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

.opencode/package-lock.json

@@ -8,6 +8,9 @@
       "name": "ecc-universal",
       "version": "1.10.0",
       "license": "MIT",
+      "dependencies": {
+        "@opencode-ai/plugin": "1.3.17"
+      },
       "devDependencies": {
         "@opencode-ai/plugin": "^1.0.0",
         "@types/node": "^20.0.0",

.opencode/package.json

@@ -66,5 +66,8 @@
   },
   "engines": {
     "node": ">=18.0.0"
+  },
+  "dependencies": {
+    "@opencode-ai/plugin": "1.4.3"
   }
-}
+}

README.md

@@ -84,6 +84,7 @@ This repo is the raw code only. The guides explain everything.
 
 ### v1.10.0 — Surface Refresh, Operator Workflows, and ECC 2.0 Alpha (Apr 2026)
 
+- **Dashboard GUI** — New Tkinter-based desktop application (`ecc_dashboard.py` or `npm run dashboard`) with dark/light theme toggle, font customization, and project logo in header and taskbar.
 - **Public surface synced to the live repo** — metadata, catalog counts, plugin manifests, and install-facing docs now match the actual OSS surface: 38 agents, 156 skills, and 72 legacy command shims.
 - **Operator and outbound workflow expansion** — `brand-voice`, `social-graph-ranker`, `connections-optimizer`, `customer-billing-ops`, `ecc-tools-cost-audit`, `google-workspace-ops`, `project-flow-ops`, and `workspace-surface-audit` round out the operator lane.
 - **Media and launch tooling** — `manim-video`, `remotion-video-creation`, and upgraded social publishing surfaces make technical explainers and launch content part of the same system.
@@ -240,6 +241,23 @@ For manual install instructions see the README in the `rules/` folder. When copy
 
 **That's it!** You now have access to 47 agents, 181 skills, and 79 legacy command shims.
 
+### Dashboard GUI
+
+Launch the desktop dashboard to visually explore ECC components:
+
+```bash
+npm run dashboard
+# or
+python3 ./ecc_dashboard.py
+```
+
+**Features:**
+- Tabbed interface: Agents, Skills, Commands, Rules, Settings
+- Dark/Light theme toggle
+- Font customization (family & size)
+- Project logo in header and taskbar
+- Search and filter across all components
+
 ### Multi-model commands require additional setup
 
 > WARNING: `multi-*` commands are **not** covered by the base plugin/rules install above.

ecc2/Cargo.lock

@@ -1286,6 +1286,15 @@ version = "0.1.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d05e27ee213611ffe7d6348b942e8f942b37114c00cc03cec254295a4a17852e"
 
+[[package]]
+name = "openssl-src"
+version = "300.6.0+3.6.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a8e8cbfd3a4a8c8f089147fd7aaa33cf8c7450c4d09f8f80698a0cf093abeff4"
+dependencies = [
+ "cc",
+]
+
 [[package]]
 name = "openssl-sys"
 version = "0.9.112"
@@ -1294,6 +1303,7 @@ checksum = "57d55af3b3e226502be1526dfdba67ab0e9c96fc293004e79576b2b9edb0dbdb"
 dependencies = [
  "cc",
  "libc",
+ "openssl-src",
  "pkg-config",
  "vcpkg",
 ]

ecc2/Cargo.toml

@@ -18,8 +18,8 @@ tokio = { version = "1", features = ["full"] }
 # State store
 rusqlite = { version = "0.32", features = ["bundled"] }
 
-# Git integration
-git2 = "0.20"
+# Git integration - use vendored openssl to avoid system dependency
+git2 = { version = "0.20", features = ["ssh", "vendored-openssl"] }
 
 # Serialization
 serde = { version = "1", features = ["derive"] }

pyproject.toml

@@ -0,0 +1,84 @@
+[project]
+name = "llm-abstraction"
+version = "0.1.0"
+description = "Provider-agnostic LLM abstraction layer"
+readme = "README.md"
+requires-python = ">=3.11"
+license = {text = "MIT"}
+authors = [
+    {name = "Affaan Mustafa", email = "affaan@example.com"}
+]
+keywords = ["llm", "openai", "anthropic", "ollama", "ai"]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+
+dependencies = [
+    "anthropic>=0.25.0",
+    "openai>=1.30.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8.0",
+    "pytest-asyncio>=0.23",
+    "pytest-cov>=4.1",
+    "ruff>=0.4",
+    "mypy>=1.10",
+    "ruff>=0.4",
+]
+test = [
+    "pytest>=8.0",
+    "pytest-asyncio>=0.23",
+    "pytest-cov>=4.1",
+    "pytest-mock>=3.12",
+]
+
+[project.urls]
+Homepage = "https://github.com/affaan-m/everything-claude-code"
+Repository = "https://github.com/affaan-m/everything-claude-code"
+
+[project.scripts]
+llm-select = "llm.cli.selector:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/llm"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+asyncio_mode = "auto"
+filterwarnings = ["ignore::DeprecationWarning"]
+
+[tool.coverage.run]
+source = ["src/llm"]
+branch = true
+
+[tool.coverage.report]
+exclude_lines = [
+    "pragma: no cover",
+    "if TYPE_CHECKING:",
+    "raise NotImplementedError",
+]
+
+[tool.ruff]
+src-path = ["src"]
+target-version = "py311"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "N", "W", "UP"]
+ignore = ["E501"]
+
+[tool.mypy]
+python_version = "3.11"
+src_paths = ["src"]
+warn_return_any = true
+warn_unused_ignores = true

