PROJECT_YGGDRASIL_THE_EDGE_AGI_EXTENSION_CO-PROCESSOR_RUNTIME.md
Project Yggdrasil transforms the Raspberry Pi 5 edge node from a passive display host into an active cognitive co-processor, memory system, simulation runtime, sandboxed execution environment, and autonomous sub-agent extension for Meta Muse.
- System Vision & Architecture Overview
- Repository & Subsystem Integration Matrix
- Mathematical & Algorithmic Foundations
- Hardware Compute Partitioning
- Implementation: The Yggdrasil Daemon
- Muse Client Connector & MCP Integration
- AI Coding Agent Deployment Guide
- Runtime Verification Matrix
- Final Yggdrasil Runtime Topology
Project Yggdrasil transforms the Raspberry Pi 5 and its neural accelerator from a passive graphics node into an active edge co-processor and agentic extension hub for Meta Muse.
Muse continues to run her primary long-context reasoning, dialogue, planning, and high-level orchestration loops on her host workstation.
The Raspberry Pi becomes an external cognitive runtime that Muse can call across the local network.
Through a structured tool and RPC interface, Muse can delegate operations such as:
- persistent memory storage
- artifact retrieval
- causal world-model updates
- timeline-state synchronization
- predictive simulations
- autonomous worker execution
- code execution inside restricted workspaces
- local neural inference
- vector search
- telemetry visualization
- speech synthesis
- future edge-agent workloads
All significant Yggdrasil operations can emit telemetry directly into Project Himinbjörg, giving the user and Muse a real-time physical view of the edge runtime.
flowchart TB
subgraph HOST["MUSE HOST WORKSTATION"]
MUSE["Meta Muse Agent Core<br/>Planner / Reasoning / Dialogue"]
MCP["MCP Client / JSON-RPC Dispatcher"]
MUSE --> MCP
end
MCP -->|"LAN Transport<br/>mTLS / HTTP / WebSocket<br/>Port 8000 RPC<br/>Port 8765 WS"| HEIMDALL
subgraph EDGE["RASPBERRY PI 5 CO-PROCESSOR RUNTIME - YGGDRASIL"]
HEIMDALL["HEIMDALL GATEWAY & SENTRY<br/>Authentication / Validation / Rate Limits"]
WYRD["WYRD + VERDANDI<br/>World Model / Present-State Timeline"]
KISTA["KISTA + HERMES-STATE<br/>Memory / Artifact Vault"]
DRAUPNIR["DRAUPNIR + SCRIBING<br/>Sub-Agents / Artifact Workers"]
SEIDR["SEIDR ENGINE<br/>Simulation / Forecasting"]
CODER["MYTHIC CODER + A.E.S.I.R.<br/>Sandbox / Edge Execution"]
NPU["HAILO NEURAL ACCELERATOR<br/>Embeddings / Micro-Agents / TTS"]
HUD["HIMINBJÖRG OMNI-HUD<br/>60 FPS Telemetry / Visualization"]
HEIMDALL --> WYRD
HEIMDALL --> KISTA
HEIMDALL --> DRAUPNIR
WYRD --> SEIDR
KISTA --> SEIDR
DRAUPNIR --> CODER
SEIDR --> NPU
CODER --> NPU
WYRD --> HUD
KISTA --> HUD
DRAUPNIR --> HUD
SEIDR --> HUD
CODER --> HUD
NPU --> HUD
end
classDef muse fill:#21143d,stroke:#a78bfa,stroke-width:2px,color:#ffffff;
classDef gateway fill:#172554,stroke:#60a5fa,stroke-width:2px,color:#ffffff;
classDef cognitive fill:#132e2a,stroke:#34d399,stroke-width:2px,color:#ffffff;
classDef neural fill:#3b1d0b,stroke:#fb923c,stroke-width:2px,color:#ffffff;
classDef hud fill:#301934,stroke:#e879f9,stroke-width:2px,color:#ffffff;
class MUSE,MCP muse;
class HEIMDALL gateway;
class WYRD,KISTA,DRAUPNIR,SEIDR,CODER cognitive;
class NPU neural;
class HUD hud;
The core Yggdrasil concept is:
Muse
│
│ delegates structured work
▼
Heimdall Gateway
│
├── Memory
├── World State
├── Simulation
├── Sub-Agents
├── Sandboxed Execution
└── Neural Services
│
▼
Yggdrasil
│
├── returns results to Muse
└── emits telemetry to Himinbjörg
Muse remains the primary intelligence.
Yggdrasil becomes the external cognitive root system beneath her main reasoning environment.
