diff --git a/fastapi_startkit/pyproject.toml b/fastapi_startkit/pyproject.toml
index 30c2b96a..679638ce 100644
--- a/fastapi_startkit/pyproject.toml
+++ b/fastapi_startkit/pyproject.toml
@@ -47,6 +47,12 @@ inertia = [
     "markupsafe>=2.0",
 ]
 
+ai = [
+    "anthropic>=0.49.0",
+    "openai>=1.0.0",
+    "google-generativeai>=0.8.0",
+]
+
 [dependency-groups]
 dev = [
     "dumpdie>=1.5.0",
diff --git a/fastapi_startkit/src/fastapi_startkit/ai/__init__.py b/fastapi_startkit/src/fastapi_startkit/ai/__init__.py
new file mode 100644
index 00000000..7cf60a7b
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/ai/__init__.py
@@ -0,0 +1,31 @@
+"""FastAPI Startkit AI module.
+
+Provides a Laravel-inspired declarative API for building AI agents backed
+by Anthropic, OpenAI, or Google provider SDKs.
+"""
+
+from .agent import Agent
+from .config import AIConfig, AnthropicConfig, GoogleConfig, OpenAIConfig
+from .decorators import max_steps, max_tokens, memory, model, provider, timeout, top_p
+from .document import Document
+from .providers.ai_provider import AIProvider
+from .response import AgentResponse, AgentSnapshot
+
+__all__ = [
+    "Agent",
+    "AgentResponse",
+    "AgentSnapshot",
+    "AIConfig",
+    "AIProvider",
+    "AnthropicConfig",
+    "Document",
+    "GoogleConfig",
+    "OpenAIConfig",
+    "max_steps",
+    "max_tokens",
+    "memory",
+    "model",
+    "provider",
+    "timeout",
+    "top_p",
+]
diff --git a/fastapi_startkit/src/fastapi_startkit/ai/agent.py b/fastapi_startkit/src/fastapi_startkit/ai/agent.py
new file mode 100644
index 00000000..05a7c664
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/ai/agent.py
@@ -0,0 +1,498 @@
+"""Agent base class — subclass this and apply decorators to build an AI agent."""
+
+from __future__ import annotations
+
+import fnmatch
+from typing import Any, Callable, Iterator, Optional, Type
+
+from .document import Document
+from .response import AgentResponse, AgentSnapshot
+
+
+class Agent:
+    """
+    Base class for all agents. Subclass this and override lifecycle methods.
+
+    Class-level configuration (set via decorators or subclass attributes)::
+
+        _provider       = "anthropic"   # LLM provider
+        _model          = ""            # model ID (empty = provider default)
+        _max_steps      = 10            # max agentic loop iterations
+        _max_tokens     = 4096          # max output tokens
+        _timeout        = 30.0          # request timeout in seconds
+        _top_p          = 1.0           # top-p nucleus sampling
+        _memory_backend = ""            # memory backend name (reserved)
+    """
+
+    _provider: str = "anthropic"
+    _model: str = ""
+    _max_steps: int = 10
+    _max_tokens: int = 4096
+    _timeout: float = 30.0
+    _top_p: float = 1.0
+    _memory_backend: str = ""
+
+    _DEFAULT_MODELS: dict[str, str] = {
+        "anthropic": "claude-sonnet-4-6",
+        "openai": "gpt-4o",
+        "google": "gemini-2.0-flash",
+    }
+
+    def __init__(self):
+        self._fakes: dict[str, AgentResponse | AgentSnapshot] = {}
+        self._call_log: list[dict] = []
+
+    # ── Lifecycle — override in subclasses ──────────────────────────────────
+
+    def messages(self) -> list[dict]:
+        """Return initial messages / few-shot examples."""
+        return []
+
+    def schema(self) -> Optional[Type]:
+        """Return a Pydantic model class for structured output, or None for plain text."""
+        return None
+
+    def tools(self) -> list[Callable]:
+        """Return a list of callable tools the agent may invoke."""
+        return []
+
+    def middleware(self) -> list[Callable]:
+        """Return middleware callables that wrap each LLM request."""
+        return []
+
+    def provider_options(self) -> dict:
+        """Return provider-specific options keyed by provider name."""
+        return {}
+
+    def before(self, message: str) -> str:
+        """Called before the message is sent. Return the (possibly modified) message."""
+        return message
+
+    def after(self, response: AgentResponse) -> AgentResponse:
+        """Called after the LLM responds. Return the (possibly modified) response."""
+        return response
+
+    # ── Public API ──────────────────────────────────────────────────────────
+
+    def prompt(
+        self,
+        message: str,
+        *,
+        system: str | None = None,
+        model: str | None = None,
+        messages: list[dict] | None = None,
+        attachments: list[Document] | None = None,
+        provider_options: dict | None = None,
+    ) -> AgentResponse:
+        """Send a prompt and return an AgentResponse."""
+        message = self.before(message)
+
+        _run_kwargs = dict(
+            system=system,
+            model=model,
+            extra_messages=messages,
+            attachments=attachments,
+            provider_options=provider_options,
+        )
+
+        match = self._match_fake(message)
+        if match is not None:
+            if isinstance(match, AgentSnapshot):
+                response = match.resolve(self, message, **_run_kwargs)
+            else:
+                response = match
+            self._log_call("prompt", message)
+            return self.after(response)
+
+        def _call(msg: str) -> AgentResponse:
+            return self._run(msg, **_run_kwargs)
+
+        response = self._apply_middleware(message, _call)
+        self._log_call("prompt", message)
+        return self.after(response)
+
+    def stream(
+        self,
+        message: str,
+        *,
+        system: str | None = None,
+        model: str | None = None,
+        provider_options: dict | None = None,
+    ) -> Iterator[str]:
+        """Stream a response token by token."""
+        message = self.before(message)
+        self._log_call("stream", message)
+        fake = self._match_fake(message)
+        if fake is not None:
+            if isinstance(fake, AgentSnapshot):
+                response = fake.resolve(self, message)
+            else:
+                response = fake
+            yield response.content
+            return
+        yield from self._stream(message, system=system, model=model, provider_options=provider_options)
+
+    def fake(self, patterns: dict[str, AgentResponse | AgentSnapshot]) -> "Agent":
+        """Register fake responses for testing. Keys are glob patterns."""
+        for pattern, value in patterns.items():
+            self._fakes[pattern] = value
+        return self
+
+    def assert_prompted(self, times: int | None = None) -> None:
+        """Assert that prompt() or stream() was called."""
+        calls = [c for c in self._call_log if c["method"] in ("prompt", "stream")]
+        if times is not None:
+            assert len(calls) == times, f"Expected {times} prompt call(s), got {len(calls)}"
+        else:
+            assert len(calls) > 0, "Expected at least one prompt() or stream() call, but none were made"
+
+    def assert_not_prompted(self) -> None:
+        """Assert that prompt() and stream() were never called."""
+        self.assert_prompted(times=0)
+
+    def reset(self) -> "Agent":
+        """Clear fakes and call log. Useful between test cases."""
+        self._fakes.clear()
+        self._call_log.clear()
+        return self
+
+    # ── Internal helpers ────────────────────────────────────────────────────
+
+    def _match_fake(self, message: str) -> Optional[AgentResponse | AgentSnapshot]:
+        for pattern, value in self._fakes.items():
+            if fnmatch.fnmatch(message.lower(), pattern.lower()):
+                return value
+        return None
+
+    def _log_call(self, method: str, message: str) -> None:
+        self._call_log.append({"method": method, "message": message})
+
+    def _apply_middleware(self, message: str, final: Callable[[str], AgentResponse]) -> AgentResponse:
+        """Build a left-to-right middleware chain and invoke it."""
