GUI Created, Dark Mode Created, npm script added, styling changed, Decoupled from Claude made more open source.

src/llm/__init__.py

@@ -0,0 +1,33 @@
+"""
+LLM Abstraction Layer
+
+Provider-agnostic interface for multiple LLM backends.
+"""
+
+from llm.core.interface import LLMProvider
+from llm.core.types import LLMInput, LLMOutput, Message, ToolCall, ToolDefinition, ToolResult
+from llm.providers import get_provider
+from llm.tools import ToolExecutor, ToolRegistry
+from llm.cli.selector import interactive_select
+
+__version__ = "0.1.0"
+
+__all__ = [
+    "LLMProvider",
+    "LLMInput",
+    "LLMOutput",
+    "Message",
+    "get_provider",
+    "ToolCall",
+    "ToolDefinition",
+    "ToolResult",
+    "ToolExecutor",
+    "ToolRegistry",
+    "interactive_select",
+]
+
+
+def gui() -> None:
+    from llm.gui.selector import main
+    main()
+

src/llm/__main__.py

@@ -0,0 +1,7 @@
+#!/usr/bin/env python3
+"""Entry point for llm CLI."""
+
+from llm.cli.selector import main
+
+if __name__ == "__main__":
+    main()

src/llm/cli/selector.py

@@ -0,0 +1,154 @@
+
+
+from __future__ import annotations
+
+import os
+import sys
+from enum import Enum
+
+
+class Color(str, Enum):
+    RESET = "\033[0m"
+    BOLD = "\033[1m"
+    GREEN = "\033[92m"
+    YELLOW = "\033[93m"
+    BLUE = "\033[94m"
+    CYAN = "\033[96m"
+
+
+def print_banner() -> None:
+    banner = f"""{Color.CYAN}
+╔═══════════════════════════════════════════╗
+║   LLM Provider Selector                   ║
+║   Provider-agnostic AI interactions       ║
+╚═══════════════════════════════════════════╝{Color.RESET}"""
+    print(banner)
+
+
+def print_providers(providers: list[tuple[str, str]]) -> None:
+    print(f"\n{Color.BOLD}Available Providers:{Color.RESET}\n")
+    for i, (name, desc) in enumerate(providers, 1):
+        print(f"  {Color.GREEN}{i}{Color.RESET}. {Color.BOLD}{name}{Color.RESET} - {desc}")
+
+
+def select_provider(providers: list[tuple[str, str]]) -> str | None:
+    if not providers:
+        print("No providers available.")
+        return None
+
+    print_providers(providers)
+
+    while True:
+        try:
+            choice = input(f"\n{Color.YELLOW}Select provider (1-{len(providers)}): {Color.RESET}").strip()
+            if not choice:
+                return None
+            idx = int(choice) - 1
+            if 0 <= idx < len(providers):
+                return providers[idx][0]
+            print(f"{Color.YELLOW}Invalid selection. Try again.{Color.RESET}")
+        except ValueError:
+            print(f"{Color.YELLOW}Please enter a number.{Color.RESET}")
+
+
+def select_model(models: list[tuple[str, str]]) -> str | None:
+    if not models:
+        print("No models available.")
+        return None
+
+    print(f"\n{Color.BOLD}Available Models:{Color.RESET}\n")
+    for i, (name, desc) in enumerate(models, 1):
+        print(f"  {Color.GREEN}{i}{Color.RESET}. {Color.BOLD}{name}{Color.RESET} - {desc}")
+
+    while True:
+        try:
+            choice = input(f"\n{Color.YELLOW}Select model (1-{len(models)}): {Color.RESET}").strip()
+            if not choice:
+                return None
+            idx = int(choice) - 1
+            if 0 <= idx < len(models):
+                return models[idx][0]
+            print(f"{Color.YELLOW}Invalid selection. Try again.{Color.RESET}")
+        except ValueError:
+            print(f"{Color.YELLOW}Please enter a number.{Color.RESET}")
+
+
+def save_config(provider: str, model: str, persist: bool = False) -> None:
+    config = f"LLM_PROVIDER={provider}\nLLM_MODEL={model}\n"
+    env_file = ".llm.env"
+
+    with open(env_file, "w") as f:
+        f.write(config)
+
+    print(f"\n{Color.GREEN}✓{Color.RESET} Config saved to {Color.CYAN}{env_file}{Color.RESET}")
