I'm still on the fence about agent frameworks, they have their place, and it depends on the nature of the agent: e.g. "Low latency, return a good enough response in 3 seconds, vs. working for 3 hours on a problem."
BUT, if you boil it down, an agent really is context building, making an LLM call, executing requested tool calls, parsing the final model output, returning it to some frontend. There's extensions like memory, async tool calls, etc, but not THAT complicated from a traditional software engineering perspective.
Everyone seems to want to build their agent framework. But if you're tasked with building an agent, I've found it much easier and more maintainable to just build 1:1 code for THAT agent: most of the abstractions you get from an agent framework purely get in the way and obfuscate core agent logic.
You end up being forced to use the abstractions chosen by the agent framework, which sometimes are a mismatch for what you're actually trying to do.
Obscuring core logic is the most egregious part of most agent frameworks. One needs a clear view of what, exactly, is being sent to the underlying language model, and what's coming back. Everything in an 'agentic' application is realized as a sequence of tokens or a call to a provider eventually. It should be clear and obvious from ~all layers of the app what that's going to look like.
I like to think of it as "AI prompting algorithms". Like instead of just this prompt gets this result it's A prompt then B prompt the C prompt gets a result.
And just like when people were trying to figure out which sorting algorithm made the most sense, we are all just trying to figure out which prompt algorithms with which models lead to good results.
Take a simple workflow. You have a query it goes to a classifier. The classifier determines what workflow it should route the request to.
Then you have a general workflow that has a set of skills (prompts) and tools. And that could be recursive.
So if you do something like "rename this file" you have to build up a workflow like:
[classifier]
what's the workflow -> rename
[rename workflow]
list files (tool call)
figure out relevant predicate (LLM)
convert predicate into a filter query give the context of the files (LLM)
figure out what you want the new name to be (LLM)
create the request body and hit the tool
approval workflow
formatting
It's a lot to manage and orchestrate and that's just one simple example. You'd like want to use the same building blocks to delete a file or move it. Even to know the right concepts is difficult as we're a bit deluded on whats going on in the background of these modern AI apps like Claude and GPT that do a lot of this stuff for you
100% agreed, the "this is what an agent looks like to write" is the wrong pitch for a new agent framework.
The better pitch would be, "this is how easy observability, guardrails, monitoring, deployment, evals, versioning, A/B testing are with our framework." What the agent code looks like is somewhat incidental.
Couldn't agree more - tried to convince a business that doubling down on OpenClaw wasn't going to solve problems except for some 0-1 stuff, and that almost immediately they'd run into roadblocks because most of the product wouldn't serve their use case.
4 months of mostly spinning their wheels later they launched a really lackluster OC product that's effectively DOA.
OpenClaw is an application, not a harness. Yes, it contains a harness, but it is a complete product.
When building an agentic workflow there are enough primitives that rewriting them from scratch every time makes zero sense.
What is a tool? How does the LLM understand the tool? Formatting a native function into a serializable input/output pattern makes sense to generalize and that does not need to exist repeated in everyones application code.
We use libraries to interact with the APIs themselves; nobody would say writing a spec-compliant API client was poor practice. Agentic harnesses are just one layer above: I need to call the API and I need to do it with certain expected conventions.
The best agent framework is Pi (pi.dev). It is minimal and doesn't assume a use case, runs fine interactively or non-interactively, has an active community building with it and supports everything you need to build whatever kind of agent you want with plugins.
Yes, Python has decorators, but they're best used as "filters" that apply to functions or methods. Cache this, serialize the output of this function always, prepare this function to be used as a tool by an agentic harness. Not registration, not flow control. You may disagree but someone has to say it; FastAPI influenced the modern use of decorators far too much in the wrong direction.
Builder patterns are a Rust convention, because Rust has no named keyword arguments. A Python function already exposes a named contract. There is very little reason to ever to sequentially pass configuration parameters in chained method calls. If you need to add state that doesn't exist yet to a constructor or factory, that is not a builder pattern. That is registration. The one place where builder patterns should be tolerated is query builders. They iteratively build on a concept and having the additional "slot" for metadata (method name plus keyword arguments) is genuinely useful. Using methods which accept single parameter instead of keyword arguments is incorrect.
Doesn't look any different than doing the same in C# or Java to me, it is kind of pointless in Python, the one thing the pattern gives you is building a class in such a way that you the developer know exactly what's what, so its really a developer ergonomics thing is how it looks like to me.