+        chain = list(self.middleware())
+
+        def build(mw_list: list, fn: Callable) -> Callable:
+            if not mw_list:
+                return fn
+            head, *tail = mw_list
+            next_fn = build(tail, fn)
+            return lambda msg: head(msg, next_fn)
+
+        return build(chain, final)(message)
+
+    def _execute_tool(self, name: str, inputs: dict) -> Any:
+        """Find a tool by function name and call it with the given inputs."""
+        for tool in self.tools():
+            if callable(tool) and tool.__name__ == name:
+                return tool(**inputs)
+        raise ValueError(f"Tool {name!r} not found")
+
+    def _resolve_model(self, override: str | None = None) -> str:
+        if override:
+            return override
+        if self._model:
+            return self._model
+        return self._DEFAULT_MODELS.get(self._provider, "")
+
+    def _get_provider_options(self, override: dict | None = None) -> dict:
+        options = dict(self.provider_options().get(self._provider, {}))
+        if override:
+            provider_specific = override.get(self._provider, override)
+            if isinstance(provider_specific, dict):
+                options.update(provider_specific)
+        return options
+
+    def _build_messages(
+        self,
+        message: str,
+        system: str | None = None,
+        extra_messages: list[dict] | None = None,
+        attachments: list[Document] | None = None,
+    ) -> tuple[str | None, list[dict]]:
+        base = self.messages()
+
+        resolved_system = system
+        if resolved_system is None:
+            sys_entries = [m for m in base if m.get("role") == "system"]
+            if sys_entries:
+                resolved_system = sys_entries[0]["content"]
+
+        history = [m for m in base if m.get("role") != "system"]
+        if extra_messages:
+            history.extend(extra_messages)
+
+        if attachments:
+            content: Any = [{"type": "text", "text": message}]
+            for doc in attachments:
+                if self._provider == "anthropic":
+                    content.append(doc.to_anthropic_block())
+                else:
+                    content.append(doc.to_openai_block())
+            history.append({"role": "user", "content": content})
+        else:
+            history.append({"role": "user", "content": message})
+
+        return resolved_system, history
+
+    def _run(
+        self,
+        message: str,
+        system: str | None = None,
+        model: str | None = None,
+        extra_messages: list[dict] | None = None,
+        attachments: list[Document] | None = None,
+        provider_options: dict | None = None,
+    ) -> AgentResponse:
+        resolved_system, messages = self._build_messages(message, system, extra_messages, attachments)
+        resolved_model = self._resolve_model(model)
+        options = self._get_provider_options(provider_options)
+
+        if self._provider == "anthropic":
+            return self._run_anthropic(resolved_system, messages, resolved_model, options)
+        if self._provider == "openai":
+            return self._run_openai(resolved_system, messages, resolved_model, options)
+        if self._provider == "google":
+            return self._run_google(resolved_system, messages, resolved_model, options)
+        raise ValueError(f"Unsupported provider: {self._provider!r}. Use 'anthropic', 'openai', or 'google'.")
+
+    def _stream(
+        self,
+        message: str,
+        system: str | None = None,
+        model: str | None = None,
+        provider_options: dict | None = None,
+    ) -> Iterator[str]:
+        resolved_system, messages = self._build_messages(message, system)
+        resolved_model = self._resolve_model(model)
+        options = self._get_provider_options(provider_options)
+
+        if self._provider == "anthropic":
+            yield from self._stream_anthropic(resolved_system, messages, resolved_model, options)
+        elif self._provider == "openai":
+            yield from self._stream_openai(resolved_system, messages, resolved_model, options)
+        elif self._provider == "google":
+            yield from self._stream_google(resolved_system, messages, resolved_model, options)
+        else:
+            raise ValueError(f"Unsupported provider: {self._provider!r}. Use 'anthropic', 'openai', or 'google'.")
+
+    # ── Anthropic ──────────────────────────────────────────────────────────
+
+    def _run_anthropic(
+        self,
+        system: str | None,
+        messages: list[dict],
+        model: str,
+        options: dict,
+    ) -> AgentResponse:
+        from anthropic import Anthropic  # noqa: PLC0415
+
+        api_key = self._resolve_api_key("anthropic")
+        client = Anthropic(api_key=api_key)
+        params: dict[str, Any] = {
+            "model": model,
+            "max_tokens": self._max_tokens,
+            "messages": messages,
+            **options,
+        }
+        if system:
+            params["system"] = system
+
+        resp = client.messages.create(**params)
+        content = "".join(b.text for b in resp.content if hasattr(b, "text"))
+        return AgentResponse(
+            content=content,
+            usage={"input": resp.usage.input_tokens, "output": resp.usage.output_tokens},
+            raw=resp,
+        )
+
+    def _stream_anthropic(
+        self,
+        system: str | None,
+        messages: list[dict],
+        model: str,
+        options: dict,
+    ) -> Iterator[str]:
+        from anthropic import Anthropic  # noqa: PLC0415
+
+        api_key = self._resolve_api_key("anthropic")
+        client = Anthropic(api_key=api_key)
+        params: dict[str, Any] = {
+            "model": model,
+            "max_tokens": self._max_tokens,
+            "messages": messages,
+            **options,
+        }
+        if system:
+            params["system"] = system
+
+        with client.messages.stream(**params) as stream:
+            for text in stream.text_stream:
+                yield text
+
+    # ── OpenAI ─────────────────────────────────────────────────────────────
+
+    def _run_openai(
+        self,
+        system: str | None,
+        messages: list[dict],
+        model: str,
+        options: dict,
+    ) -> AgentResponse:
+        from openai import OpenAI  # noqa: PLC0415
+
+        api_key = self._resolve_api_key("openai")
+        client = OpenAI(api_key=api_key)
+        all_messages: list[dict] = []
+        if system:
+            all_messages.append({"role": "system", "content": system})
+        all_messages.extend(messages)
+
+        params: dict[str, Any] = {
+            "model": model,
+            "max_tokens": self._max_tokens,
+            "messages": all_messages,
+            **options,
+        }
+        resp = client.chat.completions.create(**params)
+        content = resp.choices[0].message.content or ""
+        return AgentResponse(
+            content=content,
+            usage={
+                "input": resp.usage.prompt_tokens if resp.usage else 0,
+                "output": resp.usage.completion_tokens if resp.usage else 0,
+            },
+            raw=resp,
+        )
+
+    def _stream_openai(
+        self,
+        system: str | None,
+        messages: list[dict],
+        model: str,
+        options: dict,
+    ) -> Iterator[str]:
+        from openai import OpenAI  # noqa: PLC0415
+
+        api_key = self._resolve_api_key("openai")
+        client = OpenAI(api_key=api_key)
+        all_messages: list[dict] = []
+        if system:
+            all_messages.append({"role": "system", "content": system})
+        all_messages.extend(messages)
+
+        params: dict[str, Any] = {
+            "model": model,
+            "max_tokens": self._max_tokens,
+            "messages": all_messages,
+            "stream": True,
+            **options,
+        }
+        for chunk in client.chat.completions.create(**params):
+            delta = chunk.choices[0].delta.content
+            if delta:
+                yield delta
+
+    def _resolve_api_key(self, provider_name: str) -> str | None:
+        """Try Config.get("ai") first, fallback to None (SDK reads env var)."""
