From your first agent to production scale
RooAGI is focused on one problem: making AI agents reliable and deployable in the real world. We build the infrastructure that supports the entire agent lifecycle — from memory and development to execution, vector storage, private model hosting, and tool connectivity. Our six products are different components of that unified platform.
- ● Remember
- ● Build
- ● Run
- ● Serve
- ● Connect
- ● Store
Remember. Build. Run. Serve. Connect. Store.
One stack for running agents in production
Self-hosted by default — Agent Builder is the only one that runs as a hosted app.
Remember
Open source · GitHubLint-AI
Lint for AI-generated knowledge
Retrieval-grade memory for coding agents, plus corpus linting that flags orphaned pages and missing cross-references across your docs.
- Recall@5
- 86.9%
- Query latency
- 5.1ms
- GPU required
- No
Build
SaaS, HostedAgent Builder
Build AI agents in minutes, not months
A visual, no-code builder for production agents — pre-built agents to start from, or design your own with drag-and-drop tools.
- Tool integrations
- 10+
- Code required
- None
- Avg. build time
- 5 min
Run
GitHub releasesAgentFlow
The execution runtime for agent graphs
Fault-tolerant execution, working memory, and MCP-native tool orchestration for agent work that runs longer than a single prompt. Powers Agent Builder.
- Python support
- 3.10–3.13
- Platforms
- 2
- LLM providers
- 5+
Serve
Contact UsMogg
A native CUDA runtime for in-house model serving
Hand-tuned CUDA kernels and KV caching for in-house inference, for teams who want models running on their own hardware.
- Models
- LLaMA, Qwen, Muse Glimmer
- Precision
- FP16 / BF16
- Status
- In development
Connect
Direct downloadMCP Server
Agent tooling over the Model Context Protocol
Exposes your tools to any MCP-speaking agent over SSE, WebSocket, or stdio — with zero per-tool boilerplate to write.
- Tools exposed
- 76
- Setup
- 3 lines
- Per-tool overhead
- 0
Store
Free eval · GitHubRoo-VectorDB
A PostgreSQL-native vector database for AI agents
A PostgreSQL extension that combines relational features with vector search — vectors up to 16,000 dimensions, IVF-Flat indexing, and SIMD/OpenBLAS-accelerated queries, with native SQL WHERE-clause filtering.
- Max dimensions
- 16,000
- Distance metrics
- 4
- Multi-core gain
- 50–300%
How to Get Started
Start with the problem you need to solve, then use only the parts of the stack that move you forward.
Choose Your Starting Point
Choose the path that matches your team: build agents, connect tools and data, or run models on your own infrastructure
- Build an internal agent with Agent Builder + AgentFlow
- Connect tools and data with MCP Server
- Ground answers with Lint-AI + Roo-VectorDB
- Serve models privately with Mogg
Run a Focused Pilot
Use one workflow in your own environment and measure the result before expanding
- Connect the selected tools and knowledge sources
- Run the workflow with AgentFlow
- Measure quality, latency, and cost
- Keep your data and deployment model under control
Scale What Works
Move the proven workflow into production with the deployment model that fits your team
- Self-host the components your team needs
- Optimize serving, retrieval, and execution
- Expand from one workflow to many
- Get a technical partner when you need one
Ready to see where RooAGI fits?
Schedule Demo
Show us your workflow
Get Pricing
Plan a pilot
Talk to Sales
Discuss production deployment
Let's talk about your agents
Whether it's agent memory, a no-code build, or in-house model serving, tell us what you're working on and we'll point you to the right place to start.
Free Architecture Review
We'll look at your current agent setup and flag where memory, tooling, or serving is the bottleneck
Custom Deployment Guidance
Tailored recommendations for your use case, team size, and hardware
Enterprise Support Options
24/7 support, priority updates, and dedicated engineering resources
Built for teams running agents in production
From open-source Lint-AI users to enterprises deploying custom inference — RooAGI covers the whole stack.