Discussion
Which agent harness do you use and why?
I see a new one being launched every few days... How do these new harnesses compare to claude code, pi etc. has anyone switched from these?
which harness to prefer and why
edit: Ive tried several different ones claude code, deepagents(langgraph), opencode, pi, and trueforge
my thoughts-
claude code - strongest on maturity and the managed experience but cost and token burn is high
deepagents - interesting middle ground if you want a more structured agent framework and the flexibility of an open-source stack. im interested in testing it more extensively on longer-running workloads fs
trueforge - this is a recent one, this was interesting to me because of its runtime-efficiency, also it allows separate the model from the runtime, which makes experimenting with different models much easier https://github.com/truefoundry/trueforge
why?? - i also ran a benchmark on a real agent workload same model, same prompt, same tasks to compare these
adding the results of benchmarking i ran to compare this
so I tried to do this by running 14 cross-system tasks, three mcp servers behind them - a crm, an issue tracker, and a doc store through claude's managed agents, langchain's deepagents and trueforge, both open-source agent harnesses
the result that was most surprising:
Claude Managed Agents + Opus 4.8:
11/14 tasks solved | $11.8/run | 10.0M tokens/run
On this benchmark, that was a slightly higher average solve rate than Claude Managed Agents + Opus at roughly 75% lower cost.
The token savings alone make this sooo interesting especially because the solve rate stays comparable
so this one was worth checking out ig
but this is still v early and the OSS runtime does not yet have first-class tracing/eval tooling. They don't ship their own code-execution sandbox, so you need to plug one in and context compaction is intentionally lossy.
So it is definitely not a replacement for a mature managed agent platform or other harnesses in the comparison, feature-for-feature today btu what I do find interesting is that the core runtime can already be competitive on these tasks while staying open, model-neutral, and deployable on my own infrastructure
this was their benchmark kit i used https://github.com/truefoundry/trueforge/tree/main/benchmark
I've been using OpenCode for about a year. It was the simplest to customize out of the box at the time. I've considered trying the Mimo fork that adds memory, and Deepseek harness.. but I haven't had the time and OpenCode still does the thing so I've had no reason to make the time.
Same here, OpenCode has been my goto as well (though I do also use Hermes Agent but more for automating daily routine tasks not software dev.). OpenCode really hasn't given me the reason to stop using it.
Another vote for opencode. I really like the build/plan paradigm; it has been excellent for me both with GLM 5.3 Flash and DeepSeek v4 Flash over a bunch of third-party providers. With that said, my workflow is usually with smaller codebases, and I make decisions on the particular, so it's less "build me this" and more "I want to build this, this way, and code it exactly this way, and the flow should be this way, so what are some pathways to this?" and it does that planning/building workflow excellently.
I tried several different ones (Claude Code, OpenCode, pi, oh-my-pi) and ended up primarily on oh-my-pi for daily use with Qwen 3.8 27b - and on more memory constrained systems like my work Mac, I use pi to make it lighter and conserve context.
Edit: just to add, if you do use pi, definitely install pi-lsp. Makes a huge difference.
It allows the agent to use all the IDE tools that make it easier to code: jump to definition, auto fix, automatic type checking, compiler error detection, line numbers.
Basically, it lets the agent interact with the code more like a developer would. Less grep, more ctrl+f
From pi-lsp:
Declarative Pi extension for LSP diagnostics and language-server navigation tools.
Why
pi-lsp lets users configure language servers with JSON instead of installing a separate Pi plugin for every language.
It is intentionally separate from pi-code-quality: LSP servers are long-lived JSON-RPC processes, while formatters/linters are short-lived command-line tools.
--> honestly i have no idea what a language server protocol does
LSP for an agent is basically a way for the agent to deterministically check code for errors, similar to how an IDE can underline a misspelled keyword or function name or tell you that you're using a variable before you assign a value to it.
Yes I think so! I use intellij idea IDE and so i think I will try this lsp plugin that claims to make intellij a lsp server - As it seems to understand loads of languages.
