Agentic Labs
Flagship PlatformA Relationship Operating System — multi-agent orchestration over a single shared brain
The core product decision: agents shouldn't own their knowledge. Every agent on the platform reads from and writes to one centralized brain, so context captured during acquisition is still there when a support conversation happens a year later. LangGraph handles orchestration; the memory architecture handles continuity.
- Orchestration
- Six specialized agents — Customer Service, Executive Assistant, Lead Gen, Social Media, Business Analyst, Inventory — built as LangGraph graphs with explicit state, not prompt chains. A Marketing Suite (LinkedIn outreach, SEO, Reddit) runs coordinated campaigns on the same substrate.
- Memory architecture
- Three vertical layers — Acquisition, Operational, and Relationship Memory — with structured hand-offs between agents. When a lead converts, the lead-gen agent's context transfers to operations instead of being re-learned from scratch.
- Platform engineering
- Multi-tenant from the first schema: tenant-scoped retrieval, per-business agent configuration, and a deployment topology split across Vercel (product surface) and Railway (agent runtime).
- Bring your own LLM
- MCP-compatible, so Agentic Labs agents can be linked to whatever LLM you already use — Claude, ChatGPT, or Grok. Because memory lives in the platform rather than the model, agents retain everything they know even if you switch LLMs.
Not a concept deck — the platform is deployed and being validated on Trackply with real users before any client onboarding. Build, ship, measure, then sell.
- LangGraph
- Python
- FastAPI
- Next.js
- Supabase
- pgvector
- Claude API
- MCP
- Railway
- Vercel
