Castra

I got tired of AI agents forgetting what they were doing the moment their context window filled. The current industry solution is to write massively bloated agent harnesses full of defensive spaghetti just to stop models from drifting.The problem is treating chat history as project state. A conversation is not a ledger.Castra is a compiled Go binary that strips orchestration rights from the LLM. State lives in an encrypted, local SQLite database (castra.db). The LLM is just a stateless executor — it reads the DB, executes a highly constrained task, and the result is written back subject to rigid state-machine rules.What it actually does: - 7-Role RBAC: Hard jurisdictional boundaries (Architect plans, Engineer builds, QA tests). - Dual-Gate Approval: A task cannot reach done without explicit, sequential approval from both a QA agent and a Security agent. No self-approving code. - Cryptographic Audit Chain: Every action is logged into a SHA-256 hash-linked, Ed25519-signed ledger. - Multi-Vendor: Works with Claude, Copilot, Gemini, etc. via a standard AGENTS.md protocol. Anything that supports AGENTS.md and can run terminal commands.Proof of Work: I built this by hand up to v1.3.0. Then, I turned Castra on itself. The agents governed by this exact CLI took over and built the architecture up to v3.1.2—including the cryptographic log chain itself. The proof is in castra-log.jsonl in the repo.If you are running multi-agent workflows and hitting the context amnesia wall, stop trying to prompt-engineer your way out of it. Fix the state machine.