diff --git a/nanobot/config/schema.py b/nanobot/config/schema.py
index 674f0a931..4a9bd97a2 100644
--- a/nanobot/config/schema.py
+++ b/nanobot/config/schema.py
@@ -1,7 +1,7 @@
 """Configuration schema using Pydantic."""
 
 from pathlib import Path
-from typing import Literal
+from typing import Any, Literal
 
 from pydantic import AliasChoices, BaseModel, ConfigDict, Field
 from pydantic.alias_generators import to_camel
@@ -116,6 +116,7 @@ class ProviderConfig(Base):
     api_key: str | None = None
     api_base: str | None = None
     extra_headers: dict[str, str] | None = None  # Custom headers (e.g. APP-Code for AiHubMix)
+    extra_body: dict[str, Any] | None = None  # Extra fields merged into every request body
 
 
 class ProvidersConfig(Base):
diff --git a/nanobot/providers/factory.py b/nanobot/providers/factory.py
index f4ec6a4c2..5f97b04a3 100644
--- a/nanobot/providers/factory.py
+++ b/nanobot/providers/factory.py
@@ -69,6 +69,7 @@ def make_provider(config: Config) -> LLMProvider:
             default_model=model,
             extra_headers=p.extra_headers if p else None,
             spec=spec,
+            extra_body=p.extra_body if p else None,
         )
 
     defaults = config.agents.defaults
diff --git a/nanobot/providers/openai_compat_provider.py b/nanobot/providers/openai_compat_provider.py
index afd6a6b37..363a2e142 100644
--- a/nanobot/providers/openai_compat_provider.py
+++ b/nanobot/providers/openai_compat_provider.py
@@ -232,6 +232,25 @@ def _responses_circuit_key(
     return f"{model_name}:{effort}"
 
 
+def _deep_merge(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any]:
+    """Recursively merge *override* into *base*, returning a new dict.
+
+    Nested dicts are merged key-by-key; all other types in *override*
+    replace the corresponding key in *base*.
+    """
+    merged = dict(base)
+    for key, value in override.items():
+        if (
+            key in merged
+            and isinstance(merged[key], dict)
+            and isinstance(value, dict)
+        ):
+            merged[key] = _deep_merge(merged[key], value)
+        else:
+            merged[key] = value
+    return merged
+
+
 class OpenAICompatProvider(LLMProvider):
     """Unified provider for all OpenAI-compatible APIs.
 
@@ -246,11 +265,13 @@ class OpenAICompatProvider(LLMProvider):
         default_model: str = "gpt-4o",
         extra_headers: dict[str, str] | None = None,
         spec: ProviderSpec | None = None,
+        extra_body: dict[str, Any] | None = None,
     ):
         super().__init__(api_key, api_base)
         self.default_model = default_model
         self.extra_headers = extra_headers or {}
         self._spec = spec
+        self._extra_body = extra_body or {}
 
         if api_key and spec and spec.env_key:
             self._setup_env(api_key, api_base)
@@ -597,6 +618,15 @@ class OpenAICompatProvider(LLMProvider):
                 if msg.get("role") == "assistant" and "reasoning_content" not in msg:
                     msg["reasoning_content"] = ""
 
+        # Merge user-configured extra_body last so it can override or
+        # extend provider-specific defaults (e.g. chat_template_kwargs,
+        # guided_json, repetition_penalty).  Uses recursive merge so
+        # nested dicts like {"chat_template_kwargs": {"enable_thinking": false}}
+        # do not clobber sibling keys already set by thinking-style logic.
+        if self._extra_body:
+            existing = kwargs.get("extra_body", {})
+            kwargs["extra_body"] = _deep_merge(existing, self._extra_body)
+
         return kwargs
 
