3.3.8 pal_core_08 plan
[3.3.8] pal_core_08_plan.py // natural language → plan JSON → controlled execution (BINGO) 26.0423
Then 3.8 pal_core_08_plan.py can take the events from pal_events07.json and do:
• natural language request
• LLM returns plan JSON
• deterministic executor runs over ingested email events
That is a very nice progression.
If you want, next I’ll give you the tightened v2 of this file with:
• sender allowlist
• better subject parsing
• analyze command for status summaries.
Best Structure
• 07 = ingestion layer
Gmail → PAL events DB
• 08 = reasoning/control layer
User request → plan JSON → execute over DB
So 08 builds on 07, not replaces it.
Flow
Emails --> pal_events07.json
|
v
pal_core_08_plan.py
Why Better
Keeps demos atomic:
• 07 proves sensor ingestion
• 08 proves planning/execution
• easier to debug
• easier to explain in wiki
In Practice
08 can read:
pal_events07.json
Then user says:
Show delayed shipments in Taipei
LLM returns:
{
"steps": [
{"tool":"filter","field":"status","equals":"delayed"},
{"tool":"filter","field":"location","equals":"taipei"}
]
}
Then deterministic executor runs it.
My Recommendation
Do not merge into 07.
Make clean rung 08.
# pal_core_08_plan.py
#
# 3.8 -- natural language -> plan JSON -> controlled execution
#
# Purpose:
# Read PAL events from JSON, ask an LLM to produce a very small plan JSON,
# validate that plan, then execute it deterministically.
#
# Demo goal:
# Show that the LLM does NOT do the data processing directly.
# The LLM only proposes a constrained plan.
# Deterministic Python executes the plan.
#
# Environment variables:
# OPENAI_API_KEY=...
#
# Optional:
# PAL_EVENTS_FILE=pal_events07.json
# PAL_OPENAI_MODEL=gpt-4.1-mini
#
# Commands:
# python pal_core_08_plan.py reset_demo_data
# python pal_core_08_plan.py show_events
# python pal_core_08_plan.py run --prompt "Show delayed events in taipei"
# python pal_core_08_plan.py run --prompt "Compare delayed vs blocked events"
#
# Notes:
# - Input file must contain {"events":[...]}
# - This file can read the output of pal_core_07_gmail_alerts.py
# - If you do not want to use real 07 data yet, use reset_demo_data first
import argparse
import json
import os
import sys
from collections import Counter, defaultdict
from typing import Any, Dict, List, Tuple
try:
from openai import OpenAI
except ImportError:
OpenAI = None
# --------------------------------------------------
# 1. Config
# --------------------------------------------------
EVENTS_FILE = os.getenv("PAL_EVENTS_FILE", "pal_events07.json")
DEFAULT_MODEL = os.getenv("PAL_OPENAI_MODEL", "gpt-4.1-mini")
# --------------------------------------------------
# 2. JSON helpers
# --------------------------------------------------
def load_json(path: str, default: Any) -> Any:
if not os.path.exists(path):
return default
with open(path, "r", encoding="utf-8") as f:
return json.load(f)
def save_json(path: str, data: Any) -> None:
with open(path, "w", encoding="utf-8") as f:
json.dump(data, f, indent=2, ensure_ascii=False)
def load_events() -> List[Dict[str, Any]]:
data = load_json(EVENTS_FILE, {"events": []})
events = data.get("events", [])
if not isinstance(events, list):
raise ValueError(f"{EVENTS_FILE} must contain an 'events' list")
return events
# --------------------------------------------------
# 3. Demo seed data
# --------------------------------------------------
def cmd_reset_demo_data() -> None:
demo = {
"events": [
{
"entity": "truck_12",
"event_type": "logistics_alert",
"status": "delayed",
"priority": 7,
"location": "taipei",
"sender": "alerts@example.com",
"subject": "Shipment truck_12 delayed in Taipei",
"note": "Carrier delay due to traffic.",
"source": "demo",
"timestamp": "2026-04-20T10:00:00Z",
},
{
"entity": "truck_18",
"event_type": "logistics_alert",