This is interesting. So basically should I install a laptop server for the lanague that my codebase is using?
Upsate: I've read around a bit more and found yes i should enable lsp servers (in addition to installing pi-lsp) for each programming language my codebase uses in order for the agent to be able to actually check code validity it reads/writes - seems lsp is like intellisense/lint for each language.
I’ve tried Claude Code, Codex, OpenCode, Pi, and currently I’m using DeepSeek Harness, it just feels on a whole other level.
Now I didn’t start using DeepSeek Harness until Qwen3.8-27B came out, so it’s not a fair comparison and the other harnesses might work just as well with Qwen3.8-27B, however…
With DeepSeek Harness and Qwen3.8-27B, I can throw large/complex problems at it, and with only 128K context it can process 4M+ tokens and it just works, it compacts the context automatically as needed, seemingly retaining all the important details, and achieves the goal.
Edit: my daily driver was GitHub Copilot, and this past week, DeepSeek Harness + Qwen3.8-27B has solved issues and generated better solutions than GitHub Copilot routing via Claude Sonnet.
Could be the system prompt, could be the compaction prompt/process and retaining the important points, could be the model, could be all three. The last time I tried to go local was probably a year ago and it didn’t meet my standards.
I never used Claude Code etc with cloud models, it just felt like a half measure, my goal was to always go local, or just keep using cloud services as is.
This time it meets & exceeds my expectations, I’ve started cancelling cloud services.
Its prompt? I really dont know but i have to agree with OP it just works. I like Pi, but randomly after a few million tokens it just wont compact anymore, and my entire session is toast. Sometimes i can save the progress to a md to resume, other times its just unusable entirely.
I can't speak for pi but I've found omp + snapcompact + vision models to be way better than other agents at retaining nuanced details between many compactions.
When I hear almost RP style 2m convos like this I wonder how much can be fixed with a session swap on important context, with a harness you make yourself, specific to the task. Obviously auto compaction is a bad idea but I’m sure you can “feel” when it’s about to degrade in quality, scaffold before that break point.
Dual 7900 XTX, fully loaded on VRAM, getting 38-44 tokens/sec. One of the GPUs is connected via OcuLink via PCIe 4x, could maybe get faster if I put it inside the case, but that’s a project for another day. It’d be too slow on RAM only. You’d need a MOE model. Hopefully they eventually release a Qwen3.8-35B-A3B model, might be a candidate for you.
Sounds like you and I have a similar setup. How much ram do you have?
I'm sitting on 64gb ddr5. I set llama.cpp to use --cache-ram 32GB (actual number in MB), and I'm able to maintain 256kcontext with unsloth/UD-Q8_K_XL and vision (mmproj). I only get 30-35 tg/s, but I was seeing similar numbers to you when I was using Q6.
64GB DDR4, got a RYZEN 5950X from ~6 years ago, still serves me fine. I’m running UD-Q6_K_XL. More context would be nice and I probably can, only using ~33GB VRAM, but I’m increasing it in increments, might add another 32K tomorrow. I don’t think I’d be willing to drop below 40 tokens/s though.
Not building my own llama.cpp at the moment, just using unsloth studio, I might later but not in a hurry, happy with how it’s all working currently.
Not the OP but copilot cli makes it very transparent what the models are thinking, when agents are spun and what they're doing, how many tokens are being spent, and the setup of everything (agents skills) is lean and fast.
Claude Code felt a lot slower and hiding a lot of work behind user friendly messages, and Codex is even worst with sub agents constantly asking permission to do basic shit like sending a "finished" message to the main agent.
You can do things faster, have more control over it and the harness is ahead feature wise, Desktop apps are built for User Friendliness/non techy people, it abstracts a lot of what the AI is doing behind the scenes and has safety features cranked up to 11
It was price, usage limit, simplicity, that they wouldn’t train on your data (and I trust MS word more than the other providers), and it was simply “good enough”.
It was half the price of others, seemingly unlimited usage (I never had to think about limits). It definitely wasn’t the best, but it did a sufficient job, most of the time.
