axiomesh
Environment Design Pattern (EDP) + Model Environment Protocol (MEP)
OneOrigine / ImperialSchool Research — I.S. License
"A perfect balance between rationality and probability is necessary to fully leverage the logic of artificial intelligence." — Seikatsu-One
Install
pip install axiomesh
pip install axiomesh[ollama] # + local Ollama
pip install axiomesh[all] # + all LLM providers
Quick start — Multi-agent CLI
# Interactive school scenario (admin + teacher + accountant)
axiomesh-cli --demo
axiomesh-cli --scenario school --model gemma3:12b
axiomesh-cli --scenario bank
axiomesh-cli --scenario drone
# Run a task file
axiomesh-cli --task school_demo.axm
# Generate a demo task file
axiomesh-cli --gen-task --scenario school
Chat mode commands
| Command | Description |
|---|---|
/agents |
List all active agents |
/focus admin |
Direct chat to the admin agent |
/focus all |
Broadcast mode (all agents) |
/broadcast <msg> |
Send goal to ALL agents simultaneously |
/ctx |
Show context + harmony-ranked actions |
/switch fin accountant |
Switch accountant to financial context |
/task file.axm |
Run a task file |
/spawn analyst fin |
Spawn a new agent |
/impact |
Session impact matrix |
/causal |
Causal graph (DOT) |
/why student.enroll |
Causal trace |
/savoir |
SAVOIR knowledge base |
/export |
Export session JSON |
Task files (.axm)
# school_demo.axm
@agent admin
@context admin
@goal Add student Alice Chen
@parallel
@agent admin @context enrl
@agent admin : Enroll student STU001 courseId=CRS001
@agent accountant @context fin
@agent accountant : Process fee payment studentId=STU001
@end
Python API
from axiomesh import Environment, Context, Action, Reaction, Element
from axiomesh import MepGateway, SenseVector, Circumstance, EnvironmentKind, ContextKind
env = Environment("MyEnv", EnvironmentKind.REACTIVE)
ctx = env.create_context("Main", ContextKind.SEMANTIC,
basis=SenseVector.normative("operations", 0.9))
# Add action
async def handler(actor, payload, ctx, frame):
return Reaction.ok("my.action", f"Done: {payload}")
ctx.reg(Action("my.action", "command", "Do something",
SenseVector.normative("action", 0.9), handler=handler))
# MEP gateway
gw = MepGateway(env)
session = gw.connect("agent-1")
envelope = gw.build_envelope(session, actor, ctx)
reaction = await gw.dispatch(session, actor, "my.action", {}, ctx)
Mathematical Foundations
- Central equation:
E_{t+1} = 𝔘(E_t, 𝔯(x,c,Σ, 𝔄(x,c,Σ,Γ,Ψ(D,c,Σ)))) - Harmony:
H = α·cos(A,C) + β·cos(A,S) + γ·cos(R̂,R) − δ·D - Admissibility:
Adm(a) = ∧_{γ∈Γ_a} γ(E,c,Σ,x) - Optimum:
a* = argmax_{a∈Avail} H(a,c,s)
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