AI · alternatives · 2026
24 alternatives to A Karpathy-style LLM wiki your agents maintain (Markdown and Git)
I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
This is a wiki system designed for AI agents that uses markdown files and Git as its core storage layer, with BM25 full-text search and SQLite indexing on top. Agents maintain private notebooks and access shared team… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier.
- 1
Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…
2025 · github.com · its alternatives →
- 2HL
All content is based on Andrej Karpathy's "Intro to Large Language Models" lecture (youtube.com/watch?v=7xTGNNLPyMI). I downloaded the transcript and used Claude Code to generate the entire interactive site from it — single HTML file. I find it useful to revisit this content time to time.
Apr 2026 · ynarwal.github.io · its alternatives →
- 3
hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…
May 2026 · github.com · its alternatives →
- 4
Context Gateway▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
Mar 2026 · github.com · its alternatives →
- 5
GitAgent by Lyzr▲231Hey HN, We built GitAgent — a git-native, framework-agnostic open standard for defining AI agents. The idea is simple: your repo IS the agent. npx @open-gitagent/gitagent@latest run -r https://github.com/shreyas-lyzr/architect -a claude That one command clones the repo, reads agent.yaml + SOUL.md + skills/, and launches it with Claude Code. Switch `-a openai` or `-a lyzr` to run the same agent on a different framework. No code changes. Goal: is keep the gitagent as a open standard ledger for an AI Agent in a VM, with versioned memories, skills etc. An agent is…
Mar 2026 · gitagent.sh · its alternatives →
- 6
Hey HN! We're Paul, Preston, and Daniel from Zep. We've just open-sourced Graphiti, a Python library for building temporal Knowledge Graphs using LLMs. Graphiti helps you create and query graphs that evolve over time. Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to build a knowledge graph while handling changing relationships and maintaining historical context. At Zep, we build a memory layer for LLM applications. Developers use Zep to recall relevant user information from past conversations without including the entire…
2024 · github.com · its alternatives →
- 7
Viberia▲146Hey HN, This is my take on the agent harness. Everything on an isometric map. Agents are grouped into "buildings" that run in a sequence or a loop; e.g., the CodeForge has an agent that writes a PRD, another one that implements, and a third that reviews. Everything is customizable, you build your own buildings/teams however you want. It's a Tauri app, really light (about 8x less energy than the closest competitor I benchmarked, so it actually runs from a coffee shop on battery). It's macOS only for now, but ping me if you are willing to test the Windows or Linux version. I've been…
May 2026 · getviberia.com · its alternatives →
- 8
Trails▲81Hey all, Justin here. I previously built Phind, the AI search engine for developers. One of the biggest problems we had there was figuring out what went wrong with bad searches. We had tons of searches per day, but less than 1% of users gave any explicit feedback. So we were either manually digging through searches or making general system improvements and hoping they helped. This problem gets harder with agents. Traces are longer and more complex. It takes more effort to review them, so I'm building a tool that lets you analyze LLM outputs directly to help developers of LLM apps and agents…
Jan 2026 · trails-red.vercel.app · its alternatives →
- 9
I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built OzBrain to do: + Create a central place for agent reasoned knowledge to live + Be agnostic…
Aug 2026 · ozbrain.com · its alternatives →
- 10
Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…
2025 · github.com · its alternatives →
- 11
Free, open-source implementation of Karpathy's LLM Wiki. Upload documents and build a compounding wiki directly via Claude.