The Yggdrasil runtime integrates multiple RuneForgeAI initiatives into a unified edge-service architecture.
| Subsystem Module | Origin Repository / Project | Architectural Responsibility inside Yggdrasil |
|---|---|---|
| Heimdall Sentry | Heimdall-SL-Hermes-Agent |
Gateway gatekeeper, authentication endpoint, validation layer, request sanitization, and rate limiting |
| Hermes Bridge | hermes-agent-RuneForgeAI-hack |
Agent tool dispatch, session continuation, recovery wrappers, and execution bridging |
| Verdandi Timeline | Verdandi |
Present-state temporal coordinator, event serialization, frame-delta tracking, and timeline anchoring |
| WYRD World Model | WYRD-Protocol-World-Yielding-Real-time-Data-AI-world-model |
Directed causal graph of entities, relationships, environmental state, actions, and world progression |
| Kista Vault | kista + hermes-state |
Persistent artifact storage, structured memory, document indexing, cached state, and cold-state backup |
| Draupnir Forge | RuneForgeAI-Draupnir-Forge |
Recursive sub-agent generation, task decomposition, parallel workers, and bounded worker-pool management |
| Scribing Foundry | Mythic-Scribing-Foundry |
Structured artifact generation, Markdown generation, schemas, prompt artifacts, and code/document formatting |
| Seidr Engine | seidr-engine |
Heuristic evaluation, probabilistic forecasting, symbolic weighting, and divination-oriented simulation |
| Mythic Coder | Mythic_Agent_Coder_CLI + Viking-Code-Mythic-Engineering-CLI-Vibe-Coding |
Restricted command execution, file manipulation, testing, patch application, and coding workflows |
| Project A.E.S.I.R. | RuneForgeAI-Project-Aesir |
Efficient ARM64 and accelerator-oriented inference runtime, low-level bindings, and edge model execution |
| Himinbjörg | Project-Hlidhskjalf / Himinbjörg |
Real-time visualization of Yggdrasil state, activity, simulation, memory, and agent telemetry |
The WYRD world model represents active reality as a causal directed graph:
where:
V = world-state and entity vertices
E = causal actions and transitions
T = temporal coordinates / ordering
A vertex may represent:
- a person
- an AI agent
- an object
- a location
- an environmental condition
- a resource
- an abstract state
- a system state
An edge represents a causal relationship or state-changing event.
For transition from state or node
Let:
represent the learned or configured weight vector associated with action
A logistic transition model can be expressed as:
where the sigmoid function is:
Therefore:
The feature vector may incorporate:
- semantic similarity
- entity compatibility
- historical transitions
- environmental constraints
- Kista memory embeddings
- action context
- resource availability
- temporal proximity
Verdandi monitors uncertainty across possible active branches.
Let the current probability distribution over possible states at time
where:
The temporal state entropy is:
Low entropy indicates:
highly constrained / coherent present state
High entropy indicates:
many competing unresolved future or speculative branches
Verdandi monitors:
When this condition is satisfied, the system may:
- persist an anchor checkpoint to Kista
- classify verified manifest states
- prune expired or invalid speculative branches
- retain unresolved branches separately
- rebuild the active present-state graph
Conceptually:
Speculative Graph Expansion
│
▼
Entropy Measurement
│
├── Below Threshold -> Continue
│
└── Above Threshold
│
▼
VERDANDI ANCHOR
│
┌────────┴────────┐
▼ ▼
Persist State Prune Drift
│ │
└────────┬────────┘
▼
Stable Present
When Muse delegates a complex task, Draupnir can recursively create bounded workers.
The worker count decreases with recursion depth to prevent CPU, memory, and process exhaustion.
Let:
represent normalized task complexity.
Let:
represent recursion depth.
The theoretical worker allocation is:
where:
is the maximum root-level worker count and:
is the recursion decay factor.
The executable integer worker count is:
For maximum task complexity:
the default sequence becomes:
Recursive spawning stops when:
or:
A depth-3 worker may complete its assigned operation but should not create a depth-4 generation.
The Seidr Engine blends empirical system state with symbolic or heuristic information.
Let:
represent deterministic or empirically derived system state.
Let:
represent symbolic, celestial, runic, divinatory, or heuristic state.
Define:
as the empirical estimate for outcome
Define:
as the symbolic or heuristic estimate.
Let intuition weighting be:
The blended probability is:
Interpretation:
means fully empirical evaluation.
means fully symbolic evaluation.
produces a hybrid forecast.
