Orchestrix
Open Beta

Engineering agents thatunderstand and improveyour GitHub repos.

Pre-register for early access — there is no cap on signups. The first beta cohort is 20 invited accounts, chosen manually from applicants.

Beta seats filled: 2 of 20
Filled seats
2/20

Trusted by early AI builders

Join the BetaSee how it works
Orchestrix
Runs today
12
Success rate
94%
PRs opened
8
Memory entries
2.4k
●perceptionScanning repo structure and recent diffs
●planningGenerated 4-step execution plan
◐executionRunning step 2/4: write_file → src/api/health.ts
○reflectionAwaiting execution completion
2/4 steps

5-phase

Agent cognition loop

DAG

Parallel execution

pgvector

Architectural memory

GitHub

Native integration

Early access

Join the beta

Anyone can join the waitlist. Invites are limited: we hand-pick up to 20 participants for the first beta from all pre-registrations.

Beta seats filled: 2 of 20
Filled seats
2/20

How it works

From repo to shipped improvements in three steps.

01

Connect your repositories

Install the GitHub App and link repos to your workspace. Each repository gets isolated memory, embeddings, and execution context.

02

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.

03

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.

acme/backend — Agent Console
Agent Console
Execution Graph
Knowledge Map
Pull Requests
perception
→planning
→execution
→reflection
→memory
✓ perception · Loaded 847 embeddings for acme/backend
✓ planning · Generated 4-step plan · risk: low · parallel groups: 2
▸ execution · step 2/4 · write_file → src/api/health.ts
○ execution · step 3/4 · run_tests (awaiting)
○ reflection · pending
run_3f8a · main · 2/4 steps1,247 tokens · 3.2s elapsed
Join the Beta →

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.

Multi-agent reasoning
Perception → Planning → Execution → Reflection → Memory
Deterministic skills
Typed commands: search, read, write, test, create PR
Cost-aware planning
Token quotas, usage metering, priority execution levels
Persistent memory graph
Nodes, edges, decay, evolution history, per-repo namespace

Ready to automate your engineering?

Connect your GitHub repositories, define an objective, and let the agent plan, execute, and ship.

Join the Beta