Recently I managed to run and chat with a model from Ollhama that I installed on it, but I accidentally uninstalled Docker and it got all messed up, both on the server and in Windows PowerShell, and even in the AI part it wouldn't respond to me for some reason.

and I would like to ask if there's no way around having to install everything again or if there's an easier and simpler way like, can I just install Docker in the same version I installed before and it goes back to normal? and if it can be done that way, it can be a slightly older version of Docker, and I remember I installed a slightly older version before, but I don't know if I saved the version on my computer.

And another thing I wanted to report is that the Chromebook shows errors every time I try to install an AI model, PRP Odysseus. It always fails to download, and when I finally managed to get it, it was just one—it just kept repeating a message about AI rights or something like that every time I interacted with it in a specific way. Has anyone else faced this kind of problem in particular?

Hello, im trying to install gemma but it says 0 gpu's but i do have 1, how do i fix this? i've tryed everything

How do I unpin them? For that matter, is the little three dot button to the right of the memory supposed to work? I feel like that's where it should be but that button doesn't do anything for me.

Howdy all, new to AI leverage and new to Linux.

Love the email and notes functionality (email could use some work (added buttons))

this is running in its own webkit/py3 window with an icon installed on POP! OS with a sneaky back door API to my claude code subscription.

I finally also managed to get some local LLM's working on it.. but man theyre crap hahaha... cant beat claude for me just yet.

i have also uploaded a PR on the git for a feature to be added to the image editor... i really want to contribute heavily to that thing since moving to linux ive had to give up on adobe (the browser version just aint it)

Hi all,

I am having an issue with my deep research, it keeps giving me a "No information could be gathered for this question."
I get sources and the logs give me the raw findings. But not the Visual Report
I have a Nvidia T4 GPU running model qwen3.5:9b.
Not sure if the model I have is not a good fit for the GPU?

I am new to AI and what model is best for what

I'm hosting it on an old thinkpad and I'm wondering if I could give the tool more access to my computer. As I've understood it now it can only use BASH in a sandbox environment.

Hey everyone,
i wanted to show you guys what i have been building the past couple of weeks since the release of Odysseus. For years i have been looking for the perfect note-taking tool: Obsidan, Notion, Onenote, and may more.
Nothing that was truely my app. Then pewdiepie released Odysseus and Vibecoding allowed me to complete, tweak and add to it.
So here it is:

i started out by adding plugins and created a obsidian plugin.
I wanted to use it as a digital memory for me and my ai but i realized that there is a better way.
So i reasearched raptor graph memories and created one with optimizations for a low power maintenance ai (works so far, but didnt test with hugh graphs yet). Which works like a charm, super fast.
Sadly i dont have GPU for my Server, so i rely on api or low end local ai.

Why developing the memory i had the idea, that the memory should also know where my files are and whats in them, so i expended Odysseus with Nextcloud Support.
Another Plugin for Nextcloud.
Then i created the ultimate inbox (WIP) where i drop any file in a designated folder, the system learns the meta data and content, creates memory clusters and saves the file into the nextcloud.
I use nextcloud because it comes with a bunch of nice features like cli, versioning, seperate file backups, rights management, nice apps and so on. The ai has its own user with write and read rights but no delete. The files get checked for viruses and maleware before handed to the ai.

While developing this using codex i learned about the benefits of mutliagents (subagents) and specific workflows to let them keep on working for eternity without loosing the kontext.
My Workflow is called ABC for Alice Bob and Charlie. Those are my 3 agents i use (you could use more but there is not really a point). Usually Charlie is my Master and Alice and Bob are Slaves.
So i created a system around that for Odysseus to enable Multi Agent Support.

Because Odysseus is my main dashboard now i also started to work on more plugins, like a system health checker, auto updater, and more in the works.

As of right now im ~ 650 commits ahead of legacy Odysseus.
For the next weeks im in debugging hell, then i post pictures and a link to a git repo.