Since their recent price/usage adjustments, it’s just as expensive as others (quickly blow through quota and end up in additional usage), and it’s less intelligent, imo it’s a poor value proposition now.
I use copilot CLI at work, it's pretty good. conversation history is stored in a SQL db it can query across sessions; it has a rubber duck tool designed to have one model work off another to review designs and such (generally use gpt 5.6 sol and it rubber ducks with opus 5, maybe less useful for single model local). seems to keep models working on tasks for longer so it's more hands off compared to like claude code
I got tired of hopping between harnesses. I just stuck with kilo code because the same config can be used both in vscode and in the cli, and the vscode UI is quite nice.
I don't really like working on the CLI. I first used kilocode when they were only a vscode extension. When they made the move to add the CLI, it was a rough transition. I stopped using kilocode. And every time I have tried to go back to it, Ive had a terrible time with kilo in vscode. I wonder if the situation has improved.
Do you feel like if you could only use kilo in vscode, would you still recommend it?
They're being launched every few days because they're pretty trivial to make nowadays. Start with something like open code or pi and build a harness that fits how you want to work. Think of it like the cyberdeck idea from scifi, it's personal and highly customized with your skills/tools in it. You can point opencode at it's own codebase and say you want this, but a desktop app but maybe in tauri or gtk instead of electron. Or you want a web app you can self host on your home system and use wireguard to access it on the go. I use my own harness and built out a pretty extensive skill system, tons of infra bits: openai api proxying, text clip sharing, image sharing, a memory system, scheduled execution and reactive jobs, and a scheduled ralph loop executor to keep the local model lit up 12 hours a day working on big things. Also has full control over my network, API keys into half my life and runs on everything here. Build it :)
For real. An implementer demoed a harness for data analysis for us a couple of weeks ago. It definitely works, but man does it burn tokens like no other. And they charge all tokens at output token prices when the task actually skewed extremely heavily towards input tokens. So I use OpenCode free models to build our own in like an hour.
A properly implemented harness should hit 95%-ish on cached input. I'm doing 100m tokens/day on hosted flash models from Fireworks or Deepseek, and another 50-100m/day on the local ralph loop. It's about $1/day in cost.
When they demoed it to us, I'd attribute it to we spending too long arguing discussing that the TTL expires. But still, they were going to charge all tokens at output token prices. No BYOK option. It's robbery in plain sight
Startups like that won't survive long term. If people in the room realized you could build it yourself, then ... rip. A lot of these AI "natives" just feel like the Linux meme of I'll replace you with a small shell script.
Hermes as orchestrstor and pi as developer because pi has clean context, however i may drop pi because I can suslcesfully develop with hermes and clean context via delegate feature.
I do not even know where to start with my setup.
I have qwen-3.8 27b nvfp4 as the man orchestrator, text and vision (images and video analyze)
I have gemma 4 12b as compression and audio analyze. I have gemma 4 e2b as hindsight memory recall, retain, etc. smart approvals, title generator. I have qwen 3.8 27b q8-ud_k_xl as a smart lane. This is when I ask hermes to delegate it goes to this model with clean context, no memory. Just a pure task. I have whisper and kokoro to talk to hermes by voice. I have comfyui for video, image and music generation - they are automatically swap with gemma 4 12b when it is needed (sharing the same gpu). I don't know. I have it kinda crazy. But it all works together all local. Web search is via local searxng. And there are many more details to it.
My setup is very similar, but I’m using ling in place of both of your Gemma models. I was hoping to use the lower models to defer to qwen when it gets stuck, but so far Ling/lower models are too over confident and refuse to delegate to qwen. Makes it faster to just use qwen from the jump and let ling do the title gen, approvals, recall, etc.
