Engineering agents thatunderstand and improveyour GitHub repos.
Our first private-beta cohort is full—we're all-in with those teams. Want Orchestrix in your inbox? Drop your email for release notes, the next invite window, and notes from the build. Low volume, no filler, unsubscribe anytime.
Trusted by early AI builders
5-phase
Agent cognition loop
DAG
Parallel execution
pgvector
Architectural memory
GitHub
Native integration
Product news & next access
The beta is closed. The conversation isn’t.
We aren't adding new beta participants right now—the first cohort is full and we're focused on shipping with them. Still curious? Leave your email and we'll send product updates, roadmap highlights, and a heads-up when we widen access. We read what you write; we don't send noise.
How it works
From repo to shipped improvements in three steps.
Connect your repositories
Install the GitHub App and link repos to your workspace. Each repository gets isolated memory, embeddings, and execution context.
Define an engineering objective
Describe what you want — a feature, a refactor, or tech debt cleanup. The agent generates a structured execution plan with risk assessment.
Watch the agent ship
The agent runs perception, planning, execution, reflection, and memory update phases. Stream logs in real time, inspect the execution graph, and review pull requests.
Product
Your AI engineering control center
Real-time execution streams, knowledge graphs, and metrics — all unified in one workspace per repository.
Capabilities
Built for serious engineering.
Parallel execution engine
DAG-based scheduler runs independent steps concurrently with per-skill timeouts, retries, and circuit breakers.
Architectural memory
Vector-backed persistent memory with clustering, pattern emergence, decision confidence scoring, and temporal decay.
Evaluation & metrics
Per-run evaluation: success rate by category, reflection accuracy, skill reliability heatmap, and token usage analysis.
Continuous learning
Per-repo embeddings evolve with every run. Persistent knowledge graph captures decisions, patterns, and tradeoffs.
Deterministic skills
Typed, testable skill commands: search repo, read/write files, run tests, create PRs. Each skill is independently verifiable.
Cost-aware planning
Usage-based billing with token quotas, execution priority levels, and real-time cost visibility per agent run.
Under the hood
Engineering-grade stack.
No black box. Every execution step is traceable, every skill is testable, every decision is persisted.
Want the important stuff, not the noise?
Same product—fewer emails. Jump to the form for release notes, roadmap, and the next time we open the doors.