Embacle — LLM Runners

Standalone Rust library that wraps AI CLI tools and SDKs as pluggable LLM providers.

Instead of integrating with LLM APIs directly (which require API keys, SDKs, and managing auth), Embacle delegates to CLI tools that users already have installed and authenticated — getting model upgrades, auth management, and protocol handling for free. For GitHub Copilot, an optional headless mode communicates via the ACP (Agent Client Protocol) for SDK-managed tool calling.

Supported Runners

CLI Runners (subprocess-based)

HTTP API Runners (feature-flagged)

ACP Runners (persistent connection)

Quick Start

Add to your Cargo.toml:

[dependencies]
embacle = "0.9"

Use a CLI runner:

use std::path::PathBuf;
use embacle::{ClaudeCodeRunner, RunnerConfig};
use embacle::types::{ChatMessage, ChatRequest, LlmProvider};

#[tokio::main]
async fn main() -> Result<(), embacle::types::RunnerError> {
    let config = RunnerConfig::new(PathBuf::from("claude"));
    let runner = ClaudeCodeRunner::new(config);

    let request = ChatRequest::new(vec![
        ChatMessage::user("What is the capital of France?"),
    ]);

    let response = runner.complete(&request).await?;
    println!("{}", response.content);
    Ok(())
}

OpenAI API (feature flag)

Enable the openai-api feature for HTTP-based communication with any OpenAI-compatible endpoint:

[dependencies]
embacle = { version = "0.9", features = ["openai-api"] }

use embacle::{OpenAiApiConfig, OpenAiApiRunner};
use embacle::types::{ChatMessage, ChatRequest, LlmProvider};

#[tokio::main]
async fn main() -> Result<(), embacle::types::RunnerError> {
    // Reads OPENAI_API_BASE_URL, OPENAI_API_KEY, OPENAI_API_MODEL from env
    let config = OpenAiApiConfig::from_env();
    let runner = OpenAiApiRunner::new(config).await;

    let request = ChatRequest::new(vec![
        ChatMessage::user("What is the capital of France?"),
    ]);

    let response = runner.complete(&request).await?;
    println!("{}", response.content);
    Ok(())
}

Works with any OpenAI-compatible endpoint — OpenAI, Groq, Google Gemini, Ollama, vLLM, and more. To inject a shared HTTP client (e.g. from a connection pool), use OpenAiApiRunner::with_client(config, client).

Copilot Headless (feature flag)

Enable the copilot-headless feature for ACP-based communication with SDK-managed tool calling:

[dependencies]
embacle = { version = "0.9", features = ["copilot-headless"] }

use embacle::{CopilotHeadlessRunner, CopilotHeadlessConfig};
use embacle::types::{ChatMessage, ChatRequest, LlmProvider};

#[tokio::main]
async fn main() -> Result<(), embacle::types::RunnerError> {
    // Reads COPILOT_HEADLESS_MODEL, COPILOT_GITHUB_TOKEN, etc. from env
    let runner = CopilotHeadlessRunner::from_env().await;

    let request = ChatRequest::new(vec![
        ChatMessage::user("Explain Rust ownership"),
    ]);

    let response = runner.complete(&request).await?;
    println!("{}", response.content);
    Ok(())
}

The headless runner spawns copilot --acp per request and communicates via NDJSON-framed JSON-RPC. Configuration via environment variables:

MCP Server (embacle-mcp)

A standalone MCP server binary that exposes embacle runners via the Model Context Protocol. Connect any MCP-compatible client (Claude Desktop, editors, custom agents) to use all embacle providers.

Usage

# Stdio transport (default — for editor/client integration)
embacle-mcp --provider copilot

# HTTP transport (for network-accessible deployments)
embacle-mcp --transport http --host 0.0.0.0 --port 3000 --provider claude_code

MCP Tools

Client Configuration

Add to your MCP client config (e.g. Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "embacle": {
      "command": "embacle-mcp",
      "args": ["--provider", "copilot"]
    }
  }
}

REST API Server (embacle-server)

An OpenAI-compatible HTTP server that proxies requests to embacle runners. Any client that speaks the OpenAI chat completions API can use it without modification.

Usage

# Start with default provider (copilot) on localhost:3000
embacle-server

# Specify provider and port
embacle-server --provider claude_code --port 8080 --host 0.0.0.0

Endpoints

Model Routing

The model field determines which provider handles the request. Use a provider:model prefix to target a specific runner, or pass a bare model name to use the server's default provider.

# Explicit provider
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "claude:opus", "messages": [{"role": "user", "content": "hello"}]}'

# Default provider
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-5.4", "messages": [{"role": "user", "content": "hello"}]}'

Multiplex

Pass an array of models to fan out the same prompt to multiple providers concurrently. Each provider runs in its own task; failures in one don't affect others.

curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": ["copilot:gpt-4o", "claude:opus"], "messages": [{"role": "user", "content": "hello"}]}'

The response uses object: "chat.completion.multiplex" with per-provider results and timing.