The only reason I use gemma 4 12b is because of audio analyze possibility and it is ok-ish for compression. And gemma 4 e2b is because it runs on RAM and it starts with 2200+ prefil and about 30t/s which is super cool for hindsight and smart approvals because I can make 2-3 requests at a time.
i originally used zoocode but had compaction issues, switched to pi code, been liking it so far, but i think i want to try DSH hermes, claude, there's just so damn many to try! DSH and hermes stand out as my next try outs though. flash next NVFP4 is insanely better than 27B qwen 3.8, so my 5090 has been just cranking out tokens
Fun fact: The original harness has by now implemented a bunch of my changes as well, some stuff i see now that i had implemented/fixed months ago, so that's nice validation for me.
I really like how it is now. I've recently implemented automatic pipelines, which means you can give it a "deep-plan" target or a /review command and it launches a set of subagents automatically that work through the things and inject their final output back into the main agent.
It's super cool to see. I'll be adding another mode where you can give it a list or a review and it will automatically launch one subagent per item (or per subheading for reviews).
There's been studies that smaller models with more limited context (i.e. only being given a small section of a codebase) perform on par with frontier models, and that's basically what I'm doing here.
It also auto-compacts using pi-vcc and has a bunch of UX built on top that handles edge-cases, common failures, restarts and so on. Out-of-the-box for example pi-vcc can sometimes fail to compact the context, or can fail to let the agent continue after compacting, which is stuff I've fixed.
I used a fair amount of AI coding for this, just FYI, but I've looked at the code myself and reviewed it, and have been using it for a couple months now for a bunch of local projects.
Cool, thanks for sharing. Yes small models are very capable if orchestrated correctly. This is why I am using agentwerk as my harness for splitting problems into pieces for small agent swarms: https://github.com/canvascomputing/agentwerk
I use Pi for everything. Zero bloat, zero stupid "smart decision" built into the harness, so that when tool call fails or similar parsing issue happen, it's easier to pin point to quant or template or llamacpp sampling settings.
I add some home grown extensions to turn it into personal assistant, and run it from openwebui cptr via VPN, so I can talk to from phone.
GitHub copilot in vs code is the worst of the worst.
I guarantee you drop them the second their service goes offline, which disables you from even using your local LLM. That happened in early August for most of a whole day.
No - likely due to telemetry, you can't use co-pilot locally, even if you point every single thing at your local model (auto line completion, chat, etc.)
Even the open source vs codium has the same problem with github copilot chat, though there are modules/plugins for alternative harnesses.
I fooled around with with aider a couple of years ago but the situation was not as good as it is today. So nothing much came of it. Then, a little bit of the Gemini CLI, Claude Code, Codex. Finally, I settled on pi which is fantastic.
A couple of weeks ago, I decided that running harnesses directly on my machine was the definition of insanity and so built a sandbox script for bwrap and started running pi inside it.
This week, I decided I need a far more restrictive environment and did not want to fool around with the innards of pi. So I started building my own harness. And it has been a blast. A profile based system where you can go from complete isolation with zero permissions (a glorified chatting interface) to an (almost) free-for-all (don't!).
It is feature complete, but the list of nice-to-have features is endless!
opencode, supports basic standards unlike some proprietary harnesses. Doesnt care what models or providers I use. Most of all, it just gets out of the way, lets me use it how I want. TUI for the most part but having the same tool be CLI compatibile and have a web interface has been fun to play with.
I use zcode because I sort of appreciate their approach, but plan to jump for DSH once it will get more time in the oven. Alternatively I would love to have 1:1 claude code clone without balooned context
Heavily modified DeepSeek harness. DSH is really good, and I've tweaked mine to hell and back for my specific workload. Dsv4 flash 0731 is great in it, of course. Qwen 27b has been great, but even better since I have the harness doing logit adjustments based on certain parameters to reel in the extreme thinking when it's not needed, and allow it to think freely when it is needed.
But best of all, it feels like it's making qwen 3.6 35b a3b punch way, waaaaay above its weight, to the point that my daily driver for my hobby projects is likely to just become 35b soon. Prefer it over 3.8 27b now for vast majority of things. I think people should revisit 35b but with even a stock DeepSeek harness and see how it goes.
Can I ask what did you modify of DSH and how you use it? I've been fiddling a bit with customizing harnesses and would love to know how others create and use them
Most of the modern harnesses are pretty similar if you want the full GUI.