+
+    if persist:
+        os.environ["LLM_PROVIDER"] = provider
+        os.environ["LLM_MODEL"] = model
+        print(f"{Color.GREEN}✓{Color.RESET} Config loaded to current session")
+
+
+def interactive_select(
+    providers: list[tuple[str, str]] | None = None,
+    models_per_provider: dict[str, list[tuple[str, str]]] | None = None,
+    persist: bool = False,
+) -> tuple[str, str] | None:
+    print_banner()
+
+    if providers is None:
+        providers = [
+            ("claude", "Anthropic Claude ( Sonnet, Opus, Haiku)"),
+            ("openai", "OpenAI GPT (4o, 4o-mini, 3.5-turbo)"),
+            ("ollama", "Local Ollama models"),
+        ]
+
+    if models_per_provider is None:
+        models_per_provider = {
+            "claude": [
+                ("claude-opus-4-5", "Claude Opus 4.5 - Most capable"),
+                ("claude-sonnet-4-7", "Claude Sonnet 4.7 - Balanced"),
+                ("claude-haiku-4-7", "Claude Haiku 4.7 - Fast"),
+            ],
+            "openai": [
+                ("gpt-4o", "GPT-4o - Most capable"),
+                ("gpt-4o-mini", "GPT-4o-mini - Fast & affordable"),
+                ("gpt-4-turbo", "GPT-4 Turbo - Legacy powerful"),
+                ("gpt-3.5-turbo", "GPT-3.5 - Legacy fast"),
+            ],
+            "ollama": [
+                ("llama3.2", "Llama 3.2 - General purpose"),
+                ("mistral", "Mistral - Fast & efficient"),
+                ("codellama", "CodeLlama - Code specialized"),
+            ],
+        }
+
+    provider = select_provider(providers)
+    if not provider:
+        return None
+
+    models = models_per_provider.get(provider, [])
+    model = select_model(models)
+    if not model:
+        return None
+
+    print(f"\n{Color.GREEN}Selected: {Color.BOLD}{provider}{Color.RESET} / {Color.BOLD}{model}{Color.RESET}")
+
+    save_config(provider, model, persist)
+
+    return (provider, model)
+
+
+def main() -> None:
+    result = interactive_select(persist=True)
+
+    if result:
+        print(f"\n{Color.GREEN}Ready to use!{Color.RESET}")
+        print(f"  export LLM_PROVIDER={result[0]}")
+        print(f"  export LLM_MODEL={result[1]}")
+    else:
+        print("\nSelection cancelled.")
+        sys.exit(0)
+
+
+if __name__ == "__main__":
+    main()

src/llm/core/__init__.py

@@ -0,0 +1 @@
+"""Core module for LLM abstraction layer."""

src/llm/core/interface.py

@@ -0,0 +1,60 @@
+"""LLM Provider interface definition."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any
+
+from llm.core.types import LLMInput, LLMOutput, ModelInfo, ProviderType
+
+
+class LLMProvider(ABC):
+    provider_type: ProviderType
+
+    @abstractmethod
+    def generate(self, input: LLMInput) -> LLMOutput: ...
+
+    @abstractmethod
+    def list_models(self) -> list[ModelInfo]: ...
+
+    @abstractmethod
+    def validate_config(self) -> bool: ...
+
+    def supports_tools(self) -> bool:
+        return True
+
+    def supports_vision(self) -> bool:
+        return False
+
+    def get_default_model(self) -> str:
+        raise NotImplementedError(f"{self.__class__.__name__} must implement get_default_model")
+
+
+class LLMError(Exception):
+    def __init__(
+        self,
+        message: str,
+        provider: ProviderType | None = None,
+        code: str | None = None,
+        details: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(message)
+        self.message = message
+        self.provider = provider
+        self.code = code
+        self.details = details or {}
+
+
+class AuthenticationError(LLMError): ...
+
+
+class RateLimitError(LLMError): ...
+
+
+class ContextLengthError(LLMError): ...
+
+
+class ModelNotFoundError(LLMError): ...
+
+
+class ToolExecutionError(LLMError): ...

