Strands Harness

I am increasingly hesitant to use non-native harnesses - model providers are now starting to train their agents for use within the harness. An eval like terminal bench can only capture so much data. I don't want to have to assess each harness every model release to make sure it's working as well as it can.

Why is Pi not in the benchmarks? Deepseek beats Strands and its built on Pi so that’s all I needed to know.

> With Fable 5, Strands harness cost 77% less than Claude Code and scored higher on Terminal Bench 2.1.

Terminal Bench 2.1 is saturated. Many token saving techniques would save money and score basically the same running Fable 5 against Terminal Bench 2.1. (They claim a better score but don’t say how much better. I’d bet my favorite hat that it’s not statistically significant.)

This is at least the fourth time I’ve seen a project hit front page with a “save money with same score on saturated benchmark” claim.

I remember reading about strands SDK and it looked great in terms how everything is an event that you can extend, so this harness feels quite about right.

However, for this kind of customisation, Pi is actually quite great. One of the most things I love about Pi is ability to ask it to create an extension and it does it quite well as it’s part of their docs. Also ability to customise the system prompt to avoid the clutter that Claude Code add (around 20k system prompt that mostly had nothing to do with the code).

The demo was showing something I have created for my Pi setup, which is asking me in each new session which skills and MCP I would to enable for the session. This works quite well if you have multiple projects where you don’t need all skills but just a small subset

Including Ollama support is the new token "open option", like a cross-platform application that claims to support Windows, macOS, and Linux, and targets Ubuntu 16.04 only. Just target the OpenAI-compatible API with custom endpoints, it's not that hard. https://schizo.cooking/schizo-takes/9.html

> But the moment you build your own agent, you’re on your own. It’s tricky wiring up the right primitives just well enough to match that “it just worked” feeling.

It's wild to me to claim that it's tricky to customize one of these harnesses and for that to be the entire justification for an entirely different harness.

It's really not that hard. If you want to reduce costs then all you need to do is practice delegation: instead of using the strong model, all the time to do everything, instead, you have the stronger model delegate well-defined tasks to a weaker model. Patterns like these are really easy to wire up.

How are people using custom harnesses cost effectively? Do they avoid Anthropic models so they can use OpenAI subscription pricing and open weights stuff?

Does anyone else take these kinds of articles, drop them into ChatGPT, crank it up to Pro, and then have it write issues against your personal harness?

Can you also compare in the charts https://maki.sh?

Should give you some competition.

Man, I'm sorry for the lack of original thought, but I am so sick of AI websites.

I would seriously rather see a plain unstyled html page with a marquee tag saying "A HUMAN WROTE THE NEXT THREE PARAGRAPHS"

I switch between Pi, Autolith, and my own harness. I come to this website. I don't know what the hell a strand agent is. I don't know who is putting this out. I don't understand whether I'm meant to use a GUI or write code in order to configure it. All the screenshots show a GUI. "use one line of python or javascript to configure." I don't know if this is a harness, an orchestrator, an agent framework, or all of the above. I think it's hard. People get so caught up in making their own thing - they lose track of the state of the art. I have no idea what the value prop for Strands Harness is. But maybe I'm not the target audience.

Also, I don't really care about 30% token efficiency gains. Maybe I should. But this is not my experience. Don't get me wrong, I'm conservative with tokens. But I also have a LOT of convenience / essentials which bloat up context. And I'd rather bloat context and have it work well than not have it work well. And when GLM 5.3 Flash came out - all of a sudden I had more tokens than I knew what to do with. That's when I started using OpenClaw type agents for the first time.

I don't have the tokens/cash to throw at something like Gas Town or OpenRig. And I'm still not at all convinced that those software factories are actually the future of everything. Maybe so. But I just don't see why I would invest the time to learn a piece of software without really understanding the value prop. It's crazy to say, but it's just not enough anymore for software to be free. Because the cost of me implementing my own software is rapidly approaching free. And I like mine better.

"Oh, well I just need something that works out of the box." Wait, why? What does it need to work for? For making software? lol.

I'm more and more hesitant to use third party harnesses. It's so easy to take opencode or pi and build my own for exactly what I want that I'm seeing projects like this to be noisy and less valuable over time.

I wish this wasn't the case. I love software, but it's becoming increasingly easy to just build my own.

So, basically:

1. Any tool result longer than 1500 tokens gets a link instead.

2. Forced compaction at 85%, removing all past messages apart from the last 4?

I'm sure these guys made it look good on the benchmarks, but the frontier labs have been hill-climbing this game for a while now, so odds are this just doesn't work for long-horizon tasks labs are optimizing for.

is anyone using strands ? I looked into it and ended up picking mastra as it had a better story / seems more complete for people making agents in typescript.

Their highest starred repo, the SDK, has 7.8K stars. Yet they felt the need to show 13000+ on this landing page (summing up all repo stars). Technically correct inflating it? Well, that's confidence boosting.

finally paying up for chatGPT and using the Codex desktop app was my real "I'm sold" moment with AI.

Setting up projects and working with the AI on local files has been great, but only for my personal account. I've been trying to get it set up for work that provides OpenAI models through a 3rd party tool, company hosted models, as local-machine models and the UX is just straight up awful.

there's no GUI for profiles or custom endpoints, the config.toml sucks and the overall experience is primitive.

At this stage I really want a Codex-like harness but I need more fluid control over the models, I want features like pinning a project to a provider, as well as pulling in all the models from that provider, also having all providers available.

So if I need to pop over to one project to consult about product A, then pop to another project to do some code analysis on product B I can do so fluidly and have my tokens billed to the right place for each concern.

or a project that can span all of the resources. like having the OpenAi models orchestrate sub-agents on the local or hosted models.

Is strands the only option if you're trying to scale up agents? Thinking 1000s of agents hosted on AWS / other cloud providers.

This seems to be from AWS team. Is that right?

why would you include oh-my-pi in the comparison but not vanilla pi?

Tell your harness to fix overflow in header between 800-1400px.

I’m sure it was not a coincidence that this was released the day after Kimi 3 was added to bedrock.

Will they block me if I build an agent with this that shops on Amazon?

Supported provider list is nowhere close to pi's or opencode's

Amazon is so hopelessly behind in AI, nothing they produce aside from cloud infrastructure is actually good

The big threat to AWS is that coding agents dont need all of their complicated infrastructure, which was built for humans. Agents can use low level primitives, i.e. just a raw server

so far I have seen many harnesses that are built to provide similar quality to the provider's harness for lower cost. Has there been any projects that prioritizes quality and quality consistency?

"We noticed builders often wished their Claude Code or Codex setup could run in the cloud because locally their agent idea just “worked” with those harnesses.

But the moment you build your own agent, you’re on your own. It’s tricky wiring up the right primitives just well enough to match that “it just worked” feeling."

Im sorry, but who is saying this? If you just throw this statement into agent of your choice- and ask what native integrations exist to cover this use: OAI and Anthropic both have a handful of options here. Claude Agent SDK, Claude managed agents, Codex exec, Codex sdk, Codex app server, openai agents sdk, openai agents api.

Pretty crazy amazon is advertising an open source repo. Not suggesting this is an ad but I've seen ads on reddit for it.

Ive heard from a 25 yoe consultant in a meetup group in person that aws agentcore was THE best way to handle enterprise agentic workflows with all of the proper knobs for governance etc since it comes with the iam integrations and arns etc.

I their web icon a pepe frog? hard pass if so..

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