"status": "blocked",
"priority": 9,
"location": "tainan",
"sender": "alerts@example.com",
"subject": "Shipment truck_18 blocked in Tainan",
"note": "Road closure blocked route.",
"source": "demo",
"timestamp": "2026-04-20T10:05:00Z",
},
{
"entity": "supplier_2",
"event_type": "supplier_alert",
"status": "warning",
"priority": 5,
"location": "taipei",
"sender": "ops@example.com",
"subject": "Supplier risk warning in Taipei",
"note": "Late paperwork may impact delivery.",
"source": "demo",
"timestamp": "2026-04-20T10:10:00Z",
},
{
"entity": "truck_12",
"event_type": "resolution_notice",
"status": "resolved",
"priority": 2,
"location": "taipei",
"sender": "alerts@example.com",
"subject": "Shipment truck_12 recovered in Taipei",
"note": "Delay resolved.",
"source": "demo",
"timestamp": "2026-04-20T11:00:00Z",
},
{
"entity": "api_gateway",
"event_type": "system_alert",
"status": "failed",
"priority": 7,
"location": "site_1",
"sender": "monitor@example.com",
"subject": "API gateway failure at site_1",
"note": "HTTP 500 spike detected.",
"source": "demo",
"timestamp": "2026-04-20T11:05:00Z",
},
]
}
save_json(EVENTS_FILE, demo)
print(json.dumps({"ok": True, "reset_demo_data": True, "file": EVENTS_FILE}, indent=2))
def cmd_show_events() -> None:
events = load_events()
print(json.dumps({"ok": True, "count": len(events), "events": events}, indent=2, ensure_ascii=False))
# --------------------------------------------------
# 4. Allowed plan schema
# --------------------------------------------------
ALLOWED_TOOLS = {
"filter_equals",
"sort_by",
"limit",
"count_by",
"compare_counts",
}
ALLOWED_FIELDS = {
"entity",
"event_type",
"status",
"priority",
"location",
"sender",
"subject",
"source",
"timestamp",
}
def validate_plan(plan: Dict[str, Any]) -> Tuple[bool, str]:
if not isinstance(plan, dict):
return False, "Plan must be a JSON object"
steps = plan.get("steps")
if not isinstance(steps, list) or not steps:
return False, "Plan must contain non-empty steps"
for i, step in enumerate(steps):
if not isinstance(step, dict):
return False, f"Step {i} must be an object"
tool = step.get("tool")
if tool not in ALLOWED_TOOLS:
return False, f"Step {i}: unsupported tool '{tool}'"
if tool == "filter_equals":
if step.get("field") not in ALLOWED_FIELDS:
return False, f"Step {i}: invalid field for filter_equals"
if "value" not in step:
return False, f"Step {i}: missing value for filter_equals"
elif tool == "sort_by":
if step.get("field") not in ALLOWED_FIELDS:
return False, f"Step {i}: invalid field for sort_by"
direction = step.get("direction", "asc")
if direction not in ("asc", "desc"):
return False, f"Step {i}: direction must be asc or desc"
elif tool == "limit":
n = step.get("n")
if not isinstance(n, int) or n <= 0:
return False, f"Step {i}: limit n must be positive integer"
elif tool == "count_by":
if step.get("field") not in ALLOWED_FIELDS:
return False, f"Step {i}: invalid field for count_by"
elif tool == "compare_counts":
if step.get("field") not in ALLOWED_FIELDS:
return False, f"Step {i}: invalid field for compare_counts"
values = step.get("values")
if not isinstance(values, list) or len(values) < 2:
return False, f"Step {i}: compare_counts needs at least 2 values"
return True, "ok"
# --------------------------------------------------
# 5. LLM plan generation
# --------------------------------------------------
def build_prompt(user_prompt: str) -> str:
return f"""
You are planning for a deterministic event-analysis engine.
Return JSON only, no markdown, no prose.
Allowed tools:
1. filter_equals
args: field, value
2. sort_by
args: field, direction
3. limit
args: n
4. count_by
args: field
5. compare_counts
args: field, values
Allowed fields:
entity, event_type, status, priority, location, sender, subject, source, timestamp
Rules:
- Output exactly one JSON object.
