ROADMAP.
Public phases, honest scoping, no vaporware promises. If a phase is marked exploring, there is a real chance it never ships. If it is marked active, the code is being written right now.
Phase 0 — Agent OS core
✓ SHIPPEDshipped Q4 2025The foundation: PGE trinity loop, six memory tiers, 145 MCP tools, 18 channel adapters, skill registry, gatekeeper with 4-class risk model.
- ▸Planner → Gatekeeper → Executor loop
- ▸6-tier memory architecture (chat, episode, vault, identity, tactical, entity)
- ▸145 MCP tools across 14 modules
- ▸18 channel adapters (Telegram, Discord, Slack, WhatsApp, Signal, Voice, ...)
- ▸Built-in skill library (MIT)
Phase 1 — Flutter Command Center
✓ SHIPPEDshipped Q1 2026Rebuilt the UI layer in Flutter — 33 screens, Material 3 dark theme, i18n across en/de/zh/ar. Replaces the legacy React UI.
- ▸33 Flutter screens covering every daily-use surface
- ▸WebSocket chat, voice, vision, push notifications
- ▸Mobile pairing via HMAC-signed QR
- ▸Multi-interface networking (Tailscale / ZeroTier / WireGuard / LAN)
Phase 2 — Evolution engine + autonomous learning
✓ SHIPPEDshipped Q1 2026The assistant that improves itself while you sleep. Idle-gated, budget-capped, checkpoint-interruptible.
- ▸System detector (CPU / RAM / battery / active windows)
- ▸Autonomous Learner reviews last 72 hours of runs
- ▸Meta-Learner scores strategy efficiency over time
- ▸Budget caps: seconds / tokens / cost / energy
- ▸Checkpoint + resume across idle windows
Phase 3 — Creator marketplace
▶ ACTIVEQ4 2026Third-party developers can publish and sell their own agent packs. 70/30 revenue share, no exclusivity, Ed25519-signed pack verification, one-time purchases through Gumroad.
- ▸Community pack validator (5-check pipeline: syntax, prompt-injection, tool allowlist, safety, signature)
- ▸Publisher identity + trust levels
- ▸Pack registry with install / search / report tools
- ▸Creator dashboard with sales + payout data
- ▸Reddit Lead Hunter as the first case study
Phase 4 — Windowed reasoning
? EXPLORINGexploringResearch track for long-horizon reasoning on a local model — essentially, how to make a 27B planner work on tasks that need 100k tokens of context without blowing up the KV cache.
- ▸Contextual summarization via the Episode tier
- ▸Chunked context with sliding-window promotion
- ▸ARC-AGI-3 integration as a reasoning testbed
- ▸Offline CNN training for keyboard-driven games
Phase 5 — Federated learning
? EXPLORINGexploringOpt-in federation where multiple Cognithor instances share anonymous learning signals without sharing data. Still in brainstorming — there is a real chance this never ships if we cannot prove it can be done without compromising the local-first commitment.
- ▸Differential privacy on strategy signals
- ▸User consent per signal class
- ▸No raw data ever leaves the machine, ever