src/llm/core/types.py

@@ -0,0 +1,140 @@
+"""Core type definitions for LLM abstraction layer."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+
+class Role(str, Enum):
+    SYSTEM = "system"
+    USER = "user"
+    ASSISTANT = "assistant"
+    TOOL = "tool"
+
+
+class ProviderType(str, Enum):
+    CLAUDE = "claude"
+    OPENAI = "openai"
+    OLLAMA = "ollama"
+
+
+@dataclass(frozen=True)
+class Message:
+    role: Role
+    content: str
+    name: str | None = None
+    tool_call_id: str | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        result: dict[str, Any] = {"role": self.role.value, "content": self.content}
+        if self.name:
+            result["name"] = self.name
+        if self.tool_call_id:
+            result["tool_call_id"] = self.tool_call_id
+        return result
+
+
+@dataclass(frozen=True)
+class ToolDefinition:
+    name: str
+    description: str
+    parameters: dict[str, Any]
+    strict: bool = True
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "description": self.description,
+            "parameters": self.parameters,
+            "strict": self.strict,
+        }
+
+
+@dataclass(frozen=True)
+class ToolCall:
+    id: str
+    name: str
+    arguments: dict[str, Any]
+
+
+@dataclass(frozen=True)
+class ToolResult:
+    tool_call_id: str
+    content: str
+    is_error: bool = False
+
+
+@dataclass(frozen=True)
+class LLMInput:
+    messages: list[Message]
+    model: str | None = None
+    temperature: float = 1.0
+    max_tokens: int | None = None
+    tools: list[ToolDefinition] | None = None
+    stream: bool = False
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        result: dict[str, Any] = {
+            "messages": [msg.to_dict() for msg in self.messages],
+            "temperature": self.temperature,
+            "stream": self.stream,
+        }
+        if self.model:
+            result["model"] = self.model
+        if self.max_tokens is not None:
+            result["max_tokens"] = self.max_tokens
+        if self.tools:
+            result["tools"] = [tool.to_dict() for tool in self.tools]
+        return result | self.metadata
+
+
+@dataclass(frozen=True)
+class LLMOutput:
+    content: str
+    tool_calls: list[ToolCall] | None = None
+    model: str | None = None
+    usage: dict[str, int] | None = None
+    stop_reason: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def has_tool_calls(self) -> bool:
+        return bool(self.tool_calls)
+
+    def to_dict(self) -> dict[str, Any]:
+        result: dict[str, Any] = {"content": self.content}
+        if self.tool_calls:
+            result["tool_calls"] = [
+                {"id": tc.id, "name": tc.name, "arguments": tc.arguments}
+                for tc in self.tool_calls
+            ]
+        if self.model:
+            result["model"] = self.model
+        if self.usage:
+            result["usage"] = self.usage
+        if self.stop_reason:
+            result["stop_reason"] = self.stop_reason
+        return result | self.metadata
+
+
+@dataclass(frozen=True)
+class ModelInfo:
+    name: str
+    provider: ProviderType
+    supports_tools: bool = True
+    supports_vision: bool = False
+    max_tokens: int | None = None
+    context_window: int | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "provider": self.provider.value,
+            "supports_tools": self.supports_tools,
+            "supports_vision": self.supports_vision,
+            "max_tokens": self.max_tokens,
+            "context_window": self.context_window,
+        }