- Use this shape:
{{
"goal": "...",
"steps": [ ... ]
}}
- Keep plan short.
- Do not invent tools.
- Do not invent fields.
- Prefer filter_equals for narrowing.
- Use count_by for grouped summaries.
- Use compare_counts for requests like "compare delayed vs blocked".
- If the prompt is vague, still produce the safest minimal valid plan.
User request:
{user_prompt}
""".strip()
def generate_plan_with_openai(user_prompt: str, model: str) -> Dict[str, Any]:
if OpenAI is None:
raise RuntimeError("openai package not installed. Run: pip install openai")
api_key = os.getenv("OPENAI_API_KEY", "").strip()
if not api_key:
raise RuntimeError("Missing OPENAI_API_KEY")
client = OpenAI(api_key=api_key)
resp = client.responses.create(
model=model,
input=build_prompt(user_prompt),
)
text = resp.output_text.strip()
return json.loads(text)
# --------------------------------------------------
# 6. Deterministic executor
# --------------------------------------------------
def op_filter_equals(rows: List[Dict[str, Any]], field: str, value: Any) -> List[Dict[str, Any]]:
value_norm = str(value).strip().lower()
out = []
for row in rows:
row_val = str(row.get(field, "")).strip().lower()
if row_val == value_norm:
out.append(row)
return out
def op_sort_by(rows: List[Dict[str, Any]], field: str, direction: str) -> List[Dict[str, Any]]:
reverse = (direction == "desc")
return sorted(rows, key=lambda r: str(r.get(field, "")), reverse=reverse)
def op_limit(rows: List[Dict[str, Any]], n: int) -> List[Dict[str, Any]]:
return rows[:n]
def op_count_by(rows: List[Dict[str, Any]], field: str) -> Dict[str, int]:
c = Counter(str(r.get(field, "")) for r in rows)
return dict(sorted(c.items(), key=lambda kv: (-kv[1], kv[0])))
def op_compare_counts(rows: List[Dict[str, Any]], field: str, values: List[Any]) -> Dict[str, int]:
normalized = [str(v).strip().lower() for v in values]
counts = {v: 0 for v in normalized}
for row in rows:
row_val = str(row.get(field, "")).strip().lower()
if row_val in counts:
counts[row_val] += 1
return counts
def execute_plan(plan: Dict[str, Any], events: List[Dict[str, Any]]) -> Dict[str, Any]:
rows = list(events)
trace = []
final_result: Any = rows
for step in plan["steps"]:
tool = step["tool"] ##### ALLOWED_TOOLS = { "filter_equals", .....
if tool == "filter_equals":
rows = op_filter_equals(rows, step["field"], step["value"])
final_result = rows
trace.append({
"tool": tool,
"field": step["field"],
"value": step["value"],
"remaining": len(rows),
})
elif tool == "sort_by":
rows = op_sort_by(rows, step["field"], step.get("direction", "asc"))
final_result = rows
trace.append({
"tool": tool,
"field": step["field"],
"direction": step.get("direction", "asc"),
"remaining": len(rows),
})
elif tool == "limit":
rows = op_limit(rows, step["n"])
final_result = rows
trace.append({
"tool": tool,
"n": step["n"],
"remaining": len(rows),
})
elif tool == "count_by":
final_result = op_count_by(rows, step["field"])
trace.append({
"tool": tool,
"field": step["field"],
"remaining": len(rows),
})
elif tool == "compare_counts":
final_result = op_compare_counts(rows, step["field"], step["values"])
trace.append({
"tool": tool,
"field": step["field"],
"values": step["values"],
"remaining": len(rows),
})
else:
raise ValueError(f"Unsupported tool at runtime: {tool}")
return {
"goal": plan.get("goal", ""),
"trace": trace,
"result": final_result,
}
# --------------------------------------------------
# 7. Fallback planner
# --------------------------------------------------
def fallback_plan(user_prompt: str) -> Dict[str, Any]:
"""
Deterministic fallback in case API is unavailable.
