An ML intern that autonomously researches, writes, and ships good quality ML related code using the Hugging Face ecosystem — with deep access to docs, papers, datasets, and cloud compute.
git clone git@github.com:huggingface/ml-intern.git
cd ml-intern
uv sync
uv tool install -e .ml-internCreate a .env file in the project root (or export these in your shell):
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.
Interactive mode (start a chat session):
ml-internHeadless 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 --model openai/gpt-5.5 "your prompt"
ml-intern --max-iterations 100 "your prompt"
ml-intern --no-stream "your prompt"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:
/share-traces # show current visibility + dataset URL
/share-traces public # publish (anyone can view)
/share-traces private # lock it back downYou 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):
{ "share_traces": false }To override the destination repo, set:
{ "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.
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 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:
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:
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=trueFor 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:
{
"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
}
}
}
}┌─────────────────────────────────────────────────────────────┐
│ 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 │ │ │ │
│ │ └────────────────────────────────────────┘ │ │ │
│ └──────────────────────────────────────────────┘ │ │
└────────────────────────────────────────────────────┴──┘
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 ─────────────────┐ ║
║ ↑ │ ║
║ └───────────────────────┘ ║
╚═══════════════════════════════════════════╝
The agent emits the following events via event_queue:
processing- Starting to process user inputready- Agent is ready for inputassistant_chunk- Streaming token chunkassistant_message- Complete LLM response textassistant_stream_end- Token stream finishedtool_call- Tool being called with argumentstool_output- Tool execution resulttool_log- Informational tool log messagetool_state_change- Tool execution state transitionapproval_required- Requesting user approval for sensitive operationsturn_complete- Agent finished processingerror- Error occurred during processinginterrupted- Agent was interruptedcompacted- Context was compactedundo_complete- Undo operation completedshutdown- Agent shutting down
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
]Edit configs/cli_agent_config.json for CLI defaults, or
configs/frontend_agent_config.json for web-session defaults:
{
"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.