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New tools for building agents

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Re: New tools for building agents

#151

Post by koconder »

zellyn wrote: post #7

Notably not mentioned: Model Context Protocol https://www.anthropic.com/news/model-context-protocol

100% but this is not the same thing, nor is this going to replace the agent SDK (or visa versa). Agents will always need some form of communication protocol, if we look at the world and agentic frameworks its a sea of logos and without some forms of open standards this would be hard.

I'm currently at Comet and I have personally worked on MCP implementations AND have made some contributions to Agent SDK in the form of a native integration and improvement to test suite.

- https://github.com/comet-ml/opik-mcp

- https://github.com/openai/openai-agents-python/pull/91

Our recent integration shipped on day 1:

- https://www.comet.com/docs/opik/tracing/integrations/openai_...

I think the key to what OpenAI is pushing towards is simplicity for developers through very easy to use components. I won't comment on the strategy or pricing etc, but on first glance as a developer the simple modular approach and lack of bloat in their SDK is refreshing.

Kudos to the team and people working on the edge to innovate and think differently in an already crowded and shifting landscape.

Re: New tools for building agents

#152

Post by cowpig »

TeeWEE wrote: post #134
cowpig wrote: post #66

Earlier quoted context omitted.

MCP is a protocol, and Anthropic has provided SDKs for implementing that protocol. In practice, I find the MCP protocol to be pretty great, but it leaves basically everything except the model parts out. I.e. MCP really only addresses how "agentic" systems interact with one another, nothing else. This SDK is trying to provide a bunch of code for implementing specific agent codebases. There are a bunch of open source o…

Correction: the MCP is a protocol for function calling and getting context into a model. It’s can run locally or over a network. It does not specify how “agentic” systems interact with each other. Depending on what you mean there.

People are using the word "agentic" to mean this, I think. But yeah it's a dumb, overloaded primarily marketing word. I keep going back and forth on whether I should use the word "agentic"/"agent" at all

Re: New tools for building agents

#154

Post by esafak »

cowpig wrote: post #77
esafak wrote: post #69

Earlier quoted context omitted.

Thank you. Which open source ones do you recommend?

Here's a fairly comprehensive list: https://github.com/slavakurilyak/awesome-ai-agents CrewAI is a popular VC-backed one, but two that I think are kind of interesting in the open source space are: https://github.com/i-am-bee/beeai-framework https://github.com/lastmile-ai/mcp-agent ... However I think the vast majority of "AI Agent" use-cases in practice right now are actually just workflows, and imo dify is great for…

Thanks. mcp-agent lacks tests so I'm skipping it for now: https://github.com/lastmile-ai/mcp-agent/tree/main/tests

I just realized BeeAI is IBM's project: https://www.ibm.com/think/news/beeai-open-source-multiagent

I also see there's https://ai.pydantic.dev/ but it lacks MCP support. Finally, the MCP site maintains a nice client list:

https://modelcontextprotocol.io/clients#feature-support-matr...

Re: New tools for building agents

#156

Post by gusmally »

rglover wrote: post #30
baxtr wrote: post #5

A bit off topic but the post comes handy: can we settle the debate what an agent really is? It seems like everyone has their own definition. Ok I’ll start: an agent is a computer program that utilized LLMs heutiger for decision making.

Agents are just regular LLM chat bots that are prompted to parse user input into instructions about what functions to call in your back-end, with what data, etc. Basically it's a way to take random user input and turn it into pseudo-logic you can write code against. As an example, I can provide a system prompt that mentions a function like get_weather() being available to call. Then, I can pass whatever my user's pro…

That sounds like L1 in this article (there are six) https://www.vellum.ai/blog/levels-of-agentic-behavior

Re: New tools for building agents

#157

Post by sixhobbits »

xdavidliu wrote: post #155
oofbaroomf wrote: post #47

Earlier quoted context omitted.

Questions.

this is why I don't like NSA (non-standard acronyms). It saves half a second for the typer, but causes hours if not days of confusion when summed over all the readers.

This is an OF (online forum) my bruv, you don't need to follow a style guide to post here

Re: New tools for building agents

#158

Post by sixhobbits »

Aa lot of criticism about the potential of vendor lockin etc, but I think this is great, especially for building proof of concepts and small projects. As they said, these are the first building blocks and they look great to me.

When gpt3.5 came out these are literally the first things I built manually. I mainly use LLMs through a telegram bot. I know there are a lot of tools and frameworks out there but I wrote a few hundred lines of hacky python to give my bot memory, web search, image analysis. It's fun and useful and I agree that these are the basic building blocks that many apps need.

Sure you can find better stuff elsewhere with less lock in and more control, but now it "just works" and this responses api is cleaner and more powerful than the chatcompletions one, so personally I'm happy to give openai credit for this, I just don't know why they couldn't have released it two years ago

Re: New tools for building agents

#159

Post by sixhobbits »

ElijahLynn wrote: post #98
bradyriddle wrote: post #71

Earlier quoted context omitted.

The nitter link is appreciated!

TIL about Nitter, so grateful as I have Twitter blocked on my computer and phone.

oh cool, I thought nitter died with the API changes. Glad they have it working again.

Re: New tools for building agents

#160

Post by rglover »

gusmally wrote: post #156
rglover wrote: post #30

Earlier quoted context omitted.

Agents are just regular LLM chat bots that are prompted to parse user input into instructions about what functions to call in your back-end, with what data, etc. Basically it's a way to take random user input and turn it into pseudo-logic you can write code against. As an example, I can provide a system prompt that mentions a function like get_weather() being available to call. Then, I can pass whatever my user's pro…

That sounds like L1 in this article (there are six) https://www.vellum.ai/blog/levels-of-agentic-behavior

Relative to that scale, L2 is how I've come to understand it. It's kind of soft-sold as L3 but that will require quite a bit of work on the vendor side (e.g., implementing an AWS Lambda style setup for authoring functions the LLM can call).
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