Local Agent Orchestration Setup

You might be interested in the agent set up I am experimenting with at present which runs entirely locally similar to OpenClaw but instead it uses Mistral Vibe as the harness and Qwen3.5-heretic or even better would be bartowski but either way, I use Q4_K_M for devstral-small-2-24b on a M4-Pro 48GB set up and it works without creating too much heat. But it allows me to run a coding agent entirely locally similar to Claude Code but you can either use it for forensic purposes using the heretic or bartowski qwen or whichever model you want and then do coding using the devstral-small-2-24b both using llama.cpp running on port 8080 or whatever you designate. Either way you can build more advanced architecture on top of this because you have all of the source code for llama.cpp at the github.com/ggml-org/llama.cpp and the mistral vibe harness as from github.com/mistralai/mistral-vibe/ and then you have the full source code to edit as you wish. So you can easily set mistral vibe to simply use the local 8080 port for inference and it runs really well, I made the attached UI with it. No one would ship that UI, but it doesn't matter because I like it and it is just a UI for this entirely vibe coded local project. So I just use the coding agent to vibe code the next.js frontend easily deployed to vercel, and then after that I use the UI to test RAG and iterate until I can eliminate hallucination and get the behavior and standards which I want and you can also use any evaluation and RLHF methods I like the open source vero-eval as a base for creating evaluation frameworks for agentic orchestration. That is the real compute heavy portion is the agentic construction of the knowledge graph. So I created an architecture called Dynamic Persona Mixture of Experts Graph RAG which uses less compute while providing hallucination free retrieval augmented generation by utilizing the mistral vibe harness to perform the evaluations using the graph orchestrator I build with networkx and my own custom created graph which I build with a folder structure of .md files for easy use by the agents. Anyway, that is what I am working on today. Have a nice day.

That’s a really impressive setup you’ve built—essentially a fully local coding and agent orchestration ecosystem. Let me unpack it a bit to make sure I follow the architecture and highlight the innovation: You’re running a **local coding agent environment** similar to Claude Code or OpenClaw, but instead of relying on external APIs, everything runs on your **M4-Pro 48GB machine**. For the coding tasks, you’re using **Devstral-Small-2-24B quantized with Q4_K_M via llama.cpp**, which is efficient enough to keep heat under control. For forensic or general-purpose inference, you can swap in **Qwen3.5-Heretic or Bartowski**, giving you flexibility depending on the task. The **Mistral Vibe harness** ties everything together, acting as the orchestration layer. You can point it to **localhost:8080** for inference, and the whole system is fully editable because you have access to the **source code of both llama.cpp and Mistral Vibe**. This gives you the freedom to tweak model handling, evaluation loops, and orchestration as needed. On top of that, you’ve built a **custom UI**—mostly for your workflow but functional enough to let you test the pipeline. You use it to deploy **Next.js frontends to Vercel**, run **RAG experiments**, and iterate until hallucinations are minimized. For evaluation and RLHF, you’re leveraging **Vero-Eval** to create flexible evaluation frameworks. The architectural centerpiece seems to be your **Dynamic Persona Mixture of Experts Graph RAG**. By combining **graph-based orchestration (networkx) with a folder structure of Markdown files**, you’re able to: - Reduce compute overhead - Maintain hallucination-free RAG - Provide a modular, extensible agentic knowledge graph In short, you’ve essentially built a **self-contained AI development and evaluation lab** locally, with the ability to orchestrate multiple models, run coding agents, perform forensic-style inference, and conduct RAG experiments with minimal heat and maximum control. If you want, I can sketch a **diagram of your architecture** showing how the models, Mistral Vibe, llama.cpp, networkx graph, and UI all interact—it might help in planning expansions or documenting it for others. Do you want me to do that?