+        try:
+            from fastapi_startkit.facades.Config import Config  # noqa: PLC0415
+
+            ai_config = Config.get("ai")
+            return ai_config.providers[provider_name].key or None
+        except Exception:
+            return None
+
+    # ── Google ─────────────────────────────────────────────────────────────
+
+    def _run_google(
+        self,
+        system: str | None,
+        messages: list[dict],
+        model: str,
+        options: dict,
+    ) -> AgentResponse:
+        import google.generativeai as genai  # noqa: PLC0415
+
+        api_key = self._resolve_api_key("google")
+        if api_key:
+            genai.configure(api_key=api_key)
+
+        generation_config: dict[str, Any] = {}
+        if self._max_tokens:
+            generation_config["max_output_tokens"] = self._max_tokens
+        if self._top_p != 1.0:
+            generation_config["top_p"] = self._top_p
+        generation_config.update(options)
+
+        google_model = genai.GenerativeModel(
+            model_name=model,
+            system_instruction=system,
+            generation_config=generation_config if generation_config else None,
+        )
+
+        google_messages = _to_google_messages(messages)
+        response = google_model.generate_content(google_messages)
+        content = response.text if hasattr(response, "text") else ""
+        usage: dict[str, Any] = {}
+        if hasattr(response, "usage_metadata"):
+            meta = response.usage_metadata
+            usage = {
+                "input": getattr(meta, "prompt_token_count", 0),
+                "output": getattr(meta, "candidates_token_count", 0),
+            }
+        return AgentResponse(content=content, usage=usage, raw=response)
+
+    def _stream_google(
+        self,
+        system: str | None,
+        messages: list[dict],
+        model: str,
+        options: dict,
+    ) -> Iterator[str]:
+        import google.generativeai as genai  # noqa: PLC0415
+
+        api_key = self._resolve_api_key("google")
+        if api_key:
+            genai.configure(api_key=api_key)
+
+        generation_config: dict[str, Any] = {}
+        if self._max_tokens:
+            generation_config["max_output_tokens"] = self._max_tokens
+        if self._top_p != 1.0:
+            generation_config["top_p"] = self._top_p
+        generation_config.update(options)
+
+        google_model = genai.GenerativeModel(
+            model_name=model,
+            system_instruction=system,
+            generation_config=generation_config if generation_config else None,
+        )
+
+        google_messages = _to_google_messages(messages)
+        for chunk in google_model.generate_content(google_messages, stream=True):
+            if chunk.text:
+                yield chunk.text
+
+
+# ─── Utilities ─────────────────────────────────────────────────────────────────
+
+
+def _to_google_messages(messages: list[dict]) -> list[dict]:
+    """
+    Convert OpenAI-style messages to Google GenerativeAI content format.
+    Maps 'assistant' role → 'model'; omits 'system' (handled via system_instruction).
+    """
+    result = []
+    for msg in messages:
+        role = msg.get("role", "user")
+        content = msg.get("content", "")
+        if role == "system":
+            continue  # system_instruction is set at model-construction level
+        google_role = "model" if role == "assistant" else "user"
+        if isinstance(content, list):
+            # Multi-part content — extract text parts only
+            text = " ".join(p.get("text", "") for p in content if isinstance(p, dict) and "text" in p)
+            result.append({"role": google_role, "parts": [{"text": text}]})
+        else:
+            result.append({"role": google_role, "parts": [{"text": str(content)}]})
+    return result
diff --git a/fastapi_startkit/src/fastapi_startkit/ai/config.py b/fastapi_startkit/src/fastapi_startkit/ai/config.py
new file mode 100644
index 00000000..af1a1acf
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/ai/config.py
@@ -0,0 +1,48 @@
+"""AI configuration dataclasses for the FastAPI Startkit AI module."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from fastapi_startkit.environment import env
+
+
+@dataclass
+class AnthropicConfig:
+    """Configuration for the Anthropic provider."""
+
+    driver: str = "anthropic"
+    key: str = field(default_factory=lambda: env("ANTHROPIC_API_KEY", ""))
+    url: str = field(default_factory=lambda: env("ANTHROPIC_BASE_URL", "https://api.anthropic.com"))
+
+
+@dataclass
+class OpenAIConfig:
+    """Configuration for the OpenAI provider."""
+
+    driver: str = "openai"
+    key: str = field(default_factory=lambda: env("OPENAI_API_KEY", ""))
+    url: str = field(default_factory=lambda: env("OPENAI_BASE_URL", "https://api.openai.com/v1"))
+
+
+@dataclass
+class GoogleConfig:
+    """Configuration for the Google / Gemini provider."""
+
+    driver: str = "google"
+    key: str = field(default_factory=lambda: env("GEMINI_API_KEY", "") or env("GOOGLE_API_KEY", ""))
+
+
+@dataclass
+class AIConfig:
+    """Top-level AI configuration — selects the default provider and holds per-provider configs."""
+
+    default: str = field(default_factory=lambda: env("AI_PROVIDER", "google"))
+
+    providers: dict = field(
+        default_factory=lambda: {
+            "openai": OpenAIConfig(),
+            "anthropic": AnthropicConfig(),
+            "google": GoogleConfig(),
+        }
+    )
diff --git a/fastapi_startkit/src/fastapi_startkit/ai/decorators.py b/fastapi_startkit/src/fastapi_startkit/ai/decorators.py
new file mode 100644
index 00000000..bd427bb6
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/ai/decorators.py
@@ -0,0 +1,73 @@
+"""Declarative class decorators for Agent configuration."""
+
+from __future__ import annotations
+
+
+def provider(name: str):
+    """Set the LLM provider: 'anthropic', 'openai', 'google', etc."""
+
+    def decorator(cls):
+        cls._provider = name
+        return cls
+
+    return decorator
+
+
+def model(name: str = ""):
+    """Set the model identifier (e.g. 'claude-sonnet-4-6', 'gpt-4o')."""
+
+    def decorator(cls):
+        cls._model = name
+        return cls
+
+    return decorator
+
+
+def max_steps(n: int = 10):
+    """Maximum agentic loop iterations before stopping."""
+
+    def decorator(cls):
+        cls._max_steps = n
+        return cls
+
+    return decorator
+
+
+def max_tokens(n: int = 4096):
+    """Maximum output tokens per response."""
+
+    def decorator(cls):
+        cls._max_tokens = n
+        return cls
+
+    return decorator
+
+
+def timeout(seconds: float = 30.0):
+    """Request timeout in seconds."""
+
+    def decorator(cls):
+        cls._timeout = seconds
+        return cls
+
+    return decorator
+
+
+def top_p(value: float = 1.0):
+    """Top-p nucleus sampling parameter."""
+
+    def decorator(cls):
+        cls._top_p = value
+        return cls
+
+    return decorator
+
+
+def memory(backend: str = ""):
+    """Attach a named memory backend to this agent."""
+
+    def decorator(cls):
+        cls._memory_backend = backend
+        return cls
+
+    return decorator
diff --git a/fastapi_startkit/src/fastapi_startkit/ai/document.py b/fastapi_startkit/src/fastapi_startkit/ai/document.py
new file mode 100644
index 00000000..f6dffee9
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/ai/document.py
@@ -0,0 +1,43 @@
+"""Document helper — attach files or text to agent prompts."""
+
+from __future__ import annotations
+
+
+class Document:
+    """Attach documents to agent.prompt() calls."""
+
+    def __init__(self, content: str, name: str = "", media_type: str = "text/plain"):
+        self.content = content
+        self.name = name
+        self.media_type = media_type
+
+    @classmethod
+    def from_path(cls, path: str) -> "Document":
+        """Load a document from a local file path."""
+        with open(path) as f:
+            content = f.read()
+        return cls(content=content, name=path)
+
+    @classmethod
+    def from_storage(cls, key: str) -> "Document":
+        """Load a document from application storage (storage/<key>)."""
+        return cls.from_path(f"storage/{key}")
+
+    def to_anthropic_block(self) -> dict:
+        """Return an Anthropic-compatible content block for this document."""
+        return {
+            "type": "document",
+            "source": {
+                "type": "text",
+                "media_type": self.media_type,
+                "data": self.content,
+            },
+            "title": self.name,
+        }
+
+    def to_openai_block(self) -> dict:
+        """Return an OpenAI-compatible content block for this document."""
