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albertvillanovaย 
posted an update 1 day ago
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๐Ÿš€ SmolAgents v1.19.0 is live!
This release brings major improvements to agent flexibility, UI usability, streaming architecture, and developer experience: making it easier than ever to build smart, interactive AI agents. Here's what's new:

๐Ÿ”ง Agent Upgrades
- Support for managed agents in ToolCallingAgent
- Context manager support for cleaner agent lifecycle handling
- Output formatting now uses XML tags for consistency

๐Ÿ–ฅ๏ธ UI Enhancements
- GradioUI now supports reset_agent_memory: perfect for fresh starts in dev & demos.

๐Ÿ”„ Streaming Refactor
- Streaming event aggregation moved off the Model class
- โžก๏ธ Better architecture & maintainability

๐Ÿ“ฆ Output Tracking
- CodeAgent outputs are now stored in ActionStep
- โœ… More visibility and structure to agent decisions

๐Ÿ› Bug Fixes
- Smarter planning logic
- Cleaner Docker logs
- Better prompt formatting for additional_args
- Safer internal functions and final answer matching

๐Ÿ“š Docs Improvements
- Added quickstart examples with tool usage
- One-click Colab launch buttons
- Expanded reference docs (AgentMemory, GradioUI docstrings)
- Fixed broken links and migrated to .md format

๐Ÿ”— Full release notes:
https://github.com/huggingface/smolagents/releases/tag/v1.19.0

๐Ÿ’ฌ Try it out, explore the new features, and let us know what you build!

#smolagents #opensource #AIagents #LLM #HuggingFace
cfahlgren1ย 
posted an update 1 day ago
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I ran the Anthropic Misalignment Framework for a few top models and added it to a dataset: cfahlgren1/anthropic-agentic-misalignment-results

You can read the reasoning traces of the models trying to blackmail the user and perform other actions. It's very interesting!!

cfahlgren1ย 
posted an update 23 days ago
albertvillanovaย 
posted an update 30 days ago
cfahlgren1ย 
posted an update about 1 month ago
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Yesterday, we dropped a new conversational viewer for datasets on the hub! ๐Ÿ’ฌ

Actually being able to view and inspect your data is extremely important. This is a big step in making data more accessible and actionable for everyone.

Here's some datasets you can try it out on:
โ€ข mlabonne/FineTome-100k
โ€ข Salesforce/APIGen-MT-5k
โ€ข open-thoughts/OpenThoughts2-1M
โ€ข allenai/tulu-3-sft-mixture

Any other good ones?
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albertvillanovaย 
posted an update about 1 month ago
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New in smolagents v1.16.0:
๐Ÿ” Bing support in WebSearchTool
๐Ÿ Custom functions & executor_kwargs in LocalPythonExecutor
๐Ÿ”ง Streaming GradioUI fixes
๐ŸŒ Local web agents via api_base & api_key
๐Ÿ“š Better docs

๐Ÿ‘‰ https://github.com/huggingface/smolagents/releases/tag/v1.16.0
albertvillanovaย 
posted an update 2 months ago
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smolagents v1.14.0 is out! ๐Ÿš€
๐Ÿ”Œ MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
๐Ÿชจ Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. ๐Ÿ’ผ

Full release ๐Ÿ‘‰ https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI
severoย 
posted an update 3 months ago
albertvillanovaย 
posted an update 4 months ago
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๐Ÿš€ New smolagents update: Safer Local Python Execution! ๐Ÿฆพ๐Ÿ

With the latest release, we've added security checks to the local Python interpreter: every evaluation is now analyzed for dangerous builtins, modules, and functions. ๐Ÿ”’

Here's why this matters & what you need to know! ๐Ÿงต๐Ÿ‘‡

1๏ธโƒฃ Why is local execution risky? โš ๏ธ
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.

2๏ธโƒฃ New Safety Layer in smolagents ๐Ÿ›ก๏ธ
We now inspect every return value during execution:
โœ… Allowed: Safe built-in types (e.g., numbers, strings, lists)
โ›” Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)

3๏ธโƒฃ Immediate Benefits ๐Ÿ’ก
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities

4๏ธโƒฃ Security Disclaimer โš ๏ธ
๐Ÿšจ Despite these improvements, local Python execution is NEVER 100% safe. ๐Ÿšจ
If you need true isolation, use a remote sandboxed executor like Docker or E2B.

5๏ธโƒฃ The Best Practice: Use Sandboxed Execution ๐Ÿ”
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.

6๏ธโƒฃ Upgrade Now & Stay Safe! ๐Ÿš€
Check out the latest smolagents release and start building safer AI agents today.