Very crude on purpose.
"""
p = user_prompt.lower()
steps: List[Dict[str, Any]] = []
if "delayed" in p:
steps.append({"tool": "filter_equals", "field": "status", "value": "delayed"})
elif "blocked" in p:
steps.append({"tool": "filter_equals", "field": "status", "value": "blocked"})
elif "failed" in p:
steps.append({"tool": "filter_equals", "field": "status", "value": "failed"})
elif "resolved" in p:
steps.append({"tool": "filter_equals", "field": "status", "value": "resolved"})
known_locations = ["taipei", "tainan", "site_1", "site_2", "site_3"]
for loc in known_locations:
if loc in p:
steps.append({"tool": "filter_equals", "field": "location", "value": loc})
break
if "compare" in p and "delayed" in p and "blocked" in p:
return {
"goal": user_prompt,
"steps": [
{"tool": "compare_counts", "field": "status", "values": ["delayed", "blocked"]}
]
}
if "count by status" in p or "status summary" in p:
steps.append({"tool": "count_by", "field": "status"})
elif "count by location" in p or "location summary" in p:
steps.append({"tool": "count_by", "field": "location"})
if "top" in p or "highest" in p:
steps.append({"tool": "sort_by", "field": "priority", "direction": "desc"})
steps.append({"tool": "limit", "n": 5})
if not steps:
steps = [{"tool": "count_by", "field": "status"}]
return {
"goal": user_prompt,
"steps": steps,
}
# --------------------------------------------------
# 8. Main run command
# --------------------------------------------------
def cmd_run(user_prompt: str, model: str, no_llm: bool) -> None:
events = load_events()
if no_llm:
plan = fallback_plan(user_prompt)
plan_source = "fallback"
else:
try:
plan = generate_plan_with_openai(user_prompt, model=model)
plan_source = "openai"
except Exception as e:
plan = fallback_plan(user_prompt)
plan_source = f"fallback_after_error: {str(e)}"
ok, msg = validate_plan(plan)
if not ok:
print(json.dumps({
"ok": False,
"error": msg,
"plan": plan,
"plan_source": plan_source,
}, indent=2, ensure_ascii=False))
return
execution = execute_plan(plan, events)
print(json.dumps({
"ok": True,
"plan_source": plan_source,
"event_count": len(events),
"plan": plan,
"execution": execution,
}, indent=2, ensure_ascii=False))
# --------------------------------------------------
# 9. CLI
# --------------------------------------------------
def main() -> None:
parser = argparse.ArgumentParser(description="PAL Core 08 - plan JSON and controlled execution")
sub = parser.add_subparsers(dest="cmd", required=True)
sub.add_parser("reset_demo_data")
sub.add_parser("show_events")
p_run = sub.add_parser("run")
p_run.add_argument("--prompt", required=True, help="Natural language request")
p_run.add_argument("--model", default=DEFAULT_MODEL, help="OpenAI model name")
p_run.add_argument("--no_llm", action="store_true", help="Use deterministic fallback planner only")
args = parser.parse_args()
if args.cmd == "reset_demo_data":
cmd_reset_demo_data()
elif args.cmd == "show_events":
cmd_show_events()
elif args.cmd == "run":
cmd_run(user_prompt=args.prompt, model=args.model, no_llm=args.no_llm)
if __name__ == "__main__":
main()
Recommended workflow
1. Clean old test emails
In the test Gmail account:
• delete or archive old messages
• leave only the test emails you want 07 to ingest
2. Send yourself several test emails
From another account, send a few emails to the test account.
3. Run 07
Either one-shot:
python pal_core_07_gmail_alerts.py fetch
or continuous polling:
python pal_core_07_gmail_alerts.py loop --interval 10
For testing, 10 seconds is better than 60.
4. Check what 07 stored
python pal_core_07_gmail_alerts.py show
Make sure pal_events07.json contains the new events.
5. Run 08 on those events
Using fallback planner first:
python pal_core_08_plan.py run --prompt "Show delayed events in taipei" --no_llm
python pal_core_08_plan.py run --prompt "Compare delayed vs blocked events" --no_llm
python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
Then later with LLM if desired.
