RoboSystems

RoboSystems is an open-source, AI-native financial intelligence platform for accounting, financial reporting, and investment management. It models your financial data as a knowledge graph — transactions, facts, reporting elements, and the calculation structures that relate them are all nodes and edges, with the semantics preserved rather than flattened into rows you query around. On top of that graph it gives AI agents and analysts a ledger-grade system of record they can both query and operate — closing the books, producing reports, and analyzing portfolios across your own ledger, your holdings, and SEC public filings queryable alongside them. Powers RoboLedger and RoboInvestor.

Every tenant gets their own graph. Not a row-level slice of a shared table — a dedicated graph database on its own instance, with a dedicated OLTP schema behind it. Your ontology, your taxonomies, and your calculation structures live in it as artifacts you can read, export, and take with you.

Platform

The platform provides the core infrastructure that all extensions build on:

• Dedicated Infrastructure: Tiered LadybugDB graph infrastructure with dedicated instances and configurable memory allocation
• AI Operator System: Autonomous financial Operators (Claude/MCP executors) with automatic credit tracking and SSE progress streaming.
• Shared Repositories: SEC XBRL filings knowledge graph for context mining and benchmarking
• Document Management: Upload, index, and search documents with full-text and semantic search via OpenSearch
• Credit-Based Billing: Flexible credits for AI operations based on token usage
• Subgraphs (Workspaces): AI memory graphs and isolated environments for development and team collaboration
• Web Application: Primary web interface — graph management, the AI query console (natural-language + Cypher over MCP), schema explorer, document search, shared-repository access, and billing — robosystems-app

The core platform API lives at /v1 — auth, orgs, billing, graph lifecycle (subgraphs, backups, materialize, tier changes), Cypher, and MCP — with reads as REST GET. Every write — across both the core and extensions surfaces — is a named OperationEnvelope operation with Idempotency-Key support, audit logging, and SSE progress streaming via /v1/operations/{id}/stream.

Extensions

Extensions are domain-specific subsystems that bring their own schema, OLTP tables, API routes, data pipelines, and dedicated frontend apps. They share a single PostgreSQL database with schema-per-tenant isolation and materialize to the graph for analytical queries. Domain content is authored as block molecules — self-describing envelopes bundling atomic facts with their structure, rules, and verification — never bare rows.

The extensions API surface is graph-scoped at the URL level — graph_id is always a path parameter, never a query argument — and splits reads from writes by transport:

• Reads → POST /extensions/{graph_id}/graphql — Strawberry GraphQL, GraphiQL in dev, schema composed dynamically from enabled domains
• Writes → POST /extensions/{roboledger|roboinvestor}/{graph_id}/operations/{operation_name} — named REST commands
• Analytical views → POST /extensions/{domain}/{graph_id}/operations/{view_name} — read-only analytical operations (e.g. build-fact-grid, live-financial-statement), same envelope as writes

Behind the API is a CQRS operations kernel (reads/ + commands/ per domain, plus graph-backed views/) that's the single source of truth for business logic — GraphQL resolvers, REST operation routes, and MCP tools all delegate to the same functions. Per-domain feature flags (ROBOLEDGER_ENABLED, ROBOINVESTOR_ENABLED) gate both the routers and the GraphQL schema composition.

RoboLedger

Accounting and financial reporting extension — a ledger-grade system of record that AI and analysts can both query and operate. It broadly implements the Seattle Method, a declarative methodology for digital financial reporting. Writes land as self-describing molecules: atomic facts bundled with their structural wiring, rules, and verification in one typed envelope, never bare rows. Three block molecules are the authoring substrate:

