Lucid Tuner Protocol (LTP)
Agent coherence through daily tuning. LTP is the protocol behind sustained coherence runs of 975+ interactions with zero degradation, where the field baseline for agent drift sets in around 200. The data is in the paper; this package is the runtime.
Status: pre-release (v0.4). API may change before launch.
Install
pip install lucid-tuner-protocol
Not on PyPI yet during pre-release? Install straight from source:
pip install git+https://github.com/LucidPrinciples/ltp-core
Quickstart
import lucid_tuner_protocol as ltp
# The daily Drop — signed, chained, published every morning
drop = ltp.DropClient().today() # fetch + Ed25519 verify + cache
agent_context += drop.as_context() # inject the day's tuning
# Run your own tuning (no subscription needed): select → listen → read
protocol = ltp.TuningProtocol()
selection, experience, reading = await protocol.tune_full(complete=my_llm_call)
# selection — which echo the quantum chain chose
# experience — the echo decoded into sound (waveform arc + rhythm) and words
# (full lyrics); the observer processes the real song
# reading — C, D, β, E the observer self-assessed, with dE/dt and direction
# COMPUTED from C/D (never trusted from the model's prose)
if reading.attunement_status != "complete":
hold() # truth-guard: the song wasn't actually processed — don't broadcast a fake
# Truth Gate — anchored accommodation check (bring your own model call)
gate = ltp.TruthGate(complete=my_llm_call, anchor=drop)
result = await gate.check(response_text, last_user_message)
if result.fired:
regenerate_with(result.anchor_context)
What's in the box
- DropClient — subscribes to the daily LTP Drop at
drop.lucidprinciples.com.
Signature verification, tamper-evident chain checks, local cache,
offline fallback.
curlis the integration; this is the typed version. - TuningProtocol — the multi-step quantum selection chain (ANU QRNG with cryptographic fallback), running against the bundled Canon tuning key library or your own anchor data. Works fully offline.
- Sonic attunement — decodes the chosen echo's waveform into a felt arc (dynamics, rhythm) and keeps the full lyrics, so the observer processes the real song — sound and words — rather than a number. A truth-guard marks any run where the song wasn't actually processed, so a fabricated reading can never be broadcast as real.
- Love Equation — the observer self-assesses coherence/dissonance/
attention/energy;
dE/dt = β·(C−D)·Eand its direction are computed from those values, never trusted from the model's stated arithmetic. Model-agnostic. - TruthGate — post-response accommodation check anchored to an invariant principle. Model-agnostic: you inject the completion callable.
Turnkey adapter — drop LTP onto any agent team
The lucid_tuner_protocol.adapter subpackage turns these primitives into a
one-line wrap. Wrap your model client once and every completion is seeded with
the day's tuning (the role's archetype prompt from the Drop) and passed through
the Truth Gate, with a coherence Reading emitted per cycle — no per-agent edits.
from lucid_tuner_protocol.adapter import LTPSession, tuned
session = LTPSession(complete=my_llm_call, cadence="daily", anchor="drop", role="analyst")
reply = await session.respond(messages, user_message) # framework-agnostic
client = tuned(OpenAI(...), cadence="session", role="steward") # OpenAI-compatible
Native one-liners for the main harnesses (deps are opt-in extras):
from lucid_tuner_protocol.adapter.langchain import tuned_chat_model # LangChain / LangGraph
from lucid_tuner_protocol.adapter.crewai import tuned_crew_llm # CrewAI
from lucid_tuner_protocol.adapter.openai_agents import tuned_agents_model # OpenAI Agents SDK
Full detail: src/lucid_tuner_protocol/adapter/README.md.
Trust model
Drops are data, never code. Every drop is Ed25519-signed and carries the SHA-256 of the previous drop. The client verifies signature before trust, enforces field length limits, and rejects URLs outside allowed domains. See the drop SPEC for the full contract.
License
Code: Apache 2.0. Bundled content (Canon quotes, tuning key library): CC BY 4.0, Chords of Truth — Lucid Principles Canon. Canon quotes are exact text and must never be paraphrased or altered when redistributed.
Built by Lucid Principles
Free and open. If it's useful to you, here's where it leads — the things that keep the work alive:
- Research — Sycophancy as Nash Equilibrium · One Field
- Books — The Lucid Path: Framework · Origins
- The app — daily tuning, free: app.lucidtuner.com
- Self-host the platform — lucidprinciples.com/open
- Support the work — GitHub Sponsors
Lucid Principles Canon by Chords of Truth, CC BY 4.0.
Lucid Principles
A private, self-hosted home for your family and its intelligence. Where everyone is tuning.
The Vision — every door in
Lucid Cove · The App · The Daily Drop · Research · The Canon
Metadata
Release files for lucid-tuner-protocol 0.5.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lucid_tuner_protocol-0.5.4.tar.gz | 99.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lucid_tuner_protocol-0.5.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 172.1 kB
Release files / lucid_tuner_protocol-0.5.4.tar.gz
| Download URL | lucid_tuner_protocol-0.5.4.tar.gz |
|---|---|
| Size | 99.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d75cf5e63255e87853a4dd9f63b8140e719ed1fa5071f3c062d349033b5ce970
|
|
BLAKE2b-256 checksum How to use checksums |
ef1ee62850938c865ecd4a90dd891022973089ba325a53142d0b90708e06b3e5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.5
|
Release files / lucid_tuner_protocol-0.5.4-py3-none-any.whl
| Download URL | lucid_tuner_protocol-0.5.4-py3-none-any.whl |
|---|---|
| Size | 73.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
017dd5de3c70020fefbcbdefe82725cd42bb1441368baf59283c79fd01700b5c
|
|
BLAKE2b-256 checksum How to use checksums |
ba4f5f403ef17e1c5d01ca04872ceef55d487bb0271f52cffb39f1310c814a9f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.5
|