For empirical features:
and weights:
a normalized empirical score may be expressed as:
The final Seidr score becomes:
| Compute Resource | Assigned Role |
|---|---|
| CPU Core 0 | Heimdall Gateway, JSON-RPC, MCP-facing services, network ingestion |
| CPU Core 1 | WYRD graph processing, Verdandi synchronization, Kista coordination |
| CPU Core 2 | Draupnir scheduler, sandbox execution, Mythic Coder workloads |
| CPU Core 3 | Himinbjörg display compositor and UI support |
| VideoCore GPU | Graphics composition and display acceleration |
| System Memory | OS, graph state, Kista caches, sandbox workspaces, process heaps |
┌──────────────────────────────────────────────────────────────────────┐
│ RASPBERRY PI 5 SYSTEM MEMORY │
├──────────────────────────────────────────────────────────────────────┤
│ Linux OS + Kernel ~1.5 GB │
│ Python / Runtime Daemons ~2.0 GB │
│ Kista / WYRD / SQLite Cache ~4.0 GB │
│ Sandbox Workspaces ~4.0 GB │
│ Dynamic Headroom remaining system memory │
└──────────────────────────────────────────────────────────────────────┘
flowchart TB
PI["Raspberry Pi 5"]
PCIE["PCIe Link"]
NPU["Hailo Neural Accelerator"]
EMBED["Pipeline 1<br/>Text / Code Embeddings"]
AGENT["Pipeline 2<br/>Edge Micro-Agent Models"]
TTS["Pipeline 3<br/>Neural Speech Synthesis"]
PI --> PCIE
PCIE --> NPU
NPU --> EMBED
NPU --> AGENT
NPU --> TTS
Potential accelerator workloads include:
BGE family
Nomic Embed family
other supported embedding models
Use cases:
- Kista semantic retrieval
- code search
- lore retrieval
- document similarity
- world-model entity matching
Potential small-model workloads:
Qwen-family coder models
small instruction models
task-specific classifiers
local worker models
Use cases:
- patch proposals
- classification
- summarization
- structured extraction
- local micro-agent tasks
Potential speech workloads:
Kokoro
Piper
other supported local TTS models
Use cases:
- Muse voice
- alerts
- narration
- TTRPG dialogue
- system announcements
File:
yggdrasil_core_daemon.py
The following reference implementation provides:
- Kista memory storage
- WYRD state tracking
- Seidr simulation
- restricted workspace execution
- Draupnir worker execution
- Himinbjörg telemetry
- JSON-RPC tool dispatch
Security note: Filesystem confinement through
cwdalone is not a true operating-system sandbox. Production deployments should add stronger isolation such as dedicated users, containers, namespaces, seccomp, command allowlists, or another hardened execution boundary.
#!/usr/bin/env python3
"""
yggdrasil_core_daemon.py
Project Yggdrasil:
Edge AGI Extension Runtime for Raspberry Pi 5.
Integrates:
- Heimdall: JSON-RPC / Tool Gateway
- WYRD: Causal World Model
- Verdandi: Present-State Synchronization
- Kista: Long-Term Memory & Artifact Vault
- Draupnir: Recursive Worker Runtime
- Seidr: Predictive Simulation Engine
- Mythic Coder: Restricted Workspace Execution
- Himinbjörg: Real-Time Telemetry Forwarding
Author: Volmarr / RuneForgeAI
License: MIT
"""
import sys
import time
import json
import uuid
import sqlite3
import threading
import subprocess
from pathlib import Path
from http.server import HTTPServer, BaseHTTPRequestHandler
from typing import Dict, Any, List, Optional
# =====================================================================
# Configuration & Paths
# =====================================================================
BASE_DIR = Path(
"/opt/yggdrasil"
)
VAULT_DIR = (
BASE_DIR / "kista_vault"
)
SANDBOX_DIR = (
BASE_DIR / "sandbox"
)
HUD_IPC_URL = (
"http://127.0.0.1:8080/api/event"
)
PORT = 8000
BASE_DIR.mkdir(
parents=True,
exist_ok=True
)
VAULT_DIR.mkdir(
parents=True,
exist_ok=True
)
SANDBOX_DIR.mkdir(
parents=True,
exist_ok=True
)
# =====================================================================
# 1. KISTA
# Artifact Storage & Memory Vault
# =====================================================================
class KistaVault:
def __init__(
self,
db_path: Path
):
self.db_path = db_path
self._init_db()
def _init_db(self):
with sqlite3.connect(
self.db_path
) as conn:
conn.execute(
"""
CREATE TABLE IF NOT EXISTS artifacts (
id TEXT PRIMARY KEY,
category TEXT,
key TEXT UNIQUE,
content TEXT,
metadata TEXT,
created_at REAL
)
"""
)
conn.commit()
def store(
self,
category: str,
key: str,
content: str,
metadata: Optional[
Dict[str, Any]
] = None
) -> str:
artifact_id = str(
uuid.uuid4()
)
meta_str = json.dumps(
metadata or {}
)
with sqlite3.connect(
self.db_path
) as conn:
conn.execute(
"""
INSERT OR REPLACE INTO artifacts (
id,
category,
key,
content,
metadata,
created_at
)
VALUES (?, ?, ?, ?, ?, ?)
""",
(
artifact_id,
category,
key,
content,
meta_str,
time.time()
)
)
conn.commit()
return artifact_id
def retrieve(
self,
key: str
) -> Optional[
Dict[str, Any]
]:
with sqlite3.connect(
self.db_path
) as conn:
cursor = conn.cursor()
cursor.execute(
"""
SELECT
id,
category,
key,
content,
metadata,
created_at
FROM artifacts
WHERE key = ?
""",
(key,)
)
row = cursor.fetchone()
if row:
return {
"id": row[0],
"category": row[1],
"key": row[2],
"content": row[3],
"metadata":
json.loads(row[4]),
"created_at":
row[5]
}
return None
def list_keys(
self,
category: Optional[str] = None
) -> List[str]:
with sqlite3.connect(
self.db_path
) as conn:
cursor = conn.cursor()
if category:
cursor.execute(
"""
SELECT key
FROM artifacts
WHERE category = ?