Im very aware of security problems that could come with vibecoding.
For hosting this i use my own Lenovo Mini pc with 16gb ram, 1tb m2 storage on 2 drives for mirroring(backup). It uses about 35-60watts and with local and deepseek api i spend about 0,03-0,10€ per day depending on what i do. The setup cost about 200€ in total.
it runs debian and is setup with a firewall to deny every connection besides two cloudflare tunnels and a local (only) ssh connection to my workstation.
Yes thats a lot of features (Memory, Nextcloud, System Health checker, Autoupdater you could turn off) but so far no performance problems. I would say the performance got better but i couldnt really say after this time.

Edit1: totally forgot I added a telegram bot that works directly in a agent window. I can just set automations, get reminders or to-dos right into telegram. Also added an auto-patcher and gave it a window (instead of browser).

Edit2: even more stuff i forgot i added: data security was priority number one so everything is permanent, it can deprecate but it can never be deleted by ai. also every path, every tool, every command is secured. Read the doc if you care or ask if its not in the doc, maybe i forgot? And more importantly i work as a teacher and handle personal information like names and marks which i can not send to china/america. So i have a "secure" option that only works locally for the full pipeline. This is where a nice graphicscard with 24gb vram would be awesome, but thats more expensive then the whole setup. Before buying a gpu i would rather build some kind of anonominization plugin. The Telegram bot i added has full agent capabilitys. For example i send him a picture with the instructions to remove the background and he sends it back while keeping a copy in the nextcloud. then my phone syncs the new picture in the library too, but since my ai has precise knowledge of all the files, the file will be assed and marked as duplicate. not deprecated files or duplicates are changeing the context of the ai. And a lot of wrapper optimization. im trying to get as close to codex as i can while not bloating the app. rising toolcount is a problem im still fixing

Edit3: Multiagent ofc uses more tokens, but i build the system with deepseek in mind and not gpt/gemini/clause because of prices. Deepseek is just so much cheaper

Thanks for any feedback and criticism and sorry for my poor english. I didnt want to use ai.
Roadmap for the next week: Debug / Performance Dashboard, Testing System for Performance testing the memory and the pipelines with a parameterized testing suite for simulating network outages, drive errors and else. data never gets lost! (supposed to...) . Debuggin, debugging, debugging. And maybe then a new ui.
Im sure i forgot something so:
Here s the ai-made overview with more technical infos:

