DocsQuick Start
Quick Start
Get your first agent running in under 60 seconds.
Prerequisites
- Python 3.10 or higher
- 6GB disk space (for the AI model)
- 8GB RAM minimum (16GB recommended)
- macOS, Linux, or Windows (WSL)
1
Install tp-ops
Install the TernaryPhysics CLI using pip:
Terminal
$ pip install tp-opsThis installs the tp-ops command-line tool.
2
SSH to your resource
Connect to the server, VM, or machine where you want to drop an agent:
Terminal
$ ssh my-server.example.comAgents run on the resource they monitor. This is what makes them so effective — they have direct access to logs, metrics, and system state.
3
Drop an agent
Run tp-ops drop on the resource. The agent auto-detects what type of resource it's running on:
Terminal
$ tp-ops drop
Detecting resource type...
✓ Detected: Kubernetes cluster (kubectl configured)
✓ Agent type: k8s-agent
Downloading TernaryPhysics-7B model...
████████████████████████████████ 100% (4.2GB)
Checking network requirements...
✓ Mesh discovery port - open
✓ Mesh messaging port - open
Agent dropped successfully!
Name: prod-cluster
Type: k8s-agent
Status: activeFirst run: The TernaryPhysics-7B model (~4GB) downloads once per resource. Subsequent runs start instantly.
4
Talk to your agent
Start a conversation with your agent using natural language:
Interactive session
$ tp-ops ask prod-cluster
prod-cluster > What's the cluster health?
Checking cluster health...
Cluster: prod-cluster
Nodes: 6/6 healthy
Pods: 142 running, 3 pending, 0 failed
CPU: 45% average utilization
Memory: 62% average utilization
No critical issues detected.
prod-cluster > Why are 3 pods pending?
Investigating pending pods...
3 pods pending due to resource constraints:
├─ payment-api-7d9f8 (needs 2Gi memory, node has 1.5Gi)
├─ worker-batch-3k2j (needs GPU, no GPU nodes available)
└─ cache-warm-x92m (node selector: zone=us-east-1a, no nodes match)
Recommendation: Scale node pool or adjust resource requests.
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