๐Ÿ”— https://github.com/huggingface/smolagents

What security measures do you take when running AI-generated code? Letโ€™s discuss! ๐Ÿ‘‡

#AI #smolagents #Python #Security
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albertvillanovaย 
posted an update 4 months ago
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๐Ÿš€ Big news for AI agents! With the latest release of smolagents, you can now securely execute Python code in sandboxed Docker or E2B environments. ๐Ÿฆพ๐Ÿ”’

Here's why this is a game-changer for agent-based systems: ๐Ÿงต๐Ÿ‘‡

1๏ธโƒฃ Security First ๐Ÿ”
Running AI agents in unrestricted Python environments is risky! With sandboxing, your agents are isolated, preventing unintended file access, network abuse, or system modifications.

2๏ธโƒฃ Deterministic & Reproducible Runs ๐Ÿ“ฆ
By running agents in containerized environments, you ensure that every execution happens in a controlled and predictable settingโ€”no more environment mismatches or dependency issues!

3๏ธโƒฃ Resource Control & Limits ๐Ÿšฆ
Docker and E2B allow you to enforce CPU, memory, and execution time limits, so rogue or inefficient agents donโ€™t spiral out of control.

4๏ธโƒฃ Safer Code Execution in Production ๐Ÿญ
Deploy AI agents confidently, knowing that any generated code runs in an ephemeral, isolated environment, protecting your host machine and infrastructure.

5๏ธโƒฃ Easy to Integrate ๐Ÿ› ๏ธ
With smolagents, you can simply configure your agent to use Docker or E2B as its execution backendโ€”no need for complex security setups!

6๏ธโƒฃ Perfect for Autonomous AI Agents ๐Ÿค–
If your AI agents generate and execute code dynamically, this is a must-have to avoid security pitfalls while enabling advanced automation.

โšก Get started now: https://github.com/huggingface/smolagents

What will you build with smolagents? Let us know! ๐Ÿš€๐Ÿ’ก
albertvillanovaย 
posted an update 5 months ago
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๐Ÿš€ Introducing @huggingface Open Deep-Research๐Ÿ’ฅ

In just 24 hours, we built an open-source agent that:
โœ… Autonomously browse the web
โœ… Search, scroll & extract info
โœ… Download & manipulate files
โœ… Run calculations on data

55% on GAIA validation set! Help us improve it!๐Ÿ’ก
https://huggingface.co/blog/open-deep-research
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cfahlgren1ย 
posted an update 5 months ago
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If you haven't seen yet, we just released Inference Providers ๐Ÿ”€

> 4 new serverless inference providers on the Hub ๐Ÿคฏ
> Use your HF API key or personal key with all providers ๐Ÿ”‘
> Chat with Deepseek R1, V3, and more on HF Hub ๐Ÿ‹
> We support Sambanova, TogetherAI, Replicate, and Fal.ai ๐Ÿ’ช

Best of all, we don't charge any markup on top of the provider ๐Ÿซฐ Have you tried it out yet? HF Pro accounts get $2 of free usage for the provider inference.
cfahlgren1ย 
posted an update 6 months ago
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Wow, I just added Langfuse tracing to the Deepseek Artifacts app and it's really nice ๐Ÿ”ฅ

It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.

It was just added as a one click Docker Space template, so it's super easy to self host ๐Ÿ’ช
albertvillanovaย 
posted an update 6 months ago
cfahlgren1ย 
posted an update 6 months ago
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You'll notice the AI in the SQL Console is much better at working with chatml conversations:

Here's example of unnesting the cfahlgren1/react-code-instructions in less than 10 seconds by asking it. Check it out here: cfahlgren1/react-code-instructions

- "show me the average assistant response length"
- "extract user, system, and assistant messages into separate columns"

It's super easy to work with conversational datasets now with natural language ๐Ÿ—ฃ๏ธ





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cfahlgren1ย 
posted an update 6 months ago
lhoestqย 
posted an update 7 months ago
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Made a HF Dataset editor a la gg sheets here: lhoestq/dataset-spreadsheets

With Dataset Spreadsheets:
โœ๏ธ Edit datasets in the UI
๐Ÿ”— Share link with collaborators
๐Ÿ Use locally in DuckDB or Python

Available for the 100,000+ parquet datasets on HF :)
cfahlgren1ย 
posted an update 7 months ago
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You can just ask things ๐Ÿ—ฃ๏ธ

"show me messages in the coding category that are in the top 10% of reward model scores"

Download really high quality instructions from the Llama3.1 405B synthetic dataset ๐Ÿ”ฅ

argilla/magpie-ultra-v1.0