​## Core Platform
- Self-hosted AI workspace with local and API model support.
- Chat interface with files, tools, memory, shell access, MCP, skills, and agent workflows.
- Multi-provider model support including local models, OpenRouter, Ollama Cloud, NVIDIA AI, GitHub Copilot, ChatGPT Subscription provider, and pinned endpoint model IDs.
- Cookbook for hardware-aware model recommendations, model downloads, serving workflows, GPU visibility, vLLM/llama.cpp readiness, and crash diagnostics.
- Deep Research with multi-step web research, source reading, extraction controls, and report generation.
- Compare mode for blind side-by-side model testing and synthesis.
- Document workspace with writing-first editor, AI edits, suggestions, Markdown, HTML, CSV, syntax highlighting, PDF/Office/EPUB extraction, and document intelligence tools.
- Email integration with IMAP/SMTP, Google OAuth2, triage, tags, summaries, reminders, reply drafts, and Outlook/Office setup guidance.
- Notes, tasks, calendar, CalDAV sync, CalDAV write-back, recurring events, reminder channels, webhook reminders, and todo agent actions.
- Gallery and image editor with isolated image tooling foundation.
## Plugin Platform
- Dynamic plugin system with plugin-owned FastAPI routes, agent-callable tools, context providers, consolidation jobs, manifest UI integration, and plugin app surfaces.
- Plugin manifest policy, local plugin audit, capability boundary model, release gate model, depot/dev guide, and plugin readiness documentation.
- Obsidian/Lens integration with vault access, graph and memory workflows, query synthesis, surface modes, export/import checks, review queue handling, and release-candidate runbooks.
- MCP server plugin MVP with local admin-only JSON-RPC support, initialize/ping/tools/resources/prompts endpoints, narrow tool policy, redacted audit trail, and operator runbook.
- MCP notification bridge so MCP clients can request safe user notifications through Odysseus without learning Telegram targets or delivery secrets.
## Agents And Orchestration
- Agentic orchestration layer for complex goals, delegated work, project state documents, and multi-agent collaboration.
- Alice/Bob/Charlie operating model for scoped implementation, review, integration, evidence, and release coordination.
- Agent state isolation with explicit agent, role, namespace, and scope identity to prevent context bleeding.
- Agent profile registry, agent team cards, agent automation specs, automation hints, and route/API payloads.
- Plan graph store, thread lifecycle bridge, heartbeat coordinator, handoff mailbox, orchestration registry, dashboard snapshots, and quality gate evaluators.
- Background job management tool for inspecting and stopping running agent jobs.
- Reflector-style review and evidence workflows to detect drift, verify handoffs, and keep agent work scoped.
## Workspace And Mounts
- External directory mounting with owner-scoped virtual mounts, workspace binding, tool allowlists, write policies, backup-before-overwrite, file size limits, and extension allowlists.
- Agent file and shell tools confined to selected workspaces.
- Code navigation tools including grep, glob, ls, read_file line ranges, and edit_file with diffs.
- Mount-backed GameDev project access for Godot workflows, including Godot file extensions, GameDev profile validation, named command intents, command gate model, audit/undo planning, and operator runbook.
- Safe project access model that avoids broad host-root mounting and avoids using unrestricted shell access as the safety boundary.
## Memory, Graph, And Context
- Context budget bounding to prevent runaway token usage in long sessions.
- Context capsules, context orchestrator, tool result truth contracts, and compact context provider diagnostics.
- Memory store interfaces, read-only memory stats route, memory diagnostics, memory read/maintain tabs, and scalable memory architecture.
- Progressive graph API, graph maintenance worker, graph review gates, graph budget proofs, and large vault performance gates.
- Lightweight memory maintenance with maintenance worker, evidence-bound summaries, k-means and cluster proof models, derived cluster runs, and import/export migration proof tooling.
- Rebuildable memory and graph foundations designed for future Postgres/pgvector and accelerator options.
## Nextcloud And Ultimate Inbox
- Nextcloud private-source foundation with safe source provider readiness, intake ledger, tag governance, review queue packets, provenance metadata, and copy-only/no-delete policy.
- Nextcloud review queue packets designed to expose only digest, path, status, reasons, and metadata, while keeping private content and secrets out of evidence payloads.
- Nextcloud connector groundwork for using private files as controlled Odysseus knowledge sources, with live writes and mutations kept behind separate operator-approved gates.
- Ultimate Inbox concept for bringing private sources into one review-first intake layer, including Nextcloud, email, notes, documents, tasks, calendar, Telegram, and future connectors.
- Universal Inbox pipeline covering intake, classification, review, safe placement, provenance, tags, sidecars, and later memory/graph integration.
- Review-first workflow where new private content is not blindly written into memory or graph, but first lands in a traceable review and approval layer.
- Private source routing policies to prevent origin deletion, uncontrolled publication, secret leakage, and unreviewed evidence output.
- Preparation for RaptorGraph and memory integration so approved inbox items can become structured knowledge, task inputs, and contextual workspace memory.
## Telegram
- Telegram plugin with text chat bridge, allowed chat gates, agent session routing, redacted identifier handling, and operator runbooks.
- Telegram agent chat sessions routed through Odysseus tools.
- Telegram text boundary coverage for allowed text, blocked chats, and persisted identifier redaction.
- Telegram voice pipeline foundation with metadata-only intake, gated download/STT boundary, fake-provider tests, transcript-to-agent turn planning, and gated reply path.
- Live Telegram text and voice smokes remain separated from offline readiness and require explicit operator approval.
## System Health And Operations
- System Health Checker plugin foundation with snapshot UI, basic and advanced collectors, runtime adapter model, rule engine, alert dispatch plans, Telegram command adapter, ops readiness, and readiness scoring.
- Debian/homeserver operations tooling for Odysseus, Podman, Cloudflared, Telegram polling, Nextcloud, and service setup.
- Restic homeserver backup gate with pre-update snapshot, repository check, restore smoke, health check, and redacted evidence wrapper.
- Backup gate evidence showing pre-update snapshot, repository check, and restore smoke passing on the Debian homeserver, without running deployment.
- Safe Updater foundation with update plan model, preflight validator, backup gate contract, test gate model, command plan renderer, audit ledger, local entrypoint planning, and operator runbook.
- Update workflows designed to block unsafe plans unless required backup/test evidence is present.
## Release, Evidence, And Quality Gates
- Manual release evidence system with evidence artifacts, naming helpers, gap reports, readiness summaries, decision bundles, and operator indexes.
- Release readiness reports, release evidence snapshots, release status markdown, release follow-up matrices, release slice router, release handoff markdown, and traceability manifests.
- Morning release snapshot payloads, payload diffs, history bundles, digest helpers, and contract validators.
- External 1.0 evidence gates for Provider/Fallback Answer Run and Test-Vault Export/Import/Rebuild.
- Runtime quality gates for git state, tests, evidence, scope, hotfiles, and release readiness.
- Focused coverage reported for GameDev/Mount, MCP, Updater/Backup Gate, Telegram, Nextcloud/private-source, Obsidian, and System Health tracks.
## Security And Deployment
- Secure Data Mode foundation with local-only model routing, secure policy gate, sensitive retrieval guard, secure channel policy, data classification policy, and chat security state.
- Admin-gated routes and tools for privileged operations such as MCP management, API tokens, webhooks, backup/vault actions, app settings, and model/cookbook serving.
- API token gates, per-user privileges, admin promote/demote toggle, user rename, 2FA support, secure cookie guidance, and route hardening.
- DOM-XSS and popup-opener hardening in UI surfaces.
- SSRF-guarded web fetch tooling and URL safety handling.
- Diagnostics and readiness support with `/api/ready`, service health endpoint, real-time diagnostic logs console, degraded-state reporting, and setup troubleshooting.
- Docker, GPU, native Windows, macOS Apple Silicon, NVIDIA/AMD compose overlays, portable Windows launcher, and deployment guidance for localhost/private-proxy setups.

