# Mithril

> *"Mithril! All folk desired it. It could be beaten like copper, and polished like glass; and the Dwarves could make of it a metal, light and yet harder than tempered steel."* — Gandalf

**A multi-model orchestration engine.** Combine any mix of LLM providers (Gemini, OpenAI, Anthropic, Groq, local GGUF) into a single Ollama-compatible API endpoint. Configure who does what in a YAML file, then point any AI tool at it.

[![Build](https://img.shields.io/badge/build-cargo-orange)](https://doc.rust-lang.org/cargo/)
[![License](https://img.shields.io/badge/license-MIT-blue)](LICENSE)
[![API](https://img.shields.io/badge/API-Ollama%20%7C%20OpenAI%20%7C%20MCP-green)]()

---

## What It Does

You define a **fellowship** — a team of AI models working together:

```yaml
# .mithril/fellowship.yaml
name: "my-team"
controller:
  provider: local          # Free GGUF model routes requests
  model: qwen-1.5b

agents:
  - name: coder
    provider: gemini
    model: gemini-2.5-flash
    when: "coding tasks"
    tools: ["*"]

  - name: reviewer
    provider: openai
    model: gpt-4o
    when: "code review requested"
    tools: ["read_psi", "grep_files"]
```

Then you start the engine:

```bash
mithril serve
```

Now any Ollama-compatible client sees your fellowship as a model:

```bash
# From Junie, OpenCode, Open WebUI, or any Ollama client:
curl http://localhost:16180/api/tags
# → {"models": [{"name": "my-team:latest", "details": {"family": "mithril-fellowship"}}]}
```

---

## Use Cases

| Use Case | How |
|----------|-----|
| **Backend for Junie** | Point Junie at `http://localhost:16180`, select your fellowship as the model |
| **Backend for OpenCode** | Same — Ollama API compatible |
| **Backend for Open WebUI** | Add as Ollama connection |
| **Backend for LangChain** | Use OpenAI API at `http://localhost:16180/v1/chat/completions` |
| **MCP server for Claude Desktop** | `mithril mcp-stdio` |
| **Standalone CLI** | `mithril chat` — built-in terminal interface |
| **Docker service for teams** | `docker compose up` — shared orchestration backend |
| **Telegram bot** | `mithril telegram` — chat via Telegram with same fellowship |

---

## Architecture

```mermaid
graph TB
    subgraph "Clients (any Ollama/OpenAI consumer)"
        J[Junie]
        O[OpenCode]
        W[Open WebUI]
        L[LangChain]
        C[Claude Desktop]
        T[Telegram]
        CLI[Mithril CLI]
    end

    subgraph "Mithril Engine"
        API[API Layer<br/>Ollama + OpenAI + MCP]
        ORCH[Orchestrator<br/>GGUF Classifier → Agent Routing]
        TOOLS[24 Built-in Tools<br/>File, Git, Web, Code, Terminal]
    end

    subgraph "Cloud API Providers"
        G[Gemini]
        GPT[OpenAI]
        A[Anthropic]
        GR[Groq]
    end

    subgraph "Local"
        LOCAL[Local GGUF]
    end

    subgraph "CLI Providers"
        K[Kiro]
        JN[Junie]
        ANY[Any CLI]
    end

    J -->|Ollama API| API
    O -->|Ollama API| API
    W -->|Ollama API| API
    L -->|OpenAI API| API
    C -->|MCP stdio| API
    T -->|Internal| API
    CLI -->|Internal| API

    API --> ORCH
    ORCH --> G
    ORCH --> GPT
    ORCH --> A
    ORCH --> GR
    ORCH --> LOCAL
    ORCH --> K
    ORCH --> JN
    ORCH --> ANY
    ORCH --> TOOLS
```

---

## Installation

### One-liner
```bash
curl -fsSL https://raw.githubusercontent.com/GiacomoSaccaggi/mithril/main/install.sh | bash
```

### Manual
```bash
# macOS (Apple Silicon)
curl -L https://github.com/GiacomoSaccaggi/mithril/releases/latest/download/mithril-macos-arm64.tar.gz | tar xz
sudo mv mithril /usr/local/bin/

# Linux (x86_64)
curl -L https://github.com/GiacomoSaccaggi/mithril/releases/latest/download/mithril-linux-x64.tar.gz | tar xz
sudo mv mithril /usr/local/bin/
```

### Docker
```bash
git clone https://github.com/GiacomoSaccaggi/mithril.git
cd mithril
docker compose up -d
# API available at http://localhost:16180
```

### Build from source
```bash
git clone https://github.com/GiacomoSaccaggi/mithril.git
cd mithril && cargo build --release
```

---

## Quick Start

### 1. Configure providers

```bash
# API keys — stored encrypted with Argon2id + AES-256-GCM
mithril config set gemini "AIza..."
mithril config set openai "sk-..."

# Or via environment variables (for Docker/CI):
export MITHRIL_KEY_GEMINI="AIza..."
export MITHRIL_KEY_OPENAI="sk-..."
```

### 2. Create a fellowship

```bash
mithril fellowship init
# Creates .mithril/fellowship.yaml with sensible defaults
```

### 3. Start the engine

```bash
mithril serve
# → http://localhost:16180 (Ollama + OpenAI + MCP)
```

### 4. Connect your tools

**Junie / OpenCode / Open WebUI:**
- Ollama URL: `http://localhost:16180`
- Model: select your fellowship name from the list

