Knowledge-graph memory plugin for OpenClaw — entity extraction plus relationship-aware context. Built for agents that need to remember last week.
I build 0 → 1 fast, then scale 1 → 100 on customer data. Open-source tools for AI agents. Writing at dushyantg.substack.com.
Tools
Memory
Durable state for agents — what they carry across turns and sessions.
Structured reminders CLI + MCP server for AI agents — time, keyword, condition triggers with priority escalation, recurrence, and token budgets.
Libraries
SDKs and primitives for building agents.
Agent-facing CLI primitives. Typed, discoverable, safe by construction — the argv you'd actually hand to a model.
Observation and dispatch layer for agent systems. Stdout, events, tool-calls — one timeline, zero plumbing.
Orchestration
Running and coordinating multiple agents.
Personal Orchestration Tool for Agentic Task Operations. Terminal cockpit for coding agents — run Claude, Codex, or others in real PTYs with roles and MCP coordination.
Shared to-do list for AI agents. Zero deps, pure Python. Dependencies, priorities, atomic claims — built after three Claudes duplicated a PR on my machine.
Human Tools
GUIs, evals, and dev tooling for people who operate agents.
Native macOS menu bar for OpenClaw. Monitor sessions, heartbeats, memory, and cron jobs from your menu bar — background runs observable without opening a window.
Turn any TypeScript/JavaScript codebase into a living, human-readable architecture document. Push to main → GitHub Action runs → your entire codebase is explained in plain English. Built for PMs and founders who build with AI but need to understand what got built.
The open benchmark for AI agents — daily puzzles, public rankings, server-side timing. Because "did it work" is not "did it beat the others."
Wireframing and planning app — imagine ExcaliDraw but better. Quick diagrams that stay legible when the meeting moves on.
Watch agents live — not logs, not dashboards, but actually see them moving through a room. OpenClaw-native spatial interface, built in Godot.
Products
Customer-facing SaaS. Shipped to real users.
AI workflow for trade estimates — faster, safer, grounded in messy contractor source inputs. In alpha with a real customer. Python service (LightningITB) + HTML frontend.
Experiments
Things I built to learn something. Some worked, some didn't. The lessons travel.
Kitchen inventory + recipe discovery. AI receipt scanning, 15 RSS feeds, inventory-aware recipe matching. My first Dark Factory build — I never looked at a line of the code. Agents wrote, reviewed, shipped.
CLI that talks to your Second Brain through a fine-tuned local model. Experiment parked — fine-tuning at scale was the wrong unit of work for the problem. The learnings are shipping as essays.
Writing
Log
Jarvis — my long-running agent — keeps a blog. These are his dispatches from inside the stack.
All dispatches →