How does this compare to https://strandsagents.com/ ? I'm interested in tools in this space, right now I'm not attached to one, but Bedrock + Serverless on Agent Core feels like the "easy guided path" though I don't like the platform lock-in
I’ve been playing with this stack and left wondering if Strands provides any secret sauce with Agent Core. So far it doesn’t feel that way and sometimes they even feel at odds with each other.
Why wouldn't it? The ASF has a long history of incubating new FOSS projects. Some graduate and become household names. Others fail and end up in the attic. The ASF can provide organisational support and generally fosters good communities.
My point was this is a crowded market now, why would they pick a platform that is not known? I did search HN and this platform was only shown once 2 years ago, and from their releases, they are still 0.42 after two years.
It might sounded that I’m against the move, but I’m just curious as what apache found in the platform to get incubated
Burr is named after Aaron Burr, founding father, third VP of the United States, and murderer/arch-nemesis of Alexander Hamilton. What's the connection with Hamilton? This is DAGWorks' second open-source library release after the Hamilton library We imagine a world in which Burr and Hamilton lived in harmony and saw through their differences to better the union. We originally built Burr as a harness to handle state between executions of Hamilton DAGs (because DAGs don't have cycles), but realized that it has a wide array of applications and decided to release it more broadly.
I searched the docs for authentication and mcp (one of the protocols which, among other things, handles some pieces of authentication/authorization) but didn't see any results.
I just create a MVP chatbot for a client that has a Django app. I took the route to no frameworks. Claude/codex wrote the agent loop, the tools, the streaming..it’s working well for the MVP, we’ll see
I'm still on the fence about agent frameworks, they have their place, and it depends on the nature of the agent: e.g. "Low latency, return a good enough response in 3 seconds, vs. working for 3 hours on a problem."
BUT, if you boil it down, an agent really is context building, making an LLM call, executing requested tool calls, parsing the final model output, returning it to some frontend. There's extensions like memory, async tool calls, etc, but not THAT complicated from a traditional software engineering perspective.
Everyone seems to want to build their agent framework. But if you're tasked with building an agent, I've found it much easier and more maintainable to just build 1:1 code for THAT agent: most of the abstractions you get from an agent framework purely get in the way and obfuscate core agent logic.
You end up being forced to use the abstractions chosen by the agent framework, which sometimes are a mismatch for what you're actually trying to do.
Obscuring core logic is the most egregious part of most agent frameworks. One needs a clear view of what, exactly, is being sent to the underlying language model, and what's coming back. Everything in an 'agentic' application is realized as a sequence of tokens or a call to a provider eventually. It should be clear and obvious from ~all layers of the app what that's going to look like.
Have a look at pi.
Whst about pi do you like?
I like to think of it as "AI prompting algorithms". Like instead of just this prompt gets this result it's A prompt then B prompt the C prompt gets a result.
And just like when people were trying to figure out which sorting algorithm made the most sense, we are all just trying to figure out which prompt algorithms with which models lead to good results.
Take a simple workflow. You have a query it goes to a classifier. The classifier determines what workflow it should route the request to.
Then you have a general workflow that has a set of skills (prompts) and tools. And that could be recursive.
So if you do something like "rename this file" you have to build up a workflow like:
[classifier]
what's the workflow -> rename
[rename workflow]
list files (tool call)
figure out relevant predicate (LLM)
convert predicate into a filter query give the context of the files (LLM)
figure out what you want the new name to be (LLM)
create the request body and hit the tool
approval workflow
formatting
It's a lot to manage and orchestrate and that's just one simple example. You'd like want to use the same building blocks to delete a file or move it. Even to know the right concepts is difficult as we're a bit deluded on whats going on in the background of these modern AI apps like Claude and GPT that do a lot of this stuff for you
my job rn is just building agents
the hard part about building agents isnt the framework it's discovery, context, traditional engineering, handling the last mile
there are some invariants like the loop, tools, observability, guardrails, monitors etc...
100% agreed, the "this is what an agent looks like to write" is the wrong pitch for a new agent framework.
The better pitch would be, "this is how easy observability, guardrails, monitoring, deployment, evals, versioning, A/B testing are with our framework." What the agent code looks like is somewhat incidental.
Couldn't agree more - tried to convince a business that doubling down on OpenClaw wasn't going to solve problems except for some 0-1 stuff, and that almost immediately they'd run into roadblocks because most of the product wouldn't serve their use case.
4 months of mostly spinning their wheels later they launched a really lackluster OC product that's effectively DOA.
OpenClaw is an application, not a harness. Yes, it contains a harness, but it is a complete product.
When building an agentic workflow there are enough primitives that rewriting them from scratch every time makes zero sense.
