Org MCP Server

Valem

A spreadsheet engine for AI agents. An LLM describes a domain as a JSON spec (fields, formulas, invariants); Valem compiles it into a live model that recomputes every dependent value on each change, refuses changes that break a rule, and can explain why any number is what it is.

Why

LLMs are good at describing a domain and bad at keeping state consistent over it. Ask one to maintain a quote, an order, or a loan application across twenty turns and the total drifts from its line items, a limit checked three messages ago is forgotten, and arithmetic is off by a rounding step.

Valem moves the rules out of the conversation and into a deterministic runtime:

• Derivations are JSONata formulas over JSON paths, recomputed in dependency order (only what a change touches).
• Constraints are invariants checked before a change commits: roll it back or flag it.
• Effects (HTTP calls, LLM calls, timers) are emitted as data and run after the commit, so replay never re-fires I/O.
• Explain and audit: every value has a derivation trace, and the audit trail is hash-chained.

The agent writes the spec; Valem validates it, tests it, and keeps it true.

Try it in 30 seconds

In the browser, no install. Open valem.run, describe a model in plain words ("a mortgage with overpayments", "an invoice with VAT and discounts"), then type into the live form and watch every derived value update.

From your agent (MCP, hosted, nothing to download):

# Claude Code
claude mcp add --transport http valem https://valem.run/mcp

Claude Desktop: Settings → Connectors → Add custom connector → https://valem.run/mcp. Then ask the agent to call pair_browser and it will build models you see live in a browser tab. The authoring tools (validate_spec, eval_expression, test_spec, dry_run) work without pairing. Guide: Connect your agent.

Self-hosted with Docker:

docker run --rm -p 8080:8080 ghcr.io/vlad-public-code/valem:latest
# → http://localhost:8080 (management UI, REST + WebSocket API, and /mcp)

What a spec looks like

{
  "id": "order",
  "version": "1.0.0",
  "schema": {},
  "derivations": [
    { "path": "$.order.items[*].lineTotal", "expr": "$parent.qty * $parent.price" },
    { "path": "$.order.total", "expr": "$sum(order.items.(qty * price))" }
  ],
  "constraints": [
    { "id": "max-order", "expr": "order.total <= 5000",
      "message": "Order exceeds the cap", "policy": "rollback" }
  ]
}

Write qty on any line item and lineTotal and total recompute; a write that pushes the total over 5,000 is rejected before it commits. Full format: ModelSpec reference.

Ways to run it

Clients: TypeScript and Java SDKs.

Documentation

Published at valem.run/docs (source in docs/), organised in six chapters: Getting started, Usage scenarios, Model guide, Reference, Deployment, Extending. Good first reads:

• What is Valem?: the idea in five minutes
• Valem vs alternatives: how it compares to code interpreters, rules engines, form libraries and spreadsheets
• FAQ, and when not to use Valem
• ModelSpec format: the spec format, single source of truth
• API reference: REST, WebSocket, and console protocol
• MCP tools: every tool and resource the MCP server exposes
• Configuration: every valem.* property
• Security model: auth, effect egress/SSRF, limits
• Architecture: component map, data flow, design decisions
• For agents: llms.txt

Building from source

Prerequisites

• Java 21+
• Maven 3.9+
• Node.js 20+ and npm 9+ (UI only)

Running the console app (no HTTP server required)

The console app is the fastest way to use Valem from a script or an AI agent. It reads one JSON command per line from stdin and writes one JSON response per line to stdout. No HTTP, no browser, no server process. All state is held in memory for the lifetime of the process.

# Build the fat jar (first time)
mvn install -pl valem-core,valem-service -q
mvn package -pl valem-console -q

# Run interactively
java -jar valem-console/target/valem-console-1.0.0-SNAPSHOT.jar

# Or pipe commands
echo '{"cmd":"list-models"}' | java -jar valem-console/target/valem-console-1.0.0-SNAPSHOT.jar

Example session:

// stdin
{"cmd":"create-model","spec":{"id":"order","version":"1.0.0","schema":{},"derivations":[{"path":"$.total","expr":"subtotal + tax"}]}}
{"cmd":"mutate","id":"order","mutations":{"$.subtotal":100,"$.tax":8}}
{"cmd":"get-state","id":"order"}

// stdout
{"ok":true,"result":{"id":"order","status":"created"}}
{"ok":true,"result":{"success":true,"mutatedPaths":["$.subtotal","$.tax"],"derivedUpdated":["$.total"],...}}
{"ok":true,"result":{"subtotal":100,"tax":8,"total":108}}

Full command list: console JSON protocol.