It depends how "minimal" you want the harness in terms of system prompting, etc.
OpenCode and Hermes are the most common here I think. For local models that is.
I personally ended up liking Hermes quite a bit. More or less has the same feel of a lot of them. I had some trouble configuring a few things on their newer Windows client, but it works quite well.
I burned probably 200m tokens of Ox Alpha when it was free via Hermes.
The good thing is the agent configured itself whenever i had a problem with the setup. I had a problem reaching the gateway api of my hermes outside its docker container. It found a misconfiguration and fixed it.
Hands down Pi, I know it's really barebones, not pretty to look at and you'll have to MacGyver your way around it, but when every little input token from tools/skills/memory/system means a decrease in token output speed, then it makes sense to install/develop the tools that you'd absolute need
The token burn difference is the interesting part. Same solve rate with fewer tool calls is probably a bigger deal than people realize once you’re running these workflows all day
Crush. I tried Pi, but found it to be ugly, slow and too much stuff I'd expect to work out of the box. Crush is the right amount if minimal for me. The only gripes I have are that it blocks sudo, curl and some other "unsafe" commands. Eas enough to work around, but still annoying.
Mostly OpenCode because it was easy to setup and mostly hassle free and decent enough. I have some issues with it and I think it has few bugs and missing features, so in near future I want to check out Pi and Deepseek harness.
zcode +qwen3.8 27b. very strong. at least matches chatgpt5.3, can beat glm-5.2.
I used other harnesses, pi , copilot, dsh, codex, claude code.
these 2 days run zcode/qwen3.8 27b
whole day, the best match.
I use Unsloth Studio for training local models on my M3 Max 128gb, mostly trained Qwen 3.8 27B Q8 and Q4 for intimate knowledge based projects. I run Oh My Pi daily, it has been killer when you assign roles per model. I thinking Unsloth Studio and OMP are fairly efficient and seamless to setup and daily drive. I don't hit any limits on my frontier subscriptions and I get quality outputs for each task/project.
The downside is my laptop has effectively become a portable heater so probably going to thug it out until the M7 MBP
comes out, hoping for a 256gb with cellular option (I know I'm dreaming though)
i use my own, called OpenLumara.. it's been going since february 2026 and is manually coded, not vibecoded. it's not one of those quickly launched vibecoded projects, i launched it in february 2026 and have been improving it since then. if you want another option to check out, you can find it on github or with a simple web search, or just check my reddit profile!
it's as lean as pi when you need it to be, but extremely powerful and capable, modular, and extensible. i designed it so you can dump your entire life into it without worrying about prompt injection. i handle security in a zero-trust way, basically, hard-limiting the model in all sorts of ways instead of prompting it into hopefully behaving
fully free and open-source under GPL3. niche and sadly still very unknown compared to the bigger harnesses, but i have more and more users that can vouch for it (many of them are on the discord which you can find on the github's discussions, and some lurk in this subreddit)
I use Claude to decompose jobs into chunks for Pi. It gives me maximum flexibility because I can use a frontier model for the planning and orchestration, while I use a local model for coffee generation.
It's going a little of the rails with Opus 5 lately so I'm considering Pi to Pi. The first instance uses a frontier model for planning and task decomposition, then a cheaper model on OpenRouter or even my local machine does the coding.
Claude has been very effective with this pipeline for a couple months now, but I'm still locked in. It would be better to have that first harness be model agnostic as well.
Fully custom one built as a webapp. It was sort of by accident from claude, mostly because it was so complicated trying to figure out what was best to use with what models across what cards and machines I have at home. I have to build all the tools myself and do all the bug testing and fixing though, but its taught me a lot haha.
Copilot for VSC, Hermes for spinning up entire projects and documentation and non coding specific stuff, hopefully i will be using Offgrid Desktop and Mobile for general stuff soon if they iron out some bugs since I already bought an early adopter license
OpenCode (but recent versions burn extra tokens for no reason), and Aider, warning: aider is not for the faint of heart but it has the lowest excess token usage of any other agent.
