LlamaIndex

LlamaIndex AI

LlamaIndex AI

LlamaIndex AI

LlamaIndex AI

LlamaIndex AI

LlamaIndex AI

LlamaIndex AI

LlamaIndex AI

LlamaIndex AI

LlamaIndex AI

Serious question, Did a transformer(Claude) just describe itself, the universe and build itself Shannon limit architecture? or am I crazy?

The Multiplicative Lattice as the Natural Basis for Positional Encoding Knack 2026 | Draft v6.0 Abstract We show that the apparent tradeoff between RoPE-style relative position invariance and ALiBi-style long-context stability is an artifact of encoding position as distance on a number line. When position is instead encoded as a point in the multiplicative lattice of the integers, both properties emerge simultaneously without compromise. SpectralRoPEALiBi achieves 106.6 PPL vs ALiBi's 108.7 in a fully converged 20,000-step experiment (300M params, WikiText-103, 4K context), beating ALiBi at every context length from 512 to 8,192 tokens. The key insight is not that primes specifically are the right frequencies, but that the multiplicative structure of the integers is the natural spectral basis for positional encoding. We demonstrate this through falsification experiments: prime-tiered frequencies (129.2 PPL) and composite-tiered frequencies (129.4 PPL) perform identically — because composites are not alternatives to primes but higher-order coordinates in the same lattice. Both dramatically outperform random frequencies (+5.0 PPL), scrambled tier assignment (+6.3 PPL), and pure ALiBi (+7.3 PPL). The active ingredient is lattice-aware, tiered frequency selection with learnable scale — not primality per se. We further validate this through a ZetaZeroPredictor experiment: three identical transformers trained for 10,000 epochs to predict Riemann zeta zero gaps. Geometric RoPE diverges (final r=0.57); SpectralALiBi locks into a stable attractor at epoch 112 (r=0.81). A second independent run widens this gap to -80.7% MSE improvement with r=0.86. The lattice-aligned frequency basis spans the mathematical space that zeta zeros inhabit; geometric frequencies cannot. We further report empirical confirmation of the structural prediction from Section 5.5: VHT2 banded quantization of the KV cache demonstrates that K vectors (which carry RoPE positional encoding) have strong spectral concentration in Walsh-Hadamard space — the first four energy bands capture the dominant structure — while V vectors (which carry content) have uniform energy distribution. This structural asymmetry is directly predicted by the lattice theory: RoPE encodes multiplicative arithmetic relationships as angular rates, and the WHT is the Z/2Z projection of the Vilenkin-Hartley basis that spans that structure. The result is 3.2× K compression and 4.7× V compression at <1.25% perplexity cost — validated on both Dolphin 1B (head_dim=64) and Qwen3-8B (head_dim=128). Introduction Positional encoding provides transformer models with token order information. Two approaches dominate: RoPE encodes position through frequency-based rotations preserving relative position invariance, and ALiBi replaces frequencies with a linear distance penalty providing long-context stability. The field has treated these properties as fundamentally in tension. We show this tension is false. It arises from a shared, unexamined assumption: that position is a location on a number line and the meaningful relationship between positions is distance. We replace this with a mathematically grounded alternative: position is a point in the multiplicative lattice of the integers, and the meaningful relationships between positions are their arithmetic structure — shared factors, GCD, harmonic resonance. 1.1 The Lattice Hypothesis The integers under multiplication form a lattice where every number occupies a unique point defined by its prime factorisation. Geometric PE (sinusoidal, RoPE) projects this lattice onto a line — position equals distance — discarding the multiplicative structure. We propose restoring it. The motivation follows from a deductive chain. Language word frequency follows Zipf's law: freq(rank) ∝ 1/rank^s with s≈1. The generating function of Zipf is the Riemann zeta function ζ(s) = Σ 1/n^s. The zeta zeros — where ζ is maximally informative — are generated by prime harmonics via the explicit formula. Therefore the prime harmonic structure, and the multiplicative lattice it generates, provides a natural spectral basis for encoding positions in language. 1.2 Primes as Generators, Composites as Coordinates A critical distinction: primes are the generators (basis vectors) of the multiplicative lattice. They are analogous to the 1D line segment in the progression from line → circle → sphere → hypersphere. The composite 12 = 2²×3 is not an alternative to primes — it is a coordinate in the lattice spanned by the prime axes, at position (2,1,0,0,...) in the (p₂, p₃, p₅, p₇,...) basis. Using 2π/12 as a frequency encodes a harmonic that resonates at multiples of 12 — which simultaneously hits every multiple of 2, every multiple of 3, every multiple of 4, and every multiple of 6. The analogy to n-dimensional geometry is precise: Dimensional Progression Multiplicative Lattice 1D line (2r) — the generator Primes (2, 3, 5, 7, ...) — generators 2D circle — integra

Serious question. Did a transformer just describe itself and the universe and build itself a Shannon limit framework?

