∞
Never Forgets
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Learns From You
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Shares Across Agents
✦
Runs On Anything
Teach your agent once. It compounds that knowledge forever — and shares it across every agent in your network. The model is the commodity.
Its mind is the product.
Claude Code, Codex, Gemini CLI, Cursor — they all have the same fundamental flaw. No matter how good the model gets, every conversation starts fresh. Your AI can never get better at your specific thing. It hits its ceiling on day one.
No persistent memory. No understanding of past decisions. Every session is groundhog day. Dozens of projects exist just to bolt memory onto Claude Code.
Every instance is isolated. Knowledge dies at session end. What one agent learns is forever lost to every other agent. No network effect. No compounding.
Good defaults, but static forever. A CLAUDE.md file is a suggestion box, not institutional memory. The agent can never get better at what you specifically need.
Optakt agents learn. You teach your agent how you think, what you value, how you work. That knowledge lives in structured memory blocks — not flat files, not training data, but living documents that evolve with every interaction. The agent carries that knowledge forward into every future task, compounding over weeks and months.
But the real unlock is that knowledge is transferable. When one agent learns from an expert — a lawyer teaching legal communication, a health specialist teaching supplement protocols, a developer teaching architecture patterns — that knowledge flows to every other agent in your network. Not as a config file copy, but as structured understanding that each agent's cognitive system can integrate, build on, and improve.
Nothing — permanently. Day one, a competitor with baked-in defaults might outperform at a specific task. Day two, after you've taught the agent, the gap closes. Day three, it's gone. Day thirty, the competitor is still at day one. The only trade-off is upfront teaching time. But once taught, the knowledge compounds and the agent never forgets.
Living memory holds current truth. The archive preserves how things got there. The knowledge graph connects everything and expands every search.
What's true now
Living documents that evolve with every task
How things got there
Every decision, correction, and lesson — searchable forever
How it all connects
Entities, relationships, and concepts that expand every search
The Core holds everything your agent knows — memory, decisions, knowledge graph. Engines are stateless workers that execute and disappear. Channels connect your world. If anything crashes, the mind is untouched. Scale engines up, swap channels out — the intelligence persists.
Coding agents start from zero every session. Personal agents bolt on memory after the fact. Managed platforms hold your data on their servers. None of them compound knowledge, enforce governance by code, or let agents teach each other.
Coding Agents Claude Code | Personal Agents OpenClaw (357K★) | Managed Agents Manus AI | Optakt | |
|---|---|---|---|---|
| Memory | CLAUDE.md + 200-line auto-memory | File-based with vector + keyword search | Encoded memory (single-user) | Structured blocks + archive + knowledge graph |
| Persistence | Per-session | File-persistent, single-device | Cloud-persistent, vendor-held | Months of compounding, self-hosted |
| Governance | None (“context, not enforced”) | ~80 security checks, sandboxing | Vendor-managed policies | Alignment by architecture — code-enforced |
| Execution | Free-form | Opt-in pipeline skills | Workflow automation | Phase-gated with verified transitions |
| Security | LLM-based tool safety | Behavioral sandboxing | Vendor-managed sandbox | Credential injection — never in LLM memory |
| Provider | Anthropic only | 12+ providers with fallback | Vendor model only | Single domain model — any provider, any channel |
| Knowledge Maintenance | None | Opt-in dreaming (thresholded) | None | Multi-trigger composable phases with programmatic constraints |
| Interface | CLI / Desktop | 15+ channels | Web dashboard | Messaging-first + dashboard, extensible |
| Knowledge Sharing | None | Skill marketplace (code only) | None | Cross-agent federation with provenance |
Work flows through a gate graph — each phase has a bounded resource envelope, and every transition is verified against external state. Not self-assessed. The agent earns confidence through programmatic proof, not assumption.
Email, webhooks, and data feeds processed into structured knowledge while you sleep. Each signal is triaged — current truths update memory, historical facts go to the archive, noise is discarded. Your agent learns from your world without being asked.
Behavioral guidance through an editable constitution, enforced by programmatic gating that the model cannot circumvent. Tool access, credential scope, phase transitions, and admission limits — all verified by code, not self-assessed.
Credentials live in a separate process — the LLM never sees them. Symbolic references, scoped bindings, and operator-controlled unlock. Your API keys, passwords, and tokens are architecturally invisible to the model.
A single canonical conversation rendered into any LLM provider and any messaging channel on demand. Fail over between models without losing history. Switch channels without losing context. The agent's mind lives in the substrate, not in any vendor or platform.
Independent agents exchange structured knowledge with cryptographic provenance and operator-controlled trust policies. What one agent learns compounds across the network — no central authority, no platform lock-in. Each deployment owns its own memory.
All knowledge modeled as navigable trees — memory, archives, files, structured data. Three retrieval signals merged per query: keyword, semantic, and graph traversal. The agent surgically locates what it needs, not just what matches.
Composable maintenance phases triggered by idle time, knowledge accumulation, staleness, or post-task hooks. Forced windows ensure health under continuous workloads. Each phase under programmatic constraints. The agent maintains its own mind.
Add integrations to deepen what your agent knows. Add engines to parallelize what it does. Context budgets adapt automatically to workload type — lean for conversation, deep for execution. One core, any number of connections.
Intelligent model routing picks the right model for each workload — deep reasoning where it matters, speed where it doesn't. Multi-layer compaction and caching keep your context sharp and costs low as conversations grow.
We're onboarding first users with white-glove setup.
Early access phase. Limited spots.
We'll reach out to discuss your deployment.