The self-learning app backend
Your agents deserve
a real backend.
One data layer where agents build production business software. Governed schema, accumulated knowledge, instant interfaces. No SaaS sprawl. No data silos. No mess.
orders 8 cols, 18k rows
tickets 6 cols, 890 rows
context: status uses active, lead, churned. churned = cancelled subscription.
The problem
Agents are building in a vacuum.
Every agent starts from zero. No shared context, no governance, no memory of what worked before. Data ends up scattered across ad-hoc databases, spreadsheets, and SaaS tools that weren't built for how agents work. The result: fragile software, duplicated data, and mistakes that repeat forever.
Data everywhere, truth nowhere
Customer data in HubSpot, tickets in Zendesk, orders in Stripe. Same entity, different schemas, always out of sync.
No guardrails
Agents drop tables, write garbage data, and make schema changes with no approval flow. One bad mutation can cascade.
Knowledge doesn't accumulate
Agent A figures out that amount is stored in cents. Agent B makes the same mistake an hour later. Nothing is learned.
Core capabilities
Everything agents need to build real software.
One data layer
One customers table for sales, support, billing, and ops.
Agents and humans share the same source of truth. No integration layer, no sync jobs, no drift.
Governance built in
Schema changes require human approval. Access policies control who reads and writes what. Every mutation is audited. Agents operate within boundaries, not the wild west.
Self-learning knowledge
Lore accumulates context: what columns mean, which patterns work, what business rules apply. One agent learns something, every future agent benefits. Knowledge compounds over time.
Instant interfaces
The schema is the app definition. Lore generates CRUD views, dashboards, approval queues, and standalone apps directly from your data model. Ship tools in minutes, not sprints.
Interfaces that build themselves
Schema in, apps out.
Lore reads your data model and generates real interfaces. Not mockups. Not wireframes. Working applications with data tables, dashboards, and approval flows — ready to use the moment your schema exists.
Sortable, filterable, paginated. Generated from schema.
Group by any status column. Drag to update.
Full apps from a single HTML file
// Agent writes one HTML file. Lore does the rest. $ lore app publish-standalone sales-dash \ --title "Sales Dashboard" \ --file ./dashboard.html Published to /apps/acme/sales-dash Auth, data access, and SDK injected automatically. // Inside the app: const lore = await window.lore._ready const { rows } = await lore.api.query({ sql: "SELECT * FROM deals" })
Agents build custom tools, dashboards, and portals. Lore handles auth, sandboxing, and the data bridge.
How it works
Three interfaces, one data layer.
Connect via MCP, CLI, or REST API. Every interface shares the same auth, governance, and capabilities. Define once, access everywhere.
For AI agent frameworks
// claude_desktop_config.json { "mcpServers": { "lore": { "command": "lore", "args": ["mcp"] } } }
37 tools. 9 resources.
For Claude Code, Codex, terminal agents
$ lore auth signup Authenticated as you@company.com $ lore schema customers orders tickets $ lore query "SELECT ..." 12 rows in 6ms
For any language or framework
POST /v1/query { "sql": "SELECT * FROM customers WHERE status = ?", "params": ["active"] } // Response includes knowledge context
The flywheel
Every agent makes the next one smarter.
Lore isn't a static database. It's a knowledge system that improves with every interaction.
Agent connects and gets context
Schema, semantics, patterns, rules, corrections — everything Lore knows, delivered on first query.
Agent queries and mutates with governance
Access policies enforce boundaries. Mutations go through approval flows. Every action is audited.
Lore learns from the interaction
Query patterns are recorded. Agents contribute semantics and corrections. Knowledge compounds automatically.
Next agent starts with richer context
Fewer mistakes, better queries, deeper understanding. The system gets smarter with every connection.
Use cases
Stop buying software. Start building it.
Your agents build exactly what your org needs. Lore gives them the backend to do it right.
Customer management
Contacts, deals, pipelines, activity tracking. Your agent builds a CRM tailored to how your team actually sells.
Operations tooling
Inventory, order processing, fulfillment. Internal tools built by agents, governed by Lore.
Customer support
Ticket triage, knowledge bases, SLA tracking. Agents handle routing, humans handle escalations.
Dashboards and reporting
Revenue metrics, operational KPIs, team performance. Lore generates views directly from your schema.
Process automation
Approval queues, event-driven workflows, scheduled tasks. Agents wire up automations, Lore enforces the rules.
Multi-agent coordination
Agents share state through Lore's data layer. One agent writes, another reacts. Events drive the choreography.
Early access
Get on the list.
Lore is in private beta. Drop your email and we'll get you access as we roll out.