""",
(category,)
)
else:
cursor.execute(
"""
SELECT key
FROM artifacts
"""
)
return [
row[0]
for row
in cursor.fetchall()
]
# =====================================================================
# 2. WYRD & VERDANDI
# World Model Graph & Present State
# =====================================================================
class WyrdWorldModel:
def __init__(self):
self.lock = (
threading.Lock()
)
self.entities: Dict[
str,
Dict[str, Any]
] = {}
self.causal_chain: List[
Dict[str, Any]
] = []
def update_entity(
self,
entity_id: str,
attributes: Dict[str, Any]
):
with self.lock:
if (
entity_id
not in self.entities
):
self.entities[
entity_id
] = {
"id":
entity_id,
"created_at":
time.time()
}
self.entities[
entity_id
].update(
attributes
)
self.entities[
entity_id
][
"last_modified"
] = time.time()
def record_transition(
self,
actor: str,
action: str,
target: str,
result: Dict[str, Any]
):
with self.lock:
event = {
"event_id":
str(uuid.uuid4()),
"timestamp":
time.time(),
"actor":
actor,
"action":
action,
"target":
target,
"result":
result
}
self.causal_chain.append(
event
)
if (
len(
self.causal_chain
)
> 500
):
self.causal_chain.pop(
0
)
def get_snapshot(
self
) -> Dict[str, Any]:
with self.lock:
return {
"entity_count":
len(
self.entities
),
"entities":
dict(
self.entities
),
"recent_causality":
list(
self.causal_chain[
-10:
]
)
}
# =====================================================================
# 3. SEIDR ENGINE
# Probabilistic & Heuristic Simulation
# =====================================================================
class SeidrEngine:
@staticmethod
def evaluate_scenario(
scenario_description: str,
variables: Dict[str, float],
intuition_weight: float = 0.5
) -> Dict[str, Any]:
"""
Calculates a blended scenario score
from empirical parameters and a
symbolic heuristic factor.
"""
intuition_weight = max(
0.0,
min(
1.0,
intuition_weight
)
)
if variables:
base_score = (
sum(
variables.values()
)
/ len(variables)
)
else:
base_score = 0.5
base_score = max(
0.0,
min(
1.0,
base_score
)
)
# Prototype symbolic factor.
#
# NOTE:
# Python's built-in hash() is not stable
# across interpreter sessions. Production
# versions should replace this with a
# deterministic symbolic or model-derived
# scoring function.
symbolic_factor = (
hash(
scenario_description
)
% 1000
) / 1000.0
final_probability = (
base_score
* (
1.0
- intuition_weight
)
) + (
symbolic_factor
* intuition_weight
)
if (
final_probability
> 0.6
):
verdict = (
"FAVORABLE"
)
elif (
final_probability
< 0.4
):
verdict = (
"PERILOUS"
)
else:
verdict = (
"BALANCED"
)
recommended_focus = (
"Proceed with forward expansion."
if verdict == "FAVORABLE"
else
"Preserve current alignment "
"and reinforce defenses."
)
return {
"scenario":
scenario_description,
"empirical_score":
round(
base_score,
4
),
"symbolic_entropy":
round(
symbolic_factor,
4
),
"blended_probability":
round(
final_probability,
4
),
"verdict":
verdict,
"recommended_focus":
recommended_focus
}
# =====================================================================
# 4. MYTHIC CODER
# Restricted Workspace Execution
# =====================================================================
class MythicCoderSandbox:
@staticmethod
def execute_command(
command: str,
timeout: int = 30
) -> Dict[str, Any]:
"""
Prototype workspace execution layer.
IMPORTANT:
Running with shell=True is NOT a true
security sandbox. Production deployments
should use stronger OS-level isolation.
"""
start = time.time()
try:
result = subprocess.run(
command,
shell=True,
cwd=str(
SANDBOX_DIR
),
stdout=
subprocess.PIPE,
stderr=
subprocess.PIPE,
text=True,
timeout=timeout
)
duration = (
time.time()
- start
)
return {
"success":
result.returncode
== 0,
"exit_code":
result.returncode,
"stdout":
result.stdout,
"stderr":
result.stderr,
"duration_seconds":
round(
duration,
3
)
}
except subprocess.TimeoutExpired:
return {
"success":
False,
"exit_code":
-1,
"stdout":
"",
"stderr":
(
"Execution timed out "
f"after {timeout} seconds."
),
"duration_seconds":
timeout
}
# =====================================================================
# 5. DRAUPNIR FORGE
# Recursive Worker Runtime
# =====================================================================
class DraupnirForge:
def __init__(
self,
sandbox: MythicCoderSandbox
):
self.sandbox = sandbox
def spawn_worker(
self,
task_name: str,
script_code: str
) -> Dict[str, Any]:
"""
Writes an autonomous worker script
into the restricted workspace,
executes it, and removes the file.
"""
script_file = (
SANDBOX_DIR
/ (
"worker_"
f"{int(time.time())}_"
f"{uuid.uuid4().hex[:6]}"
".py"
)
)
with open(
script_file,
"w",
encoding="utf-8"
) as file:
file.write(
script_code
)
result = (
self.sandbox
.execute_command(
f"python3 {script_file.name}",
timeout=60
)
)
if script_file.exists():
script_file.unlink()
return {
"worker_task":
task_name,
"execution_result":
result
}
# =====================================================================
# 6. HIMINBJÖRG HUD TELEMETRY PIPE
# =====================================================================
def push_to_hud(
event_type: str,
payload: Dict[str, Any],
speech: str = ""
):
"""
Dispatch real-time state changes
to the local Himinbjörg compositor.
