daavoo

daavoo

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posted an update 1 day ago
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1215
Another day, another release in
⭐https://github.com/mozilla-ai/any-agent ⭐

You can now use MCP (Model Context Protocol) tools via SSE (Server-Sent Events):

from any_agent import AgentConfig, AnyAgent
from any_agent.config import MCPSseParams

agent = AnyAgent.create(
    "smolagents",
    AgentConfig(
        model_id="gpt-4o-mini",
        tools=[
            MCPSseParams(
                url="http://localhost:8000/sse"
            ),
        ]
    )
)
agent.run("What do MCP and SSE mean?")


See SuperGateway for an easy way to turn a Stdio server into an SSE server.
replied to their post 4 days ago
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Currently, we only support 2 patterns that can be implemented (almost) consistently across frameworks:
single agent and multi-agent in the form of "manager" + "managed agents".

Don't hesitate to open an issue https://github.com/mozilla-ai/any-agent/issues to discuss what other patterns would be useful

reacted to Xenova's post with 🤗🚀🚀 5 days ago
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1838
Reasoning models like o3 and o4-mini are advancing faster than ever, but imagine what will be possible when they can run locally in your browser! 🤯

Well, with 🤗 Transformers.js, you can do just that! Here's Zyphra's new ZR1 model running at over 100 tokens/second on WebGPU! ⚡️

Giving models access to browser APIs (like File System, Screen Capture, and more) could unlock an entirely new class of web experiences that are personalized, interactive, and run locally in a secure, sandboxed environment.

For now, try out the demo! 👇
webml-community/Zyphra-ZR1-WebGPU
  • 1 reply
·
posted an update 6 days ago
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1292
New release in https://github.com/mozilla-ai/any-agent 🤖

You can now use "managed_agents" also in langchain and llama_index, in addition to the other frameworks:

from any_agent import AgentConfig, AgentFramework, AnyAgent
from any_agent.tracing import setup_tracing

framework = AgentFramework("langchain")  # also in AgentFramework("llama_index") and the rest of frameworks
setup_tracing(framework)

agent = AnyAgent.create(
    framework,
    AgentConfig(
        model_id="gpt-4.1-mini",
        instructions="You are the main agent. Use the other available agents to find an answer",
    ),
    managed_agents=[
        AgentConfig(
            name="search_web_agent",
            description="An agent that can search the web",
            model_id="gpt-4.1-nano",
            tools=["any_agent.tools.search_web"]
        ),
        AgentConfig(
            name="visit_webpage_agent",
            description="An agent that can visit webpages",
            model_id="gpt-4.1-nano",
            tools=["any_agent.tools.visit_webpage"]
        )
    ]
)
agent.run("Which Agent Framework is the best??")
  • 2 replies
·
reacted to stefan-french's post with 😎 8 days ago
reacted to etemiz's post with 👀 11 days ago
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2165
It looks like Llama 4 team gamed the LMArena benchmarks by making their Maverick model output emojis, longer responses and ultra high enthusiasm! Is that ethical or not? They could certainly do a better job by working with teams like llama.cpp, just like Qwen team did with Qwen 3 before releasing the model.

In 2024 I started playing with LLMs just before the release of Llama 3. I think Meta contributed a lot to this field and still contributing. Most LLM fine tuning tools are based on their models and also the inference tool llama.cpp has their name on it. The Llama 4 is fast and maybe not the greatest in real performance but still deserves respect. But my enthusiasm towards Llama models is probably because they rank highest on my AHA Leaderboard:

https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08

Looks like they did a worse job compared to Llama 3.1 this time. Llama 3.1 has been on top for a while.

Ranking high on my leaderboard is not correlated to technological progress or parameter size. In fact if LLM training is getting away from human alignment thanks to synthetic datasets or something else (?), it could be easily inversely correlated to technological progress. It seems there is a correlation regarding the location of the builders (in the West or East). Western models are ranking higher. This has become more visible as the leaderboard progressed, in the past there was less correlation. And Europeans seem to be in the middle!

Whether you like positive vibes from AI or not, maybe the times are getting closer where humans may be susceptible to being gamed by an AI? What do you think?
·
posted an update 12 days ago
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2800
Wondering how the new Google Agent Development Toolkit (ADK) compares against other frameworks? 🤔You can try it in any-agent 🚀

https://github.com/mozilla-ai/any-agent

agent = AnyAgent.create(
    AgentFramework("google"),
    AgentConfig(
        model_id="gpt-4o-mini"
    )
)
agent.run("Which Agent Framework is the best??")

  • 1 reply
·
updated a Space 13 days ago
posted an update 14 days ago
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1830
After working on agent evaluation🔍🤖 the last weeks, we started to accumulate code to make trying different agent frameworks easier. From that code, we have built and just released a small library called any-agent.


Give it a try and a ⭐: https://github.com/mozilla-ai/any-agent

from any_agent import AgentConfig, AgentFramework, AnyAgent

agent = AnyAgent.create(
    AgentFramework("smolagents"),  # or openai, langchain, llama_index
    AgentConfig(
        model_id="gpt-4o-mini"
    )
)
agent.run("Which Agent Framework is the best??")
reacted to stefan-french's post with 🔥🚀 about 1 month ago
reacted to sharpenb's post with 🔥 about 1 month ago
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3087
We open-sourced the pruna package that can be easily installed with pip install pruna :) It allows to easily ccompress and evaluate AI models including transformers and diffusers.

- Github repo: https://github.com/PrunaAI/pruna
- Documentation: https://docs.pruna.ai/en/stable/index.html

With open-sourcing, people can now inspect and contribute to the open code. Beyond the code, we provide detailed readme, tutorials, benchmarks, and documentation to make transparent compression, evaluation, and saving/loading/serving of AI models.

Happy to share it with you and always interested in collecting your feedback :)
  • 2 replies
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posted an update about 1 month ago
reacted to chansung's post with 👍 about 1 month ago
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1570
Gemma 3 Release in a nutshell
(seems like function calling is not supported whereas the announcement said so)