6. Send more emails and repeat
Yes. Send more test emails, let 07 ingest them, then run more 08 commands.
________________________________________
Important detail
Do not run: python pal_core_08_plan.py reset_demo_data
if you want to use real 07 email data.
That command overwrites pal_events07.json with demo seed data.
________________________________________
Even better test order
1) python pal_core_07_gmail_alerts.py reset
2) python pal_core_07_gmail_alerts.py loop --interval 10
3) Then send emails.
4) Then in another terminal: python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
________________________________________
3) Suggested first 4 test emails
Email 1
Subject:
Shipment truck_12 delayed in Taipei
Body:
Carrier delay due to traffic. truck_12 is delayed in Taipei.
Email 2
Subject:
Shipment truck_18 blocked in Tainan
Body:
Road closure blocked truck_18 in Tainan.
Email 3
Subject:
Supplier warning in Taipei
Body:
supplier_2 warning in Taipei. Late paperwork may impact delivery.
Email 4
Subject:
API gateway failure at site_1
Body:
api_gateway failure at site_1. HTTP 500 spike detected.
________________________________________
4) First 08 commands to try
python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
python pal_core_08_plan.py run --prompt "Show delayed events in taipei" --no_llm
python pal_core_08_plan.py run --prompt "Compare delayed vs blocked events" --no_llm
python pal_core_08_plan.py run --prompt "Show failed events at site_1" --no_llm
One small note: if 07 is using subject keyword filters, make sure your test subjects contain words like delay, blocked, warning, failure, or shipment, which these do.
Yes. You understood correctly.
You want one clean live demo timeline.
(1) 0E Delete old emails (no AI in this test)
(2) Terminal A
1A python pal_core_07_gmail_alerts.py reset
$ python pal_core_07_gmail_alerts.py reset
{
"ok": true,
"reset": true
}
(venv)
terry@LAPTOP-HKPDHF7M MINGW64 ~/Downloads/d1_agent (main)
pal_email_state07.json
{
"seen_email_ids": []
}
pal_events07.json
{
"events": []
}
2A python pal_core_07_gmail_alerts.py loop --interval 30
terry@LAPTOP-HKPDHF7M MINGW64 ~/Downloads/d1_agent (main)
$ python pal_core_07_gmail_alerts.py loop --interval 30
[pal_core_07] polling gmail every 30s...
{
"ok": true,
"fetched_messages": 0,
"added_events": 0,
"skipped": 0,
"events": []
}
(3) Email Actions
3E Send Email #1
Subject:
(1) Shipment truck_12 delayed in Taipei
Body:
(1) Carrier delay due to traffic.
Wait ~10 sec.
(4) Terminal A
{
"ok": true,
"fetched_messages": 1,
"added_events": 1,
"skipped": 0,
"events": [
{
"source": "gmail",
"source_message_id": "<,,,,,,,,,,,,,,,,,,,,,,,,,,,,,@mail.gmail.com>",
"timestamp": "2026-04-23T22:23:25.893166+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:23:06 -0400",
"entity": "truck_12",
"event_type": "logistics_alert",
"status": "delayed",
"priority": 7,
"location": "taipei",
"subject": "(1) Shipment truck_12 delayed in Taipei",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(1) Carrier delay due to traffic."
}
]
}
{
"ok": true,
"fetched_messages": 1,
"added_events": 0,
"skipped": 1,
"events": []
}
pal_email_state07.json
{
"seen_email_ids": [
"<CAK,,,,,,,,,,,,,,,,sh9w@mail.gmail.com>"
]
}
pal_events07.json
{
"events": [
{
"source": "gmail",
"source_message_id": "<CAK,,,,,,,,,,,,,,,,sh9w@mail.gmail.com>",
"timestamp": "2026-04-23T22:23:25.893166+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:23:06 -0400",
"entity": "truck_12",
"event_type": "logistics_alert",
"status": "delayed",
"priority": 7,
"location": "taipei",
"subject": "(1) Shipment truck_12 delayed in Taipei",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(1) Carrier delay due to traffic."