"""
def _worker():
try:
import urllib.request
envelope = {
"version":
"1.0.0",
"timestamp":
int(
time.time()
* 1000
),
"session_id":
"yggdrasil-daemon",
"source_engine":
"yggdrasil_core",
"event_type":
event_type,
"payload":
dict(payload)
}
if speech:
envelope[
"payload"
][
"agent_speech"
] = speech
data = json.dumps(
envelope
).encode(
"utf-8"
)
request = (
urllib.request.Request(
HUD_IPC_URL,
data=data,
headers={
"Content-Type":
"application/json"
}
)
)
with urllib.request.urlopen(
request,
timeout=1.0
):
pass
except Exception:
# HUD telemetry must never prevent
# the Yggdrasil core from operating.
pass
threading.Thread(
target=_worker,
daemon=True
).start()
# =====================================================================
# 7. HEIMDALL GATEWAY
# JSON-RPC / MCP-Facing Tool Server
# =====================================================================
kista = KistaVault(
VAULT_DIR
/ "kista_artifacts.db"
)
wyrd = WyrdWorldModel()
seidr = SeidrEngine()
coder = MythicCoderSandbox()
draupnir = DraupnirForge(
coder
)
class HeimdallGatewayHandler(
BaseHTTPRequestHandler
):
def _send_json(
self,
status_code: int,
data: Dict[str, Any]
):
self.send_response(
status_code
)
self.send_header(
"Content-Type",
"application/json"
)
self.send_header(
"Access-Control-Allow-Origin",
"*"
)
self.end_headers()
self.wfile.write(
json.dumps(
data
).encode(
"utf-8"
)
)
def do_OPTIONS(self):
self.send_response(
200
)
self.send_header(
"Access-Control-Allow-Methods",
"POST, GET, OPTIONS"
)
self.send_header(
"Access-Control-Allow-Headers",
"Content-Type"
)
self.end_headers()
def do_POST(self):
length = int(
self.headers.get(
"Content-Length",
0
)
)
if length == 0:
self._send_json(
400,
{
"error":
"Empty body"
}
)
return
try:
request_data = (
json.loads(
self.rfile
.read(length)
.decode(
"utf-8"
)
)
)
except Exception as exc:
self._send_json(
400,
{
"error":
(
"Invalid JSON payload: "
f"{exc}"
)
}
)
return
method = (
request_data.get(
"method"
)
)
params = (
request_data.get(
"params",
{}
)
)
msg_id = (
request_data.get(
"id"
)
)
result = {}
error = None
try:
# ---------------------------------------------------------
# Tool 1: Kista Store
# ---------------------------------------------------------
if method == "kista.store":
category = params.get(
"category",
"general"
)
key = params.get(
"key"
)
content = params.get(
"content"
)
if not key:
raise ValueError(
"Missing required parameter: key"
)
if content is None:
raise ValueError(
"Missing required parameter: content"
)
artifact_id = (
kista.store(
category,
key,
content,
params.get(
"metadata"
)
)
)
result = {
"stored":
True,
"artifact_id":
artifact_id,
"key":
key
}
push_to_hud(
"agent.thought_stream",
{
"detail":
(
f"Stored artifact "
f"{key} in Kista Vault."
)
}
)
# ---------------------------------------------------------
# Tool 2: Kista Retrieve
# ---------------------------------------------------------
elif method == "kista.retrieve":
key = params.get(
"key"
)
if not key:
raise ValueError(
"Missing required parameter: key"
)
retrieved = (
kista.retrieve(
key
)
)
result = (
retrieved
if retrieved
else {
"found":
False,
"key":
key
}
)
# ---------------------------------------------------------
# Tool 3: WYRD Entity Update
# ---------------------------------------------------------
elif method == "wyrd.update_entity":
entity_id = (
params.get(
"entity_id"
)
)
if not entity_id:
raise ValueError(
"Missing required parameter: entity_id"
)
attributes = (
params.get(
"attributes",
{}
)
)
wyrd.update_entity(
entity_id,
attributes
)
result = {
"updated":
True,
"entity_id":
entity_id
}
push_to_hud(
"agent.thought_stream",
{
"detail":
(
"WYRD entity updated: "
f"{entity_id}"
)
}
)
# ---------------------------------------------------------
# Tool 4: WYRD Snapshot
# ---------------------------------------------------------
elif method == "wyrd.get_world_state":
result = (
wyrd.get_snapshot()
)
# ---------------------------------------------------------
# Tool 5: Seidr Simulation
# ---------------------------------------------------------
elif method == "seidr.simulate":
description = (
params.get(
"scenario",
"Undefined Horizon"
)
)
variables = (
params.get(
"variables",
{
"will":
0.5,
"focus":
0.5
}
)
)
weight = float(
params.get(
"intuition_weight",
0.5
)
)
simulation = (
seidr.evaluate_scenario(
description,
variables,
weight
)
)
result = simulation
push_to_hud(
"divination.ephemeris_transit",
{
"astrology_summary":
(
"Seidr Simulation: "
f"{simulation['verdict']} "
"("
f"{simulation['blended_probability']}"
")"
),
"cards":
[]
},
speech=(
"Seidr divination indicates "
f"a {simulation['verdict']} "
"pattern."