Streaming is not supported for multiplex requests.

SSE Streaming

Set "stream": true for Server-Sent Events output in OpenAI streaming format (data: {json}\n\n with data: [DONE] terminator).

Authentication

Optional. Set EMBACLE_API_KEY to require bearer token auth on all endpoints. When unset, all requests are allowed through (localhost development mode). The env var is read per-request, so key rotation doesn't require a restart.

EMBACLE_API_KEY=my-secret embacle-server
curl http://localhost:3000/v1/models -H "Authorization: Bearer my-secret"

Docker

Pull the image from GitHub Container Registry:

docker pull ghcr.io/dravr-ai/embacle:latest

The image includes embacle-server and embacle-mcp with Node.js pre-installed for adding CLI backends.

Adding a CLI Backend

The base image doesn't include CLI tools. Install them in a derived image:

FROM ghcr.io/dravr-ai/embacle
USER root
RUN npm install -g @anthropic-ai/claude-code
USER embacle

Build and run:

docker build -t my-embacle .
docker run -p 3000:3000 my-embacle --provider claude_code

Auth and Configuration

CLI tools store auth tokens in their config directories. Mount them from the host, or set provider-specific env vars:

# Mount Claude Code auth from host
docker run -p 3000:3000 \
  -v ~/.claude:/home/embacle/.claude:ro \
  my-embacle --provider claude_code

# Or pass env vars if the CLI supports them
docker run -p 3000:3000 \
  -e GITHUB_TOKEN=ghp_... \
  -e EMBACLE_API_KEY=my-secret \
  my-embacle --provider copilot

Running embacle-mcp

Override the entrypoint to run the MCP server instead:

docker run --entrypoint embacle-mcp ghcr.io/dravr-ai/embacle --provider copilot

Architecture

Your Application
    └── embacle (this library)
            │
            ├── CLI Runners (subprocess per request)
            │   ├── ClaudeCodeRunner    → spawns `claude -p "prompt" --output-format json`
            │   ├── CopilotRunner       → spawns `copilot -p "prompt"`
            │   ├── CursorAgentRunner   → spawns `cursor-agent -p "prompt" --output-format json`
            │   ├── OpenCodeRunner      → spawns `opencode run "prompt" --format json`
            │   ├── GeminiCliRunner     → spawns `gemini -p "prompt" -o json -y`
            │   ├── CodexCliRunner      → spawns `codex exec "prompt" --json --full-auto`
            │   ├── GooseCliRunner      → spawns `goose run --quiet --no-session`
            │   ├── ClineCliRunner      → spawns `cline task --json --act --yolo`
            │   ├── ContinueCliRunner   → spawns `cn -p --format json`
            │   └── WarpCliRunner       → spawns `oz agent run --prompt "..." --output-format json`
            │
            ├── HTTP API Runners (behind feature flag)
            │   └── OpenAiApiRunner       → reqwest to any OpenAI-compatible endpoint
            │
            ├── ACP Runners (persistent connection, behind feature flag)
            │   └── CopilotHeadlessRunner → NDJSON/JSON-RPC to `copilot --acp`
            │
            ├── Provider Decorators (composable wrappers)
            │   ├── FallbackProvider    → ordered chain, first success wins
            │   ├── MetricsProvider     → latency, token, and error tracking
            │   └── QualityGateProvider → response validation with retry
            │
            ├── Agent Loop
            │   └── AgentExecutor       → multi-turn tool calling with configurable max turns
            │
            ├── Structured Output
            │   └── request_structured_output()  → schema-validated JSON extraction with retry
            │
            ├── MCP Tool Bridge
            │   └── McpToolBridge       → MCP tool definitions ↔ text-based tool loop
            │
            ├── MCP Server (separate binary crate)
            │   └── embacle-mcp         → JSON-RPC 2.0 over stdio or HTTP/SSE
            │
            ├── REST API Server (separate binary crate)
            │   └── embacle-server      → OpenAI-compatible HTTP, SSE streaming, multiplex
            │
            └── Tool Simulation (text-based tool calling for CLI runners)
                └── execute_with_text_tools()  → catalog injection, XML parsing, tool loop

All runners implement the same LlmProvider trait:

• complete() — single-shot completion
• complete_stream() — streaming completion
• health_check() — verify the runner is available and authenticated

For detailed API docs — fallback chains, structured output, agent loop, metrics, quality gates, tool simulation, and more — see docs.rs/embacle.

Tested With

Embacle has been tested with mirroir.dev, an MCP server for AI-powered iPhone automation.

License

Licensed under the Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0).