src/llm/prompt/__init__.py

@@ -0,0 +1,13 @@
+"""Prompt module for prompt building and normalization."""
+
+from llm.prompt.builder import PromptBuilder, adapt_messages_for_provider, get_provider_builder
+from llm.prompt.templates import TEMPLATES, get_template, get_template_or_default
+
+__all__ = [
+    "PromptBuilder",
+    "adapt_messages_for_provider",
+    "get_provider_builder",
+    "TEMPLATES",
+    "get_template",
+    "get_template_or_default",
+]

src/llm/prompt/builder.py

@@ -0,0 +1,101 @@
+"""Prompt builder for normalizing prompts across providers."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from llm.core.types import LLMInput, Message, Role, ToolDefinition
+from llm.providers.claude import ClaudeProvider
+from llm.providers.openai import OpenAIProvider
+from llm.providers.ollama import OllamaProvider
+
+
+@dataclass
+class PromptConfig:
+    system_template: str | None = None
+    user_template: str | None = None
+    include_tools_in_system: bool = True
+    tool_format: str = "native"
+
+
+class PromptBuilder:
+    def __init__(self, config: PromptConfig | None = None) -> None:
+        self.config = config or PromptConfig()
+
+    def build(self, messages: list[Message], tools: list[ToolDefinition] | None = None) -> list[Message]:
+        if not messages:
+            return []
+
+        result: list[Message] = []
+        system_parts: list[str] = []
+
+        if self.config.system_template:
+            system_parts.append(self.config.system_template)
+
+        if tools and self.config.include_tools_in_system:
+            tools_desc = self._format_tools(tools)
+            system_parts.append(f"\n\n## Available Tools\n{tools_desc}")
+
+        if messages[0].role == Role.SYSTEM:
+            system_parts.insert(0, messages[0].content)
+            result.extend(messages[1:])
+        else:
+            if system_parts:
+                result.insert(0, Message(role=Role.SYSTEM, content="\n\n".join(system_parts)))
+            result.extend(messages)
+
+        return result
+
+    def _format_tools(self, tools: list[ToolDefinition]) -> str:
+        lines = []
+        for tool in tools:
+            lines.append(f"### {tool.name}")
+            lines.append(tool.description)
+            if tool.parameters:
+                lines.append("Parameters:")
+                lines.append(self._format_parameters(tool.parameters))
+        return "\n".join(lines)
+
+    def _format_parameters(self, params: dict[str, Any]) -> str:
+        if "properties" not in params:
+            return str(params)
+        lines = []
+        required = params.get("required", [])
+        for name, spec in params["properties"].items():
+            prop_type = spec.get("type", "any")
+            desc = spec.get("description", "")
+            required_mark = "(required)" if name in required else "(optional)"
+            lines.append(f"  - {name}: {prop_type} {required_mark} - {desc}")
+        return "\n".join(lines) if lines else str(params)
+
+
+_PROVIDER_TEMPLATE_MAP: dict[str, dict[str, Any]] = {
+    "claude": {
+        "include_tools_in_system": False,
+        "tool_format": "anthropic",
+    },
+    "openai": {
+        "include_tools_in_system": False,
+        "tool_format": "openai",
+    },
+    "ollama": {
+        "include_tools_in_system": True,
+        "tool_format": "text",
+    },
+}
+
+
+def get_provider_builder(provider_name: str) -> PromptBuilder:
+    config_dict = _PROVIDER_TEMPLATE_MAP.get(provider_name.lower(), {})
+    config = PromptConfig(**config_dict)
+    return PromptBuilder(config)
+
+
+def adapt_messages_for_provider(
+    messages: list[Message],
+    provider: str,
+    tools: list[ToolDefinition] | None = None,
+) -> list[Message]:
+    builder = get_provider_builder(provider)
+    return builder.build(messages, tools)

src/llm/prompt/templates/__init__.py

@@ -0,0 +1 @@
+# Templates module for provider-specific prompt templates

src/llm/providers/__init__.py

@@ -0,0 +1,14 @@
+"""Provider adapters for multiple LLM backends."""
+
+from llm.providers.claude import ClaudeProvider
+from llm.providers.openai import OpenAIProvider
+from llm.providers.ollama import OllamaProvider
+from llm.providers.resolver import get_provider, register_provider
+
+__all__ = [
+    "ClaudeProvider",
+    "OpenAIProvider",
+    "OllamaProvider",
+    "get_provider",
+    "register_provider",
+]