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",
+        )

tests/conftest.py

@@ -0,0 +1,4 @@
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent / "src"))

tests/test_builder.py

@@ -0,0 +1,61 @@
+import pytest
+from llm.core.types import LLMInput, Message, Role, ToolDefinition
+from llm.prompt import PromptBuilder, adapt_messages_for_provider
+
+
+class TestPromptBuilder:
+    def test_build_without_system(self):
+        messages = [Message(role=Role.USER, content="Hello")]
+        builder = PromptBuilder()
+        result = builder.build(messages)
+
+        assert len(result) == 1
+        assert result[0].role == Role.USER
+
+    def test_build_with_system(self):
+        messages = [
+            Message(role=Role.SYSTEM, content="You are helpful."),
+            Message(role=Role.USER, content="Hello"),
+        ]
+        builder = PromptBuilder()
+        result = builder.build(messages)
+
+        assert len(result) == 2
+        assert result[0].role == Role.SYSTEM
+
+    def test_build_adds_system_from_config(self):
+        messages = [Message(role=Role.USER, content="Hello")]
+        builder = PromptBuilder(system_template="You are a pirate.")
+        result = builder.build(messages)
+
+        assert len(result) == 2
+        assert "pirate" in result[0].content
+
+    def test_build_with_tools(self):
+        messages = [Message(role=Role.USER, content="Search for something")]
+        tools = [
+            ToolDefinition(name="search", description="Search the web", parameters={}),
+        ]
+        builder = PromptBuilder(include_tools_in_system=True)
+        result = builder.build(messages, tools)
+
+        assert len(result) == 2
+        assert "search" in result[0].content
+        assert "Available Tools" in result[0].content
+
+
+class TestAdaptMessagesForProvider:
+    def test_adapt_for_claude(self):
+        messages = [Message(role=Role.USER, content="Hello")]
+        result = adapt_messages_for_provider(messages, "claude")
+        assert len(result) == 1
+
+    def test_adapt_for_openai(self):
+        messages = [Message(role=Role.USER, content="Hello")]
+        result = adapt_messages_for_provider(messages, "openai")
+        assert len(result) == 1
+
+    def test_adapt_for_ollama(self):
+        messages = [Message(role=Role.USER, content="Hello")]
+        result = adapt_messages_for_provider(messages, "ollama")
+        assert len(result) == 1

tests/test_executor.py

@@ -0,0 +1,86 @@
+import pytest
+from llm.core.types import ToolCall, ToolDefinition, ToolResult
+from llm.tools import ToolExecutor, ToolRegistry
+
+
+class TestToolRegistry:
+    def test_register_and_get(self):
+        registry = ToolRegistry()
+
+        def dummy_func() -> str:
+            return "result"
+
+        tool_def = ToolDefinition(
+            name="dummy",
+            description="A dummy tool",
+            parameters={"type": "object"},
+        )
+        registry.register(tool_def, dummy_func)
+
+        assert registry.has("dummy") is True
+        assert registry.get("dummy") is dummy_func
+        assert registry.get_definition("dummy") == tool_def
+
+    def test_list_tools(self):
+        registry = ToolRegistry()
+        tool_def = ToolDefinition(name="test", description="Test", parameters={})
+        registry.register(tool_def, lambda: None)
+
+        tools = registry.list_tools()
+        assert len(tools) == 1
+        assert tools[0].name == "test"
+
+
+class TestToolExecutor:
+    def test_execute_success(self):
+        registry = ToolRegistry()
+
+        def search(query: str) -> str:
+            return f"Results for: {query}"
+
+        registry.register(
+            ToolDefinition(
+                name="search",
+                description="Search",
+                parameters={"type": "object", "properties": {"query": {"type": "string"}}},
+            ),
+            search,
+        )
+
+        executor = ToolExecutor(registry)
+        result = executor.execute(ToolCall(id="1", name="search", arguments={"query": "test"}))
+
+        assert result.tool_call_id == "1"
+        assert result.content == "Results for: test"
+        assert result.is_error is False
+
+    def test_execute_unknown_tool(self):
+        registry = ToolRegistry()
+        executor = ToolExecutor(registry)
+
+        result = executor.execute(ToolCall(id="1", name="unknown", arguments={}))
+
+        assert result.is_error is True
+        assert "not found" in result.content
+
+    def test_execute_all(self):
+        registry = ToolRegistry()
+
+        def tool1() -> str:
+            return "result1"
+
+        def tool2() -> str:
+            return "result2"
+
+        registry.register(ToolDefinition(name="t1", description="", parameters={}), tool1)
+        registry.register(ToolDefinition(name="t2", description="", parameters={}), tool2)
+
+        executor = ToolExecutor(registry)
+        results = executor.execute_all([
+            ToolCall(id="1", name="t1", arguments={}),
+            ToolCall(id="2", name="t2", arguments={}),
+        ])
+
+        assert len(results) == 2
+        assert results[0].content == "result1"
+        assert results[1].content == "result2"

