huggingface / ml-intern

пятница, 24 апреля 2026 г. в 00:00:02

https://github.com/huggingface/ml-intern

🤗 ml-intern: an open-source ML engineer that reads papers, trains models, and ships ML models

ML Intern

An ML intern that autonomously researches, writes, and ships good quality ML releated code using the Hugging Face ecosystem — with deep access to docs, papers, datasets, and cloud compute.

Quick Start

Installation

git clone git@github.com:huggingface/ml-intern.git
cd ml-intern
uv sync
uv tool install -e .

That's it. Now `ml-intern` works from any directory:

ml-intern

Create a .env file in the project root (or export these in your shell):

ANTHROPIC_API_KEY=<your-anthropic-api-key> # if using anthropic models
HF_TOKEN=<your-hugging-face-token>
GITHUB_TOKEN=<github-personal-access-token>

If no HF_TOKEN is set, the CLI will prompt you to paste one on first launch. To get a GITHUB_TOKEN follow the tutorial here.

Usage

Interactive mode (start a chat session):

ml-intern

Headless mode (single prompt, auto-approve):

ml-intern "fine-tune llama on my dataset"

Options:

ml-intern --model anthropic/claude-opus-4-6 "your prompt"
ml-intern --max-iterations 100 "your prompt"
ml-intern --no-stream "your prompt"

Architecture

Component Overview

┌─────────────────────────────────────────────────────────────┐
│                         User/CLI                            │
└────────────┬─────────────────────────────────────┬──────────┘
             │ Operations                          │ Events
             ↓ (user_input, exec_approval,         ↑
      submission_queue  interrupt, compact, ...)  event_queue
             │                                          │
             ↓                                          │
┌────────────────────────────────────────────────────┐  │
│            submission_loop (agent_loop.py)         │  │
│  ┌──────────────────────────────────────────────┐  │  │
│  │  1. Receive Operation from queue             │  │  │
│  │  2. Route to handler (run_agent/compact/...) │  │  │
│  └──────────────────────────────────────────────┘  │  │
│                      ↓                             │  │
│  ┌──────────────────────────────────────────────┐  │  │
│  │         Handlers.run_agent()                 │  ├──┤
│  │                                              │  │  │
│  │  ┌────────────────────────────────────────┐  │  │  │
│  │  │  Agentic Loop (max 300 iterations)     │  │  │  │
│  │  │                                        │  │  │  │
│  │  │  ┌──────────────────────────────────┐  │  │  │  │
│  │  │  │ Session                          │  │  │  │  │
│  │  │  │  ┌────────────────────────────┐  │  │  │  │  │
│  │  │  │  │ ContextManager             │  │  │  │  │  │
│  │  │  │  │ • Message history          │  │  │  │  │  │
│  │  │  │  │   (litellm.Message[])      │  │  │  │  │  │
│  │  │  │  │ • Auto-compaction (170k)   │  │  │  │  │  │
│  │  │  │  │ • Session upload to HF     │  │  │  │  │  │
│  │  │  │  └────────────────────────────┘  │  │  │  │  │
│  │  │  │                                  │  │  │  │  │
│  │  │  │  ┌────────────────────────────┐  │  │  │  │  │
│  │  │  │  │ ToolRouter                 │  │  │  │  │  │
│  │  │  │  │  ├─ HF docs & research     │  │  │  │  │  │
│  │  │  │  │  ├─ HF repos, datasets,    │  │  │  │  │  │
│  │  │  │  │  │  jobs, papers           │  │  │  │  │  │
│  │  │  │  │  ├─ GitHub code search     │  │  │  │  │  │
│  │  │  │  │  ├─ Sandbox & local tools  │  │  │  │  │  │
│  │  │  │  │  ├─ Planning               │  │  │  │  │  │
│  │  │  │  │  └─ MCP server tools       │  │  │  │  │  │
│  │  │  │  └────────────────────────────┘  │  │  │  │  │
│  │  │  └──────────────────────────────────┘  │  │  │  │
│  │  │                                        │  │  │  │
│  │  │  ┌──────────────────────────────────┐  │  │  │  │
│  │  │  │ Doom Loop Detector               │  │  │  │  │
│  │  │  │ • Detects repeated tool patterns │  │  │  │  │
│  │  │  │ • Injects corrective prompts     │  │  │  │  │
│  │  │  └──────────────────────────────────┘  │  │  │  │
│  │  │                                        │  │  │  │
│  │  │  Loop:                                 │  │  │  │
│  │  │    1. LLM call (litellm.acompletion)   │  │  │  │
│  │  │       ↓                                │  │  │  │
│  │  │    2. Parse tool_calls[]               │  │  │  │
│  │  │       ↓                                │  │  │  │
│  │  │    3. Approval check                   │  │  │  │
│  │  │       (jobs, sandbox, destructive ops) │  │  │  │
│  │  │       ↓                                │  │  │  │
│  │  │    4. Execute via ToolRouter           │  │  │  │
│  │  │       ↓                                │  │  │  │
│  │  │    5. Add results to ContextManager    │  │  │  │
│  │  │       ↓                                │  │  │  │
│  │  │    6. Repeat if tool_calls exist       │  │  │  │
│  │  └────────────────────────────────────────┘  │  │  │
│  └──────────────────────────────────────────────┘  │  │
└────────────────────────────────────────────────────┴──┘

