Anti-Hallucination Guardrails
Auto-detect ambiguous foreign keys and write explicit negative rules to stop AI agents from querying invalid JOINs.
schemap agents
The dangerous kind of wrong
Looks right. Is it or is it not?
AI can confidently generate a query that matches familiar naming patterns—but misses the relationships that are true in your database.
Plausible query, wrong relationship
SELECT orders.id, users.email
FROM orders
JOIN users
ON orders.customer_id = users.id;The query looks reasonable. Your schema has no
orders.customer_id.
Explicit relationship context
orders.account_id
↓
accounts.id
↓
users.account_idSchemap gives the agent the relationship map it was missing.
The missing context
Your AI doesn’t know what your tables mean.
Raw dumps describe structure, but bury the context an agent needs to make reliable database decisions.
01
Relationships are easy to miss
Foreign keys and join paths disappear inside a wall of DDL.
02
Names are not business context
acct_id may mean something completely different in your domain.
03
More tokens are not more clarity
Noise consumes the context window before the real problem begins.
A context layer for your database
From raw structure to useful context.
01Your databaseSchema metadata
→
02SchemapInspect · score
· compile
→
03AI-ready contextMaps · joins ·
rules
Claude Code · Cursor · Codex · Copilot · Custom agents
One command
Give your agent the database it actually has.
Schemap runs locally, extracts the relationships that matter, and writes context files your existing workflow can use.
Try it in your project ↗$ schemap context
→ Inspecting schema...
→ Mapping foreign-key relationships...
→ Compiling AI context...
✓ Context written to schemap_database_context.md
$ schemap benchmark
Measure raw vs compiled tokens on your schema.
Measure, don’t guess
Make room for the problem you’re solving.
Token savings vary by schema. The built-in benchmark reports your raw estimate, compiled context size, relationship coverage, readiness score, and compile latency.
↓Token footprintMeasured per database
◎AI readinessActionable schema score
↗Relationship clarityExplicit join paths
Database Intelligence Capabilities
Built to stop AI SQL errors and cut token costs.
Role-Scoped Profiles
Filter schema context outputs by role (e.g. analytics vs backend) to keep prompt context hyper-focused.
schemap context --scope analytics
Token Savings & Cost Inspector
Measure raw vs. compiled context tokens (80%+ savings) and calculate dollar savings per prompt.
schemap benchmark --cost
Shortest JOIN Path Solver
Solve multi-hop foreign key paths across tables and output canonical reference SQL JOIN clauses instantly.
schemap join users payments
AI Readiness & Auto-Fix
Diagnose schema health (0-100 score) and interactively accept inferred foreign keys and abbreviation mappings.
schemap doctor / fix
CI/CD Migration Risk Gate
PROAnalyze migration diffs for breaking schema changes that threaten AI context maps in automated CI/CD builds.
schemap diff --risk --fail-on-breaking
Built for developer workflows
Your schema stays in your workflow.
Extraction and compilation run locally by default. Schemap produces files you can inspect, commit, review, and inject into the tools you already use.
Supported databases
PostgreSQL · SQLite · MySQL · Turso/libSQL · Oracle
LLM Enrichment PRO--enrich applies LLM architecture descriptions & analytical business journeys using OpenAI.
CI/CD License Checks PRO
Licensed CI/CD builds verify your activated key online; free local usage does not require online checks.
Start locally in 60 seconds
Add an AI database context layer.
Install the CLI, point it at your schema, and compile instant AI-ready context.
Free (Works immediately)
# 1. Install developer CLI globally
pipx install schemap-tool# 2. Verify installation
schemap --version# 3. Initialize & compile context
schemap initschemap context
Pro (Optional activation)
# Activate Pro key anytime for unlimited tables & CI/CD
schemap activate YOUR_LICENSE_KEY
Requirements: Python 3.10+
Already installed? Upgrade anytime:
pipx upgrade schemap-tool
2026 Founder Program
Start free. Upgrade when your workflow grows.
The full local CLI is available for free. Pro unlocks larger schemas, CI/CD automation, and production workflow support.
Free
$0
For local projects and evaluation.
- Up to 100 tables
- Full local CLI
- Inspect, score, context, diffs, benchmarks, and exports
- No account or API key required
Pro
$7.99/mo
Flexible monthly billing.
- Unlimited tables (large DB support)
- CI/CD execution & build gates
- LLM Schema Enrichment (
--enrich) - Production schema workflows & profiles
- Priority support & founder benefits
Limited Founders
0/200 Claimed
Founder Lifetime
$79.99 once
One payment for lifetime Pro access.
200 seats remaining
Cap: 200
- Everything in Pro
- Future Pro updates
- Founder feature access
- Available during 2026
Technical Guides & Architecture Benchmarks
Master AI Database Context Engineering
In-depth technical guides, token economics benchmarks, and architectural comparisons vs alternatives.
Stop AI SQL Hallucinations
Learn how to prevent Claude Code, Cursor, and Copilot from hallucinating invalid JOINs or exposing credentials with automated guardrails.
Read Technical Guide ↗Cut Token Costs by 80%
Discover prompt token economics, measure monetary savings ($3.00/1M tokens), and supply minimum effective context with role profiles.
Read Token Economics Guide ↗vs. LLMSchema & DbSketch
Why raw Markdown dumpers fail on complex multi-table schemas and how Schemap's Database Intelligence Layer outperforms passive scripts.
View Comparison ↗vs. tbls (Human Docs)
Comparing human documentation web generators against sub-3ms AI agent context compilers. Stop wasting 80% of context window tokens.
View Comparison ↗vs. Raw pg_dump DDL
Why piping raw SQL DDL into LLM prompts wastes 10,000+ tokens, confuses join paths, and leads to invalid multi-table queries.
View Comparison ↗vs. MCP SQL Servers
Comparing zero-latency static local context maps against runtime RPC daemons requiring active database connection credentials.
View Comparison ↗Developer FAQ
A few practical answers.
How do I update Schemap to the latest version?
Run pipx upgrade schemap-tool (or uv tool upgrade schemap-tool / pip install --upgrade schemap-tool). You can verify your active CLI version anytime with schemap --version.
What is Schemap and how does it work?
Schemap is a local-first developer CLI compiler. It extracts schema metadata from PostgreSQL, SQLite, MySQL, Turso, or Oracle databases, computes AI Readiness scores, resolves foreign key join paths, and compiles compressed context files (schemap_database_context.md, CLAUDE.md, AGENTS.md) to prevent AI coding agents from guessing schema structures.
How do I update Schemap to the latest version?
Run pipx upgrade schemap-tool (or uv tool upgrade schemap-tool / pip install --upgrade schemap-tool). You can verify your active CLI version anytime with schemap --version.
Does Schemap send my database data to an AI?
Not by default. Extraction and compilation run locally. Optional --enrich sends
compressed schema metadata to the configured OpenAI API. Licensed CI/CD usage also performs
an online license check.
Does Schemap replace my ORM?
No. Schemap creates context for agents and SQL workflows. Your ORM, migrations, and application remain the source of truth for runtime behavior.
Can I use it with Cursor, Claude Code, Codex, or Copilot?
Yes. Generate Markdown context, CLAUDE.md, AGENTS.md, JSON, MCP, or framework exports for the workflow your agent already reads.
Which databases are supported?
PostgreSQL, SQLite, MySQL, Turso/libSQL, and Oracle are supported by the current extractors.
Give your database a context layer
Your AI is only as good as the context you give it.
Make the relationships explicit before your agent starts writing SQL.