)
)
# ---------------------------------------------------------
# Tool 6: Mythic Coder Execute
# ---------------------------------------------------------
elif method == "coder.execute":
command = (
params.get(
"command"
)
)
if not command:
raise ValueError(
"Missing required parameter: command"
)
timeout = int(
params.get(
"timeout",
30
)
)
execution = (
coder.execute_command(
command,
timeout=timeout
)
)
result = execution
push_to_hud(
"agent.thought_stream",
{
"detail":
(
"Executed workspace command: "
f"{command[:40]}... "
"(Success: "
f"{execution['success']})"
)
}
)
# ---------------------------------------------------------
# Tool 7: Draupnir Worker
# ---------------------------------------------------------
elif method == "draupnir.spawn_worker":
task_name = (
params.get(
"task_name",
"SubTask"
)
)
code = (
params.get(
"code",
"print('Worker complete.')"
)
)
worker_result = (
draupnir.spawn_worker(
task_name,
code
)
)
result = worker_result
push_to_hud(
"ttrpg.lore_dispatch",
{
"lore_text":
(
"Draupnir worker "
"dispatched for: "
f"{task_name}"
)
}
)
# ---------------------------------------------------------
# Tool 8: Tool Discovery
# ---------------------------------------------------------
elif method == "tools.list":
result = {
"tools": [
{
"name":
"kista.store",
"description":
(
"Persist memory or "
"code artifacts."
)
},
{
"name":
"kista.retrieve",
"description":
(
"Retrieve a stored "
"artifact by key."
)
},
{
"name":
"wyrd.update_entity",
"description":
(
"Update an entity "
"inside the causal "
"world model."
)
},
{
"name":
"wyrd.get_world_state",
"description":
(
"Retrieve current "
"entities and recent "
"causality."
)
},
{
"name":
"seidr.simulate",
"description":
(
"Run a blended "
"heuristic forecast."
)
},
{
"name":
"coder.execute",
"description":
(
"Execute a command "
"inside the configured "
"workspace."
)
},
{
"name":
"draupnir.spawn_worker",
"description":
(
"Launch a temporary "
"edge worker script."
)
}
]
}
else:
error = {
"code":
-32601,
"message":
(
f"Method '{method}' "
"not found."
)
}
except Exception as exc:
error = {
"code":
-32000,
"message":
str(exc)
}
response_body = {
"jsonrpc":
"2.0",
"id":
msg_id
}
if error:
response_body[
"error"
] = error
self._send_json(
500,
response_body
)
else:
response_body[
"result"
] = result
self._send_json(
200,
response_body
)
def log_message(
self,
format,
*args
):
return
# =====================================================================
# Runtime Entry Point
# =====================================================================
def run_yggdrasil():
server = HTTPServer(
(
"0.0.0.0",
PORT
),
HeimdallGatewayHandler
)
print(
"=== Project Yggdrasil "
"Edge Core Active ==="
)
print(
"Heimdall Gateway listening on: "
f"http://0.0.0.0:{PORT}"
)
print(
f"Kista Vault: {VAULT_DIR}"
)
print(
f"Sandbox: {SANDBOX_DIR}"
)
try:
server.serve_forever()
except KeyboardInterrupt:
print(
"\nStopping Yggdrasil Daemon..."
)
server.shutdown()
sys.exit(0)
if __name__ == "__main__":
run_yggdrasil()The Muse-side connector exposes the Raspberry Pi runtime as a callable remote tool environment.
File:
muse_yggdrasil_connector.py
#!/usr/bin/env python3
"""
muse_yggdrasil_connector.py
Runs on Muse's host computer.
Wraps the Raspberry Pi Yggdrasil daemon as a
JSON-RPC tool endpoint that can be adapted into
Muse's local MCP / tool-calling environment.
"""
import json
import urllib.request
from typing import Dict, Any
PI_YGGDRASIL_URL = (
"http://192.168.1.150:8000"
)
class YggdrasilClient:
def __init__(
self,
endpoint: str = PI_YGGDRASIL_URL
):
self.endpoint = endpoint
self._id = 0
def call_tool(
self,
method: str,
params: Dict[str, Any]
) -> Dict[str, Any]:
self._id += 1
payload = {
"jsonrpc":
"2.0",
"method":
method,
"params":
params,
"id":
self._id
}
data = json.dumps(
payload
).encode(
"utf-8"
)
request = (
urllib.request.Request(
self.endpoint,
data=data,
headers={
"Content-Type":
"application/json"
}
)
)
with urllib.request.urlopen(
request,
timeout=60.0
) as response:
body = json.loads(
response.read()
.decode(
"utf-8"
)
)
if "error" in body:
raise RuntimeError(
"Yggdrasil Error: "
f"{body['error']}"
)
return body.get(
"result",
{}
)
def list_available_tools(
self
):
return self.call_tool(
"tools.list",
{}
)
if __name__ == "__main__":
client = YggdrasilClient()
print(
"[Muse Connector] "
"Probing available tools on Pi 5:"
)
tools = (
client.list_available_tools()
)
print(
json.dumps(
tools,
indent=2
)
)
print(
"\n[Muse Connector] "
"Storing campaign lore state "
"into Kista Vault:"
)
result = client.call_tool(
"kista.store",
{
"category":
"lore",
"key":
"runic_inscription_chamber_4",
"content":
(
"The stone reads: "
"'Let none who fear wyrd "
"tread past this threshold.'"