+        return {
+            "type": "text",
+            "text": f"[Document: {self.name}]\n{self.content}",
+        }
diff --git a/fastapi_startkit/src/fastapi_startkit/ai/providers/__init__.py b/fastapi_startkit/src/fastapi_startkit/ai/providers/__init__.py
new file mode 100644
index 00000000..2cdf2487
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/ai/providers/__init__.py
@@ -0,0 +1,3 @@
+from .ai_provider import AIProvider
+
+__all__ = ["AIProvider"]
diff --git a/fastapi_startkit/src/fastapi_startkit/ai/providers/ai_provider.py b/fastapi_startkit/src/fastapi_startkit/ai/providers/ai_provider.py
new file mode 100644
index 00000000..d45f2e73
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/ai/providers/ai_provider.py
@@ -0,0 +1,30 @@
+"""AIProvider — registers AI configuration into the service container."""
+
+from __future__ import annotations
+
+from fastapi_startkit.providers import Provider
+
+
+class AIProvider(Provider):
+    """Service provider that bootstraps the AI module.
+
+    Registers :class:`~fastapi_startkit.ai.config.AIConfig` under the ``ai``
+    key in the application container so it is accessible via ``Config.get('ai')``.
+
+    Register it in your application::
+
+        app = Application(providers=[AIProvider])
+    """
+
+    provider_key = "ai"
+
+    def register(self) -> None:
+        """Bind AIConfig into the container under the 'ai' key."""
+        from fastapi_startkit.ai.config import AIConfig
+
+        self.app.bind("ai", AIConfig())
+
+    def boot(self) -> None:
+        """Merge AI config into the shared Config store."""
+        ai_config = self.app.make("ai")
+        self.app.make("config").set("ai", ai_config)
diff --git a/fastapi_startkit/src/fastapi_startkit/ai/response.py b/fastapi_startkit/src/fastapi_startkit/ai/response.py
new file mode 100644
index 00000000..77383bdb
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/ai/response.py
@@ -0,0 +1,92 @@
+"""AgentResponse and AgentSnapshot — response containers for AI agents."""
+
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from .agent import Agent
+
+
+@dataclass
+class AgentResponse:
+    """Returned by Agent.prompt(). Wraps the LLM response."""
+
+    content: str = ""
+    tool_calls: list[dict] = field(default_factory=list)
+    usage: dict = field(default_factory=dict)
+    raw: Any = None
+
+    def text(self) -> str:
+        """Return the text content."""
+        return self.content
+
+    def json(self) -> Any:
+        """Parse the content as JSON."""
+        return json.loads(self.content)
+
+    def __str__(self) -> str:
+        return self.content
+
+    def __bool__(self) -> bool:
+        return bool(self.content)
+
+
+@dataclass
+class AgentSnapshot:
+    """
+    Record-and-replay snapshot for testing.
+
+    - If the file at ``path`` **does not exist**: the agent calls the real API,
+      saves the response as JSON, then returns it.
+    - If the file **exists**: the saved response is loaded and returned without
+      hitting the API.
+
+    Example::
+
+        agent.fake({"*analyze*": AgentSnapshot(path="tests/fixtures/analysis.json")})
+    """
+
+    path: str
+
+    def exists(self) -> bool:
+        """Return True if the snapshot file is already recorded."""
+        return os.path.exists(self.path)
+
+    def load(self) -> AgentResponse:
+        """Load the recorded response from disk."""
+        with open(self.path) as f:
+            data = json.load(f)
+        return AgentResponse(
+            content=data.get("content", ""),
+            tool_calls=data.get("tool_calls", []),
+            usage=data.get("usage", {}),
+        )
+
+    def save(self, response: AgentResponse) -> None:
+        """Persist a real API response to disk for future replays."""
+        os.makedirs(os.path.dirname(self.path) or ".", exist_ok=True)
+        with open(self.path, "w") as f:
+            json.dump(
+                {
+                    "content": response.content,
+                    "tool_calls": response.tool_calls,
+                    "usage": response.usage,
+                },
+                f,
+                indent=2,
+            )
+
+    def resolve(self, agent: "Agent", message: str, **run_kwargs: Any) -> AgentResponse:
+        """
+        Return the response — from disk if recorded, or from the real API
+        (which is then saved for future runs).
+        """
+        if self.exists():
+            return self.load()
+        response = agent._run(message, **run_kwargs)
+        self.save(response)
+        return response
diff --git a/fastapi_startkit/src/fastapi_startkit/facades/AI.py b/fastapi_startkit/src/fastapi_startkit/facades/AI.py
new file mode 100644
index 00000000..739e468c
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/facades/AI.py
@@ -0,0 +1,5 @@
+from .Facade import Facade
+
+
+class AI(metaclass=Facade):
+    key = "ai"
diff --git a/fastapi_startkit/src/fastapi_startkit/facades/AI.pyi b/fastapi_startkit/src/fastapi_startkit/facades/AI.pyi
new file mode 100644
index 00000000..b6b8ab22
--- /dev/null
+++ b/fastapi_startkit/src/fastapi_startkit/facades/AI.pyi
@@ -0,0 +1,19 @@
+from fastapi_startkit.ai.config import AIConfig
+
+class AI:
+    """Facade for accessing the AI configuration registered under the 'ai' key."""
+
+    @staticmethod
+    def config() -> AIConfig:
+        """Return the full AIConfig object."""
+        ...
+
+    @staticmethod
+    def default() -> str:
+        """Return the default AI provider name."""
+        ...
+
+    @staticmethod
+    def providers() -> dict:
+        """Return the configured provider configs (OpenAIConfig, AnthropicConfig, GoogleConfig)."""
+        ...
diff --git a/fastapi_startkit/tests/ai/__init__.py b/fastapi_startkit/tests/ai/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/fastapi_startkit/tests/ai/test_agent_decorators.py b/fastapi_startkit/tests/ai/test_agent_decorators.py
new file mode 100644
index 00000000..0ce11bc4
--- /dev/null
+++ b/fastapi_startkit/tests/ai/test_agent_decorators.py
@@ -0,0 +1,206 @@
+"""Tests for Agent class decorators."""
+
+from fastapi_startkit.ai.agent import Agent
+from fastapi_startkit.ai.decorators import (
+    max_steps,
+    max_tokens,
+    memory,
+    model,
+    provider,
+    timeout,
+    top_p,
+)
+
+
+# ─── Decorator: @provider ──────────────────────────────────────────────────────
+
+
+def test_provider_decorator_sets_provider():
+    @provider("openai")
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._provider == "openai"
+
+
+def test_provider_decorator_sets_anthropic():
+    @provider("anthropic")
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._provider == "anthropic"
+
+
+def test_provider_decorator_sets_google():
+    @provider("google")
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._provider == "google"
+
+
+# ─── Decorator: @model ────────────────────────────────────────────────────────
+
+
+def test_model_decorator_sets_model():
+    @model("gpt-4o")
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._model == "gpt-4o"
+
+
+def test_model_decorator_sets_claude_model():
+    @model("claude-sonnet-4-6")
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._model == "claude-sonnet-4-6"
+
+
+# ─── Decorator: @max_tokens ───────────────────────────────────────────────────
+
+
+def test_max_tokens_decorator_sets_value():
+    @max_tokens(2048)
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._max_tokens == 2048
+
+
+def test_max_tokens_decorator_overrides_default():
+    @max_tokens(512)
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._max_tokens == 512
+
+
+# ─── Decorator: @max_steps ────────────────────────────────────────────────────
+
+
+def test_max_steps_decorator_sets_value():
+    @max_steps(5)
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._max_steps == 5
+
+
+def test_max_steps_decorator_sets_one():
+    @max_steps(1)
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._max_steps == 1
+
+
+# ─── Decorator: @timeout ──────────────────────────────────────────────────────
+
+
+def test_timeout_decorator_sets_seconds():
+    @timeout(60.0)
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._timeout == 60.0
+
+
+def test_timeout_decorator_sets_fractional():
+    @timeout(2.5)
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._timeout == 2.5
+
+
+# ─── Decorator: @top_p ────────────────────────────────────────────────────────
+
+
+def test_top_p_decorator_sets_value():
+    @top_p(0.9)
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._top_p == 0.9
+
+
+def test_top_p_decorator_sets_zero():
+    @top_p(0.0)
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._top_p == 0.0
+
+
+# ─── Decorator: @memory ───────────────────────────────────────────────────────
+
+
+def test_memory_decorator_sets_backend():
+    @memory("redis")
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._memory_backend == "redis"
+
+
+def test_memory_decorator_sets_custom_backend():
+    @memory("postgres")
+    class MyAgent(Agent):
+        pass
+
+    assert MyAgent._memory_backend == "postgres"
+
+
+# ─── Stacking multiple decorators ─────────────────────────────────────────────
+
+
+def test_multiple_decorators_can_be_stacked():
+    @provider("openai")
+    @model("gpt-4o")
+    @max_tokens(1024)
+    @max_steps(3)
+    @timeout(15.0)
+    @top_p(0.95)
+    @memory("redis")
+    class FullyConfiguredAgent(Agent):
+        pass
+
+    assert FullyConfiguredAgent._provider == "openai"
+    assert FullyConfiguredAgent._model == "gpt-4o"
+    assert FullyConfiguredAgent._max_tokens == 1024
+    assert FullyConfiguredAgent._max_steps == 3
+    assert FullyConfiguredAgent._timeout == 15.0
+    assert FullyConfiguredAgent._top_p == 0.95
+    assert FullyConfiguredAgent._memory_backend == "redis"
+
+
+def test_stacking_does_not_affect_base_class():
+    """Decorator-applied values must not leak into the Agent base class."""
