diff --git a/examples/README.md b/examples/README.md
index 728cc41..9b98d24 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -25,6 +25,7 @@ One example per supported provider. All follow the same three-agent (architect /
 |---------|----------|---------|
 | [`providers/ollama`](providers/ollama.ts) | Ollama (local) + Claude | `ANTHROPIC_API_KEY` |
 | [`providers/gemma4-local`](providers/gemma4-local.ts) | Gemma 4 via Ollama (100% local) | — |
+| [`providers/local-quantized`](providers/local-quantized.ts) | Quantized MoE on vLLM / llama-server with tuned sampling (`topK` / `minP` / `frequencyPenalty` / `extraBody.repetition_penalty`) | — |
 | [`providers/copilot`](providers/copilot.ts) | GitHub Copilot (GPT-4o + Claude) | `GITHUB_TOKEN` |
 | [`providers/azure-openai`](providers/azure-openai.ts) | Azure OpenAI | `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT` (+ optional `AZURE_OPENAI_API_VERSION`, `AZURE_OPENAI_DEPLOYMENT`) |
 | [`providers/grok`](providers/grok.ts) | xAI Grok | `XAI_API_KEY` |
diff --git a/examples/providers/local-quantized.ts b/examples/providers/local-quantized.ts
new file mode 100644
index 0000000..301ec91
--- /dev/null
+++ b/examples/providers/local-quantized.ts
@@ -0,0 +1,121 @@
+/**
+ * Local Quantized Model with Tuned Sampling (vLLM / llama-server)
+ *
+ * Demonstrates the sampling parameters exposed in v1.3+ — `topK`, `minP`,
+ * `frequencyPenalty`, `presencePenalty`, and the `extraBody` escape hatch —
+ * applied to a quantized MoE model served by an OpenAI-compatible local
+ * inference server.
+ *
+ * Why this example exists:
+ *   Highly quantized MoE models (e.g. Qwen2.5-MoE @ Q4, DeepSeek-MoE @ Q4)
+ *   on consumer hardware tend to fall into repetition loops or hallucinate
+ *   tool-call schemas when sampling defaults are too permissive. The knobs
+ *   below clamp the distribution and discourage repetition. Cloud OpenAI
+ *   users do not need any of these — defaults are tuned for full-precision
+ *   models. This is a local-quantized concern.
+ *
+ * Run:
+ *   no_proxy=localhost npx tsx examples/providers/local-quantized.ts
+ *
+ * Prerequisites — pick one OpenAI-compatible local server:
+ *   • vLLM:        `vllm serve Qwen/Qwen2.5-7B-Instruct-AWQ --port 8000`
+ *   • llama-server: `llama-server -m model.gguf --port 8080`
+ *   • LM Studio, Ollama, etc. — any server that accepts OpenAI chat
+ *     completions on a local port.
+ *   Then update LOCAL_BASE_URL and LOCAL_MODEL below.
+ *
+ * Provider compatibility note (per `AgentConfig` JSDoc):
+ *   • topP                                 — universal
+ *   • topK                                 — Anthropic + OpenAI-compatible local
+ *   • minP                                 — OpenAI-compatible local only
+ *   • frequencyPenalty / presencePenalty  — OpenAI track only
+ *   • extraBody                            — adapter-specific escape hatch
+ *
+ * Cloud OpenAI rejects `top_k` and `min_p`; this example is not portable to
+ * `api.openai.com`.
+ */
+
+import { OpenMultiAgent } from '../../src/index.js'
+import type { AgentConfig } from '../../src/types.js'
+
+// ---------------------------------------------------------------------------
+// Configuration — adjust to your local server
+// ---------------------------------------------------------------------------
+
+const LOCAL_BASE_URL = 'http://localhost:8000/v1'    // vLLM default; llama-server: 8080, LM Studio: 1234
+const LOCAL_MODEL = 'Qwen/Qwen2.5-7B-Instruct-AWQ'   // any model your local server has loaded
+
+// ---------------------------------------------------------------------------
+// Agent — each sampling knob is annotated with the loop / hallucination
+// failure mode it counters on quantized models.
+// ---------------------------------------------------------------------------
+
+const assistant: AgentConfig = {
+  name: 'assistant',
+  model: LOCAL_MODEL,
+  provider: 'openai',
+  baseURL: LOCAL_BASE_URL,
+  apiKey: 'local',  // placeholder — local servers ignore this, but the OpenAI SDK requires a non-empty value
+
+  // Standard sampling — sane defaults for instruction-tuned quantized models.
+  temperature: 0.7,
+  maxTokens: 1024,
+
+  // Nucleus + top-k jointly clamp the candidate pool. On Q4 quants the raw
+  // distribution often has long, noisy tails that produce off-topic tokens.
+  topP: 0.95,
+  topK: 40,
+
+  // Min-p drops any token whose probability is below `minP * max_prob`. Cuts
+  // the tail more aggressively than top-k alone. vLLM / llama-server expose
+  // this; cloud OpenAI does not.
+  minP: 0.05,
+
+  // Frequency penalty discourages repeated tokens — the most common failure
+  // mode is the model getting stuck emitting the same tool_call schema or
+  // sentence fragment over and over. Value range is -2..2; 0.3 is a mild
+  // nudge that does not noticeably hurt fluency.
+  frequencyPenalty: 0.3,
+
+  // Presence penalty is usually 0 unless you specifically want to push the
+  // model toward novel topics. Left here to make the contract explicit.
+  presencePenalty: 0,
+
+  // extraBody: adapter-specific escape hatch. vLLM and llama-server accept a
+  // `repetition_penalty` parameter that is not in the OpenAI spec — it
+  // multiplies logits of recently emitted tokens. Slightly redundant with
+  // frequencyPenalty but operates on logits instead of token counts, so the
+  // two compose. Anything you put here is merged into the request body and
+  // can override standard sampling fields, but cannot override transport
+  // fields (`model`, `messages`, `tools`, `stream`).
+  extraBody: {
+    repetition_penalty: 1.05,
+  },
+
+  systemPrompt: `You are a concise assistant. Answer in one short paragraph.
+Do not repeat yourself.`,
+
+  maxTurns: 4,
+}
+
+// ---------------------------------------------------------------------------
+// Run
+// ---------------------------------------------------------------------------
+
+const orchestrator = new OpenMultiAgent({
+  defaultModel: LOCAL_MODEL,
+  maxConcurrency: 1,  // most local servers serve one request at a time
+})
+
+console.log(`Calling ${LOCAL_MODEL} at ${LOCAL_BASE_URL}`)
+console.log('Sampling: topP=0.95 topK=40 minP=0.05 freqPenalty=0.3 + repetition_penalty=1.05\n')
+
+const result = await orchestrator.runAgent(
+  assistant,
+  'In one paragraph, explain what min-p sampling does and when it helps.',
+)
+
+console.log('--- response ---')
+console.log(result.output)
+console.log()
+console.log(`tokens: ${result.tokenUsage.input_tokens} in / ${result.tokenUsage.output_tokens} out`)