AI & ML interests

None defined yet.

albertvillanovaΒ 
posted an update 2 days ago
view post
Post
1318
πŸš€ 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:

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

πŸ–₯️ UI Enhancements
- GradioUI now supports reset_agent_memory: perfect for fresh starts in dev & demos.

πŸ”„ Streaming Refactor
- Streaming event aggregation moved off the Model class
- ➑️ Better architecture & maintainability

πŸ“¦ Output Tracking
- CodeAgent outputs are now stored in ActionStep
- βœ… More visibility and structure to agent decisions

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

πŸ“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

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

πŸ’¬ Try it out, explore the new features, and let us know what you build!

#smolagents #opensource #AIagents #LLM #HuggingFace
albertvillanovaΒ 
posted an update about 1 month ago
clefourrierΒ 
posted an update about 1 month ago
view post
Post
753
Always surprised that so few people actually read the FineTasks blog, on
✨how to select training evals with the highest signal✨

If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!

An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!

The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"πŸ‘Œ
(to know on your use case how to select the best evals for you)

Blog: HuggingFaceFW/blogpost-fine-tasks
  • 2 replies
Β·
albertvillanovaΒ 
posted an update about 1 month ago
view post
Post
2476
New in smolagents v1.16.0:
πŸ” Bing support in WebSearchTool
🐍 Custom functions & executor_kwargs in LocalPythonExecutor
πŸ”§ Streaming GradioUI fixes
🌐 Local web agents via api_base & api_key
πŸ“š Better docs

πŸ‘‰ https://github.com/huggingface/smolagents/releases/tag/v1.16.0
albertvillanovaΒ 
posted an update 2 months ago
view post
Post
2782
smolagents v1.14.0 is out! πŸš€
πŸ”Œ MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
πŸͺ¨ Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. πŸ’Ό

Full release πŸ‘‰ https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI
clefourrierΒ 
posted an update 4 months ago
view post
Post
2507
Gemma3 family is out! Reading the tech report, and this section was really interesting to me from a methods/scientific fairness pov.

Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)

For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)

Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!

Because if your model knows its evals by heart, you're not testing for generalization.
albertvillanovaΒ 
posted an update 4 months ago
view post
Post
4131
πŸš€ New smolagents update: Safer Local Python Execution! 🦾🐍

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. πŸ”’

Here's why this matters & what you need to know! πŸ§΅πŸ‘‡

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

2️⃣ New Safety Layer in smolagents πŸ›‘οΈ
We now inspect every return value during execution:
βœ… Allowed: Safe built-in types (e.g., numbers, strings, lists)
β›” Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)

3️⃣ Immediate Benefits πŸ’‘
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities

4️⃣ Security Disclaimer ⚠️
🚨 Despite these improvements, local Python execution is NEVER 100% safe. 🚨
If you need true isolation, use a remote sandboxed executor like Docker or E2B.

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

6️⃣ Upgrade Now & Stay Safe! πŸš€
Check out the latest smolagents release and start building safer AI agents today.

πŸ”— https://github.com/huggingface/smolagents

What security measures do you take when running AI-generated code? Let’s discuss! πŸ‘‡

#AI #smolagents #Python #Security
  • 2 replies
Β·
albertvillanovaΒ 
posted an update 4 months ago
view post
Post
4035
πŸš€ Big news for AI agents! With the latest release of smolagents, you can now securely execute Python code in sandboxed Docker or E2B environments. πŸ¦ΎπŸ”’

Here's why this is a game-changer for agent-based systems: πŸ§΅πŸ‘‡

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

2️⃣ Deterministic & Reproducible Runs πŸ“¦
By running agents in containerized environments, you ensure that every execution happens in a controlled and predictable settingβ€”no more environment mismatches or dependency issues!

3️⃣ Resource Control & Limits 🚦
Docker and E2B allow you to enforce CPU, memory, and execution time limits, so rogue or inefficient agents don’t spiral out of control.

4️⃣ Safer Code Execution in Production 🏭
Deploy AI agents confidently, knowing that any generated code runs in an ephemeral, isolated environment, protecting your host machine and infrastructure.

5️⃣ Easy to Integrate πŸ› οΈ
With smolagents, you can simply configure your agent to use Docker or E2B as its execution backendβ€”no need for complex security setups!

6️⃣ Perfect for Autonomous AI Agents πŸ€–
If your AI agents generate and execute code dynamically, this is a must-have to avoid security pitfalls while enabling advanced automation.

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

What will you build with smolagents? Let us know! πŸš€πŸ’‘
albertvillanovaΒ 
posted an update 5 months ago
view post
Post
4110
πŸš€ Introducing @huggingface Open Deep-ResearchπŸ’₯

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

55% on GAIA validation set! Help us improve it!πŸ’‘
https://huggingface.co/blog/open-deep-research
  • 3 replies
Β·
albertvillanovaΒ 
posted an update 6 months ago