Agentic Loop Flow

User Message
     ↓
[Add to ContextManager]
     ↓
     ╔═══════════════════════════════════════════╗
     ║      Iteration Loop (max 300)             ║
     ║                                           ║
     ║  Get messages + tool specs                ║
     ║         ↓                                 ║
     ║  litellm.acompletion()                    ║
     ║         ↓                                 ║
     ║  Has tool_calls? ──No──> Done             ║
     ║         │                                 ║
     ║        Yes                                ║
     ║         ↓                                 ║
     ║  Add assistant msg (with tool_calls)      ║
     ║         ↓                                 ║
     ║  Doom loop check                          ║
     ║         ↓                                 ║
     ║  For each tool_call:                      ║
     ║    • Needs approval? ──Yes──> Wait for    ║
     ║    │                         user confirm ║
     ║    No                                     ║
     ║    ↓                                      ║
     ║    • ToolRouter.execute_tool()            ║
     ║    • Add result to ContextManager         ║
     ║         ↓                                 ║
     ║  Continue loop ─────────────────┐         ║
     ║         ↑                       │         ║
     ║         └───────────────────────┘         ║
     ╚═══════════════════════════════════════════╝

Events

The agent emits the following events via event_queue:

processing - Starting to process user input
ready - Agent is ready for input
assistant_chunk - Streaming token chunk
assistant_message - Complete LLM response text
assistant_stream_end - Token stream finished
tool_call - Tool being called with arguments
tool_output - Tool execution result
tool_log - Informational tool log message
tool_state_change - Tool execution state transition
approval_required - Requesting user approval for sensitive operations
turn_complete - Agent finished processing
error - Error occurred during processing
interrupted - Agent was interrupted
compacted - Context was compacted
undo_complete - Undo operation completed
shutdown - Agent shutting down

Development

Adding Built-in Tools

Edit agent/core/tools.py:

def create_builtin_tools() -> list[ToolSpec]:
    return [
        ToolSpec(
            name="your_tool",
            description="What your tool does",
            parameters={
                "type": "object",
                "properties": {
                    "param": {"type": "string", "description": "Parameter description"}
                },
                "required": ["param"]
            },
            handler=your_async_handler
        ),
        # ... existing tools
    ]

Adding MCP Servers

Edit configs/main_agent_config.json:

{
  "model_name": "anthropic/claude-sonnet-4-5-20250929",
  "mcpServers": {
    "your-server-name": {
      "transport": "http",
      "url": "https://example.com/mcp",
      "headers": {
        "Authorization": "Bearer ${YOUR_TOKEN}"
      }
    }
  }
}

Note: Environment variables like ${YOUR_TOKEN} are auto-substituted from .env.