}
]
}
(5) Terminal B
4B python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
$ python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
{
"ok": true,
"plan_source": "fallback",
"event_count": 11,
"plan": {
"goal": "Give me a status summary",
"steps": [
{
"tool": "count_by",
"field": "status"
}
]
},
"execution": {
"goal": "Give me a status summary",
"trace": [
{
"tool": "count_by",
"field": "status",
"remaining": 11
}
],
"result": {
"alert": 9,
"delayed": 2
}
}
}
(venv)
terry@LAPTOP-HKPDHF7M MINGW64 ~/Downloads/d1_agent (main)
(6) Remaining loops: Email Actions / Terminal A/B
5A Send Email #2
Subject:
(2) Shipment truck_18 blocked in Tainan
Body:
(2) Road closure blocked route.
{
"ok": true,
"fetched_messages": 1,
"added_events": 0,
"skipped": 1,
"events": []
}
{
"ok": true,
"fetched_messages": 2,
"added_events": 1,
"skipped": 1,
"events": [
{
"source": "gmail",
"source_message_id": "<,,,,,,,,,,,,,,,@mail.gmail.com>",
"timestamp": "2026-04-23T22:35:17.424352+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:34:42 -0400",
"entity": "truck_18",
"event_type": "logistics_alert",
"status": "blocked",
"priority": 9,
"location": "tainan",
"subject": "(2) Shipment truck_18 blocked in Tainan",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(2) Road closure blocked route."
}
]
}
pal_email_state07.json
{
"seen_email_ids": [
"<,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,@mail.gmail.com>"
]
}
pal_events07.json
{
"events": [
{
,,,,,,,,,,,,,,,,,,,,,,
},
{
"source": "gmail",
"source_message_id": "<CAK,,,,,,,,,f57w@mail.gmail.com>",
"timestamp": "2026-04-23T22:35:17.424352+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:34:42 -0400",
"entity": "truck_18",
"event_type": "logistics_alert",
"status": "blocked",
"priority": 9,
"location": "tainan",
"subject": "(2) Shipment truck_18 blocked in Tainan",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(2) Road closure blocked route."
}
]
}
6B python pal_core_08_plan.py run --prompt "Compare delayed vs blocked events" --no_llm
$ python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
{
"ok": true,
"plan_source": "fallback",
"event_count": 2,
"plan": {
"goal": "Give me a status summary",
"steps": [
{
"tool": "count_by",
"field": "status"
}
]
},
"execution": {
"goal": "Give me a status summary",
"trace": [
{
"tool": "count_by",
"field": "status",
"remaining": 2
}
],
"result": {
"blocked": 1,
"delayed": 1
}
}
}
(venv)
terry@LAPTOP-HKPDHF7M MINGW64 ~/Downloads/d1_agent (main)
GPT: Perfect.
That means:
• 07 ingested 2 emails
• 08 read pal_events07.json
• fallback planner created valid plan
• deterministic executor counted statuses correctly
Current state:
{
"blocked": 1,
"delayed": 1
}
Next run:
python pal_core_08_plan.py run --prompt "Compare delayed vs blocked events" --no_llm
7E Send Email #3
Subject:
(3) API gateway failure at site_1
Body:
(3) HTTP 500 spike detected.
pal_email_state07.json
{
"seen_email_ids": [
"<,,,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>"
]
}
pal_events07.json
{
"events": [
{
,,,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
"source": "gmail",
"source_message_id": "<,,,,,,,,,,,,,,,,,,,-ow@mail.gmail.com>",
"timestamp": "2026-04-23T22:48:08.149064+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:47:48 -0400",
"entity": "site_1",
"event_type": "system_alert",
"status": "failed",
"priority": 7,
"location": "site_1",
"subject": "(3) API gateway failure at site_1",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(3) HTTP 500 spike detected."
