AsahiLM

Meme token narrative intelligence engine for Solana.

$ASAHI

Language models read sequences. They weight context, measure coherence, track how meaning accumulates. AsahiLM applies the same lens to on-chain meme tokens — measuring whether signals are coherent and directional, or just noise.

Launch AppDocumentation@AsahiDotLM
Phases
NOISE>SIGNAL>NARRATIVE>VIRAL
Score
0-100
Tests
130+
Signal Architecture

Four signals.
One narrative score.

Measuring whether a token's signals are coherent and directional, or just noise. Weighted and averaged into a single 0-100 score and phase classification.

MemeTokenContext
    |
+-------------------------------------------+
|  velocity_signal      35%   price momentum |
|  resonance_signal     30%   volume spike   |
|  depth_signal         20%   holder growth  |
|  coherence_signal     15%   organic ratio  |
+-------------------------------------------+
    |
NarrativeScore (0-100)  >  Phase  >  Insight
0135%

Velocity Signal

|1h price change| >= 15% triggers. Fast moves in either direction carry narrative weight. A dump is a narrative event too.

score = min(abs(change) x 2, 100)
0230%

Resonance Signal

1h volume >= 2.5x hourly baseline triggers. Volume above baseline means the market is processing this token as signal — not background noise.

baseline = volume_24h / 24
0320%

Depth Signal

Holder count grew >= 4% vs previous snapshot. Narratives that spread past early adopters have structural depth. Context window expansion — on-chain.

score = min(growth_pct x 5, 100)
0415%

Coherence Signal

Holders / (market_cap_usd / 1000) >= 8 triggers. Community-to-value alignment. Many holders per dollar of mcap = organic traction.

score = min(ratio x (100/8), 100)
Narrative Phases
Phase
Score
Meaning
NOISE
0-25
Below the noise floor. Could be accumulation. Could be dead.
SIGNAL
26-50
Early signal detected. Narrative assembling. Not confirmed.
NARRATIVE
51-75
Coherent narrative in motion. Community and momentum aligned.
VIRAL
76-100
Viral propagation. Maximum attention. Late entries carry risk.
Installation

Get Started

Pure Python 3.10+. 130+ tests. No external dependencies. MIT License.

01

Install

Install via pip

pip install asahi
02

Import

Import core modules

from asahi import classify, MemeTokenContext
03

Classify

Classify and read narrative

report = classify(ctx)
print(report.state)         # NARRATIVE
print(report.narrative_score)  # 67.4

Usage Example

main.py
from asahi import classify, MemeTokenContext

ctx = MemeTokenContext(
    token_mint="AsahiXyz...pump",
    symbol="ASAHI",
    price_usd=0.00082,
    price_change_1h_pct=38.0,
    price_change_24h_pct=180.0,
    volume_1h=480_000,
    volume_24h=1_200_000,
    holder_count=4_200,
    holder_count_prev=3_100,
    market_cap_usd=560_000,
)

report = classify(ctx)

print(report.state)            # NARRATIVE
print(report.narrative_score)  # ~67.4
print(report.insight)          # "Coherent narrative in motion..."
print(report.triggered_count)  # 3
print(report.is_coherent)      # True

Quick Reference

pip install asahi
from asahi import classify
report = classify(ctx)
report.state
report.narrative_score
report.insight
pytest tests/ -v

Start analyzing meme token narratives

Follow us on X for updates

Follow @AsahiDotLMView on GitHub