     def _should_use_responses_api(
diff --git a/tests/providers/test_extra_body_config.py b/tests/providers/test_extra_body_config.py
new file mode 100644
index 000000000..08ca33408
--- /dev/null
+++ b/tests/providers/test_extra_body_config.py
@@ -0,0 +1,214 @@
+"""Tests for provider extra_body config injection into request payloads."""
+
+from __future__ import annotations
+
+from typing import Any
+from unittest.mock import MagicMock
+
+from nanobot.providers.openai_compat_provider import (
+    OpenAICompatProvider,
+    _deep_merge,
+)
+
+# ---------------------------------------------------------------------------
+# _deep_merge unit tests
+# ---------------------------------------------------------------------------
+
+
+class TestDeepMerge:
+    """Verify recursive dict merge semantics."""
+
+    def test_flat_merge(self) -> None:
+        assert _deep_merge({"a": 1}, {"b": 2}) == {"a": 1, "b": 2}
+
+    def test_override_scalar(self) -> None:
+        assert _deep_merge({"a": 1}, {"a": 2}) == {"a": 2}
+
+    def test_nested_merge(self) -> None:
+        base = {"outer": {"a": 1, "b": 2}}
+        override = {"outer": {"b": 3, "c": 4}}
+        assert _deep_merge(base, override) == {"outer": {"a": 1, "b": 3, "c": 4}}
+
+    def test_deeply_nested(self) -> None:
+        base = {"l1": {"l2": {"a": 1}}}
+        override = {"l1": {"l2": {"b": 2}}}
+        assert _deep_merge(base, override) == {"l1": {"l2": {"a": 1, "b": 2}}}
+
+    def test_override_replaces_non_dict_with_dict(self) -> None:
+        assert _deep_merge({"a": 1}, {"a": {"nested": True}}) == {"a": {"nested": True}}
+
+    def test_override_replaces_dict_with_scalar(self) -> None:
+        assert _deep_merge({"a": {"nested": True}}, {"a": "flat"}) == {"a": "flat"}
+
+    def test_empty_base(self) -> None:
+        assert _deep_merge({}, {"a": 1}) == {"a": 1}
+
+    def test_empty_override(self) -> None:
+        assert _deep_merge({"a": 1}, {}) == {"a": 1}
+
+    def test_does_not_mutate_inputs(self) -> None:
+        base = {"a": {"x": 1}}
+        override = {"a": {"y": 2}}
+        _deep_merge(base, override)
+        assert base == {"a": {"x": 1}}
+        assert override == {"a": {"y": 2}}
+
+
+# ---------------------------------------------------------------------------
+# Provider construction
+# ---------------------------------------------------------------------------
+
+
+class TestExtraBodyInit:
+    """Verify the provider stores extra_body from config."""
+
+    def test_default_is_empty(self) -> None:
+        provider = OpenAICompatProvider(api_key="test")
+        assert provider._extra_body == {}
+
+    def test_none_becomes_empty(self) -> None:
+        provider = OpenAICompatProvider(api_key="test", extra_body=None)
+        assert provider._extra_body == {}
+
+    def test_dict_stored(self) -> None:
+        body = {"chat_template_kwargs": {"enable_thinking": False}}
+        provider = OpenAICompatProvider(api_key="test", extra_body=body)
+        assert provider._extra_body == body
+
+
+# ---------------------------------------------------------------------------
+# _build_kwargs integration
+# ---------------------------------------------------------------------------
+
+
+def _make_provider(extra_body: dict[str, Any] | None = None) -> OpenAICompatProvider:
+    return OpenAICompatProvider(
+        api_key="test-key",
+        default_model="test-model",
+        extra_body=extra_body,
+    )
+
+
+def _simple_messages() -> list[dict[str, Any]]:
+    return [{"role": "user", "content": "hello"}]
+
+
+class TestBuildKwargsExtraBody:
+    """Verify extra_body flows into _build_kwargs output."""
+
+    def test_no_extra_body_no_key(self) -> None:
+        provider = _make_provider()
+        kwargs = provider._build_kwargs(
+            messages=_simple_messages(),
+            tools=None, model=None, max_tokens=100,
+            temperature=0.1, reasoning_effort=None, tool_choice=None,
+        )
+        assert "extra_body" not in kwargs
+
+    def test_extra_body_injected(self) -> None:
+        provider = _make_provider({"chat_template_kwargs": {"enable_thinking": False}})
+        kwargs = provider._build_kwargs(
+            messages=_simple_messages(),
+            tools=None, model=None, max_tokens=100,