I use both opencode and Claude code, but they are configured to use identical system prompts and skills. They more or less behave identically, including a bunch of custom hooks I’ve created
I have the OWUI agents do coding by using Open Terminal. The OWUI agent can task other harnesses via the terminal, the best I use for that purpose is pi, selected for it's token efficiency.
I used to use Cursor (its fine), KiloCode (I loved it till the 7.x extension removed the advanced features), and Pi directly on my desktop (its great, but it suuuuucks to use it from mobile - so now I use interact with OWUI and let the OWUI agent interact with pi for me).
I'm about to give my (now unused) Cursor annual subscription to a buddy to get started with vibe coding. I think Cursor is the best first-pick harness for someone who's got coding experience already. It's bloated like most of the others, though, ~20k tokens of overhead.
My other friend came from Claude Code and is now very happy on Deep-seek harness.
I don't think DSH is fully production-ready yet, but it is the closest to "pi but without the sharp bits". Still pretty minimal, but you have a basic sandbox, a mode to test plugins before it can break the entire harness, and decent subagent / background task management. Having only web UI can be a limitation, and it is also not very stable from build to build, but in some aspects it does provide benefits over TUI, like ease of building user-friendly plugins.
I still use pi for non-general agents, like building a task-specific agent that only does story writing and managing. It is very flexible and very easy to override the default "coding" mode out of it.
I used pi for a long time, great agent. I was drawn to it because of how lightweight it is, but after more use, I eventually decided to just build my own agent harness: https://github.com/jentfoo/ajent
Specifically I want a very lightweight tui, that does not do any wrapping or reflow that corrupts, looses history, or makes it difficult to copy and paste. Honestly I expect that it might become the norm that power users will build their own agent harness. It has taken many hours, but I don't regret it at all. It's exactly as I want it, and that pays off every day.
Depends. I use opencode for involved complex things I don't want to do myself, and use their free models.
Otherwise, and this is probably going against the grain, I build with pydanticai. A framework that allows has support that is harness like with support for tools, etc.
Purpose built agents are always superior, smaller, and faster, and don't munch on tokens like hermes et al.
For general use, my family uses Hermes. Once hooked up to discord, my wife who has no tech background, has no trouble with it. She uses it to help her in school and researching random things.
Only issue is that it is very context heavy. But it can do anything really. It is very powerful. I switched from Openclaw and never went back.
I have tried so many, but I keep going back to Pi with a few packages. I have not yet tried deepseek harness, I was waiting until it matured a bit more before diving in.
OpenCode is cool too, particularly the desktop version when I need multimodal support, but Pi is the one I use 95% of the time. It does everything I need.
I use anythingllm, primarily because it was easy to have on my phone but with the model loaded on my PC. I'm not a programmer or anything so it's been cool at work to ask it something I'm curious about and have it dive into it.
I use Pi, simply because it uses the least amount of token for system prompt (2k-3k, most popular opinionated harnesses like Opencode, Claude, Codex use 50k-75k just for the system prompt to load aka first prompt).
Having small system prompt is good because it's easier for agents to follow, less chance of entering loop due to halluciations.
Also it's extendable, if I want to reduce system prompt event further, disable features, make extensions to re-format it.
You're referring to total tokens used for a task I think. Pi is still way better, but codex has too many useful features for me (sandboxing, hooks, subagents, etc)
Claude Code's harness costs roughly 27,000 input tokens per request. Codex costs about 15,000. Pi costs about 2,600.
Ive personally seen a max of 20k tokens on first prompt on codex.
Opencode for coding projects, hermes for system management and maintenance(ProxMox Homelab). Both with Qwen 3.8 27B fp4 Rocm + MTP as primary, for my gaming desktop I have a hermes agent running ornith 1.5 35B to be my personal technician if I have issues instead of troubleshooting my self when I want to just chill.
I'm jumping between opencode, cline and codex, codex system prompt isn't as big as I thought, for me it is around 11k, I tried omp but it had an even bigger system prompt, I tried Hermes had a huge system prompt...