src/llm/providers/claude.py

@@ -0,0 +1,103 @@
+"""Claude provider adapter."""
+
+from __future__ import annotations
+
+import os
+from typing import Any
+
+from anthropic import Anthropic
+
+from llm.core.interface import (
+    AuthenticationError,
+    ContextLengthError,
+    LLMProvider,
+    RateLimitError,
+)
+from llm.core.types import LLMInput, LLMOutput, Message, ModelInfo, ProviderType, ToolCall
+
+
+class ClaudeProvider(LLMProvider):
+    provider_type = ProviderType.CLAUDE
+
+    def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
+        self.client = Anthropic(api_key=api_key or os.environ.get("ANTHROPIC_API_KEY"), base_url=base_url)
+        self._models = [
+            ModelInfo(
+                name="claude-opus-4-5",
+                provider=ProviderType.CLAUDE,
+                supports_tools=True,
+                supports_vision=True,
+                max_tokens=8192,
+                context_window=200000,
+            ),
+            ModelInfo(
+                name="claude-sonnet-4-7",
+                provider=ProviderType.CLAUDE,
+                supports_tools=True,
+                supports_vision=True,
+                max_tokens=8192,
+                context_window=200000,
+            ),
+            ModelInfo(
+                name="claude-haiku-4-7",
+                provider=ProviderType.CLAUDE,
+                supports_tools=True,
+                supports_vision=False,
+                max_tokens=4096,
+                context_window=200000,
+            ),
+        ]
+
+    def generate(self, input: LLMInput) -> LLMOutput:
+        try:
+            params: dict[str, Any] = {
+                "model": input.model or "claude-sonnet-4-7",
+                "messages": [msg.to_dict() for msg in input.messages],
+                "temperature": input.temperature,
+            }
+            if input.max_tokens:
+                params["max_tokens"] = input.max_tokens
+            if input.tools:
+                params["tools"] = [tool.to_dict() for tool in input.tools]
+
+            response = self.client.messages.create(**params)
+
+            tool_calls = None
+            if response.content and hasattr(response.content[0], "type"):
+                if response.content[0].type == "tool_use":
+                    tool_calls = [
+                        ToolCall(
+                            id=getattr(response.content[0], "id", ""),
+                            name=getattr(response.content[0], "name", ""),
+                            arguments=getattr(response.content[0].input, "__dict__", {}),
+                        )
+                    ]
+
+            return LLMOutput(
+                content=response.content[0].text if response.content else "",
+                tool_calls=tool_calls,
+                model=response.model,
+                usage={
+                    "input_tokens": response.usage.input_tokens,
+                    "output_tokens": response.usage.output_tokens,
+                },
+                stop_reason=response.stop_reason,
+            )
+        except Exception as e:
+            msg = str(e)
+            if "401" in msg or "authentication" in msg.lower():
+                raise AuthenticationError(msg, provider=ProviderType.CLAUDE) from e
+            if "429" in msg or "rate_limit" in msg.lower():
+                raise RateLimitError(msg, provider=ProviderType.CLAUDE) from e
+            if "context" in msg.lower() and "length" in msg.lower():
+                raise ContextLengthError(msg, provider=ProviderType.CLAUDE) from e
+            raise
+
+    def list_models(self) -> list[ModelInfo]:
+        return self._models.copy()
+
+    def validate_config(self) -> bool:
+        return bool(self.client.api_key)
+
+    def get_default_model(self) -> str:
+        return "claude-sonnet-4-7"