Running the backend

# From the repo root — build and start the Spring Boot server on port 8080.
# valem-web is the runnable deployable; valem-api is the headless library it wraps.
mvn install -pl valem-core,valem-service -q
mvn spring-boot:run -pl valem-web

The API is now available at http://localhost:8080, with the management UI served at /. Storage is in-memory by default; other backends are à-la-carte adapter jars (mvn -Pweb-postgres -pl valem-web package, then --valem.storage.type=postgres) — see configuration.md. For durable setups, persistence layout, and the hardening checklist see operations.md.

To enable LLM-powered spec generation, configure a provider before starting (the key is read from valem.llm.api-key, settable as VALEM_LLM_API_KEY; there is no provider-specific env fallback):

# Anthropic (default provider)
export VALEM_LLM_API_KEY=sk-ant-...
mvn spring-boot:run -pl valem-web

# OpenAI
VALEM_LLM_PROVIDER=openai VALEM_LLM_MODEL=gpt-4o \
  VALEM_LLM_API_KEY=$OPENAI_API_KEY mvn spring-boot:run -pl valem-web

# Ollama (local, no API key needed; start `ollama serve` first)
VALEM_LLM_PROVIDER=ollama VALEM_LLM_MODEL=llama3 mvn spring-boot:run -pl valem-web

Without any provider configured the server starts normally; the /models/generate* endpoints return 503. All LLM knobs (providers, tool budgets, retries, temperatures): configuration.md.

Running the developer UI

In a separate terminal:

cd valem-ui
npm install        # first time only
npm run dev

Open http://localhost:5173 in your browser.

The UI proxies all /models and /blobs requests (including WebSocket connections) to the backend, which must be running.

Generating a model spec with an LLM

The UI's ✦ Generate button drives a human-in-the-loop workflow: enter a model ID and a plain-text domain description → Preview Prompt (editable) → send to the LLM → review/edit the generated spec → Register Model. The same workflow is available over REST (POST /models/generate/preview → /models/generate → /models).

See generating-specs-with-llm.md for the workflow and provider setup, and llm-prompts.md for the exact prompts and the validate-and-repair loop.

Example: create and mutate a model over REST

# Create a model
curl -s -X POST http://localhost:8080/models \
  -H 'Content-Type: application/json' \
  -d '{
    "id": "order",
    "version": "1.0.0",
    "schema": {},
    "derivations": [
      { "path": "$.order.total", "expr": "order.subtotal + order.tax" }
    ],
    "constraints": [
      { "id": "max-order", "expr": "order.total <= 5000",
        "message": "Order exceeds the cap", "policy": "rollback" }
    ]
  }'

# Mutate base fields — total is derived automatically
curl -s -X POST http://localhost:8080/models/order/mutations \
  -H 'Content-Type: application/json' \
  -d '{ "$.order.subtotal": 200, "$.order.tax": 20 }'

# Read merged state
curl -s http://localhost:8080/models/order/state | python -m json.tool

Every endpoint (audit, snapshots, views, blobs, spec evolution, composition, …): api-reference.md.

Running tests

# All modules
mvn test

# Core only (faster — no Spring context)
mvn test -pl valem-core

Running end-to-end tests

The valem-e2e module contains Playwright browser tests that drive the full stack (backend + UI).

Prerequisites: the backend must be running on port 8080 (see Running the backend). The UI dev server is started automatically by Playwright.

cd valem-e2e
npm install              # first time only
npx playwright install   # download browser binaries (first time only)

npm test                 # headless Chromium
npm run test:headed      # watch the browser
npm run test:ui          # Playwright interactive UI mode
npm run report           # open the last HTML report

License

Apache-2.0 — see LICENSE.