+            temperature=0.1, reasoning_effort=None, tool_choice=None,
+        )
+        assert kwargs["extra_body"] == {
+            "chat_template_kwargs": {"enable_thinking": False},
+        }
+
+    def test_extra_body_merges_with_thinking(self) -> None:
+        """Config extra_body should merge with (and override) thinking params."""
+        from nanobot.providers.registry import ProviderSpec
+
+        spec = MagicMock(spec=ProviderSpec)
+        spec.thinking_style = "deepseek"
+        spec.supports_prompt_caching = False
+        spec.strip_model_prefix = False
+        spec.model_overrides = []
+        spec.name = "custom"
+        spec.supports_max_completion_tokens = False
+        spec.env_key = None
+        spec.default_api_base = None
+        spec.is_local = True
+        spec.detect_by_base_keyword = None
+
+        provider = OpenAICompatProvider(
+            api_key="test",
+            default_model="deepseek-v3",
+            spec=spec,
+            extra_body={"custom_param": "value"},
+        )
+        kwargs = provider._build_kwargs(
+            messages=_simple_messages(),
+            tools=None, model=None, max_tokens=100,
+            temperature=0.1, reasoning_effort="high", tool_choice=None,
+        )
+        body = kwargs.get("extra_body", {})
+        # Config param should be present
+        assert body.get("custom_param") == "value"
+
+    def test_nested_extra_body_does_not_clobber_siblings(self) -> None:
+        """Nested dict merge should preserve sibling keys."""
+        provider = _make_provider({
+            "chat_template_kwargs": {"enable_thinking": False},
+        })
+        # Simulate internal code having set a sibling key
+        # by manually calling _build_kwargs — the internal logic
+        # doesn't set chat_template_kwargs, so we test the merge path
+        # by having extra_body itself contain nested keys
+        kwargs = provider._build_kwargs(
+            messages=_simple_messages(),
+            tools=None, model=None, max_tokens=100,
+            temperature=0.1, reasoning_effort=None, tool_choice=None,
+        )
+        assert kwargs["extra_body"]["chat_template_kwargs"]["enable_thinking"] is False
+
+    def test_guided_json_injection(self) -> None:
+        """Real-world use case: vLLM guided decoding."""
+        schema = {"type": "object", "properties": {"name": {"type": "string"}}}
+        provider = _make_provider({"guided_json": schema})
+        kwargs = provider._build_kwargs(
+            messages=_simple_messages(),
+            tools=None, model=None, max_tokens=100,
+            temperature=0.1, reasoning_effort=None, tool_choice=None,
+        )
+        assert kwargs["extra_body"]["guided_json"] == schema
+
+    def test_repetition_penalty_injection(self) -> None:
+        """Real-world use case: local model sampling param."""
+        provider = _make_provider({"repetition_penalty": 1.15})
+        kwargs = provider._build_kwargs(
+            messages=_simple_messages(),
+            tools=None, model=None, max_tokens=100,
+            temperature=0.1, reasoning_effort=None, tool_choice=None,
+        )
+        assert kwargs["extra_body"]["repetition_penalty"] == 1.15
+
+
+# ---------------------------------------------------------------------------
+# Schema validation
+# ---------------------------------------------------------------------------
+
+
+class TestSchemaConfig:
+    """Verify ProviderConfig accepts extra_body."""
+
+    def test_default_is_none(self) -> None:
+        from nanobot.config.schema import ProviderConfig
+
+        config = ProviderConfig()
+        assert config.extra_body is None
+
+    def test_accepts_dict(self) -> None:
+        from nanobot.config.schema import ProviderConfig
+
+        config = ProviderConfig(extra_body={"guided_json": {"type": "object"}})
+        assert config.extra_body == {"guided_json": {"type": "object"}}
+
+    def test_nested_dict(self) -> None:
+        from nanobot.config.schema import ProviderConfig
+
+        config = ProviderConfig(
+            extra_body={"chat_template_kwargs": {"enable_thinking": False}}
+        )
+        assert config.extra_body["chat_template_kwargs"]["enable_thinking"] is False