The Multiplicative Lattice as the Natural Basis for Positional Encoding Knack 2026 | Draft v6.0 Abstract We show that the apparent tradeoff between RoPE-style relative position invariance and ALiBi-style long-context stability is an artifact of encoding position as distance on a number line. When position is instead encoded as a point in the multiplicative lattice of the integers, both properties emerge simultaneously without compromise. SpectralRoPEALiBi achieves 106.6 PPL vs ALiBi's 108.7 in a fully converged 20,000-step experiment (300M params, WikiText-103, 4K context), beating ALiBi at every context length from 512 to 8,192 tokens. The key insight is not that primes specifically are the right frequencies, but that the multiplicative structure of the integers is the natural spectral basis for positional encoding. We demonstrate this through falsification experiments: prime-tiered frequencies (129.2 PPL) and composite-tiered frequencies (129.4 PPL) perform identically — because composites are not alternatives to primes but higher-order coordinates in the same lattice. Both dramatically outperform random frequencies (+5.0 PPL), scrambled tier assignment (+6.3 PPL), and pure ALiBi (+7.3 PPL). The active ingredient is lattice-aware, tiered frequency selection with learnable scale — not primality per se. We further validate this through a ZetaZeroPredictor experiment: three identical transformers trained for 10,000 epochs to predict Riemann zeta zero gaps. Geometric RoPE diverges (final r=0.57); SpectralALiBi locks into a stable attractor at epoch 112 (r=0.81). A second independent run widens this gap to -80.7% MSE improvement with r=0.86. The lattice-aligned frequency basis spans the mathematical space that zeta zeros inhabit; geometric frequencies cannot. We further report empirical confirmation of the structural prediction from Section 5.5: VHT2 banded quantization of the KV cache demonstrates that K vectors (which carry RoPE positional encoding) have strong spectral concentration in Walsh-Hadamard space — the first four energy bands capture the dominant structure — while V vectors (which carry content) have uniform energy distribution. This structural asymmetry is directly predicted by the lattice theory: RoPE encodes multiplicative arithmetic relationships as angular rates, and the WHT is the Z/2Z projection of the Vilenkin-Hartley basis that spans that structure. The result is 3.2× K compression and 4.7× V compression at <1.25% perplexity cost — validated on both Dolphin 1B (head_dim=64) and Qwen3-8B (head_dim=128). Introduction Positional encoding provides transformer models with token order information. Two approaches dominate: RoPE encodes position through frequency-based rotations preserving relative position invariance, and ALiBi replaces frequencies with a linear distance penalty providing long-context stability. The field has treated these properties as fundamentally in tension. We show this tension is false. It arises from a shared, unexamined assumption: that position is a location on a number line and the meaningful relationship between positions is distance. We replace this with a mathematically grounded alternative: position is a point in the multiplicative lattice of the integers, and the meaningful relationships between positions are their arithmetic structure — shared factors, GCD, harmonic resonance. 1.1 The Lattice Hypothesis The integers under multiplication form a lattice where every number occupies a unique point defined by its prime factorisation. Geometric PE (sinusoidal, RoPE) projects this lattice onto a line — position equals distance — discarding the multiplicative structure. We propose restoring it. The motivation follows from a deductive chain. Language word frequency follows Zipf's law: freq(rank) ∝ 1/ranks with s≈1. The generating function of Zipf is the Riemann zeta function ζ(s) = Σ 1/ns. The zeta zeros — where ζ is maximally informative — are generated by prime harmonics via the explicit formula. Therefore the prime harmonic structure, and the multiplicative lattice it generates, provides a natural spectral basis for encoding positions in language. 1.2 Primes as Generators, Composites as Coordinates A critical distinction: primes are the generators (basis vectors) of the multiplicative lattice. They are analogous to the 1D line segment in the progression from line → circle → sphere → hypersphere. The composite 12 = 2²×3 is not an alternative to primes — it is a coordinate in the lattice spanned by the prime axes, at position (2,1,0,0,...) in the (p₂, p₃, p₅, p₇,...) basis. Using 2π/12 as a frequency encodes a harmonic that resonates at multiples of 12 — which simultaneously hits every multiple of 2, every multiple of 3, every multiple of 4, and every multiple of 6. The analogy to n-dimensional geometry is precise: Dimensional Progression Multiplicative Lattice 1D line (2r) — the generator Primes (2, 3, 5, 7, ...) — generators 2D circle — integral of l