src/llm/providers/ollama.py

@@ -0,0 +1,112 @@
+"""Ollama provider adapter for local models."""
+
+from __future__ import annotations
+
+import os
+from typing import Any
+
+from llm.core.interface import (
+    AuthenticationError,
+    ContextLengthError,
+    LLMProvider,
+    RateLimitError,
+)
+from llm.core.types import LLMInput, LLMOutput, Message, ModelInfo, ProviderType, ToolCall
+
+
+class OllamaProvider(LLMProvider):
+    provider_type = ProviderType.OLLAMA
+
+    def __init__(
+        self,
+        base_url: str | None = None,
+        default_model: str | None = None,
+    ) -> None:
+        self.base_url = base_url or os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434")
+        self.default_model = default_model or os.environ.get("OLLAMA_MODEL", "llama3.2")
+        self._models = [
+            ModelInfo(
+                name="llama3.2",
+                provider=ProviderType.OLLAMA,
+                supports_tools=False,
+                supports_vision=False,
+                max_tokens=4096,
+                context_window=128000,
+            ),
+            ModelInfo(
+                name="mistral",
+                provider=ProviderType.OLLAMA,
+                supports_tools=False,
+                supports_vision=False,
+                max_tokens=4096,
+                context_window=8192,
+            ),
+            ModelInfo(
+                name="codellama",
+                provider=ProviderType.OLLAMA,
+                supports_tools=False,
+                supports_vision=False,
+                max_tokens=4096,
+                context_window=16384,
+            ),
+        ]
+
+    def generate(self, input: LLMInput) -> LLMOutput:
+        import urllib.request
+        import json
+
+        try:
+            url = f"{self.base_url}/api/chat"
+            model = input.model or self.default_model
+
+            payload: dict[str, Any] = {
+                "model": model,
+                "messages": [msg.to_dict() for msg in input.messages],
+                "stream": False,
+            }
+            if input.temperature != 1.0:
+                payload["options"] = {"temperature": input.temperature}
+
+            data = json.dumps(payload).encode("utf-8")
+            req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
+
+            with urllib.request.urlopen(req, timeout=60) as response:
+                result = json.loads(response.read().decode("utf-8"))
+
+            content = result.get("message", {}).get("content", "")
+
+            tool_calls = None
+            if result.get("message", {}).get("tool_calls"):
+                tool_calls = [
+                    ToolCall(
+                        id=tc.get("id", ""),
+                        name=tc.get("function", {}).get("name", ""),
+                        arguments=tc.get("function", {}).get("arguments", {}),
+                    )
+                    for tc in result["message"]["tool_calls"]
+                ]
+
+            return LLMOutput(
+                content=content,
+                tool_calls=tool_calls,
+                model=model,
+                stop_reason=result.get("done_reason"),
+            )
+        except Exception as e:
+            msg = str(e)
+            if "401" in msg or "connection" in msg.lower():
+                raise AuthenticationError(f"Ollama connection failed: {msg}", provider=ProviderType.OLLAMA) from e
+            if "429" in msg or "rate_limit" in msg.lower():
+                raise RateLimitError(msg, provider=ProviderType.OLLAMA) from e
+            if "context" in msg.lower() and "length" in msg.lower():
+                raise ContextLengthError(msg, provider=ProviderType.OLLAMA) from e
+            raise
+
+    def list_models(self) -> list[ModelInfo]:
+        return self._models.copy()
+
+    def validate_config(self) -> bool:
+        return bool(self.base_url)
+
+    def get_default_model(self) -> str:
+        return self.default_model