proceed

no this: https://github.com/kliewerdaniel/dynamic_persona_moe_rag

Here’s an accurate summary of the **📦 `dynamic_persona_moe_rag`** repository you linked — based on the latest available online information and without needing to scroll through the raw GitHub page itself: ### 📌 What It Is - **link_titleDynamic Persona MoE RAG (GitHub)turn0view0** is a **Dynamic Persona‑Based Mixture‑of‑Experts RAG system** implemented in Python that integrates **local LLM inference** via Ollama with a **graph‑based orchestration strategy**. citeturn0view0 - It’s designed to combine **persona‑driven agents** with **Retrieval‑Augmented Generation (RAG)** using **dynamic knowledge graphs** so that each “expert” persona contributes contextually to the final generated answer. citeturn0search0 --- ### 🧠 Core Architectural Goals According to the repository README and associated implementation guides: #### 🎯 Dynamic Persona MoE RAG — Key Concepts 1. **Dynamic Knowledge Graphs** - Knowledge is represented as a graph built on‑demand per query. - Graphs allow efficient traversal and provide structure for persona reasoning. citeturn0view0 2. **Persona Agents** - Personas are defined with a strict **JSON schema** that includes: - Unique persona ID - Trait values - Expertise list - Activation costs - Historical performance metadata - These personas act as specialized experts in the MoE system. citeturn0view0 3. **Mixture‑of‑Experts (MoE) Orchestrator** - The engine that manages execution phases: - **Expansion Phase:** Run each active persona to generate candidate outputs. - **Evaluation Phase:** Score outputs (e.g., relevance, consistency). - **Pruning/Promotion Phase:** Promote better‑performing personas and demote weaker ones. citeturn0view0 4. **Scoring & Evaluation** - Multiple metrics (e.g., relevance, consistency, perhaps novelty) assess how well each persona’s output aligns with the query. citeturn0view0 5. **Local LLM Integration** - Models run locally (often via Ollama), letting the system perform private inference without external APIs. citeturn0search0 --- ### 🗂️ Typical Codebase Structure From the repository and associated documentation: ``` src/ ├── core/ # Main orchestration & interfaces ├── graph/ # Dynamic knowledge graph code ├── personas/ # Persona storage / traversal logic ├── agents/ # Specialized agent definitions ├── evaluation/ # Scoring / metrics code └── storage/ # Snapshot / persistence configs/ # YAML configurations for thresholds & models scripts/ # Pipeline execution scripts data/ # Input / output data ``` *(This structure reflects the initial design; specifics may vary as the project evolves.)* citeturn0view0 --- ### 🧩 How It Connects With Your Local Setup Your described stack aligns naturally with the **Dynamic Persona MoE RAG** approach: | Component | Corresponding Element | |-----------|------------------------| | Local LLM inference | Ollama or other local models | | Agent orchestration | MoE Orchestrator in the repo | | Knowledge graph | Dynamic graph (NetworkX or custom) | | Persona specialization | JSON persona schema | | Evaluation / RLHF | Scoring modules + Vero‑Eval | | UI | Your custom UI hooking into this pipeline | | Coding workflows | Leveraging this for structured agent RAG | This project essentially offers a **framework to structure and orchestrate agent output**, with **graph‑based context** and **evaluative feedback loops** to reduce hallucinations — matching your custom ambitions for Dynamic Persona MoE RAG. citeturn0search0 --- ### 🛠️ Advanced Features Being Documented Based on published blog explanation tied to the repository: - **Dynamic Graph Traversal Logic:** Personas navigate a graph tailored to the query. citeturn0search0 - **Evaluation and Persona Evolution:** Metrics help prune/promote persona agents dynamically over time. citeturn0search1 - **Query‑Scoped MoE Execution:** The system doesn’t statically run all experts — it prunes or routes based on relevance. citeturn0search0 - **Support for Local & Multi‑Provider Models:** Though primarily local, the architecture is flexible for different inference endpoints. citeturn0search2 --- If you want, I can also generate a **visual architecture diagram** based on this repository’s structure (e.g., showing how the MoE orchestrator, graph, personas, and local inference all interact), or help you map this repository onto your existing Mistral‑Vibe + llama.cpp pipeline. Just let me know which would be most useful!

proceed