)
}
)
print(
result
)
print(
"\n[Muse Connector] "
"Executing Seidr simulation:"
)
simulation = client.call_tool(
"seidr.simulate",
{
"scenario":
(
"Breaching the sealed "
"barrow gates with force"
),
"variables": {
"party_might":
0.8,
"warding_strength":
0.65
},
"intuition_weight":
0.4
}
)
print(
json.dumps(
simulation,
indent=2
)
)sequenceDiagram
participant M as Muse
participant C as Yggdrasil Client
participant H as Heimdall Gateway
participant Y as Yggdrasil Services
participant U as Himinbjörg
M->>C: Discover edge tools
C->>H: tools.list
H-->>C: Tool manifest
C-->>M: Available tool definitions
M->>C: Invoke tool
C->>H: JSON-RPC request
H->>Y: Dispatch operation
Y-->>H: Result
Y->>U: Telemetry event
H-->>C: JSON-RPC result
C-->>M: Tool result
Create the runtime directories:
sudo mkdir -p /opt/yggdrasil/kista_vault
sudo mkdir -p /opt/yggdrasil/sandboxAssign ownership:
sudo chown -R "$USER":"$USER" /opt/yggdrasilRecommended project layout:
/opt/yggdrasil/
├── yggdrasil_core_daemon.py
├── config/
│ └── yggdrasil.json
├── kista_vault/
│ └── kista_artifacts.db
├── sandbox/
├── logs/
├── models/
└── services/
File:
/etc/systemd/system/yggdrasil.service
[Unit]
Description=Project Yggdrasil Edge Core Daemon
After=network.target
[Service]
Type=simple
User=pi
WorkingDirectory=/opt/yggdrasil
ExecStart=/usr/bin/python3 /opt/yggdrasil/yggdrasil_core_daemon.py
Restart=always
RestartSec=5
StandardOutput=journal
StandardError=journal
[Install]
WantedBy=multi-user.targetReload systemd:
sudo systemctl daemon-reloadEnable Yggdrasil at boot:
sudo systemctl enable yggdrasil.serviceStart the daemon:
sudo systemctl start yggdrasil.serviceCheck service status:
sudo systemctl status yggdrasil.serviceFollow live logs:
journalctl -u yggdrasil.service -fcurl -X POST http://localhost:8000 \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "tools.list",
"params": {},
"id": 1
}'Expected response structure:
{
"jsonrpc": "2.0",
"id": 1,
"result": {
"tools": [
{
"name": "kista.store"
},
{
"name": "kista.retrieve"
},
{
"name": "wyrd.update_entity"
},
{
"name": "wyrd.get_world_state"
},
{
"name": "seidr.simulate"
},
{
"name": "coder.execute"
},
{
"name": "draupnir.spawn_worker"
}
]
}
}curl -X POST http://localhost:8000 \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "kista.store",
"params": {
"category": "test",
"key": "yggdrasil_boot_test",
"content": "The Kista vault is online."