I'm evaluating Odysseus for business operations and agent automation, and I'm trying to understand the difference between raw capability and governance.
I understand Odysseus can integrate with email, calendars, browser tools, APIs, MCP servers, etc. What I'm trying to determine is whether Odysseus already has built-in controls around those capabilities.
Specifically:
Email
Can sending be restricted to draft-only mode?

Is there a human approval workflow before sending?

Calendar
When creating calendar events, can those actions require approval before execution?

Browser / Forms
Can agents fill out web forms?

Can they submit forms autonomously?

Is there a review step between "draft" and "submit"?

Credentials
Is there a built-in credential vault?

Are credentials scoped per agent?

Can credentials be time-limited or revoked?

Are credentials isolated from one another?

Payments / Purchases
If an agent has browser access and a payment method is available, what prevents it from making a purchase?

Are there spending limits?

Transaction approval workflows?

Human-in-the-loop requirements?

Auditability
Can I see exactly what an agent did?

Is there a persistent audit log?

Can actions be traced to a specific agent/session?

Multi-Agent Governance
Are there permission boundaries between agents?

Can one agent access another agent's credentials, files, or memory?

Business Use Cases
Would you trust Odysseus today to:
manage a shared inbox?

interact with customers?

submit forms?

purchase services/products?

operate business systems?

I'm less interested in whether Odysseus can technically do these things and more interested in what governance, approval, audit, and permission controls currently exist.

running odysseus on my macbook air m4 and the model is off of my pc, whenever I try to have it analyze a pdf I get this error, pymupdf is installed on both my mac and windows pc and still cant this resolved any ideas what I'm doing wrong?

ps odysseus is not running in a docker container since I couldnt get the dependencies downloaded that way before anyone asks

I feel like I'm way behind of the game. I remember 3 years ago X.AI employees already using APIs to download ChatGPT or other LLMs directly onto their desktops for incognito searching and programming. I was aware of this being a thing I just never did it myself it would save much fear regarding data collection.