+
+    @provider("openai")
+    @model("gpt-4o")
+    class SubAgent(Agent):
+        pass
+
+    # Base Agent must retain its own defaults
+    assert Agent._provider == "anthropic"
+    assert Agent._model == ""
+
+    # Subclass has decorated values
+    assert SubAgent._provider == "openai"
+    assert SubAgent._model == "gpt-4o"
+
+
+def test_instance_inherits_class_config():
+    """Instantiating a decorated class reads the right config values."""
+
+    @provider("openai")
+    @max_tokens(256)
+    class TinyAgent(Agent):
+        pass
+
+    agent = TinyAgent()
+    assert agent._provider == "openai"
+    assert agent._max_tokens == 256
diff --git a/fastapi_startkit/tests/ai/test_agent_document.py b/fastapi_startkit/tests/ai/test_agent_document.py
new file mode 100644
index 00000000..997ed0d9
--- /dev/null
+++ b/fastapi_startkit/tests/ai/test_agent_document.py
@@ -0,0 +1,151 @@
+"""Tests for the Document attachment helper."""
+
+import os
+import tempfile
+
+import pytest
+from fastapi_startkit.ai.document import Document
+
+
+# ─── Document.from_path() ─────────────────────────────────────────────────────
+
+
+def test_from_path_reads_file_content():
+    content = "Hello from file!"
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f:
+        f.write(content)
+        path = f.name
+
+    try:
+        doc = Document.from_path(path)
+        assert doc.content == content
+    finally:
+        os.unlink(path)
+
+
+def test_from_path_sets_name_to_path():
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f:
+        f.write("content")
+        path = f.name
+
+    try:
+        doc = Document.from_path(path)
+        assert doc.name == path
+    finally:
+        os.unlink(path)
+
+
+def test_from_path_reads_multiline_content():
+    content = "line one\nline two\nline three\n"
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f:
+        f.write(content)
+        path = f.name
+
+    try:
+        doc = Document.from_path(path)
+        assert doc.content == content
+    finally:
+        os.unlink(path)
+
+
+def test_from_path_raises_on_missing_file():
+    with pytest.raises(FileNotFoundError):
+        Document.from_path("/nonexistent/path/file.txt")
+
+
+def test_from_path_reads_empty_file():
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False) as f:
+        f.write("")
+        path = f.name
+
+    try:
+        doc = Document.from_path(path)
+        assert doc.content == ""
+    finally:
+        os.unlink(path)
+
+
+# ─── Document.to_anthropic_block() ────────────────────────────────────────────
+
+
+def test_to_anthropic_block_returns_dict():
+    doc = Document(content="some text", name="report.txt")
+    block = doc.to_anthropic_block()
+    assert isinstance(block, dict)
+
+
+def test_to_anthropic_block_has_type_document():
+    doc = Document(content="some text", name="report.txt")
+    block = doc.to_anthropic_block()
+    assert block["type"] == "document"
+
+
+def test_to_anthropic_block_has_source_key():
+    doc = Document(content="some text")
+    block = doc.to_anthropic_block()
+    assert "source" in block
+
+
+def test_to_anthropic_block_source_type_is_text():
+    doc = Document(content="some text")
+    block = doc.to_anthropic_block()
+    assert block["source"]["type"] == "text"
+
+
+def test_to_anthropic_block_source_media_type_default():
+    doc = Document(content="some text")
+    block = doc.to_anthropic_block()
+    assert block["source"]["media_type"] == "text/plain"
+
+
+def test_to_anthropic_block_source_data_contains_content():
+    doc = Document(content="the actual text content")
+    block = doc.to_anthropic_block()
+    assert block["source"]["data"] == "the actual text content"
+
+
+def test_to_anthropic_block_title_is_name():
+    doc = Document(content="text", name="my_document.txt")
+    block = doc.to_anthropic_block()
+    assert block["title"] == "my_document.txt"
+
+
+def test_to_anthropic_block_custom_media_type():
+    doc = Document(content="<html/>", name="page.html", media_type="text/html")
+    block = doc.to_anthropic_block()
+    assert block["source"]["media_type"] == "text/html"
+
+
+def test_to_anthropic_block_full_structure():
+    """Assert the complete expected block structure matches exactly."""
+    doc = Document(content="contract text", name="contract.txt", media_type="text/plain")
+    block = doc.to_anthropic_block()
+
+    expected = {
+        "type": "document",
+        "source": {
+            "type": "text",
+            "media_type": "text/plain",
+            "data": "contract text",
+        },
+        "title": "contract.txt",
+    }
+    assert block == expected
+
+
+# ─── Document constructor ──────────────────────────────────────────────────────
+
+
+def test_document_name_defaults_to_empty_string():
+    doc = Document(content="text")
+    assert doc.name == ""
+
+
+def test_document_media_type_defaults_to_text_plain():
+    doc = Document(content="text")
+    assert doc.media_type == "text/plain"
+
+
+def test_document_stores_content():
+    doc = Document(content="stored content")
+    assert doc.content == "stored content"
diff --git a/fastapi_startkit/tests/ai/test_agent_fake.py b/fastapi_startkit/tests/ai/test_agent_fake.py
new file mode 100644
index 00000000..ff52563c
--- /dev/null
+++ b/fastapi_startkit/tests/ai/test_agent_fake.py
@@ -0,0 +1,276 @@
+"""Tests for Agent.fake(), assert_prompted(), assert_not_prompted(), and reset()."""
+
+import json
+import os
+import tempfile
+
+import pytest
+from fastapi_startkit.ai.agent import Agent
+from fastapi_startkit.ai.response import AgentResponse, AgentSnapshot
+
+
+# ─── Helpers ──────────────────────────────────────────────────────────────────
+
+
+class SimpleAgent(Agent):
+    """Bare-minimum agent for testing — never touches a real API."""
+
+    pass
+
+
+# ─── fake() with AgentResponse returns it without hitting the API ──────────────
+
+
+def test_fake_with_agent_response_returns_it():
+    agent = SimpleAgent()
+    expected = AgentResponse(content="Hello world!")