how to save 80% on your claude bill with better context

been building web apps with claude lately and those token limits have honestly started hitting me too. i'm using claude 4.6 sonnet for a research tool, but feeding it raw web data was absolutely nuking my limits. i'm putting together the stuff that actually worked for me to save tokens and keep the bill down: switch to markdown first. stop sending raw html. use tools like firecrawl to strip out the nested divs and script junk so you only pay for the actual text. don't let your prompt cache go cold. anthropic's prompt caching is a huge relief, but it only works if your data is consistent. watch out for the 200k token "premium" jump. anthropic now charges nearly double for inputs over 200k tokens on the new opus/sonnet 4.6 models. keep your context under that limit to avoid the surcharge strip the nav and footer. the website's "about us" and "careers" links in the footer are just burning your money every time you hit send. use jina reader for quick hits. for simple single-page reads, jina is a great way to get a clean text version without the crawler bloat. truncate your context. if a documentation page is 20k words, just take the first 5k. most of the "meat" is usually at the top anyway. clean your data with unstructured.io. if you are dealing with messy pdfs alongside web data, this helps turn the chaos into a clean schema claude actually understands. map before you crawl. don't scrape every subpage blindly. i use the map feature in firecrawl to find the specific documentation urls that actually matter for your prompt, if you use another tool, prefer doing this. use haiku for the "trash" work. use claude 4.5 haiku to summarize or filter data before feeding it into the expensive models like opus. use smart chunking. use llama-index to break your data into semantic chunks so you only retrieve the exact paragraph the ai needs for that specific prompt. cap your "extended thinking" depth. for opus 4.6, set thinking: {type: "adaptive"} with effort: "low" or "medium". the old budget_tokens param is deprecated on 4.6. thinking tokens are billed at the output rate, so if you leave effort on high, claude thinks hard on every single reply including the simple ones and your bill will hurt. set hard usage limits. set your spending tiers in the anthropic console so a buggy loop doesn't drain your bank account while you're asleep. feel free to roast my setup or add better tips if you have thembeen building web apps with claude lately and those token limits have honestly started hitting me too. i'm using claude 4.6 sonnet for a research tool, but feeding it raw web data was absolutely nuking my limits. submitted by /u/Grouchy_Subject_2777 [link] [comments]

Built a tool for testing AI agents in multi-turn conversations

We built ArkSim which help simulate multi-turn conversations between agents and synthetic users to see how it behaves across longer interactions. This can help find issues like: - Agents losing context during longer interactions - Unexpected conversation paths - Failures that only appear after several turns The idea is to test conversation flows more like real interactions, instead of just single prompts and capture issues early on. There are currently integration examples for: - OpenAI Agents SDK - Claude Agent SDK - Google ADK - LangChain / LangGraph - CrewAI - LlamaIndex you can try it out here: https://github.com/arklexai/arksim The integration examples are in the examples/integration folder would appreciate any feedback from people currently building agents so we can improve the tool! submitted by /u/Potential_Half_3788 [link] [comments]

[Feature Request]: Gemini prompt caching

### Feature Description Integrate the [Gemini prompt caching](https://ai.google.dev/gemini-api/docs/caching#cost-efficiency) to save LLM costs. ### Reason Context caching is a paid feature designed to reduce cost. Billing is based on the following factors: 1. Cache token count: The number of input tokens cached, billed at a reduced rate when included in subsequent prompts. 2. Storage duration: The amount of time cached tokens are stored (TTL), billed based on the TTL duration of cached token count. There are no minimum or maximum bounds on the TTL. 3. Other factors: Other charges apply, such as for non-cached input tokens and output tokens. ### Value of Feature By reducing both the cost and latency of processing large datasets, this integration transforms the "Context-Augmented" experience that LlamaIndex is known for.

