Replies: 23 comments 9 replies
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awesome! Indeed the potential of getting an LLM querying Odoo is certainly huge. Great you reached the OCA for this... May be we should create an OCA repo for this? May be it's still a bit early? @ayushin are you already familiar with contributing to the OCA? Did you or your company contributed some module already? some PRs you got merged may be? |
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i am happy to transfer the whole https://github.com/apexive/odoo-llm -> https://github.com/OCA/llm But we need help with creating the unit tests / translations and so on - that's exactly the man power we don't have to jump through the hoops and hoping to find help here |
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Hey there. I have seen big potential here, where LLM are integrated into Odoo environment. We could build lots of AI Agents which are very helpful. I am wondering if I could provide some help for this community, as I am now working as an Odoo Developer and I have just also started building RAG using LangChain. Looking forward to hear more. @ayushin |
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Hi I've also starting using LangChain with RAG and wondered if Model Context Protocol could play a part in AI understanding the tools available for integrating into Odoo |
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if you look at how the modules / models structure is setup (and feedback and PRs are welcome) - you will see we are building a foundation for an ecosystem that can make Odoo truly first AI-driven ERP |
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yeah from what I saw it doesn't look bad at all... Let's see what the other OCA contributors say... cc @etobella @pedrobaeza @sbidoul @simahawk @alexis-via @sebastienbeau @vdewulf |
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Hi happy to help, big potential here I feel. |
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@ayushin a repo/module that is worth checking and you could get your LLM agent interact with: https://odoo-community.org/shop/automation-oca-715899#attr=940733 |
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Hi, first of all thank you very much for sharing it. It's funny because lately I was studying how to apply this topic in Odoo, and I had started a side project, although I didn't integrate it in an Odoo module. The issue of using APIs, IMHO, with production data is dangerous, because nobody assures you that there will be a leak of such data, they can train the next model using what was sent. For this reason I had started a project that runs locally, for lack of resources I had to use the “mistralai/Mistral-7B-Instruct-v0.3” model. My main idea was to create a service with an API in FastAPI which uses an agent initialized with the ir.action.window.server as Tools. This way all data used as contex is in a sand box. In addition IMO it would be necessary to control the blocking of all use of In general it is a good starting point to be able to integrate LLMs to Odoo. A repository could be created and everything could be discussed there. Good work. |
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Hello,
In general, I see it as a fairly well-oriented idea, great work. I will be happy to review the work carried out at OCA 😄 |
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Would be great
Seems good indeed. Very trustable team also.
for this It might also be worth considering the advanced PRs to get a better webhook system in Odoo (in Odoo CE webhooks start at v17 and we could have better ones in the OCA) https://github.com/OCA/webhook/pulls |
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I didn't have that repository under control, we'll keep an eye on it, thanks. |
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I'm saddened to see all the uncritical hype for the "potential" of the lying, power-and-water-hungry plagiarism machines. I was going to list all the ways in which LLM use would go against what I expect the values of an open source project to be (you know, fairness, data privacy, openness - since none of the LLMs are actually open source) but then I realized I couldn't find any "values" on the OCA website, so fair enough. If you don't care for any of the moral arguments, LLMs WILL create technical debt for you, you WILL have to answer to your customers when they hallucinate, or when the action they "decide" to perform is wrong. LLMs are probabilistic, not deterministic, so you're just gambling. |
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@aleuffre : it's a bit like any tool: you can use nuclear power to build power plants and avoid burning coal or you can make bombs with it. As for LLMs: you can have stupid expectations or you might instead automate basic things effectively. I bet in less than 2 years nobody will want to choose an ERP where bank reconciliation will be manual or where no AI agent will help you query statistics in your database. As for resources usages, models like Mistral or Deepseek use many orders of magnitudes less resources (both for training and for running the model) than the crazy amount of resources models like OpenAI were using just a few months ago. It can even run on a Rasberry PI for smaller models and I doubt we would need to train it ourselves over the whole Internet again just for using it in Odoo, so it's probably not more resource usage than running MRP or a website on Odoo v8... Finally LLMs will be integrated with ERPs whether we like it or not I'm afraid. It can be done only in SAP and Odoo Enterprise and the OCA can marginalized or it can be done in the OCA in the best possible ways... And as for a hyped technology that created a speculative bubble, Odoo itself is not bad either :-) |
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I would be interested in integrating https://localai.io/ + https://docs.openwebui.com/ ... It may be interesting to explore the power of RAG's. |
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{{editor}}'s edit
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As I expected, this discussion is expanding rapidly, and I can already see multiple threads forming—covering architecture choices, framework integrations, ethical concerns, security, webhooks, and more. At Apexive, we've spent the last 12 months building AI-powered software with AI and integrating AI flows into Odoo modules (another good discussion on how to build Odoo modules with AI). We're happy to share our experiences with the community and collaborate to refine best practices. The main goal of our repository https://github.com/apexive/odoo-llm is to centralize and structure AI development efforts around Odoo, preventing duplicated work and ensuring a productive, open-source collaboration. To keep discussions organized, I've also opened a Discussion section where we can create dedicated threads for specific topics without overwhelming this general conversation. There are various approaches to hosting models and implementing agentic flows, and different Odoo users will likely have use cases for all of them. We've chosen Odoo as the foundation for this work because of its extensibility, modularity, and open-source nature. From what we’ve already seen with our customers in aviation and manufacturing, it’s a strong foundation for AI-driven business automation. Looking forward to more insights and contributions from the community! |
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I've create a list of topics above for convenience so we can go into separate threaded discussions:
