---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
title: SGLang for Agentic Workloads
subtitle: Priority scheduling and KV cache eviction policies for multi-turn agentic serving
---

# SGLang for Agentic Workloads

This guide covers SGLang-specific configuration for agentic serving with Dynamo. It explains which SGLang engine flags to enable and how Dynamo's [agent hints](../../components/frontend/nvext.md#agent-hints) map to SGLang behavior.

## Overview

Agentic workloads (tool-calling loops, multi-turn reasoning, code generation pipelines) have different performance characteristics than batch inference:

- **Prefix-heavy**: Successive turns share a growing conversation prefix. KV cache reuse is critical for low TTFT.
- **Priority-sensitive**: Some requests (user-facing agent turns) matter more than background tasks.
- **Long-lived**: Conversations span minutes to hours. Cache eviction under memory pressure can destroy accumulated KV state.

Dynamo's agent hints give the router per-request metadata. SGLang's engine flags control how that metadata affects scheduling and eviction on the worker.

## SGLang Engine Flags

### Priority Scheduling

Enable priority-based scheduling so the engine respects the `priority` value from `nvext.agent_hints.priority`:

```bash
python -m dynamo.sglang \
  --model-path <model> \
  --enable-priority-scheduling \
  ...
```

| Flag | Description |
|------|-------------|
| `--enable-priority-scheduling` | Enables priority-based request scheduling instead of FCFS. |

When priority scheduling is enabled, the engine uses the `priority` field from `nvext.agent_hints` to order requests in its internal queue. Requests with higher effective priority are scheduled before lower-priority ones. Ties are broken by arrival time.

### Priority-Based KV Cache Eviction

By default, SGLang evicts radix tree nodes using LRU. You can switch to priority-based eviction so that low-priority cache entries are evicted before high-priority ones:

```bash
python -m dynamo.sglang \
  --model-path <model> \
  --radix-eviction-policy priority \
  ...
```

| Flag | Values | Default | Description |
|------|--------|---------|-------------|
| `--radix-eviction-policy` | `lru`, `priority` | `lru` | Eviction strategy for the GPU radix cache. `priority` uses a heap ordered by the request's priority value. |

This does **not** require HiCache. It controls GPU-only radix tree eviction. When the GPU KV cache is full:

- **`lru`**: Evicts the least recently used leaf nodes first.
- **`priority`**: Evicts lowest-priority leaf nodes first. Nodes with equal priority fall back to LRU ordering.

#### Interaction with HiCache

When both `--radix-eviction-policy priority` and `--enable-hierarchical-cache` are enabled, priority affects eviction at both tiers:

| Event | Behavior |
|-------|----------|
| **GPU full** | Low-priority nodes are evicted (demoted to host) first. With `write_through`, all nodes survive on host -- priority only affects demotion order. |
| **Host full** | Low-priority nodes are deleted from host first. High-priority nodes survive longer. Pinned nodes are skipped entirely. |

The practical impact depends on your write policy. With `write_through`, GPU eviction is just a demotion -- the real deletion happens at host eviction, which is where priority ordering matters most.

## How Agent Hints Map to SGLang

Dynamo's `nvext.agent_hints` fields are consumed by the router and forwarded to SGLang workers. Here is how each hint interacts with the SGLang engine:

| Agent Hint | Router Behavior | SGLang Engine Behavior |
|------------|----------------|----------------------|
| `priority` | Raises router queue priority when `--router-queue-threshold` is set. | Queue ordering when `--enable-priority-scheduling` is set. Also affects radix cache eviction order when `--radix-eviction-policy priority` is set. |
| `osl` | Output block tracking for routing decisions (requires `--router-track-output-blocks`) | No direct engine effect. |
| `speculative_prefill` | After response completes, sends a `max_tokens=1` prefill to warm the KV cache for the predicted next turn. | SGLang processes the prefill request normally, populating the radix cache. |

### Example: Agentic Request with Hints

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")

response = client.chat.completions.create(
    model="Qwen/Qwen3-14B-FP8",
    messages=[
        {"role": "system", "content": "You are a coding assistant."},
        {"role": "user", "content": "Write a Python function to parse CSV files."},
    ],
    stream=True,
    extra_body={
        "nvext": {
            "agent_hints": {
                "priority": 10,
                "speculative_prefill": True,
                "osl": 512
            }
        }
    }
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
```

## See Also

- **[NVIDIA Request Extensions (nvext)](../../components/frontend/nvext.md)**: Full `nvext` field reference including agent hints
- **[Configuration and Tuning](../../components/router/router-configuration.md)**: Router configuration and CLI arguments
- **[SGLang HiCache](../../integrations/sglang-hicache.md)**: Enabling hierarchical KV cache