3 repos you should know if you're building with RAG / AI agents

I've been experimenting with different ways to handle context in LLM apps, and I realized that using RAG for everything is not always the best approach. RAG is great when you need document retrieval, repo search, or knowledge base style systems, but it starts to feel heavy when you're building agent workflows, long sessions, or multi-step tools. Here are 3 repos worth checking if you're working in this space. 1. [memvid ](https://github.com/memvid/memvid) Interesting project that acts like a memory layer for AI systems. Instead of always relying on embeddings + vector DB, it stores memory entries and retrieves context more like agent state. Feels more natural for: \- agents \- long conversations \- multi-step workflows \- tool usage history 2. [llama\_index ](https://github.com/run-llama/llama_index) Probably the easiest way to build RAG pipelines right now. Good for: \- chat with docs \- repo search \- knowledge base \- indexing files Most RAG projects I see use this. 3. [continue](https://github.com/continuedev/continue) Open-source coding assistant similar to Cursor / Copilot. Interesting to see how they combine: \- search \- indexing \- context selection \- memory Shows that modern tools don’t use pure RAG, but a mix of indexing + retrieval + state. [more ....](https://www.repoverse.space/trending) My takeaway so far: RAG → great for knowledge Memory → better for agents Hybrid → what most real tools use Curious what others are using for agent memory these days.

3 repos you should know if you're building with RAG / AI agents

I've been experimenting with different ways to handle context in LLM apps, and I realized that using RAG for everything is not always the best approach. RAG is great when you need document retrieval, repo search, or knowledge base style systems, but it starts to feel heavy when you're building agent workflows, long sessions, or multi-step tools. Here are 3 repos worth checking if you're working in this space. 1. [memvid ](https://github.com/memvid/memvid): Interesting project that acts like a memory layer for AI systems. Instead of always relying on embeddings + vector DB, it stores memory entries and retrieves context more like agent state. Feels more natural for: \- agents \- long conversations \- multi-step workflows \- tool usage history 2. [llama\_index ](https://github.com/run-llama/llama_index) Probably the easiest way to build RAG pipelines right now. Good for: \- chat with docs \- repo search \- knowledge base \- indexing files Most RAG projects I see use this. 3. [continue](https://github.com/continuedev/continue) Open-source coding assistant similar to Cursor / Copilot. Interesting to see how they combine: \- search \- indexing \- context selection \- memory Shows that modern tools don’t use pure RAG, but a mix of indexing + retrieval + state. [more ....](https://www.repoverse.space/trending) My takeaway so far: RAG → great for knowledge Memory → better for agents Hybrid → what most real tools use Curious what others are using for agent memory these days.

I built Dome: An open-source, local-first knowledge management app with a built-in AI agent workspace. Looking for feedback and testers!

Hey everyone! I wanted to share a personal project I’ve been pouring my heart into for the last few months. It's an open-source desktop app called **Dome** ([https://github.com/maxprain12/dome](https://github.com/maxprain12/dome)). **The itch I was scratching:** I deal with a lot of PDFs, research papers, and scattered notes. I wanted a unified place to not just store my knowledge, but actually interact with it using AI. More importantly, because a lot of my data is private, I needed something that could run entirely locally without sending my files to the cloud. I couldn't find a tool that did everything I wanted perfectly, so I decided to build it. **What is Dome?** It’s basically a mix between a Notion-style workspace, a local AI chat, and an AI agent builder. Here are the main features I’ve built so far (I’ve attached some screenshots so you can get a feel for the UI): * **Unified Library &amp; Editor:** A Notion-style rich text editor where you can organize notes, PDFs (with an integrated annotator), web scrapes, and even Python notebooks all in one place. * **Custom Agent Workspace:** This is the part I'm most excited about. Powered by LangGraph, you can create custom multi-agent workflows. For example, you can have a "Research Agent" scour your local PDFs and pass that info to a "Writer Agent" to draft a presentation. We even have a marketplace for pre-built workflows. * **The "Studio" (Automated Study Materials):** Dome can take any document or folder and automatically generate mind maps, quizzes, and **flashcards with spaced repetition (SM-2)** directly from your sources. * **Local AI First:** First-class support for **Ollama**, so you can run models like Llama 3 or Mistral locally for complete privacy. (It also supports OpenAI, Anthropic, and Gemini via API keys if you prefer). * **MCP Support:** You can connect external Model Context Protocol servers to give your agents even more tools. **Tech Stack:** If you're curious about the hood: It's built with Bun, Electron, React, Vite, Tiptap (for the editor), LangGraph, SQLite, Knowledge Graph, PageIndex adapted. **Why I'm posting here:** Dome is fully open-source and in active development. I’m at the stage where building in a vacuum isn't helpful anymore **I need your brutally honest feedback.** I'd love for you to download it, try breaking it, and let me know: 1. Is the UI/UX actually intuitive? 2. What essential features am I completely missing? 3. What bugs did you run into during setup or daily use? **Repo link:** [https://github.com/maxprain12/dome](https://github.com/maxprain12/dome) I’ll be hanging around the comments to answer any questions, help with setup, or just talk about the tech stack. Thanks so much for taking a look!

