Centurion
Resource-aware orchestration for AI agent fleets
Spawn, schedule, and manage 100+ AI agents with hardware-aware admission control. The infrastructure layer that Claude Code doesn't have.
Three-layer architecture
The inner loop built into Claude Code. One agent, one task, sequential execution. No parallelism, no persistent state.
Spawns background agents via the Agent tool. Multiple tasks run concurrently, but with zero resource awareness. No scheduling, no backpressure. Can OOM-kill the system.
Hardware-aware scheduling, admission control with memory-pressure detection, DAG-based task decomposition, real-time event streaming, and auto-scaling. Safe, structured parallel execution at machine scale.
How Centurion works
Centurion sits between your orchestrator and the agents it spawns. Every agent spawn request passes through hardware-aware admission control — checking RAM, CPU, and memory pressure before allowing execution. The diagram below shows the full request flow.
Multiple ways to connect
Centurion adapts to your stack. Run it as a standalone CLI, embed it as a library, or deploy it behind its REST API. Each integration mode exposes the same scheduling and admission-control guarantees, so you get identical resource safety whether you are scripting locally or orchestrating from a CI pipeline.
Why Centurion
Claude Code has zero resource management. No RAM awareness, no parallel agent limits, no memory pressure detection. Spawning 20+ subagents on a constrained machine can OOM-kill the entire system. A community request for maxParallelAgents (#15487) was closed NOT_PLANNED — Anthropic views resource scheduling as outside their application boundary.
Centurion fills this permanent gap. It operates at the infrastructure layer: probing hardware, enforcing admission control before every agent spawn, detecting memory pressure in real time, and scaling fleets automatically. It is model-independent — the same scheduler works for Claude, GPT, Gemini, or shell scripts.
Success Stories
Real Projects, Verified Metrics
Every number below comes from actual project history — task counts, test results, and commit logs.
OpenClaw Bug Fixes
An open-source Rust project had a backlog of bugs. Centurion decomposed them into a task DAG with parallel engineering tasks, each gated by QA. Progressive ramp-up scaled from 1 to 4 agents safely.
What Centurion Did
Decomposed the bug backlog into a task DAG — architecture phase for issue triage, parallel engineering tasks for fixes, and QA gates before PR submission. Hardware-aware scheduling ran the full Rust test suite (7,000+ cases) per PR without OOM kills. When memory pressure hit “warn” during concurrent test runs, auto-throttled batch size and resumed once pressure normalized.
Up and running in three steps
From zero to orchestrating AI agents in under a minute.
Install
$ pip install centurion
Installs Centurion with hardware-aware scheduling.
Launch
$ centurion quickstart
Centurion detects your hardware and configures itself automatically.
HARDWARE SUMMARY
Platform: Darwin | CPU: 10 cores | RAM: 32 GB
RECOMMENDED CONFIGURATION
Agent type: claude_cli | Max agents: 20
Centurion is ONLINE [quickstart mode]
Health: http://localhost:8100/health ✓
Use
# Check system status:
curl http://localhost:8100/api/centurion/status
# Or install as a LaunchAgent (auto-restart on boot):
./scripts/install-launchd.sh
Explore the ecosystem
Comparison
How Centurion Compares
Claude Code ships without infrastructure-level process management. Centurion fills that gap.
| Feature | Raw Agentic Loop | Claude Code Subagents | Centurion |
|---|---|---|---|
| Parallel agents | 1 | Unlimited (unmanaged) | 100+ (managed) |
| Memory awareness | ✗ None | ✗ None | ✓ RAM/CPU probing |
| Scheduling | ✗ None | ✗ None | ✓ HW-aware admission |
| Memory safety | N/A | ✗ OOM risk | ✓ Pressure detection |
| Task decomposition | Manual | Manual | ✓ Automatic DAG |
| State persistence | In-memory | ✗ None | ✓ .harness/ + SQLite |
| Cross-project | ✗ No | ✗ No | ✓ Yes |
| Auto-scaling | ✗ No | ✗ No | ✓ Yes (Optio) |
| Broadcasting | ✗ No | ✗ No | ✓ Yes |
| Circuit breaker | ✗ No | ✗ No | ✓ Yes |
Evidence
Don't take our word for it
These are open GitHub issues and discussions filed by users running Claude Code at scale. The memory management gap is well-documented.
Issue #4953
Memory leak — process grows to 120+ GB
Long-running Claude Code sessions accumulate memory without bound, eventually consuming all available RAM on the host machine.
Issue #15487
maxParallelAgents — closed NOT_PLANNED
A request for configurable parallel agent limits was closed as NOT_PLANNED, confirming this is outside Anthropic's intended scope for Claude Code.
Issue #22188
93 GB heap crash
A single Claude Code session crashed after its heap grew to 93 GB, taking down co-located processes and requiring a hard restart.
Issue #21403
16 GB RSS on Linux
On Linux workstations, a single agent session reaches 16 GB RSS, making multi-agent workflows impractical without external resource management.
Position
Infrastructure, not intelligence
Centurion operates at the infrastructure level. It does not modify prompts, intercept API calls, or depend on any specific model provider. When Anthropic closed #15487 as NOT_PLANNED, it drew a clear boundary: Claude Code is a single-agent tool, and resource orchestration is out of scope. Centurion exists on the other side of that boundary — managing processes, memory, scheduling, and state so that any agentic coding tool can run reliably at scale.