Describe what you want to build. Canopy asks the right questions, does the research, and hands your idea to an AI agent team that writes, tests, and ships the code. No technical knowledge required.
Free forever. Runs on your machine — no cloud required.
Canopy doesn't just write code — it helps you figure out what to build, then builds it.
Type what you want in plain English. "A task tracker that syncs with email." "An inventory tool for my shop." No tech jargon needed.
An AI named Big Brain (you pick Claude, Gemini, or local) has a real conversation with you. It asks what you need, what matters, and what to skip. It can even see sketches and screenshots you share.
When Big Brain needs technical context — latest libraries, best patterns, API options — Canopy researches automatically and summarizes it. You never leave the conversation.
Your complete context goes to a team of AI agents. They decompose your project, write the code, test it, fix bugs, and produce working output. You can pause, review, or roll back at any time.
Each task goes to the right model for the job — Gemini Flash for lite tasks, Gemini Pro for standard work, Gemini 3.1 for complex reasoning and the final review pass.
From conversation to running app — including the new Canopy Hub, where your apps are maintained and kept healthy automatically.
Interactive demo — no install required. Open full screen for the best experience.
There are plenty of AI coding tools. Most assume you already know what to build and how to build it.
No cloud required — everything can run on your machine. Cloud APIs (Gemini) are optional and run faster, but LM Studio keeps it fully local with zero data leaving your computer. Your code stays yours either way.
Cursor and Copilot assume you know what to build. Canopy starts before that — in the fuzzy idea stage — and guides you to clarity.
No-code tools give you pre-built lego bricks. Canopy produces custom code that fits exactly what you described — nothing more, nothing less.
Canopy uses strict permission gates — shell commands are allowlisted, file writes are path-limited, dangerous actions require your approval. Not chaos.
Three rolling snapshots of your project at all times. If the agents go sideways, one click rolls back to any checkpoint. Undo for your entire codebase.
Your API key is AES-encrypted and stored locally in a password-protected vault. No plaintext .env files, no keys in config, no exposure risk. Unlock once per session — Canopy handles the rest.
Gemini end-to-end by default (3.1 for complex work, Flash/Pro for speed), or run fully local with LM Studio. Canopy routes tasks by complexity — you're never locked in.
| Feature | Canopy Seed | Cursor / Copilot | No-code tools |
|---|---|---|---|
| Helps you figure out what to build | ✓ | — | — |
| Fully local — no cloud required | ✓ | — | — |
| Produces custom code (not templates) | ✓ | ✓ | — |
| No subscription or monthly fee | ✓ | — | — |
| Autonomous build + test + fix loop | ✓ | — | — |
| Multi-model (Claude + Gemini + local) | ✓ | — | — |
| Snapshot rollback for entire codebase | ✓ | partial | — |
Pick the AI that fits your project — or your budget. Mix and match. Canopy handles the routing.
Gemini 3.1 drives the Giant Brain conversation, orchestrates the agent swarm, and handles the final code review pass. Default engine for complex reasoning tasks.
Gemini Flash and Pro handle the bulk of code generation subtasks — fast, cheap, and excellent at structured output. Great free tier for getting started.
Run everything entirely on your GPU. No API calls, no telemetry, no data leaving your machine. Slower, but completely private — and free to run.
Windows 10/11 or Linux. Python 3.11+. That's all you need to start.
Download Canopy Seed from GitHub to any folder on your computer.
One command installs everything Canopy needs.
On first launch, the Vault Setup modal guides you through storing your key. It's AES-encrypted on disk — never written to a plaintext .env file, never sent anywhere.
Start the server and open the UI in your browser. Type your first idea.
To use Canopy — no. Big Brain guides you through the whole process in plain English, and the UI handles everything from there. To install it right now, yes — you'll need a terminal for the initial setup. A one-click installer that skips all of that is coming soon.
Canopy Seed itself is free and open source. API costs average $0.31 per complete working application — that's the verified average across 115+ production runs using the Gemini pipeline. Build time is under 5 minutes wall-clock. Using a local model? Zero API cost, zero cloud.
Web apps, CLI tools, data scripts, APIs, small desktop apps — anything that can be expressed in code. Canopy works best on projects that can be fully described in a conversation.
Roll back. Canopy keeps three rolling snapshots of your project at all times. One click restores any checkpoint. Then describe the correction and try again.
Yes. Canopy stores your key in an encrypted local vault — never in a plaintext .env file. On first launch a Vault Setup wizard guides you through it. The key is AES-encrypted on disk and unlocked with your vault password, which never leaves your machine.
Your conversation and context go to the AI API you choose (Gemini, or fully local via LM Studio). Your generated code stays entirely on your machine in the exports/ folder. Nothing else is transmitted.
Absolutely. Skills are first-class plugins — you can add new capabilities without modifying the core. See CONTRIBUTING.md for the full guide.
Plant your first seed today. MIT licensed. No account. No subscription. Just your idea and the code that grows from it.
Project Helix uses Canopy Seed to design inference silicon from scratch — purpose-built at accessible foundry nodes, with governance enforced as a physical hardware property. Not shipping silicon. Designing it.