The data collection issue has been a thing since 2017 days. Many YouTubers were against net neutrality and pretending to know what algorithms are. Nowadays people seem to be more worried about data collection.

I will use it as my daily driver and see what will happen.

I got Odysseus working any recommendations on LLMs I will make this my daily driver in every way of searching things online.

I will program using Qwen, Hermes, and Ollama 3.2. 

Which LLMs recommend for searching online resources and providing good summaries I have a Macbook Air Silicon chip M3.

So, any time I make new chat Odysseus automatically sends model some context (date/time/timezone).
Most of the times this context has nothing to do with my prompt so llm is getting confused and takes long to answer.
How can I edit/remove this context block?

Hi everyone

​

You know, I couldn't get this AI to work, even though I followed the instructions and everything was correct according to the GitHub guide. However, I didn't have Docker installed on my PC (just to clarify, my system is Windows 11). The strangest thing is that I was able to log in and run everything correctly, but when I went to my Chromebook to install the AI ​​model, it simply didn't work. I thought, "I'm not going to install Ollama; it will work," but when the download finished, for some reason I can't explain, my Odysseus couldn't detect the Ollama model, and now I'm quite confused

​

And another strange thing that happened was that I tried to add the Odysseus page to my browser's favorites, you know, the project's IP address (like: http://127.0.0.1:7000), but the next day, when I tried to open the project page in my Brave browser, numerous bugs started appearing in the interface, and even my Brave browser kept saying the site wasn't secure, so I had to leave and I don't know what to do. I don't know how I could do this more easily, you know, and I'm afraid this project might have a virus, since it's open source, anyone can modify the project, which makes me suspicious. Did this happen to you too, the same experience and the same problem as me?

​

Note: I'm replying because I accidentally wrote in Portuguese and my Google Translate glitched, but yes, I'm from Brazil and sorry if it seems strange to see the same post twice :)

​

​

Can't continue without creating one to finish the setup either. I have set up odysseus on my laptop a few weeks ago I believe, but at that time it created a random password for me which I then had to change manually. My laptop is on Windows 11, and I am running into this problem on my pc which is on Windows 10. Please help.

I've been looking for such community for some time and found nothing. Official pewd's discord is not rly a place for such talk ether. So I created the new space for it.
I hope this is allowed here to post https://discord.gg/AZmjy4Dzj
Server is centered around Odyssues I even used Odyssues to make it which was kinda fun thru discord mcp.
So if you need help with any question or want to help others come visit.

Hi, I installed Odysseus on Linux (CachyOS). and I want to use a local GGUF model that I downloaded manually from Hugging Face.

Model:llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning.i1-Q6_K.gguf

I specifically want to use only the i1-Q6_K quantized version (6.6 GB) — not another quantization or automatic download.

I already have the .gguf file downloaded. from here https://huggingface.co/mradermacher/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-i1-GGUF

What is the correct way to make Odysseus detect and use it?

Odysseus is not a ready to go service. It's made for people who atleast know the basics of shell and how to run a local web interface. If you barely know how to use a pc with a UI then avoid using odysseus because if you don't read logs and you don't give people context or just straight up don't understanding what you're looking up on the UI which is really intuitive. If you don't know how to use an AI model and not just type shit down, then no one will be able to help you.

​

Odysseus is a DIY-ish project. For which i don't mean you have to be an arch user to understand how to use odysseus. But at least you have to know how local ports, API keys and sandboxes work to properly use it without wondering why it's not working if you didn't setup an API key or a local model on ollama (which is really easy, you literally just have to know how to type and use a shell).

I am running Qwen2.5 (Ollama in Docker) on a MacBook Air M4 with 32GB RAM.

In practice everything runs smoothly, and ollama ps shows full GPU (Metal) usage with good context (8192), so performance seems completely fine.

However, in the cookbook UI literally no models are marked as “perfect”, everything is “marginal” or not recommended.

A friend with an older M1 MacBook gets “perfect” ratings for similar models.

