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smolagents/ml-intern

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App README

[!IMPORTANT] ML Intern is no longer maintained. Both the hosted web application and the ml-intern CLI have been retired. Continue with HuggingChat. This repository is retained for historical reference only. No support, bug fixes, or security updates will be provided.

ML Intern (retired)

Historical documentation (unsupported)

The documentation below is preserved for historical reference. The project is no longer supported or maintained.

ML Intern was an agent that autonomously researched, wrote, and shipped ML-related code using the Hugging Face ecosystem, with deep access to documentation, papers, datasets, and cloud compute.

Quick Start

Installation

bash

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:

bash

ml-intern

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

bash

ANTHROPIC_API_KEY=<your-anthropic-api-key> # if using anthropic models
OPENAI_API_KEY=<your-openai-api-key> # if using openai 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):

bash

ml-intern

Headless mode (single prompt, auto-approve):

bash

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

Options:

bash

ml-intern --model anthropic/claude-opus-4-6 "your prompt"
ml-intern --model openai/gpt-5.5 "your prompt"
ml-intern --max-iterations 100 "your prompt"
ml-intern --no-stream "your prompt"

Sharing Traces

Every session is auto-uploaded to your own private Hugging Face dataset in Claude Code JSONL format, which the HF Agent Trace Viewer auto-detects so you can browse turns, tool calls, and model responses directly on the Hub.

By default the dataset is named {your-hf-username}/ml-intern-sessions and is created private. You can flip it to public from inside the CLI:

bash

/share-traces            # show current visibility + dataset URL
/share-traces public     # publish (anyone can view)
/share-traces private    # lock it back down

You can also flip visibility from the dataset page on huggingface.co — the agent honours whatever you set there for subsequent uploads.

To opt out entirely, set in your CLI config (e.g. configs/cli_agent_config.json or ~/.config/ml-intern/cli_agent_config.json):

json

{ "share_traces": false }

To override the destination repo, set:

json

{ "personal_trace_repo_template": "{hf_user}/my-custom-traces" }

The shared smolagents/ml-intern-sessions dataset is unrelated and only receives anonymized telemetry rows used by the backend KPI scheduler.

Supported Gateways

ML Intern currently supports one-way notification gateways from CLI sessions. These gateways send out-of-band status updates; they do not accept inbound chat messages.

Slack

Slack notifications use the Slack Web API to post messages when the agent needs approval, hits an error, or completes a turn. Create a Slack app with a bot token that has chat:write, invite the bot to the target channel, then set:

bash

SLACK_BOT_TOKEN=xoxb-...
SLACK_CHANNEL_ID=C...

The CLI automatically creates a slack.default destination when both variables are present. Optional environment variables for the env-only default:

bash

ML_INTERN_SLACK_NOTIFICATIONS=false
ML_INTERN_SLACK_DESTINATION=slack.ops
ML_INTERN_SLACK_AUTO_EVENTS=approval_required,error,turn_complete
ML_INTERN_SLACK_ALLOW_AGENT_TOOL=true
ML_INTERN_SLACK_ALLOW_AUTO_EVENTS=true

For a persistent user-level config, put overrides in ~/.config/ml-intern/cli_agent_config.json or point ML_INTERN_CLI_CONFIG at a JSON file:

json

{
  "messaging": {
    "enabled": true,
    "auto_event_types": ["approval_required", "error", "turn_complete"],
    "destinations": {
      "slack.ops": {
        "provider": "slack",
        "token": "${SLACK_BOT_TOKEN}",
        "channel": "${SLACK_CHANNEL_ID}",
        "allow_agent_tool": true,
        "allow_auto_events": true
      }
    }
  }
}

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:

python

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/cli_agent_config.json for CLI defaults, or configs/frontend_agent_config.json for web-session defaults:

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.

ML Intern (retired)

Historical documentation (unsupported)

Quick Start

Installation

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

Usage

Sharing Traces

Supported Gateways

Slack

Architecture

Component Overview

Agentic Loop Flow

Events

Development

Adding Built-in Tools

Adding MCP Servers

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