+    agent.fake({"*": expected})
+
+    result = agent.prompt("anything")
+
+    assert result.content == "Hello world!"
+
+
+def test_fake_does_not_call_provider_run():
+    """fake() must short-circuit before _run() is ever invoked."""
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="faked")})
+
+    called = []
+
+    original_run = agent._run
+
+    def patched_run(*args, **kwargs):
+        called.append(True)
+        return original_run(*args, **kwargs)
+
+    agent._run = patched_run  # type: ignore[method-assign]
+    agent.prompt("hello")
+
+    assert called == [], "_run() must not be called when a fake matches"
+
+
+def test_fake_with_exact_pattern():
+    agent = SimpleAgent()
+    agent.fake({"hello": AgentResponse(content="matched hello")})
+
+    result = agent.prompt("hello")
+    assert result.content == "matched hello"
+
+
+# ─── fake() with glob pattern matching ────────────────────────────────────────
+
+
+def test_fake_glob_hello_wildcard():
+    agent = SimpleAgent()
+    agent.fake({"*hello*": AgentResponse(content="hi there")})
+
+    result = agent.prompt("say hello to me")
+    assert result.content == "hi there"
+
+
+def test_fake_glob_analyze_wildcard():
+    agent = SimpleAgent()
+    agent.fake({"*analyze*": AgentResponse(content="analysis done")})
+
+    result = agent.prompt("please analyze this report")
+    assert result.content == "analysis done"
+
+
+def test_fake_glob_no_match_raises_on_missing_run():
+    """When a pattern does not match and no real provider is configured, _run raises."""
+    agent = SimpleAgent()
+    agent.fake({"*hello*": AgentResponse(content="hi")})
+
+    with pytest.raises(Exception):
+        agent.prompt("goodbye")  # pattern does not match → falls through to _run
+
+
+def test_fake_glob_case_insensitive():
+    agent = SimpleAgent()
+    agent.fake({"*HELLO*": AgentResponse(content="case insensitive")})
+
+    result = agent.prompt("say hello please")
+    assert result.content == "case insensitive"
+
+
+def test_fake_first_matching_pattern_wins():
+    agent = SimpleAgent()
+    agent.fake(
+        {
+            "*hello*": AgentResponse(content="first match"),
+            "*hello world*": AgentResponse(content="second match"),
+        }
+    )
+
+    result = agent.prompt("hello world")
+    assert result.content == "first match"
+
+
+# ─── fake() with AgentSnapshot loads from fixture if file exists ───────────────
+
+
+def test_fake_with_snapshot_loads_from_file_if_exists():
+    fixture_data = {"content": "snapshot reply", "tool_calls": [], "usage": {"input": 5, "output": 10}}
+
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
+        json.dump(fixture_data, f)
+        fixture_path = f.name
+
+    try:
+        agent = SimpleAgent()
+        snapshot = AgentSnapshot(path=fixture_path)
+        agent.fake({"*": snapshot})
+
+        result = agent.prompt("any prompt")
+        assert result.content == "snapshot reply"
+        assert result.usage == {"input": 5, "output": 10}
+    finally:
+        os.unlink(fixture_path)
+
+
+def test_fake_with_snapshot_missing_file_calls_run(monkeypatch):
+    """When the snapshot file does not exist, _run() is called and the result is saved."""
+    expected_response = AgentResponse(content="live result")
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        snapshot_path = os.path.join(tmpdir, "snap.json")
+
+        agent = SimpleAgent()
+        snapshot = AgentSnapshot(path=snapshot_path)
+        agent.fake({"*": snapshot})
+
+        # Patch _run to avoid real API call
+        monkeypatch.setattr(agent, "_run", lambda *a, **kw: expected_response)
+
+        result = agent.prompt("test message")
+        assert result.content == "live result"
+
+        # Snapshot should now be saved to disk
+        assert os.path.exists(snapshot_path)
+        with open(snapshot_path) as f:
+            saved = json.load(f)
+        assert saved["content"] == "live result"
+
+
+# ─── assert_prompted() ────────────────────────────────────────────────────────
+
+
+def test_assert_prompted_passes_after_one_call():
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="ok")})
+
+    agent.prompt("first")
+    agent.assert_prompted()  # must not raise
+
+
+def test_assert_prompted_times_2_passes_after_exactly_2_calls():
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="ok")})
+
+    agent.prompt("first")
+    agent.prompt("second")
+    agent.assert_prompted(times=2)  # must not raise
+
+
+def test_assert_prompted_times_fails_when_count_mismatch():
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="ok")})
+
+    agent.prompt("only once")
+
+    with pytest.raises(AssertionError):
+        agent.assert_prompted(times=2)
+
+
+def test_assert_prompted_fails_when_never_called():
+    agent = SimpleAgent()
+
+    with pytest.raises(AssertionError):
+        agent.assert_prompted()
+
+
+def test_assert_prompted_times_zero_passes_when_never_called():
+    agent = SimpleAgent()
+    agent.assert_prompted(times=0)  # must not raise
+
+
+# ─── assert_not_prompted() ────────────────────────────────────────────────────
+
+
+def test_assert_not_prompted_passes_when_no_calls_made():
+    agent = SimpleAgent()
+    agent.assert_not_prompted()  # must not raise
+
+
+def test_assert_not_prompted_fails_after_one_call():
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="ok")})
+
+    agent.prompt("a prompt")
+
+    with pytest.raises(AssertionError):
+        agent.assert_not_prompted()
+
+
+# ─── reset() ──────────────────────────────────────────────────────────────────
+
+
+def test_reset_clears_call_log():
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="ok")})
+
+    agent.prompt("first")
+    assert len(agent._call_log) == 1
+
+    agent.reset()
+    assert agent._call_log == []
+
+
+def test_reset_clears_fakes():
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="ok")})
+    assert len(agent._fakes) == 1
+
+    agent.reset()
+    assert agent._fakes == {}
+
+
+def test_reset_returns_agent_for_chaining():
+    agent = SimpleAgent()
+    result = agent.reset()
+    assert result is agent
+
+
+def test_assert_not_prompted_passes_after_reset():
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="ok")})
+
+    agent.prompt("call before reset")
+    agent.reset()
+
+    agent.assert_not_prompted()  # call log was cleared
+
+
+def test_fake_after_reset_works_normally():
+    agent = SimpleAgent()
+    agent.fake({"*": AgentResponse(content="first fake")})
+    agent.prompt("call")
+    agent.reset()
+
+    agent.fake({"*": AgentResponse(content="second fake")})
+    result = agent.prompt("call again")
+    assert result.content == "second fake"
+
+
+# ─── stream() respects fake() ─────────────────────────────────────────────────
+
+
+def test_stream_returns_fake_response():
+    agent = SimpleAgent()
+    agent.fake({"*hello*": AgentResponse(content="Faked stream!")})
+
+    chunks = list(agent.stream("hello world"))
+
+    assert chunks == ["Faked stream!"]
+    agent.assert_prompted(times=1)
diff --git a/fastapi_startkit/tests/ai/test_agent_response.py b/fastapi_startkit/tests/ai/test_agent_response.py
new file mode 100644
index 00000000..d57f7b5b
--- /dev/null
+++ b/fastapi_startkit/tests/ai/test_agent_response.py
@@ -0,0 +1,136 @@
+"""Tests for the AgentResponse dataclass."""
+
+import pytest
+from fastapi_startkit.ai.response import AgentResponse
+
+
+# ─── AgentResponse.text() ─────────────────────────────────────────────────────
+
+
+def test_text_returns_content():
+    response = AgentResponse(content="Hello, world!")
+    assert response.text() == "Hello, world!"