src/llm/providers/openai.py

@@ -0,0 +1,113 @@
+"""OpenAI provider adapter."""
+
+from __future__ import annotations
+
+import os
+from typing import Any
+
+from openai import OpenAI
+
+from llm.core.interface import (
+    AuthenticationError,
+    ContextLengthError,
+    LLMProvider,
+    RateLimitError,
+)
+from llm.core.types import LLMInput, LLMOutput, Message, ModelInfo, ProviderType, ToolCall
+
+
+class OpenAIProvider(LLMProvider):
+    provider_type = ProviderType.OPENAI
+
+    def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
+        self.client = OpenAI(api_key=api_key or os.environ.get("OPENAI_API_KEY"), base_url=base_url)
+        self._models = [
+            ModelInfo(
+                name="gpt-4o",
+                provider=ProviderType.OPENAI,
+                supports_tools=True,
+                supports_vision=True,
+                max_tokens=4096,
+                context_window=128000,
+            ),
+            ModelInfo(
+                name="gpt-4o-mini",
+                provider=ProviderType.OPENAI,
+                supports_tools=True,
+                supports_vision=True,
+                max_tokens=4096,
+                context_window=128000,
+            ),
+            ModelInfo(
+                name="gpt-4-turbo",
+                provider=ProviderType.OPENAI,
+                supports_tools=True,
+                supports_vision=True,
+                max_tokens=4096,
+                context_window=128000,
+            ),
+            ModelInfo(
+                name="gpt-3.5-turbo",
+                provider=ProviderType.OPENAI,
+                supports_tools=True,
+                supports_vision=False,
+                max_tokens=4096,
+                context_window=16385,
+            ),
+        ]
+
+    def generate(self, input: LLMInput) -> LLMOutput:
+        try:
+            params: dict[str, Any] = {
+                "model": input.model or "gpt-4o-mini",
+                "messages": [msg.to_dict() for msg in input.messages],
+                "temperature": input.temperature,
+            }
+            if input.max_tokens:
+                params["max_tokens"] = input.max_tokens
+            if input.tools:
+                params["tools"] = [tool.to_dict() for tool in input.tools]
+
+            response = self.client.chat.completions.create(**params)
+            choice = response.choices[0]
+
+            tool_calls = None
+            if choice.message.tool_calls:
+                tool_calls = [
+                    ToolCall(
+                        id=tc.id or "",
+                        name=tc.function.name,
+                        arguments={} if tc.function.arguments == "" else tc.function.arguments,
+                    )
+                    for tc in choice.message.tool_calls
+                ]
+
+            return LLMOutput(
+                content=choice.message.content or "",
+                tool_calls=tool_calls,
+                model=response.model,
+                usage={
+                    "prompt_tokens": response.usage.prompt_tokens,
+                    "completion_tokens": response.usage.completion_tokens,
+                    "total_tokens": response.usage.total_tokens,
+                },
+                stop_reason=choice.finish_reason,
+            )
+        except Exception as e:
+            msg = str(e)
+            if "401" in msg or "authentication" in msg.lower():
+                raise AuthenticationError(msg, provider=ProviderType.OPENAI) from e
+            if "429" in msg or "rate_limit" in msg.lower():
+                raise RateLimitError(msg, provider=ProviderType.OPENAI) from e
+            if "context" in msg.lower() and "length" in msg.lower():
+                raise ContextLengthError(msg, provider=ProviderType.OPENAI) from e
+            raise
+
+    def list_models(self) -> list[ModelInfo]:
+        return self._models.copy()
+
+    def validate_config(self) -> bool:
+        return bool(self.client.api_key)
+
+    def get_default_model(self) -> str:
+        return "gpt-4o-mini"

src/llm/providers/resolver.py

@@ -0,0 +1,39 @@
+"""Provider factory and resolver."""
+
+from __future__ import annotations
+
+import os
+
+from llm.core.interface import LLMProvider
+from llm.core.types import ProviderType
+from llm.providers.claude import ClaudeProvider
+from llm.providers.openai import OpenAIProvider
+from llm.providers.ollama import OllamaProvider
+
+
+_PROVIDER_MAP: dict[ProviderType, type[LLMProvider]] = {
+    ProviderType.CLAUDE: ClaudeProvider,
+    ProviderType.OPENAI: OpenAIProvider,
+    ProviderType.OLLAMA: OllamaProvider,
+}
+
+
+def get_provider(provider_type: ProviderType | str | None = None, **kwargs: str) -> LLMProvider:
+    if provider_type is None:
+        provider_type = os.environ.get("LLM_PROVIDER", "claude").lower()
+
+    if isinstance(provider_type, str):
+        try:
+            provider_type = ProviderType(provider_type)
+        except ValueError:
+            raise ValueError(f"Unknown provider type: {provider_type}. Valid types: {[p.value for p in ProviderType]}")
+
+    provider_cls = _PROVIDER_MAP.get(provider_type)
+    if not provider_cls:
+        raise ValueError(f"No provider registered for type: {provider_type}")
+
+    return provider_cls(**kwargs)
+
+
+def register_provider(provider_type: ProviderType, provider_cls: type[LLMProvider]) -> None:
+    _PROVIDER_MAP[provider_type] = provider_cls

