Open Source

Open infrastructure for Decision Graphs

Reasoning Layer infrastructure for enterprise AI agents needs a shared substrate - an agreed-on shape for what a decision is, how decisions reference each other, and how an agent queries the chain. Naboo is publishing that substrate, open and free, under Apache 2.0.

Two artifacts, one principle. An open schema specification anyone can implement against, and a reference MCP server that plugs straight into Claude Desktop, Cursor, and any MCP-compatible agent runtime. Build your own backend, fork the reference, or run Naboo as the managed implementation - the choice stays yours.

What's shipping

Specification

decision-graph-spec

The open schema. Node types, edge types, attributes, state machine, GraphQL SDL, MCP protocol contract, worked examples. Implementations welcome. Apache 2.0.

github.com/nabooai/decision-graph-spec
Reference implementation

@naboo/mcp-server

A working MCP server that exposes the spec's tools (list_decisions, get_blockers, trace_decision_chain). Plugs into Claude Desktop, Cursor, and any MCP-compatible agent. Apache 2.0.

npx @naboo/mcp-servergithub.com/nabooai/mcp-server

Why this is open

Naboo's reasoning is simple. The category needs a shared shape before any single vendor can win it - otherwise every agent runtime ends up integrated against a different proprietary surface, and the whole stack rots into glue code.

We have a strong opinion on what that shape should be, because we've been deploying Decision Graphs inside large organizations (Global-E, Melio) for a while. So we're publishing the shape as a spec, and a reference implementation that works today, and inviting the rest of the ecosystem to ship alternatives.

The schema is the contract. The reference is the proof. The hosted Naboo product - the Forward Deployed Agent who sits with your team and encodes your organization's decisions into the graph - is the thing we sell. Those are two cleanly separable layers. We're betting the category is bigger than any one vendor.

How to get involved

Read the spec

Start with spec/0001-introduction.md in the repo.

Run the MCP server

Five-minute install. Comes with an Acme Co sandbox backend you can query immediately.

Comment on RFCs

All schema discussions live in GitHub Discussions. Issues are for bugs only.

Governance

v0.x is BDFL-governed by Naboo while the shape settles. From v1.0 onward, schema changes require an RFC and rough consensus from active implementers. We'll publish the process in CONTRIBUTING.md alongside the spec.

Related reading

Definition

Reasoning Layer for Enterprise AI Agents

Definition, architecture, and the two tiers - Topic Graph and Decision Graph.

Read more
Definition

What is a Decision Graph for AI Agents?

Decisions as first-class nodes - owners, triggers, blockers, evidence. The primitive AI agents need to act.

Read more
How-to

How to Build a Decision Graph

Seven concrete steps from elicitation to a queryable graph. Two to four weeks via Forward Deployed Agent.

Read more
CFO brief

How to Reduce LLM Token Costs

Don't meter the waste, cut the cause. Reasoning Layer vs observability and caching, compared.

Read more
Guide

Improve AI Agent Accuracy

Accuracy is upstream of evals. Four causes of enterprise AI inaccuracy and how a Reasoning Layer fixes them.

Read more
Architecture

Connect Enterprise Data Sources

Live joins vs stale copies. Warehouse, ETL, knowledge graphs, and Reasoning Layer compared.

Read more
Guide

Overcome GenAI Hallucinations

Hallucinations are a context-handoff problem, not a model problem. Four causes, one upstream fix.

Read more
ROI

How Naboo Saves Cost

Five places Naboo cuts cost in enterprise AI deployments. Four-minute explainer video.

Read more
Hub

Compare Naboo

Every category enterprise AI buyers weigh against the Reasoning Layer - in one place.

Read more
Comparison

Naboo vs Helicone

Reasoning Layer cuts the cause; Helicone measures the waste. Composable.

Read more
Comparison

Naboo vs Langfuse

Different layers. Langfuse versions + traces; Naboo grounds the agent.

Read more
Comparison

Naboo vs LlamaIndex

RAG framework vs Reasoning Layer. When to use each.

Read more
Comparison

Naboo vs LangChain

Orchestration vs substrate. Compose them.

Read more
Comparison

Naboo vs Cognee

Open-source agent memory vs enterprise Reasoning Layer. Different primitives, different jobs.

Read more
Comparison

Naboo vs Hyperspell

Cloud 'Company Brain' API vs enterprise Reasoning Layer with on-prem, RBAC, and FDA.

Read more
Comparison

Naboo vs Modern Relay

Git-style graph DB primitive vs a complete Reasoning Layer shipped end-to-end.

Read more
Background

Why retrieval was the wrong foundation

How enterprise AI agents got built on RAG, why it falls short, and what a reasoning layer fixes.

Read more
Comparison

Naboo vs RAG

Retrieval vs reasoning - head-to-head benchmarks, architecture, and when to use each.

Read more
Comparison

Naboo vs Glean

Enterprise search vs reasoning layer - when each fits.

Read more
Concept

AI Search vs Reasoning Layer

Search returns links; the reasoning layer returns the chain. When to use which.

Read more
Category

Agent Memory vs Reasoning Layer

Memory recalls what the agent saw. A Reasoning Layer returns what the company decided. Different primitives, different jobs.

Read more
Case study

Global-E case study

How Global-E (NASDAQ: GLBE) gave AI agents secure access to customer data.

Read more
Comparison

Compare alternatives

Naboo vs other enterprise AI agent infrastructure platforms.

Read more

Two ways to engage Naboo

The schema and the reference MCP server are yours to fork, extend, and ship against. Or talk to us about the hosted Reasoning Layer infrastructure - a Forward Deployed Agent who encodes your organization's Decision Graph alongside your team.