+
+
+def test_text_returns_empty_string_when_no_content():
+    response = AgentResponse()
+    assert response.text() == ""
+
+
+def test_text_returns_multiline_content():
+    content = "Line 1\nLine 2\nLine 3"
+    response = AgentResponse(content=content)
+    assert response.text() == content
+
+
+# ─── AgentResponse.json() ─────────────────────────────────────────────────────
+
+
+def test_json_parses_content_as_json():
+    response = AgentResponse(content='{"key": "value", "number": 42}')
+    parsed = response.json()
+    assert parsed == {"key": "value", "number": 42}
+
+
+def test_json_parses_list_content():
+    response = AgentResponse(content="[1, 2, 3]")
+    assert response.json() == [1, 2, 3]
+
+
+def test_json_parses_nested_object():
+    response = AgentResponse(content='{"nested": {"a": 1}}')
+    assert response.json()["nested"]["a"] == 1
+
+
+def test_json_raises_on_invalid_content():
+    response = AgentResponse(content="not valid json")
+    with pytest.raises(Exception):  # json.JSONDecodeError
+        response.json()
+
+
+def test_json_raises_on_empty_content():
+    response = AgentResponse(content="")
+    with pytest.raises(Exception):
+        response.json()
+
+
+# ─── AgentResponse.__str__() ──────────────────────────────────────────────────
+
+
+def test_str_returns_content():
+    response = AgentResponse(content="My response text")
+    assert str(response) == "My response text"
+
+
+def test_str_returns_empty_string_when_no_content():
+    response = AgentResponse()
+    assert str(response) == ""
+
+
+def test_str_works_in_f_string():
+    response = AgentResponse(content="hello")
+    assert f"Result: {response}" == "Result: hello"
+
+
+# ─── AgentResponse.__bool__() ─────────────────────────────────────────────────
+
+
+def test_bool_is_true_when_content_non_empty():
+    response = AgentResponse(content="some text")
+    assert bool(response) is True
+
+
+def test_bool_is_false_when_content_empty():
+    response = AgentResponse(content="")
+    assert bool(response) is False
+
+
+def test_bool_is_false_when_content_not_set():
+    response = AgentResponse()
+    assert bool(response) is False
+
+
+def test_bool_is_true_with_whitespace_content():
+    """A response with only whitespace still evaluates as truthy (non-empty string)."""
+    response = AgentResponse(content="   ")
+    assert bool(response) is True
+
+
+def test_bool_usable_in_conditional():
+    response = AgentResponse(content="text")
+    assert response  # truthy
+
+    empty = AgentResponse(content="")
+    assert not empty  # falsy
+
+
+# ─── AgentResponse dataclass fields ───────────────────────────────────────────
+
+
+def test_tool_calls_default_to_empty_list():
+    response = AgentResponse()
+    assert response.tool_calls == []
+
+
+def test_usage_defaults_to_empty_dict():
+    response = AgentResponse()
+    assert response.usage == {}
+
+
+def test_raw_defaults_to_none():
+    response = AgentResponse()
+    assert response.raw is None
+
+
+def test_all_fields_can_be_set():
+    raw_obj = object()
+    response = AgentResponse(
+        content="text",
+        tool_calls=[{"name": "search", "input": {"q": "test"}}],
+        usage={"input": 10, "output": 20},
+        raw=raw_obj,
+    )
+    assert response.content == "text"
+    assert response.tool_calls == [{"name": "search", "input": {"q": "test"}}]
+    assert response.usage == {"input": 10, "output": 20}
+    assert response.raw is raw_obj
diff --git a/fastapi_startkit/tests/ai/test_config.py b/fastapi_startkit/tests/ai/test_config.py
new file mode 100644
index 00000000..a5c91390
--- /dev/null
+++ b/fastapi_startkit/tests/ai/test_config.py
@@ -0,0 +1,149 @@
+"""Tests for AI configuration dataclasses."""
+
+from fastapi_startkit.ai.config import AIConfig, AnthropicConfig, GoogleConfig, OpenAIConfig
+
+
+# ─── AIConfig defaults ────────────────────────────────────────────────────────
+
+
+def test_aiconfig_default_provider_is_google(monkeypatch):
+    """Default provider is 'google' when AI_PROVIDER env var is not set."""
+    monkeypatch.delenv("AI_PROVIDER", raising=False)
+    config = AIConfig()
+    assert config.default == "google"
+
+
+def test_aiconfig_default_reads_ai_provider_env(monkeypatch):
+    monkeypatch.setenv("AI_PROVIDER", "anthropic")
+    config = AIConfig()
+    assert config.default == "anthropic"
+
+
+def test_aiconfig_providers_has_anthropic_key():
+    config = AIConfig()
+    assert "anthropic" in config.providers
+
+
+def test_aiconfig_providers_has_openai_key():
+    config = AIConfig()
+    assert "openai" in config.providers
+
+
+def test_aiconfig_providers_has_google_key():
+    config = AIConfig()
+    assert "google" in config.providers
+
+
+def test_aiconfig_providers_anthropic_is_instance():
+    config = AIConfig()
+    assert isinstance(config.providers["anthropic"], AnthropicConfig)
+
+
+def test_aiconfig_providers_openai_is_instance():
+    config = AIConfig()
+    assert isinstance(config.providers["openai"], OpenAIConfig)
+
+
+def test_aiconfig_providers_google_is_instance():
+    config = AIConfig()
+    assert isinstance(config.providers["google"], GoogleConfig)
+
+
+# ─── AnthropicConfig ──────────────────────────────────────────────────────────
+
+
+def test_anthropic_config_driver_is_anthropic():
+    config = AnthropicConfig()
+    assert config.driver == "anthropic"
+
+
+def test_anthropic_config_picks_up_api_key_from_env(monkeypatch):
+    monkeypatch.setenv("ANTHROPIC_API_KEY", "test-anthropic-key-123")
+    config = AnthropicConfig()
+    assert config.key == "test-anthropic-key-123"
+
+
+def test_anthropic_config_key_defaults_to_empty_when_env_not_set(monkeypatch):
+    monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False)
+    config = AnthropicConfig()
+    assert config.key == ""
+
+
+def test_anthropic_config_url_default():
+    config = AnthropicConfig()
+    assert config.url == "https://api.anthropic.com"
+
+
+def test_anthropic_config_url_can_be_overridden(monkeypatch):
+    monkeypatch.setenv("ANTHROPIC_BASE_URL", "https://my-proxy.example.com")
+    config = AnthropicConfig()
+    assert config.url == "https://my-proxy.example.com"
+
+
+# ─── OpenAIConfig ─────────────────────────────────────────────────────────────
+
+
+def test_openai_config_driver_is_openai():
+    config = OpenAIConfig()
+    assert config.driver == "openai"
+
+
+def test_openai_config_picks_up_api_key_from_env(monkeypatch):
+    monkeypatch.setenv("OPENAI_API_KEY", "sk-openai-test-key")
+    config = OpenAIConfig()
+    assert config.key == "sk-openai-test-key"
+
+
+def test_openai_config_key_defaults_to_empty_when_env_not_set(monkeypatch):
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    config = OpenAIConfig()
+    assert config.key == ""
+
+
+def test_openai_config_url_default():
+    config = OpenAIConfig()
+    assert config.url == "https://api.openai.com/v1"
+
+
+def test_openai_config_url_can_be_overridden(monkeypatch):
+    monkeypatch.setenv("OPENAI_BASE_URL", "https://openai-proxy.example.com/v1")
+    config = OpenAIConfig()
+    assert config.url == "https://openai-proxy.example.com/v1"
+
+
+# ─── GoogleConfig ─────────────────────────────────────────────────────────────
+
+
+def test_google_config_driver_is_google():
+    config = GoogleConfig()
+    assert config.driver == "google"
+
+
+def test_google_config_picks_up_gemini_api_key(monkeypatch):
+    monkeypatch.setenv("GEMINI_API_KEY", "gemini-key-abc")
+    monkeypatch.delenv("GOOGLE_API_KEY", raising=False)
+    config = GoogleConfig()
+    assert config.key == "gemini-key-abc"
+
+
+def test_google_config_falls_back_to_google_api_key(monkeypatch):
+    """When GEMINI_API_KEY is not set, fall back to GOOGLE_API_KEY."""