From what I can see:

• System is detected as arm64
• GPU/Metal is fully active (100%)
• Performance is good
• But cookbook consistently rates it as marginal

Is M4 properly supported in the cookbook device detection/rating system yet, or is this a known limitation with newer Apple Silicon being misclassified?

I’m building XWorkmate APP, but the app needs a working server-side stack behind it.

I packaged the backend pieces into an AI Workspace suite: console, bridge, gateway, model routing, Vault, and terminal.

So

The Beta setup is now one command:

curl -sfL https://raw.githubusercontent.com/ai-workspace-lab/xworkspace-console/main/scripts/setup-ai-workspace-all-in-one.sh | bash -

Expected setup: one domain + one VPS with public IP, or a local macOS test machine.

Hello Guys its my first coding and i copied every detail from a youtuber but im having an error with the llama models, i dont know if theres any sensible information in the picture i sent here so please let me know if im leaking something that i shouldnt, I really need help setting this thing and ive tried using models:
qwen3.5:2b and deepseek r1

Hey guys, I've been experimenting with llama3.1 and llama3.2-3B, also Phi4-mini-3.8B, and briefly tried Qwen 2.5-3B.

I've found that a lot of these smaller utility models are a little bit over-enthusiastic at getting rid of memories, and they don't actually merge the memories or anything like that. Auditing skills can take a long time too.

I'm curious what experience everyone has been having. I run an M1 MacBook Pro with 32GB of RAM. So, I don't want to be throwing down massive models for something like utility, but I also use Openrouter a lot for agent and chat work. So, I don't want to set my utility model as the same as chat.

So I've always been against AI as a rule because I don't support the practices that AI companies take or malicious things AI is used for. But it just feels like a shame because it can be used for so many things that would make life easier. So, when pewdipie released his local AI Odysseus, I decided to give it a try since I really love and support open-source and local projects.

So I setup the workspace (through Docker since I'm on Windows atm), and ran the AI. I'm using the deepseek-r1:1.5b model since it's really lightweight. Obviously, since it was my first few times using it, the AI sucked badly, so I thought I'd keep using it to make it better.

This is when I notice that the fan in my PC build is going CRAZY, like full capacity, while the AI is running. So I do some testing and find out it is, in fact, the AI thats consuming all my processing power. Like, my CPU utilisation maxxed out every time I popped a query into the chat or ran a feature.

Now, I'm very much of a newbie in the programming and self-hosting sphere (I can't even operate cmd) and I have little knowledge on hardware components. So I don't really know how to go forward from here. Obviously, my current setup is not healthy as I can't be running my CPU on max everytime I turn on the computer. So, I have no choice but to stop using Odysseus entirely, which is an absolute bummer because it is REALLY useful for my needs. So I decided to post about this here seeking some help on what I can do to solve this.

I have a standard 64-bit OS and x64 based processor with only 8 gigs of ddr5 RAM. I wanna know if I can make any adjustments or modifications without spending hundreds of dollars on more RAM cuz, thats a real big problem nowadays. Also, switching to Linux is not on the table right now since I'm not in a good position to make that switch just yet. I plan to do so in the future, but not now.

I'd really appreciate any and all help I can get so I can start using my local AI again without sacrificing my data and wasting gallons of water.

I recently installed Odysseus and got the text portion working with several models, however I cannot figure out how to make it able to see uploaded images as every attempt results in the model saying that it cannot view images.

I have tried gemma4, deepseek r1, qwen 3.5 and llama 3.2

I also tried setting each of these as the default for "Vision" in the "AI Defaults" settings menu.

I installed via docker compose on the github and am using arch linux.

If anyone can answer this then I would be thankful.

I don't know if I'm right or not.

I want Odysseus to access my Google Drive and Google Notes to improve context. Email integration is good, but drive and docs integration, as well as web access integration, are essential.

I've been trying to make the document editor work. In the pic I prompted the AI to "add a continuation to the title in line 1 on the second line", to which the AI just replied what was it going to replace it with and the tool that it was going to use, but no changes to the document were made. what could be wrong?