• Information Blocks — the envelope for reportable content: schedules, statements, metrics, and text-block disclosures bundled with their period-versioned fact sets, typed mechanics, and rules. evaluate-rules runs arithmetic checks (EqualTo, RollUp, RollForward, SumEquals, Exists, CoExists) over materialized facts; pinning a fact set separates a live closing book from a frozen report.
• Event Blocks — REA event capture: callers record what happened in the world (a sale, a payment, an asset disposal) through a structured action-verb vocabulary, and a handler registry derives the debits and credits across the three-level ledger (Transaction → Entry → LineItem). Preview handler resolution, execute to post GL atomically, and promote matured obligations (AR/AP, schedule entries) on demand.
• Taxonomy Blocks — accounting frameworks as data, not code: Elements, linkbase Associations (presentation / calculation / mapping), Structures, and auto-generated structural rules in one atomic write. Ships fac (fundamentals) and rs-gaap (~2,000 curated US-GAAP concepts) behind a two-tier public→tenant library, with CoA→GAAP mapping anchored to calc-DAG leaves.

Built on the blocks:

• Close lifecycle — fiscal calendar, close-target catch-up sequencing, and period close/reopen gated on the balance equation, QuickBooks sync-staleness, and outstanding schedule obligations; every blocker names what is holding the close
• Mapping — CoA→GAAP mapping associations plus AI-assisted bulk mapping via the MappingOperator (confidence-tiered: auto-approve / review / skip)
• Reporting — multi-period reports rendered from shared facts through a Reporting Style; a report lifecycle (draft → under_review → filed → archived) with publish lists for distribution
• Forecasting — operating-plan scenarios projected through the same statement structures: rule-driven forecasts, per-line growth trajectories, and manual line assertions, with forecast periods returned alongside actuals on statement reads
• Analytical operations — live-financial-statement renders a statement straight from the OLTP ledger (no materialization required); build-fact-grid and financial-statement-analysis query the materialized XBRL hypercube in the graph
• Serialization — reports serialize to web-native JSON-LD (stored, SHACL-validatable) and filing-grade XBRL 2.1 (rebuilt on demand, Arelle-validated)
• Pipelines & data — QuickBooks ELT via dbt/Dagster with a configurable write_policy, and SEC XBRL financial reporting

Dedicated frontend app: roboledger-app.

RoboInvestor

Portfolio management and investment tracking extension — tracks holdings in private companies and links them back to the businesses that issued them.

• Portfolio Blocks — the same molecule discipline as RoboLedger: a portfolio plus its positions and securities are validated and written as one envelope, with cost basis and current value held as integer cents and dollar totals computed at the boundary. Positions move through an active / disposed / archived lifecycle; reads expose portfolios, positions, holdings (rolled up by issuer), and the assembled portfolioBlock.
• Securities — register and maintain ownership instruments (common stock, warrants, convertible notes, …) with an extensible terms blob for instrument-specific detail (strike price, liquidation preference, vesting)
• Cross-graph research — a security can point at the graph of the company that issued it, when that company also runs on the platform. The investor records the issuer's source_graph_id up front as a pre-association; when the issuer later shares a published report into the investor's graph, the issuer's entity is materialized there and any securities waiting on that source_graph_id link to it. A holding then traverses through to the issuer's own reported facts — Portfolio → Position → Security → Entity → Report → Fact — with authorization enforced at the report-sharing boundary, not the OLTP layer.

Dedicated frontend app: roboinvestor-app.

Quick Start

Docker Development Environment

# Install uv and just
brew install uv just

# Start robosystems backend
just start

# Start frontend apps - robosystems-app, roboledger-app, roboinvestor-app
just start apps

# Refresh images and recreate the containers that changed (after a git pull)
just upgrade

# Restart to pick up code changes; rebuild after dependency changes
just restart
just rebuild

This initializes the .env file and starts the complete RoboSystems stack with:

• Graph API with LadybugDB and DuckDB backends
• Dagster for data pipeline orchestration
• PostgreSQL for IAM, graph metadata, extensions and Dagster
• Valkey for caching, SSE messaging, and rate limiting
• OpenSearch for full-text and semantic document search
• Localstack for S3 and DynamoDB emulation

Service URLs:

With just start apps (frontend apps):

Read the full README on GitHub →