+    monkeypatch.delenv("GEMINI_API_KEY", raising=False)
+    monkeypatch.setenv("GOOGLE_API_KEY", "google-api-fallback")
+    config = GoogleConfig()
+    assert config.key == "google-api-fallback"
+
+
+def test_google_config_gemini_key_takes_precedence(monkeypatch):
+    """GEMINI_API_KEY wins over GOOGLE_API_KEY when both are set."""
+    monkeypatch.setenv("GEMINI_API_KEY", "gemini-wins")
+    monkeypatch.setenv("GOOGLE_API_KEY", "google-loses")
+    config = GoogleConfig()
+    assert config.key == "gemini-wins"
+
+
+def test_google_config_key_defaults_to_empty_when_neither_set(monkeypatch):
+    monkeypatch.delenv("GEMINI_API_KEY", raising=False)
+    monkeypatch.delenv("GOOGLE_API_KEY", raising=False)
+    config = GoogleConfig()
+    assert config.key == ""
diff --git a/fastapi_startkit/tests/ai/test_provider.py b/fastapi_startkit/tests/ai/test_provider.py
new file mode 100644
index 00000000..ddf97379
--- /dev/null
+++ b/fastapi_startkit/tests/ai/test_provider.py
@@ -0,0 +1,118 @@
+"""Tests for AIProvider service provider."""
+
+from unittest.mock import MagicMock
+
+from fastapi_startkit.ai.config import AIConfig
+from fastapi_startkit.ai.providers.ai_provider import AIProvider
+from fastapi_startkit.providers import Provider
+
+
+# ─── AIProvider class contract ────────────────────────────────────────────────
+
+
+def test_ai_provider_is_a_provider():
+    assert issubclass(AIProvider, Provider)
+
+
+def test_ai_provider_key():
+    assert AIProvider.provider_key == "ai"
+
+
+# ─── AIProvider.register() ────────────────────────────────────────────────────
+
+
+def test_register_binds_ai_config_to_container():
+    """register() must call app.bind('ai', <AIConfig instance>)."""
+    fake_app = MagicMock()
+    provider = AIProvider(fake_app)
+
+    provider.register()
+
+    fake_app.bind.assert_called_once()
+    call_args = fake_app.bind.call_args
+    assert call_args[0][0] == "ai"
+    assert isinstance(call_args[0][1], AIConfig)
+
+
+def test_register_does_not_raise():
+    fake_app = MagicMock()
+    provider = AIProvider(fake_app)
+
+    # Should not raise any exception
+    provider.register()
+
+
+# ─── AIProvider.boot() ────────────────────────────────────────────────────────
+
+
+def test_boot_sets_ai_in_config_store():
+    """boot() must call config.set('ai', <AIConfig>) so Config.get('ai') works."""
+    ai_config_instance = AIConfig()
+
+    fake_config_store = MagicMock()
+    fake_app = MagicMock()
+    fake_app.make.side_effect = lambda key: ai_config_instance if key == "ai" else fake_config_store
+
+    provider = AIProvider(fake_app)
+    provider.boot()
+
+    # Verify config store received the AIConfig under the 'ai' key
+    fake_config_store.set.assert_called_once_with("ai", ai_config_instance)
+
+
+def test_boot_does_not_raise():
+    ai_config_instance = AIConfig()
+    fake_config_store = MagicMock()
+    fake_app = MagicMock()
+    fake_app.make.side_effect = lambda key: ai_config_instance if key == "ai" else fake_config_store
+
+    provider = AIProvider(fake_app)
+    provider.boot()  # must not raise
+
+
+# ─── Integration: Config.get('ai') after boot ─────────────────────────────────
+
+
+def test_config_get_ai_returns_ai_config_data_after_provider_boots():
+    """Full integration: after AIProvider boots, Config.get('ai') exposes AI config data.
+
+    The framework's Configuration.set() serialises dataclasses to a dotty-dict, so
+    Config.get('ai') returns a mapping rather than an AIConfig instance.  The test
+    verifies the 'default' key is present and the raw container binding stays typed.
+    """
+    from fastapi_startkit.application import Application
+    from fastapi_startkit.configuration.config import Config
+
+    # Use the test Application singleton (initialised by the session fixture)
+    app = Application()
+
+    ai_config_instance = AIConfig()
+
+    # Simulate what AIProvider.register() does
+    app.bind("ai", ai_config_instance)
+
+    # Simulate what AIProvider.boot() does — config.set() serialises the dataclass
+    app.make("config").set("ai", ai_config_instance)
+
+    # Config.get('ai') returns the serialised dict structure
+    result = Config.get("ai")
+    assert result is not None
+    # The 'default' field must survive serialisation
+    assert result["default"] == ai_config_instance.default
+
+    # The raw container binding retains the typed AIConfig instance
+    assert isinstance(app.make("ai"), AIConfig)
+
+
+def test_ai_provider_register_and_boot_together():
+    """register() followed by boot() produces an AIConfig in the container."""
+    from fastapi_startkit.application import Application
+
+    app = Application()
+
+    provider = AIProvider(app)
+    provider.register()
+    provider.boot()
+
+    ai_value = app.make("ai")
+    assert isinstance(ai_value, AIConfig)
diff --git a/fastapi_startkit/uv.lock b/fastapi_startkit/uv.lock
index 50813f3e..3ded87ed 100644
--- a/fastapi_startkit/uv.lock
+++ b/fastapi_startkit/uv.lock
@@ -46,6 +46,25 @@ wheels = [
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 ]
 
+[[package]]
+name = "anthropic"
+version = "0.109.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "distro" },
+    { name = "docstring-parser" },
+    { name = "httpx" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/54/0b/ce24a4f275573f5e436ca954faca60c759d58ed152b8fa36a1e3b888e261/anthropic-0.109.1.tar.gz", hash = "sha256:83e06b3d9d40ff5898f588020e0cc4e42187de954549a3b5fbe6e2685a09c785", size = 927569, upload-time = "2026-06-09T23:55:24.884Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/0f/a6110d713370bc92f074a622f8a5ebdec7e92360149b1048dca258a07b2f/anthropic-0.109.1-py3-none-any.whl", hash = "sha256:ce7d94a7657f2aa29338cca448945eac621b4f62c1794cf461cb32847223e9b8", size = 923851, upload-time = "2026-06-09T23:55:23.348Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.13.0"
@@ -117,6 +136,8 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" }
 wheels = [
+    { url = "https://files.pythonhosted.org/packages/ea/47/4f61023ea636104d4f16ab488e268b93008c3d0bb76893b1b31db1f96802/cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d", size = 185271, upload-time = "2025-09-08T23:22:44.795Z" },
+    { url = "https://files.pythonhosted.org/packages/df/a2/781b623f57358e360d62cdd7a8c681f074a71d445418a776eef0aadb4ab4/cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c", size = 181048, upload-time = "2025-09-08T23:22:45.938Z" },
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     { url = "https://files.pythonhosted.org/packages/d5/72/12b5f8d3865bf0f87cf1404d8c374e7487dcf097a1c91c436e72e6badd83/cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062", size = 220097, upload-time = "2025-09-08T23:22:48.677Z" },
     { url = "https://files.pythonhosted.org/packages/c2/95/7a135d52a50dfa7c882ab0ac17e8dc11cec9d55d2c18dda414c051c5e69e/cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e", size = 207983, upload-time = "2025-09-08T23:22:50.06Z" },
@@ -124,6 +145,11 @@ wheels = [
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