> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentkube.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Resource Recommendations
The Resource Recommender feature in Agentkube IDE provides intelligent CPU and memory resource recommendations for your Kubernetes workloads. This built-in feature analyzes historical usage patterns to suggest optimal resource requests and limits, helping you right-size your containers and improve cluster efficiency.
By analyzing usage data collected from Prometheus metrics, the Resource Recommender ensures your resource allocations match actual workload requirements, preventing both over-provisioning and resource contention.
## How to Use
## Prerequisites
To use the Resource Recommender feature, ensure:
* Prometheus is integrated with Agentkube IDE
* Prometheus is scraping cAdvisor metrics for container resource usage
* kube-state-metrics is deployed for Kubernetes object metadata
* Metric retention period covers at least 14 days for optimal recommendations
* Scrape interval is configured appropriately (typically 15-30 seconds)
To access resource recommendations in Agentkube IDE:
1. Navigate to the **Workloads** section
2. Select **Pods** from the workload types
3. Click on the **Action** button for the desired pod
4. Select **Telemetry** from the dropdown menu
5. Navigate to the **Recommendation** tab
6. View CPU and memory recommendations based on historical data
## Recommendation Strategy
Agentkube implements an intelligent recommendation strategy that balances resource efficiency with workload stability.
### CPU Recommendations
**Request Calculation:**
* Based on the **95th percentile** of historical CPU usage
* Allows pods to burst above the request when needed
* No CPU limit is set by default to permit bursting
**Required Metric:** `container_cpu_usage_seconds_total`
### Memory Recommendations
**Request/Limit Calculation:**
* Based on **peak memory usage** over the historical period
* Adds a **15% buffer** to prevent OOM kills
* Sets both request and limit to the same value for predictability
**Required Metric:** `container_memory_working_set_bytes`
## Data Requirements
The Resource Recommender requires **100+ historical datapoints** to generate reliable recommendations. By default, it analyzes **14 days** of historical data to capture regular usage patterns, peak load scenarios, and daily/weekly cycles.
## Metrics Used
The Resource Recommender leverages the following Prometheus metrics for analysis:
### Core Metrics
| Metric | Purpose | Description |
| ------------------------------------ | ------------ | --------------------------------------------------------------------- |
| `container_cpu_usage_seconds_total` | CPU Usage | Total CPU time consumed by containers |
| `container_memory_working_set_bytes` | Memory Usage | Current memory usage including cache but excluding reclaimable memory |
### Supporting Metrics
| Metric | Purpose | Description |
| -------------------------------------------------- | ------------------ | ----------------------------------------------- |
| `kube_pod_container_resource_limits` | Resource Limits | Configured resource limits for containers |
| `kube_pod_container_status_last_terminated_reason` | OOM Detection | Identifies OOMKilled events for memory analysis |
| `kube_replicaset_owner` | Pod Identification | Maps pods to their parent resources |
| `kube_pod_owner` | Pod Identification | Identifies pod controllers |
| `kube_pod_status_phase` | Pod Status | Current phase of pod lifecycle |
## OOM Kill Detection
When Out-Of-Memory (OOM) kill events are detected, the Resource Recommender automatically adjusts recommendations:
* Applies a **25% memory buffer** (instead of 15%)
* Considers historical memory limits that were exceeded
* Helps prevent future OOM kills
**Required Metrics:**
* `kube_pod_container_resource_limits`
* `kube_pod_container_status_last_terminated_reason`