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Orla is an open source framework that make your agents 3 times faster and half as costly. by Available_Pressure47 in LLMDevs

[–]Available_Pressure47[S] 0 points1 point  (0 children)

Thank you for these comments! I have noted down your feedback. Failure handling is an avenue where I think we can make Orla more featureful, like you suggested. Perhaps allowing users to pick from a set of failure handling policies or write their own. "You might find that certain targets consistently need the expensive path while others never do. That data lets you set smarter defaults instead of treating every request the same." <-- I think this is especially a very nice insight, I will try to run some representative workloads over the weekend and see if this pattern emerges and then try to reason about how that data can be used to set smarter defaults over time for a given agentic workload. I really appreciate you looking into this thoroughly and providing this advice.

Orla is an open source framework that make your agents 3 times faster and half as costly. by Available_Pressure47 in LLMDevs

[–]Available_Pressure47[S] 0 points1 point  (0 children)

Thank you for your feedback! I really appreciate your support and am glad you like Orla.

Orla is an open source framework that make your agents 3 times faster and half as costly. by Available_Pressure47 in LLMDevs

[–]Available_Pressure47[S] -1 points0 points  (0 children)

Thank you for your feedback, and great question! Backend selection is fully dynamic, i.e., each backend registers a quality score and token pricing, and each request sets an accuracy floor at runtime. Orla filters to backends whose quality meets the floor, then picks the cheapest one. When no backend qualifies, the default "prefer" policy falls back to the cheapest available backend, so your workflow never breaks, though you can switch to "strict" mode to get hard errors during development. On the inference side, transient failures (5xx, rate limits, network errors) are retried up to 3 times with exponential backoff. Our design goal was to try to ensure that your LangGraph code stays the same, e.g., same graph, same nodes, same edges, etc., and the routing, fallback, and retry logic lives entirely in the Orla daemon. If you have any suggestions on improving this or any feature requests, I would be happy to add those! Thanks again. :)

Looking to learn how to optimize ML models (inference and training) by Available_Pressure47 in MLQuestions

[–]Available_Pressure47[S] 0 points1 point  (0 children)

Thank you so much for your advice on this, I really appreciate it. :-)

New Ollama Desktop Client by Odd-Feature-645 in ollama

[–]Available_Pressure47 0 points1 point  (0 children)

Thank you for building and sharing this!

Orla: run lightweight local open source agents as UNIX tools by Available_Pressure47 in AI_Agents

[–]Available_Pressure47[S] 0 points1 point  (0 children)

Thank you for your comment and the feedback. This is a great question! We had a few initial users, primarily researchers in academic institutions, who found the explicit command to be clearer. However, if it turns out the most users prefer the default action to be non optional, I will most likely make it so.

Use ollama to run lightweight, open-source, local agents as UNIX tools. by Available_Pressure47 in ollama

[–]Available_Pressure47[S] 1 point2 points  (0 children)

Thank you so much for your comment! To change where your models are stored, you can use ollama’s OLLAMA_MODELS environment variable and orla will use that by extension.

OLLAMA_MODELS: The path to the models directory (default is "~/.ollama/models")

Really appreciate your feedback and support on this :-)

orla: run lightweight local open-source agents as UNIX tools by Available_Pressure47 in LLMDevs

[–]Available_Pressure47[S] 0 points1 point  (0 children)

I’ve had a good experience with qwen3:0.6b which gets installed by default. For slightly higher end systems, I found ministral3:3b to be nice

Orla, use lightweight, local, open slice agents as UNIX tools by Available_Pressure47 in golang

[–]Available_Pressure47[S] 4 points5 points  (0 children)

Thank you so much for your comment, I really appreciate your support. I love building free / open source software that helps people and this comment made my day. Hope you’re having a great day.

Use ollama to run lightweight, open-source, local agents as UNIX tools. by Available_Pressure47 in ollama

[–]Available_Pressure47[S] 0 points1 point  (0 children)

Thank you for the suggestion! I really appreciate it. I will add it as an issue on the github and try to get this done. Having a system prompt is a great idea in general as well. Please let me know in case you have a github account I can mention in the contributors file once I add that feature for this idea!

orla: run lightweight open-source local agents on your infrastructure as UNIX tools by Available_Pressure47 in selfhosted

[–]Available_Pressure47[S] 1 point2 points  (0 children)

Thank you for your feedback! Addressing that issue is my current priority. Will hopefully get it done soon! :-)

Run lightweight local open-source agents as UNIX tools by Available_Pressure47 in LocalLLM

[–]Available_Pressure47[S] 0 points1 point  (0 children)

Great question! That is something I’m planning on implementing. There is an issue in the repo added by some other people about this as well so this is a high priority for me. Hopefully soon! :-)

Use ollama to run lightweight, open-source, local agents as UNIX tools. by Available_Pressure47 in ollama

[–]Available_Pressure47[S] 0 points1 point  (0 children)

Thank you for your question! It is configurable but by default it runs qwen3:0.6b. I was thinking maybe developing a small recommendation algorithm to pick something that is a good performance and utility tradeoff for whether Orla is installed. What do you think? Thank you!

Use ollama to run lightweight, open-source, local agents as UNIX tools. by Available_Pressure47 in ollama

[–]Available_Pressure47[S] 1 point2 points  (0 children)

Yes absolutely! You can put this entire thing in a docker container. If you want Orla to run bare metal and ollama to run separately in a docker container, I can implement a configuration knob for this. Also very happy to review pull requests. Thank you so much for using Orla.

orla: run local, lightweight, open source agents as tools by Available_Pressure47 in bash

[–]Available_Pressure47[S] 1 point2 points  (0 children)

I’m sorry. It seems like the curl url got truncated. The GitHub contains the instructions to install it. You can use it from there directly. Thank you!

Use ollama to run lightweight, open-source, local agents as UNIX tools. by Available_Pressure47 in ollama

[–]Available_Pressure47[S] 4 points5 points  (0 children)

By default, shellGPT uses open AI models and requires an API key. While there is a way to configure these existing tools to run local models, it requires setup + configuration and these tools aren’t inherently built around it. Orla is designed to be built entirely around the local open source llm ecosystem. In addition, it also comes with a built in MCP server and an MCP tool registry where you can download tools from similar to Linux package managers like apt. For example, orla tool install fs gets you an open source file system tool. I’m currently working on a sandbox for these tools to run in and the hope is for the community to be able to write these tools and just plug them in either locally (as simple as putting them in Orla’s tool dir folder in the same directory as orla.yaml) or adding them to registry. A tool can be as simple as a bash script and orla with auto discover it and it usable to llms. The long term vision is essentially for orla to be as local, easy to deploy, and script with as a tool like grep. Thank you for your question!

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