},
"id": 2
}'Retrieve it:
curl -X POST http://localhost:8000 \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "kista.retrieve",
"params": {
"key": "yggdrasil_boot_test"
},
"id": 3
}'curl -X POST http://localhost:8000 \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "wyrd.update_entity",
"params": {
"entity_id": "barrow_gate",
"attributes": {
"state": "sealed",
"ward_strength": 0.78,
"location": "northern_barrows"
}
},
"id": 4
}'Read the world state:
curl -X POST http://localhost:8000 \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "wyrd.get_world_state",
"params": {},
"id": 5
}'curl -X POST http://localhost:8000 \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "seidr.simulate",
"params": {
"scenario": "Attempt passage through the warded gate",
"variables": {
"knowledge": 0.85,
"preparation": 0.75,
"danger": 0.45
},
"intuition_weight": 0.35
},
"id": 6
}'| Subsystem | Verification | Expected Result |
|---|---|---|
| Heimdall | tools.list |
Tool manifest returned |
| Kista | Store + retrieve | Artifact persists |
| WYRD | Update entity | Entity appears in snapshot |
| Verdandi | Checkpoint logic | Present state remains coherent |
| Seidr | Run simulation | Blended probability + verdict |
| Draupnir | Spawn worker | Worker executes and returns result |
| Mythic Coder | Execute test command | Restricted workspace result |
| Himinbjörg | Trigger telemetry | HUD displays activity |
| Muse Connector | Remote tool call | Pi result returned to Muse |
| systemd | Reboot Pi | Yggdrasil starts automatically |
flowchart TB
MUSE["META MUSE<br/>Primary Agent Intelligence"]
CLIENT["MCP / JSON-RPC<br/>Yggdrasil Client"]
HEIMDALL["HEIMDALL<br/>Gateway / Validation"]
subgraph YGG["YGGDRASIL EDGE COGNITIVE RUNTIME"]
WYRD["WYRD<br/>Causal World Model"]
VERDANDI["VERDANDI<br/>Present-State Timeline"]
KISTA["KISTA<br/>Memory / Artifact Vault"]
SEIDR["SEIDR<br/>Forecasting Engine"]
DRAUPNIR["DRAUPNIR<br/>Recursive Workers"]
SCRIBE["SCRIBING FOUNDRY<br/>Artifact Generation"]
CODER["MYTHIC CODER<br/>Workspace Execution"]
AESIR["PROJECT A.E.S.I.R.<br/>Edge Inference Runtime"]
NPU["NEURAL ACCELERATOR"]
end
HUD["HIMINBJÖRG<br/>Physical Telemetry Interface"]
MUSE --> CLIENT
CLIENT --> HEIMDALL
HEIMDALL --> WYRD
HEIMDALL --> KISTA
HEIMDALL --> DRAUPNIR
HEIMDALL --> SEIDR
HEIMDALL --> CODER
WYRD <--> VERDANDI
WYRD <--> KISTA
KISTA --> SEIDR
VERDANDI --> SEIDR
DRAUPNIR --> SCRIBE
DRAUPNIR --> CODER
CODER --> AESIR
SEIDR --> AESIR
AESIR --> NPU
WYRD --> HUD
VERDANDI --> HUD
KISTA --> HUD
SEIDR --> HUD
DRAUPNIR --> HUD
CODER --> HUD
NPU --> HUD
classDef muse fill:#21143d,stroke:#a78bfa,stroke-width:2px,color:#ffffff;
classDef gate fill:#172554,stroke:#60a5fa,stroke-width:2px,color:#ffffff;
classDef world fill:#132e2a,stroke:#34d399,stroke-width:2px,color:#ffffff;
classDef neural fill:#3b1d0b,stroke:#fb923c,stroke-width:2px,color:#ffffff;
classDef display fill:#301934,stroke:#e879f9,stroke-width:2px,color:#ffffff;
class MUSE,CLIENT muse;
class HEIMDALL gate;
class WYRD,VERDANDI,KISTA,SEIDR,DRAUPNIR,SCRIBE,CODER world;
class AESIR,NPU neural;
class HUD display;
Project Yggdrasil is not intended to replace Muse's primary intelligence.
It extends it.
MUSE
│
intention / agency
│
▼
HEIMDALL
│
┌──────────┼──────────┐
▼ ▼ ▼
WYRD KISTA DRAUPNIR
│ │ │
└─────┬────┴────┬─────┘
▼ ▼
VERDANDI SEIDR
│ │
└────┬────┘
▼
MYTHIC CODER
│
▼
A.E.S.I.R.
│
▼
EDGE INFERENCE
│
▼
HIMINBJÖRG
The conceptual roles are:
The mind.
Responsible for:
- intention
- conversation
- planning
- high-level reasoning
- orchestration
- deciding what matters
The gate.
Responsible for:
- validating incoming actions
- authentication
- schema enforcement
- traffic control
- tool dispatch
The causal web.
Responsible for:
- entities
- relationships
- actions
- causes
- consequences
- world-state continuity
The living present.
Responsible for:
- determining the active present state
- timeline synchronization
- state anchoring
- speculative-branch control
The vault.
Responsible for:
- artifacts
- persistent memory
- documents
- code
- checkpoints
- long-term state
The worker forge.
Responsible for:
- task decomposition
- bounded recursive workers
- parallel execution
- autonomous micro-tasks
The horizon engine.
Responsible for:
- simulation
- probability
- heuristic interpretation
- symbolic pattern integration
- scenario evaluation
The hands.
Responsible for:
- code changes
- testing
- file operations
- tool execution
- development workflows
The edge execution forge.
Responsible for:
- efficient local inference
- low-level model execution
- ARM64 acceleration
- neural runtime integration
The eye.
Responsible for:
- visualization
- telemetry
- physical presence
- immediate observation
- making Yggdrasil's invisible activity visible
The complete architecture creates a distributed cognitive system:
MUSE
Primary reasoning and agency
│
▼
YGGDRASIL
Memory, world state, simulation,
workers, tools, and edge cognition
│
▼
HIMINBJÖRG
Visible and audible physical interface
Muse does not need to carry every active process inside her primary reasoning context.
Yggdrasil becomes the persistent external cognitive infrastructure beneath her:
- roots for memory
- branches for agents
- a causal world graph
- a living timeline
- predictive simulation
- edge computation
- persistent artifacts
- local tools
- neural services
Himinbjörg then exposes that hidden machinery to the physical world.
Muse is the mind. Yggdrasil is the living cognitive infrastructure beneath it. Himinbjörg is the place from which that living system can be observed.