src/llm/tools/__init__.py

@@ -0,0 +1,9 @@
+"""Tools module for tool/function calling abstraction."""
+
+from llm.tools.executor import ReActAgent, ToolExecutor, ToolRegistry
+
+__all__ = [
+    "ToolRegistry",
+    "ToolExecutor",
+    "ReActAgent",
+]

src/llm/tools/executor.py

@@ -0,0 +1,108 @@
+"""Tool executor for handling tool calls from LLM responses."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any, Callable
+
+from llm.core.interface import ToolExecutionError
+from llm.core.types import LLMInput, LLMOutput, Message, Role, ToolCall, ToolDefinition, ToolResult
+
+
+ToolFunc = Callable[..., Any]
+
+
+class ToolRegistry:
+    def __init__(self) -> None:
+        self._tools: dict[str, ToolFunc] = {}
+        self._definitions: dict[str, ToolDefinition] = {}
+
+    def register(self, definition: ToolDefinition, func: ToolFunc) -> None:
+        self._tools[definition.name] = func
+        self._definitions[definition.name] = definition
+
+    def get(self, name: str) -> ToolFunc | None:
+        return self._tools.get(name)
+
+    def get_definition(self, name: str) -> ToolDefinition | None:
+        return self._definitions.get(name)
+
+    def list_tools(self) -> list[ToolDefinition]:
+        return list(self._definitions.values())
+
+    def has(self, name: str) -> bool:
+        return name in self._tools
+
+
+class ToolExecutor:
+    def __init__(self, registry: ToolRegistry | None = None) -> None:
+        self.registry = registry or ToolRegistry()
+
+    def execute(self, tool_call: ToolCall) -> ToolResult:
+        func = self.registry.get(tool_call.name)
+        if not func:
+            return ToolResult(
+                tool_call_id=tool_call.id,
+                content=f"Error: Tool '{tool_call.name}' not found",
+                is_error=True,
+            )
+
+        try:
+            result = func(**tool_call.arguments)
+            content = result if isinstance(result, str) else str(result)
+            return ToolResult(tool_call_id=tool_call.id, content=content)
+        except Exception as e:
+            return ToolResult(
+                tool_call_id=tool_call.id,
+                content=f"Error executing {tool_call.name}: {e}",
+                is_error=True,
+            )
+
+    def execute_all(self, tool_calls: list[ToolCall]) -> list[ToolResult]:
+        return [self.execute(tc) for tc in tool_calls]
+
+
+class ReActAgent:
+    def __init__(
+        self,
+        provider: Any,
+        executor: ToolExecutor,
+        max_iterations: int = 10,
+    ) -> None:
+        self.provider = provider
+        self.executor = executor
+        self.max_iterations = max_iterations
+
+    async def run(self, input: LLMInput) -> LLMOutput:
+        messages = list(input.messages)
+        tools = input.tools or []
+
+        for _ in range(self.max_iterations):
+            input_copy = LLMInput(
+                messages=messages,
+                model=input.model,
+                temperature=input.temperature,
+                max_tokens=input.max_tokens,
+                tools=tools,
+            )
+
+            output = self.provider.generate(input_copy)
+
+            if not output.has_tool_calls:
+                return output
+
+            results = self.executor.execute_all(output.tool_calls)
+
+            for result in results:
+                messages.append(
+                    Message(
+                        role=Role.TOOL,
+                        content=result.content,
+                        tool_call_id=result.tool_call_id,
+                    )
+                )
+
+        return LLMOutput(
+            content="Max iterations reached",
+            stop_reason="max_iterations",
+        )