What is a tool? How does the LLM understand the tool? Formatting a native function into a serializable input/output pattern makes sense to generalize and that does not need to exist repeated in everyones application code.
We use libraries to interact with the APIs themselves; nobody would say writing a spec-compliant API client was poor practice. Agentic harnesses are just one layer above: I need to call the API and I need to do it with certain expected conventions.
It’s painfully obvious that you can just open your coding harness and… tell it you’d like to make an agent. They’re simple to write.
Wow, such a un-apache-y homepage I've ever seen, vs. the canonical one: https://httpd.apache.org/ (And wow, they still keep releasing it!)
Is this comparable to https://dspy.ai/ ?
The best agent framework is Pi (pi.dev). It is minimal and doesn't assume a use case, runs fine interactively or non-interactively, has an active community building with it and supports everything you need to build whatever kind of agent you want with plugins.
A builder pattern and decorators.
Yes, Python has decorators, but they're best used as "filters" that apply to functions or methods. Cache this, serialize the output of this function always, prepare this function to be used as a tool by an agentic harness. Not registration, not flow control. You may disagree but someone has to say it; FastAPI influenced the modern use of decorators far too much in the wrong direction.
Builder patterns are a Rust convention, because Rust has no named keyword arguments. A Python function already exposes a named contract. There is very little reason to ever to sequentially pass configuration parameters in chained method calls. If you need to add state that doesn't exist yet to a constructor or factory, that is not a builder pattern. That is registration. The one place where builder patterns should be tolerated is query builders. They iteratively build on a concept and having the additional "slot" for metadata (method name plus keyword arguments) is genuinely useful. Using methods which accept single parameter instead of keyword arguments is incorrect.
Builder pattern isn't only used in Rust, but I agree it's hideous to use in Python.
Doesn't look any different than doing the same in C# or Java to me, it is kind of pointless in Python, the one thing the pattern gives you is building a class in such a way that you the developer know exactly what's what, so its really a developer ergonomics thing is how it looks like to me.
Fair point. I should have said "popularized in the modern software vernacular by Rust".
I think of Java immediately when I hear Builder Pattern, and I think anyone who has ever touched Java does as well.
How does this compare to https://strandsagents.com/ ? I'm interested in tools in this space, right now I'm not attached to one, but Bedrock + Serverless on Agent Core feels like the "easy guided path" though I don't like the platform lock-in
Curious about other experiences.
I’ve been playing with this stack and left wondering if Strands provides any secret sauce with Agent Core. So far it doesn’t feel that way and sometimes they even feel at odds with each other.
First time I hear about Burr, curious why it was incubated in Apache.
Why wouldn't it? The ASF has a long history of incubating new FOSS projects. Some graduate and become household names. Others fail and end up in the attic. The ASF can provide organisational support and generally fosters good communities.
My point was this is a crowded market now, why would they pick a platform that is not known? I did search HN and this platform was only shown once 2 years ago, and from their releases, they are still 0.42 after two years.
It might sounded that I’m against the move, but I’m just curious as what apache found in the platform to get incubated
I couldn't find an explicit reference for the naming, but for anyone wondering there is a Hamilton example: https://github.com/apache/burr/tree/main/examples/multi-agen...
Burr is named after Aaron Burr, founding father, third VP of the United States, and murderer/arch-nemesis of Alexander Hamilton. What's the connection with Hamilton? This is DAGWorks' second open-source library release after the Hamilton library We imagine a world in which Burr and Hamilton lived in harmony and saw through their differences to better the union. We originally built Burr as a harness to handle state between executions of Hamilton DAGs (because DAGs don't have cycles), but realized that it has a wide array of applications and decided to release it more broadly.
https://pypi.org/project/burr/
How are agents authenticated?
I searched the docs for authentication and mcp (one of the protocols which, among other things, handles some pieces of authentication/authorization) but didn't see any results.
What did I miss?
vibe coded landing page
reddit user testimonial
framework is for state machines
why man..
Don't ask the why, ask the how. How did they get acceptance into an incubation stage with what you just mentioned?
The vibe coded landing page (at least in its look) is really degrading Apache foundation image imo.
I just create a MVP chatbot for a client that has a Django app. I took the route to no frameworks. Claude/codex wrote the agent loop, the tools, the streaming..it’s working well for the MVP, we’ll see
Claude Opus really loves this template when building websites. It's very funny how many times I've seen it for recent launches.
And it lags my desktop every-time, I hate it. It's the default bootstrap theme all over again but instead with SVG's.