tests/test_resolver.py

@@ -0,0 +1,28 @@
+import pytest
+from llm.core.types import ProviderType
+from llm.providers import ClaudeProvider, OpenAIProvider, OllamaProvider, get_provider
+
+
+class TestGetProvider:
+    def test_get_claude_provider(self):
+        provider = get_provider("claude")
+        assert isinstance(provider, ClaudeProvider)
+        assert provider.provider_type == ProviderType.CLAUDE
+
+    def test_get_openai_provider(self):
+        provider = get_provider("openai")
+        assert isinstance(provider, OpenAIProvider)
+        assert provider.provider_type == ProviderType.OPENAI
+
+    def test_get_ollama_provider(self):
+        provider = get_provider("ollama")
+        assert isinstance(provider, OllamaProvider)
+        assert provider.provider_type == ProviderType.OLLAMA
+
+    def test_get_provider_by_enum(self):
+        provider = get_provider(ProviderType.CLAUDE)
+        assert isinstance(provider, ClaudeProvider)
+
+    def test_invalid_provider_raises(self):
+        with pytest.raises(ValueError, match="Unknown provider type"):
+            get_provider("invalid")

tests/test_types.py

@@ -0,0 +1,117 @@
+import pytest
+from llm.core.types import (
+    LLMInput,
+    LLMOutput,
+    Message,
+    ModelInfo,
+    ProviderType,
+    Role,
+    ToolCall,
+    ToolDefinition,
+    ToolResult,
+)
+
+
+class TestRole:
+    def test_role_values(self):
+        assert Role.SYSTEM.value == "system"
+        assert Role.USER.value == "user"
+        assert Role.ASSISTANT.value == "assistant"
+        assert Role.TOOL.value == "tool"
+
+
+class TestProviderType:
+    def test_provider_values(self):
+        assert ProviderType.CLAUDE.value == "claude"
+        assert ProviderType.OPENAI.value == "openai"
+        assert ProviderType.OLLAMA.value == "ollama"
+
+
+class TestMessage:
+    def test_create_message(self):
+        msg = Message(role=Role.USER, content="Hello")
+        assert msg.role == Role.USER
+        assert msg.content == "Hello"
+        assert msg.name is None
+        assert msg.tool_call_id is None
+
+    def test_message_to_dict(self):
+        msg = Message(role=Role.USER, content="Hello", name="test")
+        result = msg.to_dict()
+        assert result["role"] == "user"
+        assert result["content"] == "Hello"
+        assert result["name"] == "test"
+
+
+class TestToolDefinition:
+    def test_create_tool(self):
+        tool = ToolDefinition(
+            name="search",
+            description="Search the web",
+            parameters={"type": "object", "properties": {}},
+        )
+        assert tool.name == "search"
+        assert tool.strict is True
+
+    def test_tool_to_dict(self):
+        tool = ToolDefinition(
+            name="search",
+            description="Search",
+            parameters={"type": "object"},
+        )
+        result = tool.to_dict()
+        assert result["name"] == "search"
+        assert result["strict"] is True
+
+
+class TestToolCall:
+    def test_create_tool_call(self):
+        tc = ToolCall(id="1", name="search", arguments={"query": "test"})
+        assert tc.id == "1"
+        assert tc.name == "search"
+        assert tc.arguments == {"query": "test"}
+
+
+class TestToolResult:
+    def test_create_tool_result(self):
+        result = ToolResult(tool_call_id="1", content="result")
+        assert result.tool_call_id == "1"
+        assert result.is_error is False
+
+
+class TestLLMInput:
+    def test_create_input(self):
+        messages = [Message(role=Role.USER, content="Hello")]
+        input_obj = LLMInput(messages=messages, temperature=0.7)
+        assert len(input_obj.messages) == 1
+        assert input_obj.temperature == 0.7
+
+    def test_input_to_dict(self):
+        messages = [Message(role=Role.USER, content="Hello")]
+        input_obj = LLMInput(messages=messages)
+        result = input_obj.to_dict()
+        assert "messages" in result
+        assert result["temperature"] == 1.0
+
+
+class TestLLMOutput:
+    def test_create_output(self):
+        output = LLMOutput(content="Hello!")
+        assert output.content == "Hello!"
+        assert output.has_tool_calls is False
+
+    def test_output_with_tool_calls(self):
+        tc = ToolCall(id="1", name="search", arguments={})
+        output = LLMOutput(content="", tool_calls=[tc])
+        assert output.has_tool_calls is True
+
+
+class TestModelInfo:
+    def test_create_model_info(self):
+        info = ModelInfo(
+            name="gpt-4",
+            provider=ProviderType.OPENAI,
+        )
+        assert info.name == "gpt-4"
+        assert info.supports_tools is True
+        assert info.supports_vision is False