}
]
}
{
"ok": true,
"fetched_messages": 3,
"added_events": 1,
"skipped": 2,
"events": [
{
"source": "gmail",
"source_message_id": "<CAK,,,,,,,,,,,,,,,,,,,,,,,,,,,f1w-ow@mail.gmail.com>",
"timestamp": "2026-04-23T22:48:08.149064+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:47:48 -0400",
"entity": "site_1",
"event_type": "system_alert",
"status": "failed",
"priority": 7,
"location": "site_1",
"subject": "(3) API gateway failure at site_1",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(3) HTTP 500 spike detected."
}
]
}
8B python pal_core_08_plan.py run --prompt "Show failed events at site_1" --no_llm
$ python pal_core_08_plan.py run --prompt "Show failed events at site_1" --no_llm
{
"ok": true,
"plan_source": "fallback",
"event_count": 3,
"plan": {
"goal": "Show failed events at site_1",
"steps": [
{
"tool": "filter_equals",
"field": "status",
"value": "failed"
},
{
"tool": "filter_equals",
"field": "location",
"value": "site_1"
}
]
},
"execution": {
"goal": "Show failed events at site_1",
"trace": [
{
"tool": "filter_equals",
"field": "status",
"value": "failed",
"remaining": 1
},
{
"tool": "filter_equals",
"field": "location",
"value": "site_1",
"remaining": 1
}
],
"result": [
{
"source": "gmail",
"source_message_id": "<CAK,,,,,,,,,,,,,,,,,,,,,,,,,,w-ow@mail.gmail.com>",
"timestamp": "2026-04-23T22:48:08.149064+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:47:48 -0400",
"entity": "site_1",
"event_type": "system_alert",
"status": "failed",
"priority": 7,
"location": "site_1",
"subject": "(3) API gateway failure at site_1",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(3) HTTP 500 spike detected."
}
]
}
}
(venv)
terry@LAPTOP-HKPDHF7M MINGW64 ~/Downloads/d1_agent (main)
GPT: Excellent. This is a clean success.
It proves the full chain: email → PAL event → plan → controlled execution → filtered result
Also note the trace is very good for your writeup:
status=failed → 1 remaining
location=site_1 → 1 remaining
That clearly shows deterministic execution, not LLM guessing.
9E Send Email #4
Subject:
(4) Supplier warning in Taipei
Body:
(4) Late paperwork may impact delivery.
pal_email_state07.json
{
"seen_email_ids": [
"<,,,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>"
]
}
pal_events07.json
{
"events": [
{
,,,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
"source": "gmail",
"source_message_id": "<,,,,,,,,,,,,,,iCw@mail.gmail.com>",
"timestamp": "2026-04-23T22:57:33.000198+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:57:07 -0400",
"entity": "email_signal",
"event_type": "logistics_alert",
"status": "delayed",
"priority": 7,
"location": "taipei",
"subject": "(4) Supplier warning in Taipei",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(4) Late paperwork may impact delivery."
}
]
}
{
"ok": true,
"fetched_messages": 3,
"added_events": 0,
"skipped": 3,
"events": []
}
{
"ok": true,
"fetched_messages": 4,
"added_events": 1,
"skipped": 3,
"events": [
{
"source": "gmail",
"source_message_id": "<,,,,,,,,,,,,,,,,iCw@mail.gmail.com>",
"timestamp": "2026-04-23T22:57:33.000198+00:00",
"event_time_raw": "Thu, 23 Apr 2026 18:57:07 -0400",
"entity": "email_signal",
"event_type": "logistics_alert",
"status": "delayed",
"priority": 7,
"location": "taipei",
"subject": "(4) Supplier warning in Taipei",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(4) Late paperwork may impact delivery."
}
]
}
10B python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
$ python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
{
"ok": true,
"plan_source": "fallback",
"event_count": 4,
"plan": {
"goal": "Give me a status summary",
"steps": [
{
"tool": "count_by",
"field": "status"
}
]
},
"execution": {
"goal": "Give me a status summary",
"trace": [
{
"tool": "count_by",
"field": "status",
"remaining": 4
}
],
"result": {
"delayed": 2,
"blocked": 1,
"failed": 1
}
}
}
(venv)
terry@LAPTOP-HKPDHF7M MINGW64 ~/Downloads/d1_agent (main)
GPT: Good. Demo works end-to-end.