You may not think you are doing RAG with Claude Code, but once context piles up, you are in pipeline territory

TL;DR **This is meant to be a copy-paste, take-it-and-use-it kind of post.** A lot of Claude and Claude Code failures look like model failures on the surface. You retry the prompt. You add more rules. You explain the task again. You assume the model is being inconsistent. But a lot of the time, the failure did not start at the wording layer. It started earlier, in the context pipeline. That means the model may not have had the right evidence, may have kept too much stale context, may have received the task in a badly packaged form, or may have drifted across turns before the bad output ever showed up. That is exactly why I use this **long debug card.** I pair the card with one failing Claude or Claude Code session, run it through a strong model, and use it as a first-pass triage method before I start blindly rewriting prompts or retrying the whole task again. **What people think is happening vs what is often actually happening** What people think: The prompt is too weak. The model is hallucinating. I need better wording. I should add more constraints. I should retry the task. Claude is just being inconsistent. Claude Code is being random today. What is often actually happening: The right evidence never became visible. Old context is still steering the session. The final prompt stack is overloaded or badly packaged. The original task got diluted across turns. The wrong slice of context was used, or the right slice was underweighted. The failure showed up during generation, but it started earlier in the pipeline. This is the trap. A lot of people think they are still solving a prompt problem, when in reality they are already dealing with a context problem. **Why this matters for Claude and Claude Code users** Most people hear “RAG” and imagine a company chatbot answering from a vector database. That is only one narrow version of the idea. Broadly speaking, the moment a model depends on external material before deciding what to generate, you are already in retrieval or context pipeline territory. And that happens much sooner than people think. If you use Claude or Claude Code to: read repo files before patching look at docs before deciding what to write use logs or terminal output as evidence carry prior outputs into the next turn use project instructions, memory, or rules to shape a long workflow use tool results to decide the next action then you are no longer dealing with pure prompting. You are dealing with a context pipeline. That is why a lot of failures that feel like “Claude is just wrong” are not actually random. They are often failures in: what got retrieved, what stayed visible, what got dropped, what got over-weighted, and how all of that got packaged before the final answer. What this card helps me separate I use it to split messy failures into smaller buckets, like: context / evidence problems The model did not actually have the right material, or it had the wrong material. prompt packaging problems The final instruction stack was overloaded, malformed, or framed in a misleading way. state drift across turns The session moved away from the original task after a few rounds, even if early turns looked fine. setup / visibility / tooling problems The model could not see what you thought it could see, or the environment made the behavior look more confusing than it really was. This matters because the visible symptom can look almost identical, while the correct fix can be completely different. So this is not about magic auto-repair. It is about getting a cleaner first diagnosis before you start changing things blindly. A few real patterns this catches **Case 1 You ask for a targeted fix, but Claude Code edits the wrong file.** That does not automatically mean the model is “bad.” Sometimes it means the wrong file, wrong slice, or incomplete context became the visible working set. **Case 2 It looks like hallucination, but it is actually stale context.** Claude or Claude Code keeps continuing from an earlier wrong assumption because old outputs, old constraints, or outdated evidence stayed in the session and kept shaping the next answer. **Case 3 It starts fine, then drifts.** Early turns look good, but after several rounds the session slowly moves away from the real objective. That is often a state problem, not just a single bad answer problem. **Case 4 You keep rewriting prompts, but nothing improves.** That can happen when the real issue is not wording at all. The model may simply be missing the right evidence, carrying too much old context, or working inside a setup problem that prompt edits cannot fix. **Case 5 You fall into a fix loop.** Claude Code keeps offering changes that sound reasonable, but the loop never actually resolves the real issue. A lot of the time, that happens when the session is already anchored to the wrong assumption and every new step is built on top of it. This is why I like using a triage layer first. It turns “this feels broke

