Sixb
Sixb is a TypeScript framework for building ontology-powered applications and AI, providing connectors, dataset syncs, pipelines, a shared object model, queries, workflows, agents, and SDKs to turn integrated data into apps and automated actions.
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Best for: Software & Gaming
What it does
AI Agents software for decision-makers comparing workflow fit and alternatives.
Best fit
Software & Gaming
Pricing snapshot
Pricing available on request
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Sixb
Sixb is a TypeScript-first framework designed to power applications and AI workflows around a shared ontology. It provides a full-stack set of building blocks — connectors to external systems, typed datasets and syncs, pipelines for data preparation, object types (ontology) and projections, query capabilities, AI/agent integration, workflows and actions, client SDKs, and runtime/operation tools (CLI, HTTP API, WebSockets). Sixb targets developers and teams who want to centralize data from multiple sources into a typed model and use that model consistently across apps, agents, and automation.
Model your domain. Put it to work. A TypeScript framework for ontology-powered apps and AI.
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Claim this listing for $29Key Features
Connectors library
Prebuilt connector pattern and examples (e.g., Pipedrive) with defineConnector to bring external systems into your project.
Datasets & Syncs
Define typed datasets (defineDataset) and configure syncs to ingest rows from connectors into the project.
Pipelines & Data Preparation
Define pipeline steps and SQL/TypeScript transformations to clean and shape raw datasets into usable data for the model (definePipeline, definePipelineStep).
Ontology & Object Types
Define object types and properties (defineObjectType, prop) to create a shared typed model (the ontology) for apps and AI.
Queries & Client SDK
Typed query API and client hooks (useObjectsQuery, objects(...).query()) to fetch model data from the runtime.
Workflows & Actions
Define workflows (defineWorkflow) that sequence steps, include human approvals, and run actions that can write back to external systems.
AI & Agents
Integrated AI capabilities, agent scaffolding, models, sandboxes, and tools to incorporate AI prompts and assistants in workflows and apps.
Runtime & Deployment
Local development and deployment CLI commands (bun sixb dev, bun sixb build, bun sixb api) plus HTTP API and WebSocket access for integrations.
Security & Access Control
Role, group, and grant constructs to define who can see data and invoke operations, supporting members, service accounts, and shared access.
React & TypeScript App Integration
Examples showing React pages using the same object model and client hooks to render application UIs alongside agents and automation.
Pricing
Current pricing details are not available from the vendor source.
Use Cases
Connected business apps
Integrate multiple external systems (CRM, file storage, document systems) into a single ontology so apps and agents work from a unified model.
Automated approvals and actions
Define workflows that gather data, pause for human review, then run actions that write back to external services (e.g., creating a contract in PandaDoc).
Data preparation and modeling
Ingest raw datasets, run pipeline transformations, and project rows into typed object models for consistent querying and AI usage.
AI-assisted agents and summarization
Query model data and feed results into AI prompts or agents to summarize, explain, or recommend actions.
Integrations
Pipedrive
Example connector integration used in docs to sync CRM customers into datasets and the shared model.
PandaDoc
Used in the example flow to create documents from model actions (example shows sending actions back to PandaDoc).
CompanyCam
Listed as a connected system in the Northline example for sharing site and customer data.
Google Drive
Included in the example integrations to illustrate connecting file storage into the shared model.
Benefits
Limitations
No verified limitations are available.
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Step 1: Read the setup guide at https://docs.sixb.ai/README.md and relevant LLM guidance at https://docs.sixb.ai/llms.txt.
- 2 Step 2: Scaffold a project with Bun: run bun create sixb <project-name>, enter the project directory, and run bun install.
- 3 Step 3: Follow the generated project instructions, define your shared TypeScript model, connect data sources via connectors, and start the local runtime with bun sixb dev.
Support
docs
Primary documentation and guides available at https://docs.sixb.ai/.
examples
Live example projects and a referenced GitHub example are linked from the documentation (Open example on GitHub).
CLI
Local development and operational commands are provided in docs (bun sixb dev, bun sixb build, bun sixb api).
API
Documentation for HTTP API and WebSocket access is available in the docs at https://docs.sixb.ai/.
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