the harness provides:

  • SQL Intelligence Engine — deterministic SQL parsing and analysis (not LLM pattern matching). 19 rules, 100% F1, 0 false positives. Built for data engineers who've been burned by hallucinated SQL advice.
  • Column-Level Lineage — automatic extraction from SQL across dialects. 100% edge-match on 500 benchmark queries.
  • Live Warehouse Intelligence — indexed schemas, query history, and cost data from your actual warehouse. Not guesses.
  • dbt Native — manifest parsing, test generation, model scaffolding, medallion patterns, impact analysis
  • FinOps — credit consumption, expensive query detection, warehouse right-sizing, idle resource cleanup
  • PII Detection — 15 categories, 30+ regex patterns, enforced pre-execution

Works seamlessly with Claude Code and Codex. altimate is the data engineering tool layer — use it standalone in your terminal, or mount it as the harness underneath whatever AI agent you already run. The two are complementary.

altimate is a fork of OpenCode rebuilt for data teams. Model-agnostic — bring your own LLM or run locally with Ollama.

Quick demo

# Auto-detect your data stack (dbt projects, warehouse connections, installed tools)
> /discover

# Analyze a query for anti-patterns and optimization opportunities
> Analyze this query for issues: SELECT * FROM orders JOIN customers ON orders.id = customers.order_id

# Translate SQL across dialects
> /sql-translate this Snowflake query to BigQuery: SELECT DATEADD(day, 7, current_date())

# Generate dbt tests for a model
> /generate-tests for models/staging/stg_orders.sql

# Get a cost report for your Snowflake account
> /cost-report

Key Features

All features are deterministic — they parse, trace, and measure. Not LLM pattern matching.

SQL Anti-Pattern Detection

19 rules with confidence scoring — catches SELECT *, cartesian joins, non-sargable predicates, correlated subqueries, and more. 100% accuracy on 1,077 benchmark queries.

Column-Level Lineage

Automatic lineage extraction from SQL. Trace any column back through joins, CTEs, and subqueries to its source. Works standalone or with dbt manifests for project-wide lineage. 100% edge match on 500 benchmark queries.

FinOps & Cost Analysis

Credit analysis, expensive query detection, warehouse right-sizing, unused resource cleanup, and RBAC auditing.

Cross-Dialect Translation

Transpile SQL between Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, MySQL, SQL Server, and DuckDB.

PII Detection & Safety

Automatic column scanning for PII across 15 categories with 30+ regex patterns. Safety checks and policy enforcement before query execution.

dbt Native

Manifest parsing, test generation, model scaffolding, incremental model detection, and lineage-aware refactoring. 12 purpose-built skills including medallion patterns, yaml config generation, and dbt docs.

Data Visualization

Interactive charts and dashboards from SQL results. The data-viz skill generates publication-ready visualizations with automatic chart type selection based on your data.

Local-First Tracing

Built-in observability for AI interactions — trace tool calls, token usage, and session activity locally. No external services required. View traces with altimate trace.

AI Teammate Training

Teach your AI teammate project-specific patterns, naming conventions, and best practices. The training system learns from examples and applies rules automatically across sessions.

Agent Modes

Each agent has scoped permissions and purpose-built tools for its role.

Agent Role Access
Builder Create dbt models, SQL pipelines, and data transformations Full read/write
Analyst Explore data, run SELECT queries, and generate insights Read-only enforced
Validator Data quality checks, schema validation, test coverage analysis Read + validate
Migrator Cross-warehouse SQL translation, schema migration, dialect conversion Read/write for migrations
Researcher Deep-dive analysis, documentation research, and knowledge extraction Read-only
Trainer Teach project-specific patterns, naming conventions, and best practices Read + write training data
Executive Business-audience summaries — translates findings into revenue, cost, and compliance impact Read-only

New to altimate? Start with Analyst mode — it's read-only and safe to run against production connections.

Supported Warehouses

Snowflake · BigQuery · Databricks · PostgreSQL · Redshift · DuckDB · MySQL · SQL Server

First-class support with schema indexing, query execution, and metadata introspection. SSH tunneling available for secure connections.

Works with Any LLM

Model-agnostic — bring your own provider or run locally.

Anthropic · OpenAI · Google Gemini · Google Vertex AI · Amazon Bedrock · Azure OpenAI · Mistral · Groq · DeepInfra · Cerebras · Cohere · Together AI · Perplexity · xAI · OpenRouter · Ollama · GitHub Copilot

No API key? Codex is a built-in provider with no key required. Select it via /connect to start immediately.

Skills

altimate ships with built-in skills for every common data engineering task — type / in the TUI to browse available skills and get autocomplete. No memorization required.

Architecture

altimate (TypeScript CLI)
        |
   @altimateai/altimate-core (napi-rs → Rust)
   SQL analysis, lineage, PII, safety — 45 functions, ~2ms per call
        |
   Native Node.js drivers
   10 warehouses: Snowflake, BigQuery, PostgreSQL, Databricks,
   Redshift, MySQL, SQL Server, Oracle, DuckDB, SQLite

The CLI handles AI interactions, TUI, and tool orchestration. SQL analysis is powered by the Rust-based @altimateai/altimate-core engine via napi-rs bindings (no Python required). Database connectivity uses native Node.js drivers with lazy loading.

No Python dependency: All 73 tool methods run natively in TypeScript. No pip, venv, or Python installation needed.

dbt-first: When working in a dbt project, the CLI automatically uses dbt's connection from profiles.yml — no separate warehouse configuration needed.

Monorepo structure

packages/
  altimate-code/       TypeScript CLI (main entry point)
  drivers/             Shared database drivers (10 warehouses)
  dbt-tools/           dbt integration (TypeScript)
  plugin/              Plugin system
  sdk/                 SDKs (includes VS Code extension)
  util/                Shared utilities

Community & Contributing

Contributions welcome — docs, SQL rules, warehouse connectors, and TUI improvements are all needed. The contributing guide covers setup, the vouch system, and the issue-first PR policy.

Read CONTRIBUTING.md →

What's New

  • v0.4.1 (March 2026) — env-based skill selection, session caching, tracing improvements
  • v0.4.0 (Feb 2026) — data visualization skill, 99+ tools, training system
  • v0.3.x — See full changelog →

License

MIT — see LICENSE.

Acknowledgements

altimate is a fork of OpenCode, the open-source AI coding agent. We build on top of their excellent foundation to add data-team-specific capabilities.