Show HN: AgentLens – Open-source observability for AI agents

Hi HN,<p>I built AgentLens because debugging multi-agent systems is painful. LangSmith is cloud-only and paid. Langfuse tracks LLM calls but doesn&#x27;t understand agent topology — tool calls, handoffs, decision trees.<p>AgentLens is a self-hosted observability platform built specifically for AI agents:<p>- *Topology graph* — see your agent&#x27;s tool calls, LLM calls, and sub-agent spawns as an interactive DAG - *Time-travel replay* — step through an agent run frame-by-frame with a scrubber timeline - *Trace comparison* — side-by-side diff of two runs with color-coded span matching - *Cost tracking* — 27 models priced (GPT-4.1, Claude 4, Gemini 2.0, etc.) - *Live streaming* — watch spans appear in real-time via SSE - *Alerting* — anomaly detection for cost spikes, error rates, latency - *OTel ingestion* — accepts OTLP HTTP JSON, so any OTel-instrumented app works<p>Works with LangChain, CrewAI, AutoGen, LlamaIndex, and Google ADK.<p>Tech: React 19 + FastAPI + SQLite&#x2F;PostgreSQL. MIT licensed. 231 tests, 100% coverage.<p><pre><code> docker run -p 3000:3000 tranhoangtu&#x2F;agentlens-observe:0.6.0 pip install agentlens-observe </code></pre> Demo GIF and screenshots in the README.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;tranhoangtu-it&#x2F;agentlens-observe" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;tranhoangtu-it&#x2F;agentlens-observe</a> Docs: <a href="https:&#x2F;&#x2F;agentlens-observe.pages.dev" rel="nofollow">https:&#x2F;&#x2F;agentlens-observe.pages.dev</a><p>I&#x27;d love feedback on the trace visualization approach and what features matter most for your agent debugging workflow.

Engineering the Autonomous Local Enterprise: A Technical Blueprint for Agentic RAG and Sovereign AI Infrastructure