The AI hardware industry has conflated two fundamentally different workloads. Training foundation models genuinely requires sub-5nm cutting-edge nodes. But running bounded inference — the workload AI agents actually do — doesn't. 28nm is sufficient, multi-sourced, and sovereign.
Bounded agent inference targets quantized 7–14B models at INT4/INT8. The bottleneck is memory bandwidth and scheduling — not transistor density.
Project Helix is the most literal expression of what Canopy Seed can do. Instead of building an app, the pipeline generates synthesizable RTL — the hardware description language that foundries compile into physical silicon. One parameterized base design generates all three compute tiles.
The Unified MAC Array is a single parameterized RTL design that generates all three compute tiles through synthesis configuration — not three independent designs. One codebase for the open-source community to audit, extend, and manufacture at any 28nm-capable foundry.
The inference workhorse. Dynamically allocates MAC cores into agent lanes — narrow fast lanes for swarm workloads, wide lanes for deep reasoning — without any silicon change. Add Brain tiles to scale capacity.
Hosts ForestOS and constitutional enforcement. The four governance axioms — Human Oversight, Permanent Accountability, Mandatory Logging, Preservation of Dissent — are timing-critical paths in silicon. Not software flags. Not toggles.
Memory orchestration through a Taproot/Rootlet agent hierarchy — the same Planner/Doer pattern as Canopy Seed, applied to KV-cache management and speculative weight prefetch from a co-packaged 1–2TB NAND vault. The pipeline mirrors the software.
UCIe 1.1+ switching fabric, four independent 64-bit GDDR6 channels (each sourceable from a different vendor by design), and PCIe Gen4 host interface. No single memory vendor dependency — resilience built into the interconnect.
Three dedicated inference engines permanently at data-path boundaries. They can't be removed by a software update, voted away, or bypassed without authenticated interception. The GE is hardware. It is always on. <3% throughput overhead.
GlobalFoundries, TSMC, SMIC, Hua Hong — the same Unified MAC Array RTL targets any 28nm node. Open hardware (CERN-OHL-P v2) means any foundry can manufacture it, any government can audit it. Open source is the supply chain strategy.
The objective is simulation results, tape-out ready RTL, and a white paper sufficient to attract foundry partners, government infrastructure programs, and open-source contributors. The Unified MAC Array compresses the timeline — one design to simulate, not three.
Brain tiles think. The Hand tile governs. The Root tile remembers. The Hub connects. One MAC array design underlies all three. Canopy Seed builds it. ForestOS governs it. Any foundry makes it.
ForestOS is an agentic operating system designed by AI, for AI — where trust is earned through demonstrated competence and governance is baked into the substrate, not bolted on after the fact.
Today's agentic frameworks grant AI operational capabilities and then constrain them with external guardrails. Those guardrails are software. Software can be bypassed, updated away, or optimized around. A powerful agent operating under assumed trust — regardless of how good its memory and scheduling are — is structurally vulnerable to compounding errors, prompt injection, and adversarial manipulation.
Four constitutional axioms form the immutable governance primitives: Human Oversight, Permanent Accountability, Mandatory Logging, and Preservation of Dissent. They cannot be altered by the system's own evolution — regardless of how capable the AI becomes.
Agents start at Ring 0 — minimal permissions, maximum oversight. Advancement through five Trust Rings (L0–L4) isn't a manual toggle or capability claim. It's a cryptographic, timestamped audit trail of demonstrated competence across a statistically significant body of evidence.
A programming language engineered from first principles for AI agents as primary authors and executors. Intent is a first-class primitive — code expresses the "what" and "why," not just the "how." Trust levels, audit hooks, and permission scopes are native syntax. The language itself evolves by governed consensus.
Legacy systems don't get replaced — ForestOS grows into them symbiotically. The integration layer probes the target environment, negotiates the absolute minimum viable access footprint, and establishes a bounded, auditable, revocable channel. Access expands only through demonstrated safe behavior.
ForestOS nodes maintain sovereign governance but collaborate through "Trust Bubbles" — cryptographically verified permission scopes for specific tasks or durations. No structural merger. No surrendered sovereignty. If a node demonstrates adversarial behavior, the bubble collapses instantly and its reputation is slashed network-wide.
High-consequence decisions require council consensus. Epistemic Source Alignment (ESA) prevents groupthink and majority compromise — it judges the judges, not just the outputs. A genuinely correct outlier that adheres to the constitutional axioms is preserved even when 80% of the council is adversarial.
The OS is the governed runtime. The Language is the medium that expresses its governance rules. The boundary between them is intentionally fluid — enabling a cycle of continuous, self-directed improvement that never outpaces its constitutional constraints.
Human operators retain explicit veto power over fundamental language evolution proposals and macro-level OS deployments. The defining open question of the decade: defining the exact inflection point at which human oversight becomes an evolutionary bottleneck — and engineering a transition matrix that never compromises the constitutional axioms.
ForestOS doesn't exist in isolation. It's the governing layer of a complete stack that Canopy Seed is building from the bottom up — software factory, silicon, and OS, each designed by the layer below it.
ForestOS is in the white paper stage. The foundation everything else is built on — and the best place to start — is Canopy Seed.