One small issue: Email #4 became delayed, not warning.
Reason is probably body text:
Late paperwork may impact delivery.
Your parser sees late and maps it to delayed.
So current result is correct for the rules:
{
"delayed": 2,
"blocked": 1,
"failed": 1
}
For a clean warning test, use subject/body:
Supplier warning in Taipei
Supplier risk warning in Taipei. Paperwork issue may impact delivery.
11E (9E #2) Send Email #5 (small correction)
Redo with text that wont mess things up
Subject:
(5) Supplier warning in Taipei
Body:
(5) Supplier risk warning in Taipei. Paperwork issue may impact delivery.
pal_email_state07.json
{
"seen_email_ids": [
"<,,,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>",
"<,,,,,,,,,,,,,,,@mail.gmail.com>"
]
}
pal_events07.json
{
"events": [
{
,,,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
,,,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
,,,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
,,,,,,,,,,,,,,,,,,,,,,,,,,,
},
{
"source": "gmail",
"source_message_id": "<,,,,,,,,,,,,,,jJw@mail.gmail.com>",
"timestamp": "2026-04-23T23:04:51.531169+00:00",
"event_time_raw": "Thu, 23 Apr 2026 19:04:20 -0400",
"entity": "email_signal",
"event_type": "logistics_alert",
"status": "warning",
"priority": 5,
"location": "taipei",
"subject": "(5) Supplier warning in Taipei",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(5) Supplier risk warning in Taipei. Paperwork issue may impact delivery."
}
]
}
{
"ok": true,
"fetched_messages": 4,
"added_events": 0,
"skipped": 4,
"events": []
}
{
"ok": true,
"fetched_messages": 5,
"added_events": 1,
"skipped": 4,
"events": [
{
"source": "gmail",
"source_message_id": "<,,,,,,,,,,,EjJw@mail.gmail.com>",
"timestamp": "2026-04-23T23:04:51.531169+00:00",
"event_time_raw": "Thu, 23 Apr 2026 19:04:20 -0400",
"entity": "email_signal",
"event_type": "logistics_alert",
"status": "warning",
"priority": 5,
"location": "taipei",
"subject": "(5) Supplier warning in Taipei",
"sender": "T T <terrytaylorbonn@gmail.com>",
"note": "(5) Supplier risk warning in Taipei. Paperwork issue may impact delivery."
}
]
}
$ python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
{
"ok": true,
"plan_source": "fallback",
"event_count": 5,
"plan": {
"goal": "Give me a status summary",
"steps": [
{
"tool": "count_by",
"field": "status"
}
]
},
"execution": {
"goal": "Give me a status summary",
"trace": [
{
"tool": "count_by",
"field": "status",
"remaining": 5
}
],
"result": {
"delayed": 2,
"blocked": 1,
"failed": 1,
"warning": 1
}
}
}
(venv)
terry@LAPTOP-HKPDHF7M MINGW64 ~/Downloads/d1_agent (main)
12B (10B #2) python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
$ python pal_core_08_plan.py run --prompt "Give me a status summary" --no_llm
{
"ok": true,
"plan_source": "fallback",
"event_count": 5,
"plan": {
"goal": "Give me a status summary",
"steps": [
{
"tool": "count_by",
"field": "status"
}
]
},
"execution": {
"goal": "Give me a status summary",
"trace": [
{
"tool": "count_by",
"field": "status",
"remaining": 5
}
],
"result": {
"delayed": 2,
"blocked": 1,
"failed": 1,
"warning": 1
}
}
}
(venv)
terry@LAPTOP-HKPDHF7M MINGW64 ~/Downloads/d1_agent (main)
GPT: Perfect. Now it is clean.
Final status summary:
{
"delayed": 2,
"blocked": 1,
"failed": 1,
"warning": 1
}
This is a successful 3.7 + 3.8 live demo.
(7) What This Demonstrates
• live external signals arriving by email
• automatic ingestion by 07
• updated machine state
• 08 analyzing current state on demand
26.0618