**LangChain / custom:**
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:16180/v1", api_key="unused")
response = client.chat.completions.create(
    model="my-team",
    messages=[{"role": "user", "content": "Review this code"}]
)
```

---

## Credentials in Docker

Mithril reads API keys in this priority order:

1. **Environment variables** (recommended for Docker): `MITHRIL_KEY_<PROVIDER>`
2. **Encrypted config file**: `~/.mithril/config.yaml` (used by CLI)

```bash
# Docker Compose — set in .env file or environment:
MITHRIL_KEY_GEMINI=AIza...
MITHRIL_KEY_OPENAI=sk-...
MITHRIL_KEY_ANTHROPIC=sk-ant-...
MITHRIL_KEY_GROQ=gsk_...
```

No secrets are stored in the Docker image. Mount `.mithril/fellowship.yaml` for your agent configuration.

---

## Fellowship Configuration

A fellowship defines **who does what**:

```yaml
name: "code-team"
description: "Multi-model coding assistant"

controller:
  provider: local         # Routes requests (free, fast)
  model: qwen-1.5b
  context_window: 2       # Messages the router sees

agents:
  - name: worker
    provider: gemini
    model: gemini-2.5-flash
    role: "Fast coder — implements features"
    when: "any coding task"
    can_call: [reviewer]
    tools: ["*"]           # All 24 tools

  - name: reviewer
    provider: openai
    model: gpt-4o
    role: "Senior reviewer — catches bugs"
    when: "review requested or complex logic"
    can_call: []
    tools: [read_psi, grep_files, git_diff]
```

Agents communicate via the NEXT/TASK protocol:
- `NEXT: DONE` — task complete, return to user
- `NEXT: reviewer` + `TASK: check auth.rs` — delegate to another agent

---

## The CLI (Optional)

Mithril includes a full-featured terminal interface:

```bash
mithril chat              # Interactive REPL with Tab completion
mithril chat --tui        # Full-screen TUI with panels
mithril exec "fix bug"    # Non-interactive (for CI/scripts)
```

Features: `@file` expansion, `#agent` routing, `/commands`, Plan/Build modes, undo/redo, session persistence, custom commands, hooks.

See [docs/CLI.md](docs/CLI.md) for details.

---

## API Endpoints

| Endpoint | Protocol | Use |
|----------|----------|-----|
| `GET /health` | — | Health check |
| `GET /api/tags` | Ollama | List models (includes fellowships) |
| `POST /api/chat` | Ollama | Chat completion |
| `POST /api/generate` | Ollama | Text generation |
| `POST /v1/chat/completions` | OpenAI | Chat completion |
| `GET /v1/models` | OpenAI | List models |
| `POST /mcp` | MCP | JSON-RPC tool calls |

---

## Provider Types

Mithril supports three types of providers:

| Type | Examples | How It Works |
|------|----------|--------------|
| **Local GGUF** | qwen-1.5b, llama-8b | Direct inference via llama.cpp (free, private, fast for routing) |
| **Cloud API** | Gemini, OpenAI, Anthropic, Groq | HTTP calls to cloud LLM endpoints (pay-per-token) |
| **CLI Tools** | Kiro, Junie, any CLI with chat | Subprocess calls to local CLI tools that have their own model access |

```yaml
# .mithril/fellowship.yaml
name: "my-team"

controller:
  provider: local          # Local GGUF (free, used for routing)
  model: qwen-1.5b

agents:
  # Cloud API provider
  - name: coder
    provider: gemini
    model: gemini-2.5-flash

  # CLI provider (uses kiro-cli with its own auth)
  - name: reviewer
    provider: kiro
    model: claude-opus-4.6
```

CLI providers are useful when you have access to tools like Kiro or other AI CLIs with their own authentication and model access. Mithril orchestrates them as part of your fellowship without needing separate API keys.

> **Note on the controller:** The controller (classifier/router) defaults to a local GGUF model which is free, fast (~100ms), and private. You can technically use any provider as controller (e.g., `provider: gemini`), but it's not worth the cost unless you have a very large agent structure where precise routing justifies paying per-classification.

---

## 24 Built-in Tools

File: `read_file`, `write_file`, `edit_file`, `delete_file`, `apply_patch`
Terminal: `run_terminal` (sandboxed)
Discovery: `list_files`, `grep_files`, `find_file`, `file_stats`, `glob_files`
Git: `git_status`, `git_log`, `git_diff`, `git_blame`, `git_branch`
Web: `web_search`, `fetch_page`
Code: `search_symbols`, `document_outline`
Knowledge: `lore_write`, `lore_read`
Interaction: `todo_write`, `question`

---

## Documentation

| Document | Contents |
|----------|----------|
| [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) | System design and module map |
| [docs/TOOLS.md](docs/TOOLS.md) | All 24 tools with parameters |
| [docs/CLI.md](docs/CLI.md) | Terminal commands and features |
| [docs/API.md](docs/API.md) | HTTP endpoints reference |
| [docs/PROVIDERS.md](docs/PROVIDERS.md) | Provider configuration |
| [docs/SECURITY.md](docs/SECURITY.md) | Security model |
| [docs/SESSION.md](docs/SESSION.md) | Session persistence |
| [docs/ENGINE.md](docs/ENGINE.md) | GGUF inference engine |
| [CONTRIBUTING.md](CONTRIBUTING.md) | Join the Fellowship |

---

## License

MIT