Apr 2026 · llmwiki.app · its alternatives →
- 12
The knowledge compiler. Raw sources in, interlinked wiki out. Inspired by Karpathy's LLM Wiki pattern. - atomicstrata/llm-wiki-compiler
Apr 2026 · github.com · its alternatives →
- 13
Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com · its alternatives →
- 14
Hi HN, for quite some time I've been thinking how LLMs are missing the knowledge base, where I can dump CSVs, PDFs, and most important, inline web app. running on Claude Code (bring your own agent) with agents with heartbeats and jobs https://runcabinet.com It runs locally and is installable via npm. GitHub (open source): https://github.com/hilash/cabinet This is still very early. I put the first version together quickly after seeing a post by Andrej Karpathy about LLM knowledge bases, which matched closely with what I’d been building. Some people have already…
Apr 2026 · runcabinet.com · its alternatives →
- 15
Build source-backed knowledge bases with Claude Code, Codex, OpenCode, or any AI agent. Export Project Knowledge Checkpoints, apply personal specialist review methods, shape Ideas, and promote approved work into Projects.
Jun 2026 · llm-wiki.net · its alternatives →
- 16
We built a reference MCP server that lets your editor/agent learn a codebase directly from source (signatures, types, AST, comments). Docs are optional. The reference impl runs on our open-source project’s codebase. Why we built it Docs are important, but they add another abstraction layer between your code and your users. Keeping them at the right quality is hard (especially at a startup), and LLM-generated docs are often mediocre until you invest real polish. Exposing code to the model in a structured way keeps answers grounded and current, and it’s always available. You can even…
2025 · github.com · its alternatives →
- 17
Lore is the local-first memory layer for AI agents. Plain Markdown and an open SQLite database, on your machine. Run by a fair-source engine.
Jun 2026 · withlore.ai · its alternatives →
- 18
It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.
2024 · github.com · its alternatives →
- 19
A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub
2023 · github.com · its alternatives →
- 20
Hey HN, I just spent the last few weeks building a database for agents. Over the last year I built PostHog AI, the company's business analyst agent, where we experimented on giving raw SQL access to PostHog databases vs. exposing tools/MCPs. Needless to say, SQL wins. I left PostHog 3 weeks ago to work on side-projects. I wanted to experiment more with SQL+agents. I built an MVP exposing business data through DuckDB + annotated schemas, and ran a benchmark with 11 LLMs (from Kimi 2.5 to Claude Opus 4.6) answering business questions with either 1) per-source MCP access (e.g. one Stripe…
Apr 2026 · github.com · its alternatives →
- 21MI
The release of microgpt by Andrej Karpathy is a foundational moment for AI transparency. In exactly 243 lines of pure, dependency-free Python, Karpathy has implemented the complete GPT algorithm from scratch. As a PhD scholar investigating AI and Blockchain, I see this as the ultimate tool for moving beyond the "black box" narrative of Large Language Models (LLMs). The Architecture of Simplicity Unlike modern frameworks that hide complexity behind optimized CUDA kernels, microgpt exposes the raw mathematical machinery. The code implements: The Autograd Engine: A custom Value class that…
Feb 2026 · its alternatives →
- 22
A great way to enhance chatbots is to allow them to look up information for context, typically using a vectordb. If you have writings you would like to share with others, you can offer a server that allows others to do semantic lookup, and that way anyone can have a chatbot which can pull from your writing. The goal of this project is to have a protocol that makes that easy. Strictly speaking, the protocol is for semantic retrieval and doesn't require using LLMs although LLMs are the motivating application. For far more details and how to get a demo up and running, see the readme.
2024 · github.com · its alternatives →
- 23
Markdown based AI agent memory with semantic search, hybrid retrieval, and offline-first sync between agents. - sqliteai/sqlite-memory
Apr 2026 · github.com · its alternatives →
- 24
Turbular▲13Hello everyone, my startup sadly failed, so I decided to convert it to an open source project since we actually built alot of internal tools. The result is todays release Turbular. Turbular is an MCP server under the MIT license that allows you to connect your LLM agent to any database. Additional features are: Schema normalizes: translates schemas into proper naming conventions (LLMs perform very poorly on non standard schema naming conventions) Query optimization: optimizes your LLM generated queries and renormalizes them Security: All your queries (except for Bigquery) are run with…
2025 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →