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| title: ML Intern | |
| emoji: 🤖 | |
| colorFrom: yellow | |
| colorTo: blue | |
| sdk: docker | |
| app_port: 7860 | |
| hf_oauth: true | |
| hf_oauth_expiration_minutes: 43200 | |
| hf_oauth_scopes: | |
| - read-repos | |
| - write-repos | |
| - contribute-repos | |
| - manage-repos | |
| - write-collections | |
| - inference-api | |
| - jobs | |
| - write-discussions | |
| <p align="center"> | |
| <img src="frontend/public/smolagents.webp" alt="smolagents logo" width="160" /> | |
| </p> | |
| # ML Intern | |
| 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. | |
| ## 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](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-fine-grained-personal-access-token). | |
| ### 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](https://huggingface.co/changelog/agent-trace-viewer), | |
| 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`. | |