Hello!
As a handsome local AI enjoyer™ you’ve probably noticed one of the big flaws with LLMs:
It lies. Confidently. ALL THE TIME.
(Technically, it “bullshits” - https://link.springer.com/article/10.1007/s10676-024-09775-5
I’m autistic and extremely allergic to vibes-based tooling, so … I built a thing. Maybe it’s useful to you too.
The thing: llama-conductor
llama-conductor is a router that sits between your frontend (OWUI / SillyTavern / LibreChat / etc) and your backend (llama.cpp + llama-swap, or any OpenAI-compatible endpoint). Local-first (because fuck big AI), but it should talk to anything OpenAI-compatible if you point it there (note: experimental so YMMV).
Not a model, not a UI, not magic voodoo.
A glass-box that makes the stack behave like a deterministic system, instead of a drunk telling a story about the fish that got away.
TL;DR: “In God we trust. All others must bring data.”
Three examples:
1) KB mechanics that don’t suck (1990s engineering: markdown, JSON, checksums)
You keep “knowledge” as dumb folders on disk. Drop docs (.txt, .md, .pdf) in them. Then:
>>attach <kb>— attaches a KB folder>>summ new— generatesSUMM_*.mdfiles with SHA-256 provenance baked in- `>> moves the original to a sub-folder
Now, when you ask something like:
“yo, what did the Commodore C64 retail for in 1982?”
…it answers from the attached KBs only. If the fact isn’t there, it tells you - explicitly - instead of winging it. Eg:
The provided facts state the Commodore 64 launched at $595 and was reduced to $250, but do not specify a 1982 retail price. The Amiga’s pricing and timeline are also not detailed in the given facts.
Missing information includes the exact 1982 retail price for Commodore’s product line and which specific model(s) were sold then. The answer assumes the C64 is the intended product but cannot confirm this from the facts.
Confidence: medium | Source: Mixed
No vibes. No “well probably…”. Just: here’s what’s in your docs, here’s what’s missing, don’t GIGO yourself into stupid.
And when you’re happy with your summaries, you can:
>>move to vault— promote those SUMMs into Qdrant for the heavy mode.
2) Mentats: proof-or-refusal mode (Vault-only)
Mentats is the “deep think” pipeline against your curated sources. It’s enforced isolation:
- no chat history
- no filesystem KBs
- no Vodka
- Vault-only grounding (Qdrant)
It runs triple-pass (thinker → critic → thinker). It’s slow on purpose. You can audit it. And if the Vault has nothing relevant? It refuses and tells you to go pound sand:
FINAL_ANSWER:
The provided facts do not contain information about the Acorn computer or its 1995 sale price.
Sources: Vault
FACTS_USED: NONE
[ZARDOZ HATH SPOKEN]
Also yes, it writes a mentats_debug.log, because of course it does. Go look at it any time you want.
The flow is basically: Attach KBs → SUMM → Move to Vault → Mentats. No mystery meat. No “trust me bro, embeddings.”
3) Vodka: deterministic memory on a potato budget
Local LLMs have two classic problems: goldfish memory + context bloat that murders your VRAM.
Vodka fixes both without extra model compute. (Yes, I used the power of JSON files to hack the planet instead of buying more VRAM from NVIDIA).
!!stores facts verbatim (JSON on disk)??recalls them verbatim (TTL + touch limits so memory doesn’t become landfill)- CTC (Cut The Crap) hard-caps context (last N messages + char cap) so you don’t get VRAM spikes after 400 messages
So instead of:
“Remember my server is 203.0.113.42” → “Got it!” → [100 msgs later] → “127.0.0.1 🥰”
you get:
!! my server is 203.0.113.42?? server ip→ 203.0.113.42 (with TTL/touch metadata)
And because context stays bounded: stable KV cache, stable speed, your potato PC stops crying.
There’s more (a lot more) in the README, but I’ve already over-autism’ed this post.
TL;DR:
If you want your local LLM to shut up when it doesn’t know and show receipts when it does, come poke it:
- Primary (Codeberg): https://codeberg.org/BobbyLLM/llama-conductor
- Mirror (GitHub): https://github.com/BobbyLLM/llama-conductor
PS: Sorry about the AI slop image. I can’t draw for shit.
PPS: A human with ASD wrote this using Notepad++. If it the formatting is weird, now you know why.
So you’ve made a FAQ with a LLM interface? I could see that potentially being useful for cooperate “let our bot answer your questions” tools.
But the usefulness of AI isn’t just in “tell me a fact”. Like what would your AI give for "what functions would I use in Python to convert a utf16 string to utf8? Would the answer need to be in the KB already?
Correct. Curate your sources :)
I can’t LoRa stupid out of a model…but I can do this. If your model is at all obedient and non-stupid, and reasons from good sources, it will do well with the harness.
Would you like to see the benchmarks for the models I recommend in the “minimum reccs” section? They are very strong…and not chosen at random.
Like the router, I bring receipts :)
It’s a very nice concept to be able to dump a shitload of knowledge into a folder, look at “processing…” for a few seconds, then ask questions to get exactly what you’re looking for out of it rather than having to go digging through the mound of information and without having to worry that the computer just threw in a few made up facts for giggles. The idea is that the dumping happens over time mostly, allowing you to quickly find buried information from years ago with a few relevant queries.
One thing I’d do with this is dump all my emails into it, from across all my accounts. That might save me having to search keywords in 8+ accounts over 4-5 different platforms every so often…
It also might have been useful in a lawsuit I prosecuted a few years ago. Instead of going through two years of encrypted messages by hand to pull out relevant excerpts with context, I could have exported the lot and just asked for the information. If it worked it could have saved me months (I spent a few hours after work every night screenshotting, dating the screenshots in chronological order, then I’ve that was done I kept a spreadsheet that I filled with relevant excerpts and links to the screenshots, by reading every single screenshot… it was a lot).
I would be super interested to hear if it could do that. I genuinely don’t know, because I haven’t tried it.
If you can export your emails in the correct format, it might actually work. Try a small batch and report back.
PS: you DON’T HAVE TO run >>summ if you don’t want to. You can ask questions against the raw files too. It’s just a keyword match (though obviously, a curated summary of keywords is generally less noisy)
Wishing you luck! I didn’t make this enterprise grade, but if it works, use it.