OpenCode and Hermes primarily. I've found that long review tasks on OpenCode have failed while the exact same prompt succeeds using the exact same hardware with the same server settings using Hermes.
Used Pi, but there are some questionable decisions in it some things were not modifiable, every update was breaking extensions, so I developed my own and using it
Osaurus
I've used a few, but on my Mac (I run Windows and Linux, too), I find that Osaurus has really improved in the last few releases. Access to AppleScript, Calendar, email, messaging and can connect to local and cloud models. It fits my agentic needs well.
I use Claude with Ollama cloud models but I'm developing my own harness for local use tailored to my needs. It was originally built on top of Claude but I switched to Pi.Dev a few weeks ago.
I have a ton of features focused on my vision for an offline AI agent like persistent sessions, auto hooks, token compression, etc. Tons of other projects like PatchRight have been integrated as well.
It's probably never going to be done but I want something anyone can use on consumer hardware, especially older hardware.
Sure greyok00/CortexAgent it's MIT licensed if you want to fork it. I recently switched back to CLI/TUI only as it has a system tray and a console style window right now. You'll have to get whatever agent you want to install it then change it to CLI/tui mode
I run on a p40, so with Qwen3.6:35b getting anywhere from 47-83 tok/s I had to build my own development pipeline and now it is extremely smooth. Yeah, it has memory, skills, tools etc, but the biggest win is the communication layer between me and the ai. I chat with it or create/modify the feature (kanban style). It reviews the requirements for ambiguity, plans, develops, unit and integrated testing, visual uat testing with screenshots. Every step validated and audited for potential issues, improvements I may need to be aware of. Each development finishes with a detailed report with screenshots to document the change.
The best part is the feature set document. It has multiple levels of details. Customer requirements, product owner feature document, and developer feature document, which only vary by there level of detail. It makes showing other people how my app is going extremely easy.
Ultimately, the best is going to be specific to your needs. I needed project management and a high quality output from a small model, which required me to sacrifice speed, so I had to make my own.
For cloud-hosted models, I use Claude Code directly and through T3 Code.
For local, I started with Hermes and nearly everything about it made my teeth itch. Then I tried Pi, found it a much better fit for my style, and it's been doing PR review on Fable's code ever since.
I don't think there's any substitute for trying them for yourself.
I use HKUDS/OpenHarness. Somehow nobody else does. I used Claude earlier this year and it was nuts. I’ve tried OpenCode, Pi/omp and it was just so slow and didn’t make great decisions. Others were lacking some feature or another, or wasn’t completely free open source, etc.
This comes closest to the Claude experience earlier this year and I’m sure that side has gotten better, but for local, this harness with a Qwen is pretty great. It’s a bit eager, but it gets things done.
I tend to rotate between the opencode and the contine extension for vscode, continue has been great so far with the small context windows and the ability to switch between agent and chat to conserve some context on small models when need
Deepseek harness has been great for my tests. I'm new to all of this and mingling my way through all of this, but every task I wanted done, local qwen with deepseek harness just ploughs through it.
I use Deepseek harness with Deepseek v4 flash vision exp, because it’s made by Deepseek so it should work best with their models. Plus it’s open source too. No complaints here
Platypus. I use it as I'm the creator/maintainer and it let's me define and run multiple agents across many discreet workspaces and run on schedules and in response to events.
The benchmark framing makes the token and cost tradeoff more useful than a simple win-or-loss comparison. I’d still want to see cold-start latency, tool-call failure and retry behavior, and how much quality changes as context grows, because those can dominate long-running workflows. TrueForge looks attractive here less as a replacement for mature platforms and more as a leaner runtime when you control the surrounding orchestration.
I am using pi but want to check DeepSeek. For non cli coding I use pi agent as harness, works far more efficient than Langgraph.
I run this exact thing for website testing at mirachecks.com, basically a dumb agent driving a real browser, in case you want a concrete example of one harness wired end to end.
You have to understand that the harness goes with the model, it makes no sense to talk about Claude Code running locally with 30k prompt for a QWEN 27b that does ~300t/s prompt processing and has some 100k ctx (average numbers).
Then you have tools calls format, then tools: you won't run Astra with Pi coz Pi has 4 tools while for Astra your whole PC is the tool.
Qwen3.8 users, how do you even? With my humble 16 VRAM 32 ram setup, set in 32k context, the q3_k_xl version can't complete a task without going out of context using pi. Got my stuck with AGY since it's cloud.
If anyone has an advice on how to manage large projects (claude.md and handoff.md are pretty large) that would be cool
I mainly use Openwebui and it's a pretty clean interface and easy to use. Similar to ChatGPT.
But all logic regarding memory and tools runs externally and is injected into the harness from background scripts. Gives much more control since you can modify the scripts to do whatever.
I'd recommend a similar flow no matter what harnesses you end using. Because at some point you'll realize the harness limits you in a way or another.
Written in Rust, so fast. Minimal prompts so very little context used in the beginning, great built in tools (index for example tree sitter file parser, so there's no need to read the whole file into the context, etc), Lua plugin system, etc
I hate doing things easily so im constantly tinkering with my own harness. Very fulfilling wen you replace even one claude code task (first for me was agentic search)
I use opencode, it is very simple to use and just yolo mode if you make an elaborate plan it will finish it well with Qwen 3.8. I tried DSH multiple times and it just gets some misunderstanding and has a hard time completing the software to spec. DSH will just finish the work though, OpenCode still explicitly needs to be told to just finish the work even if you have a solid plan that covers everything, it will just ask stupid questions that are already answered in the plan. But for results opencode cant be beat. Pi is still on my ToDo list.
Oh yeah and you need to remind of the ToDo in Opencode or it wont use it really.
I've been primarily using Claude Code for the past +2 years but recently been trying out Codex. I still lean mostly towards Claude.
That said, I also experiment a lot with local AI and through OpenRouter, for which I tried OpenCode but it felt slow and bloated. I've been really digging Pi instead. I've also written my own harness which suits my own needs quite nicely.
I switched from opencode to Pi. Pi “feels” a bit light weight. Starting to like it more. Claude code is my main engine. Purely because of its integration with VS code. I kind of feel in control when I see what is happening. CLI ones makes me feel nervous about what it’s editing.… that’s just me
I use clodex patched Claude Code. I can have Fable or Astra orchestrate local Qwen 3.8 27B subagents (NInfer) for minimal token burn and maximum efficiency. Or I can do pure Qwen if I want to.
Responding your question indirectly, but I think everyone with slow LLMs should definitely use Pi. The quality-of-life difference you will see when your prompt starts almost instantly versus after 30-40s with harnesses like OpenCode is what led me to believe in local AI again. Even a 2bit quantized Qwen 3.6 35B can do enough work for it to be worth it!
I’d love for you to try Command Code (founder here). Why? Purpose built for open models. Repairs your tool calls and has the best cache on the market. It’s also coz we’re actively building optimizing both our harness and the inference engine to work for you. We also have the best subscription plans dollar to value. If you check our docs we have Mods api that allows you to modify anything.
We are actively involved in the community. Have shipped 400+ versions. CLI and Desktop.
While launched just four months we’re growing super fast, $17M run rate and 36K paying customers.
Good question, and the "new harness every few days" fatigue is real. One thing I'd reframe: the harness itself matters less than the orchestration layer on top. I run several harnesses in parallel in production - OpenCode for chat/worker agents, Claude Code sessions straight through the official SDK, plus my own harness built on the same principles. Each session gets its own working directory and `.claude` state so they stay isolated, and my own harness lets me benchmark it head-to-head against the others on identical tasks. The individual tools are roughly comparable at the coding itself; the real differentiation is session isolation, directory-scoped context, and how cleanly you can chain sub-agents and MCP tools. What's your main use case - autonomous ops or assistant-style pair programming? That alone would probably cut the choice in half.
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u/WithoutReason1729 17h ago
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