# Engineering the Autonomous Local Enterprise: A Technical Blueprint for Agentic RAG and Sovereign AI Infrastructure The transition from reactive large language model applications to autonomous agentic workflows represents a fundamental paradigm shift in enterprise computing. In the 2025–2026 technological landscape, the industry has moved beyond simple chat interfaces toward systems capable of planning, executing, and refining multi-step workflows over extended temporal horizons. This evolution is underpinned by the convergence of high-performance local inference, sophisticated document understanding, and multi-agent orchestration frameworks that operate within a "sovereign stack"—an infrastructure entirely controlled by the organization to ensure data privacy, security, and operational resilience. The architecture of such a system requires a nuanced understanding of hardware constraints, the mathematical implications of model quantization, and the systemic challenges of retrieving context from high-volume, complex document sets. # Executive Summary: The Rise of Sovereign Intelligence The contemporary AI landscape is increasingly bifurcated between centralized cloud-based services and a burgeoning movement toward decentralized, sovereign intelligence. For organizations managing sensitive intellectual property, legal documents, or healthcare data, the reliance on third-party APIs introduces unacceptable risks regarding data residency, privacy, and long-term cost volatility. The primary mission of this report is to define the architecture for a fully local, production-ready system that leverages the most advanced open-source components from GitHub and Hugging Face. The proposed system integrates high-fidelity document ingestion, a multi-stage RAG pipeline, and an agentic orchestration layer capable of long-horizon reasoning. By utilizing reasoning models such as DeepSeek-R1 and Llama 3.3, and optimizing them through advanced quantization, the enterprise can achieve performance levels previously reserved for high-cost cloud providers. This architecture is further enhanced by comprehensive observability through the OpenTelemetry standard, ensuring that every reasoning step and retrieval operation is transparent and verifiable. # Phase 1: The Local Discovery Engine Identifying the optimal components for a local sovereign stack requires a rigorous evaluation of active maintenance, documentation quality, and community health. The following repositories and transformers represent the current state-of-the-art for local LLM deployment with agentic RAG. # Top GitHub Repositories for Local Agentic RAG |**Repository**|**Stars**|**Last Updated**|**Primary Language**|**Key Strength**|**Critical Limitation**| |:-|:-|:-|:-|:-|:-| |**langchain-ai/langchain**|125,000|2026-01|Python/TS|700+ integrations; modular agentic workflows.|High abstraction complexity; steep learning curve.| |**langgenius/dify**|114,000|2026-01|Python/TS|Visual drag-and-drop workflow builder; built-in RAG.|Less flexibility for custom low-level Python hacks.| |**infiniflow/ragflow**|70,000|2025-12|Python|Deep document understanding; visual chunk inspection.|Resource-heavy; requires robust GPU for layout parsing.| |**run-llama/llama\_index**|46,500|2025-12|Python/TS|Superior data indexing; 150+ data connectors.|Transition from ServiceContext to Settings can be confusing.| |**zylon-ai/private-gpt**|52,000|2025-11|Python|Production-ready; 100% offline; OpenAI API compatible.|Gradio UI is basic; designed primarily for document Q&amp;A.| |**Mintplex-Labs/anything-llm**|25,000|2026-01|Node.js|All-in-one desktop/Docker app; multi-user support.|Workspace-based isolation can limit cross-context queries.| |**DSProject/Docling**|12,000|2026-01|Python|Industry-leading table extraction (97.9% accuracy).|Speed scales linearly with page count (slower than LlamaParse).| # Top Hugging Face Transformers for Reasoning and RAG |**Model**|**Downloads**|**Task**|**Base Model**|**Params**|**Hardware (4-bit)**|**Fine-tuning**| |:-|:-|:-|:-|:-|:-|:-| |**DeepSeek-R1-Distill-Qwen-32B**|2.1M|Reasoning|Qwen 2.5|32.7B|24GB VRAM (RTX 4090).|Yes (LoRA).| |**DeepSeek-R1-Distill-Llama-70B**|1.8M|Reasoning|Llama 3.3|70.6B|48GB VRAM (2x 4090).|Yes (LoRA).| |**Llama-3.3-70B-Instruct**|5.5M|General/RAG|Llama 3.3|70B|48GB VRAM (2x 4090).|Yes.| |**Qwen 2.5-72B-Instruct**|3.2M|Coding/RAG|Qwen 2.5|72B|48GB VRAM.|Yes.| |**Ministral-8B-Instruct**|800K|Edge RAG|Mistral|8B|8GB VRAM (RTX 3060).|Yes.| # Phase 2: Hardware Topographies and Inference Optimization The viability of local intelligence is strictly dictated by the memory bandwidth and VRAM capacity of the deployment target. In 2025, the release of the NVIDIA RTX 5090 introduced a significant leap in local capability, featuring 32GB of GDDR7 memory and a bandwidth of approximately 1,792 GB/s, representing a 77% improvement over its predecessor. # The Physics of Inference: Bandwidth vs. Compute A detailed 2025 NVIDIA research pap

bare-agent Lightweight enough to understand completely. Complete enough to not reinvent wheels. Not a framework, not 50,000 lines of opinions — just composable building blocks for agents.

Every AI agent — support bots, code assistants, research tools, autonomous workflows — does the same 6 things: call an LLM, plan steps, execute them in parallel, retry failures, observe progress, report back. Today you either write this plumbing from scratch (200+ lines you won't test, edge cases you'll find in production) or import LangChain/CrewAI/LlamaIndex — 50,000 lines, 20+ dependencies, eight abstraction layers between you and the actual LLM call. Something breaks and you're four files deep with no idea what's happening. You wanted a screwdriver, you got a factory that manufactures screwdrivers. bare-agent is the middle ground that didn't exist: 1,500 lines, zero dependencies, ten composable components. Small enough to read entirely in 30 minutes. Complete enough to not reinvent wheels. No opinions about your architecture. I built it, tested it in isolation, and avoided wiring it into a real system because I was sure it would break. So I gave an AI agent the documentation and a real task: replace a 2,400-line Python pipeline. Over 5 rounds it wired everything together, hit every edge case, told me exactly what was broken and how long each bug cost to find ("CLIPipe cost me 30 minutes — it flattened system prompts into text, the LLM couldn't tell instructions from content"). I shipped fixes, it rewired cleanly — zero workarounds, zero plumbing, 100% business logic. Custom code dropped 56%. What took me ages took under 2 hours. The framework went from "I think this works" to "I watched someone else prove it works, break it, and prove it again." That's what 1,500 readable lines gives you that 50,000 lines of abstractions never will. Open for feedback [https://github.com/hamr0/bareagent](https://github.com/hamr0/bareagent)