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I'm considering https://github.com/coleam00/local-ai-packaged with https://github.com/tuanle96/mcp-odoo What’s included ✅ Supabase - Open source database as a service - most widely used database for AI agents ✅ Ollama - Cross-platform LLM platform to install and run the latest local LLMs ✅ Open WebUI - ChatGPT-like interface to privately interact with your local models and N8N agents ✅ Flowise - No/low code AI agent builder that pairs very well with n8n ✅ Qdrant - Open-source, high performance vector store with an comprehensive API. Even though you can use Supabase for RAG, this was kept unlike Postgres since it's faster than Supabase so sometimes is the better option. ✅ SearXNG - Open-source, free internet metasearch engine which aggregates results from up to 229 search services. Users are neither tracked nor profiled, hence the fit with the local AI package. ✅ Caddy - Managed HTTPS/TLS for custom domains |
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so local-ai-packaged seems like a bunch of tools pre-packaged together - not a fan, but this could work for you odoo-llm is a toolbox for building AI apps and flows INSIDE of Odoo - rather then connecting external AI tools to Odoo I believe that that's the right way of doing it - you need to bring the tools into your ERP, so the ERP becomes the center of all your automations and data (MCP host in the MCP terms) We are still doing a few experiments but I expect the code to stabilise in a week time. |
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It is true that the topic of "AI" brings up many questions. In essence, this module would allow Odoo to be connected to the "AI" that is required, so the biggest question here is: do we provide this tool to any user in the community? If so, according to OCA regulations, it is the user's own responsibility if they make improper use of this tool. Is this correct? |
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This is awesome! I hope we'll open a repository soon where we can start to contribute/work on this topic. |
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@gbtechnology The repository is already there https://github.com/apexive/odoo-llm - we will transfer it to OCA as soon as it matures enough |
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seems legit, but then you better emphasize https://odoo-community.org/about/cla in your repo to avoid issues where third party contributions may be tainted later and it could not be possible to put it under the OCA umbrella. As AI is a hyped topic, better be cautious early with this... |
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I would prefer to move it ASAP in order to avoid CLA future issues |
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Thanks @ayushin for sharing and init this topic |
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Hello everyone, at Binhex we have taken a different approach than the one proposed in the odoo-llm repo. Instead of using a specific app to handle chats with agents, we have integrated agent communication directly within Discuss. Additionally, we manage agents as users, leveraging the existing user model with some modifications. This allows agents to utilize user-level endpoints for communication. Our goal is not to use LLMs directly but rather to enable external agents, with all the necessary complexity, to handle interactions effectively. So far, we have achieved promising results, and we plan to make our code publicly available soon. |
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@arielbarreiros96 welcome to the discussion and happy that we are not alone. Assigning a model or agent to a user is a good idea in some cases, but not really needed for others; the same goes for discuss - we have tried/prototyped multiple approaches and the conclusion was that LLM thread is significantly different than discuss / chatter as it also can happen as part of automated actions and you can have multiple conversation threads linked to the same database record or not linked at all (for example A/ B testing with different models and tools combination) Further I think it is important to have the architecture with various levels of abstraction in the spirit of Odoo extensibility architecture - so you can separately configure and manage providers and models without having to use the chat. Perhaps you could consider adding your modules on top of our provider/model management that is quite robust already at this moment. |
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Also a few months ago, this module was submitted for lllm agents with the crewAI library: OCA/automation#2 |
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Here is a quick explanation of what we have so far until our content team created something better: https://www.loom.com/share/212def9456fd4103aba33ad0c1abe2d5 I've started another thread apexive/odoo-llm#80 What's your use case? |
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Hi everyone! I’d like to share a short video that demonstrates how a sales agent would work using the approach we’ve taken. In this case, the interaction happens through Livechat, but the same setup works seamlessly in Discuss as well. sales_agent.mp4Rather than integrating all AI tools directly into Odoo, we’ve opted for an external API architecture. The agent lives within this API, along with the tools it needs for each specific use case. While we recognize the benefits of having a built-in automation framework inside Odoo, we believe those benefits don’t yet outweigh the downsides (especially when it comes to maintainability, support, and the ability to quickly introduce new capabilities). Given how fast the AI space is evolving, we think it would be extremely difficult to keep a newly created automation framework aligned with these rapid changes without sacrificing stability or development speed. Additionally, it’s becoming increasingly clear that Odoo is developing its own vision of how AI fits into its ecosystem. For now, their focus seems to be primarily on task automation. While this approach has its strengths, we believe it complements our vision of integrating AI into Discuss chats, allowing us to enhance the platform’s capabilities and interactions. That’s why we’ve chosen to decouple the AI logic from Odoo, which gives us the flexibility to evolve independently. Looking forward to hearing your thoughts! |
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Totally forgot to show the result lead, but the agent was capable of creating the lead with the resume of the request. This is also true for tickets and several other records the user may ask to create during a livechat session |
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Here's some update on how odoo-llm can be used to build a chat bot using https://github.com/apexive/odoo-llm/tree/16.0/llm_knowledge https://www.loom.com/share/6895612476e04a2e953442e5256aec97?sid=27be63c3-7aa4-4204-a81b-d686c4d7214f |
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Hi all,
We're getting amazing results by connecting reasoning/agent models (ChatGPT, Grok, DeepSeek, etc.) to Odoo and enabling the models to interact with the Odoo database via functions for retrieval and existing server actions for database manipulation.
Some base modules—e.g., for model/provider configuration and streaming chat—are mature, while others are being prototyped and built as we speak.
https://github.com/apexive/odoo-llm
This approach has the potential to be a game changer for managing and customizing Odoo.
Please reach out if you’re interested in helping or testing. We intend to keep this core open source.
Alex | Apexive
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