EXP-001 · active
Harness Canopy
In a forest, the canopy is where the crowns touch — separate trees, one living layer.
A Rust daemon and terminal UI that runs alongside your AI agents. It gives them persistent memory across sessions, background scheduling on cron and file events, a knowledge graph that learns from every run, and a sync protocol so multiple agents stop colliding in the same workspace.
curl -fsSL https://get.univerlab.org/canopy | sh - antigravity
- blackbox
- claude
- cline
- codex
- continue
- copilot
- cursor
- gemini
- kilo
- kiro
- mimocode
- mistral
- opencode
- qwen
The one that started it all.
It started as a folder of skills for work. Then I noticed what nobody was talking about: agent harnesses shipped a headless mode, just sitting there unused. I wired cron jobs to fire tasks through it — too much for a skill, and the models of the day choked on the instructions. So it became an MCP: task-trigger. It worked, but it ran blind in the background; only the agent ever saw what happened. Not enough. I killed it and built a TUI — then scheduling, memory, sync, identities, and a new name. Canopy. By then the twist was complete: Canopy was building Canopy, and everything else in this lab.
Memory that persists
Every session writes facts and patterns to a project-scoped knowledge graph. The next session reads them. Agents stop re-explaining the same codebase to themselves.
Background scheduling
Agents run on cron schedules or file-change triggers — not just on demand. A daemon watches the workspace so you don't have to babysit.
Multi-agent sync
Agents declare their mission, report stability, and broadcast messages. The workspace "vibe" is visible before anyone touches a file. No more silent collisions.
Watch a loop assemble itself
- 01
A spec meets an agent
Work enters as a spec — role, what, how. An implementer node picks it up on whichever harness you choose.
- 02
Deterministic gates
A check node runs your real commands — build, lint, tests. Red routes back to the implementer; only green moves forward.
- 03
Review on a second brain
A different harness reviews the diff against the spec and commits. Same-vendor blind spots stay out of your branch.
- 04
Failure is routed, not lost
If the implementer dies mid-run, a resilience node triages it: glitches retry now; a quota death schedules the loop to wake itself at the exact reset time.
- 05
Ensemble mode
Fan the spec out to several models in parallel, wait for every proposal, and let an arbiter distill the consensus before a single line is implemented.
Every node runs on the harness you pick. Mix vendors freely — the graph is yours to edit.
DAG-based automation
Define workflows as directed acyclic graphs: specs flow through agent, check, and gate nodes with pass/fail routing. Automate bug fixing, code review, and multi-step tasks.
Background execution
Loops run autonomously in the background — implement, verify, review, commit. Each node has timeouts, retries, and a resilience agent that diagnoses failures.
Human-in-the-loop
When automation hits a wall, the resilience agent reports a blocker and pauses. You decide; the loop resumes when you're ready.
FAQ
- What does Canopy actually do?
- Canopy orchestrates work across different AI coding harnesses — Claude, Codex, or any agent that runs in a terminal. It lets you use all your free tiers without learning each platform's commands, config, or MCP parsing. One daemon, all agents.
- How do I share work between Claude and Codex?
- Canopy gives each agent a shared knowledge graph and sync protocol. When Claude finishes a task, the facts and patterns it discovered are available to Codex in the next session. No manual context copying.
- Can I run AI agents on a schedule?
- Yes. Canopy fires agents on cron schedules or file-change triggers via a background daemon. Set the schedule once; the daemon watches the workspace and runs tasks automatically.
- How is Canopy different from just using Claude Code or Codex directly?
- Those are individual harnesses. Canopy is the layer that connects them — shared memory, background scheduling, and multi-agent coordination. You keep your agents; Canopy adds the infrastructure.