Remember. Build. Run. Serve. Connect.
One stack for running agents in production
Three tools to start from, and two that connect them together.
Lint-AI, AgentFlow, and Mogg run entirely on your own hardware — local by default for the MCP Server too.
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
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
AgentFlow is the engine behind Agent Builder; the MCP Server is how outside tools plug into all of them.
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+
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