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A key difference between MCP and A2A that is apparent to me after building with MCP and now reading the material on A2A: MCP is solving specific problems peopl

A key difference between MCP and A2A that is apparent to me after building with MCP and now reading the material on A2A: MCP is solving specific problems people have in practice today. LLMs need access to data that they weren't trained on, but that's really hard because there's a millions different ways you could RAG something. So MCP defines a standard by which LLMs can call APIs through clients. (and more). A2A solves a marketing problem that Google is chasing with technology partners. I think I can safely say which one will still be around in 6 months, and it's not the one whose contributors all work for the same company.

owebmaster 2y ago
> I think I can safely say which one will still be around in 6 months LangChain is still around but that doesn't mean much. MCP isn't much better.
- phillipcarter 2y ago
  Langchain has long solved (we can argue on if it's done it well, opinions vary) the problem of needing to orchestrate LLM calls into a coherent workflow. Plus it had a first mover advantage. MCP solves a data and API integration problem. Both are concrete things that people need to do today. AI agents talking to one another is not a concrete problem that organizations building features that integrate AI have today.
  - __loam 2y ago
    Langchain is one of the most hilarious libraries I've ever had the displeasure of looking through. Many of the abstractions look like they were written by a college student who took clean code way too literally. Many of the methods are so trivial and shallow that I'm shocked people use it in any serious capacity.
    - tomaskafka 2y ago
      This. I was amazed opening something like PagagraphLineReaderFactory, thinking it somehow deals smartly with paragraph boundaries and too long paragraphs etc., and finding a trivial single line regex wrapped in two screens of OOP boilerplate.
  - XCSme 2y ago
    > the problem of needing to orchestrate LLM calls into a coherent workflow. I didn't feel the need to use Langchain, chaining LLM calls is usually just a few lines of code (I think even fewer than when using Langchain).
- XCSme 2y ago
  I am still simply doing plain fetch requests to LLMs APIs, and it works great, 10/10 would recommend.
TS_Posts 2y ago
Hi there (I work on a2a) - A2A works at a different level than MCP. We are working with partners on very specific customer problems. Customers are building individual agents in different frameworks OR are purchasing agents from multiple vendors. Those agents are isolated and do not share tools, or memory, or context. For example, most companies have an internal directory and internal private APIs and tools. They can build an agent to help complete internal tasks. However, they also may purchase an "HR Agent" or "Travel Assistant Agent" or "Tax Preparation Agent" or "Facilities Control Agent". These agents aren't sharing their private APIs and data with each other. It's also difficult to model these agents as structured tools. For example, a "Tax Preparation Agent" may need to evaluate many different options and ask for specific different documents and information based on an individual users needs. Modeling this as 100s of tools isn't practical. That's where we see A2A helping. Talk to an agent as an agent. This lets a user talk to only their company agent and then have that agent work with the HR Agent or Travel Booking Agent to complete complex tasks.
- phillipcarter 2y ago
  While I can logically understand these problems and why A2A could solve them, unfortunately you're asking me to suspend disbelief about the actual agents being built and deployed.

owebmaster 2y ago

> I think I can safely say which one will still be around in 6 months LangChain is still around but that doesn't mean much. MCP isn't much better.

phillipcarter 2y ago
Langchain has long solved (we can argue on if it's done it well, opinions vary) the problem of needing to orchestrate LLM calls into a coherent workflow. Plus it had a first mover advantage. MCP solves a data and API integration problem. Both are concrete things that people need to do today. AI agents talking to one another is not a concrete problem that organizations building features that integrate AI have today.
- __loam 2y ago
  Langchain is one of the most hilarious libraries I've ever had the displeasure of looking through. Many of the abstractions look like they were written by a college student who took clean code way too literally. Many of the methods are so trivial and shallow that I'm shocked people use it in any serious capacity.
  - tomaskafka 2y ago
    This. I was amazed opening something like PagagraphLineReaderFactory, thinking it somehow deals smartly with paragraph boundaries and too long paragraphs etc., and finding a trivial single line regex wrapped in two screens of OOP boilerplate.
- XCSme 2y ago
  > the problem of needing to orchestrate LLM calls into a coherent workflow. I didn't feel the need to use Langchain, chaining LLM calls is usually just a few lines of code (I think even fewer than when using Langchain).
XCSme 2y ago
I am still simply doing plain fetch requests to LLMs APIs, and it works great, 10/10 would recommend.

TS_Posts 2y ago

Hi there (I work on a2a) - A2A works at a different level than MCP. We are working with partners on very specific customer problems. Customers are building individual agents in different frameworks OR are purchasing agents from multiple vendors. Those agents are isolated and do not share tools, or memory, or context. For example, most companies have an internal directory and internal private APIs and tools. They can build an agent to help complete internal tasks. However, they also may purchase an "HR Agent" or "Travel Assistant Agent" or "Tax Preparation Agent" or "Facilities Control Agent". These agents aren't sharing their private APIs and data with each other. It's also difficult to model these agents as structured tools. For example, a "Tax Preparation Agent" may need to evaluate many different options and ask for specific different documents and information based on an individual users needs. Modeling this as 100s of tools isn't practical. That's where we see A2A helping. Talk to an agent as an agent. This lets a user talk to only their company agent and then have that agent work with the HR Agent or Travel Booking Agent to complete complex tasks.

phillipcarter 2y ago
While I can logically understand these problems and why A2A could solve them, unfortunately you're asking me to suspend disbelief about the actual agents being built and deployed.