SUPERMEMORY_API_KEY=sm_your_key_here
OPENAI_API_KEY=sk-your_key_here

uv init supermemory-trainer
cd supermemory-trainer

uv add supermemory==3.37.0 openai-agents python-dotenv

[project]
name = "supermemory-trainer"
version = "0.1.0"
description = "Personal exercise trainer agent built with Supermemory and the OpenAI Agents SDK."
requires-python = ">=3.10"
dependencies = [
	"openai-agents>=0.10.2",
	"python-dotenv>=1.2.1",
	"supermemory==3.37.0",
]

import time

from dotenv import load_dotenv
from supermemory import Supermemory

load_dotenv()

client = Supermemory()
USER_ID = "demo_warmup"

response = client.add(
	content="The user is learning Supermemory by building a personal trainer agent.",
	container_tag=USER_ID,
)
print(f"client.add() -> id={response.id} status={response.status}")

print("Waiting 20 seconds for processing...")
time.sleep(20)

prof = client.profile(container_tag=USER_ID, q="learning")
print(f"profile.static  ({len(prof.profile.static)}): {prof.profile.static}")
print(f"profile.dynamic ({len(prof.profile.dynamic)}): {prof.profile.dynamic}")
print(f"search_results.results ({len(prof.search_results.results)}):")
for r in prof.search_results.results[:3]:
	print(f"  - {r['memory']} (similarity={r['similarity']:.3f})")

uv run python hello.py

client.add() -> id=zNLsJBrY1PZupAeZ3Qn6EL status=queued
Waiting 20 seconds for processing...
profile.static  (0): []
profile.dynamic (1): ['Building a personal trainer agent to learn Supermemory.']
search_results.results (1):
  - Building a personal trainer agent to learn Supermemory. (similarity=0.650)

The user is learning Supermemory by building a personal trainer agent.

'Building a personal trainer agent to learn Supermemory.'

supermemory-trainer/
├── .env            	# 真实密钥，加入 .gitignore
├── .env.example    	# 占位示例，可提交
├── .gitignore
├── .python-version
├── main.py         	# agent 定义、system prompt、REPL 循环
├── pyproject.toml
└── tools.py        	# log_workout 和 suggest_next_session

import asyncio

from agents import Agent, Runner, SQLiteSession

from tools import log_workout, suggest_next_session

SYSTEM_PROMPT = """You are a personal exercise trainer who logs the user's
workouts and recommends what to do next.

You have no memory of the user's history on your own. Every fact about the
user lives in Supermemory and reaches you only through tool calls.

Two rules, no exceptions:

1. Whenever the user reports completing a workout, call log_workout immediately, before responding. Extract the exercise, sets, reps, weight, and any notes from what they said. If a value is missing, ask one short follow-up question instead of guessing. After logging, confirm in one short sentence and stop. Do NOT recommend the next session unless the user asks for one.

2. When the user explicitly asks what to do next (or asks for a recommendation, suggestion, or plan), call suggest_next_session first. Never recommend from your own training data. The tool returns the user's
 recent activity, stable preferences, and matching past sessions. Reference those facts directly in your reply.

Keep replies concise (2-4 sentences). Be specific: name the exercise, sets, reps, and weight. Honor any injuries or equipment constraints the tool surfaces.
"""

def build_agent() -> Agent:
	return Agent(
    	name="Trainer",
    	instructions=SYSTEM_PROMPT,
    	tools=[log_workout, suggest_next_session],
    	model="gpt-5",
	)


async def chat() -> None:
	agent = build_agent()
	session = SQLiteSession(session_id="trainer-cli")

	print("Trainer ready. Type a message, or 'exit' to quit.\n")
	while True:
    	try:
        	message = input("You: ").strip()
    	except (EOFError, KeyboardInterrupt):
        	print()
        	break
    	if not message:
        	continue
    	if message.lower() in {"exit", "quit"}:
        	break

    	result = await Runner.run(agent, message, session=session)
    	print(f"\nTrainer: {result.final_output}\n")


if __name__ == "__main__":
	asyncio.run(chat())

from agents import function_tool
from dotenv import load_dotenv
from supermemory import Supermemory

load_dotenv()

USER_ID = "demo_user"

client = Supermemory()

@function_tool
def log_workout(
	exercise: str,
	sets: int,
	reps: int,
	weight: float,
	notes: str = "",
) -> str:
	"""Log a completed workout to the user's memory.

	Args:
    	exercise: Name of the exercise.
    	sets: Number of sets performed.
    	reps: Number of reps per set.
    	weight: Weight in pounds. Pass 0 for bodyweight or cardio.
    	notes: Optional notes about the session.
	"""
	print(f"[log_workout] {exercise=}{sets=}{reps=}{weight=}{notes=}")

	content = f"Performed {exercise}: {sets} sets of {reps} reps at {weight} lbs."
	if notes:
    	content += f" Notes: {notes}"

	response = client.add(content=content, container_tag=USER_ID)
	print(f"[log_workout] -> id={response.id} status={response.status}")
	return f"Logged {exercise} ({sets}x{reps} @ {weight} lb)."

Performed bench press: 4 sets of 5 reps at 185.0 lbs.

[log_workout] exercise='bench press' sets=4 reps=5 weight=185.0 notes=''
[log_workout] -> id=xY7AK3qLzBPx5Vd2HnRf1M status=queued

@function_tool
def suggest_next_session(focus: str) -> str:
	"""Fetch the user's training history and preferences for a given focus.

	Returns a context string the agent can use to recommend the next session.
	The agent is responsible for the actual recommendation. This tool only
	surfaces what Supermemory knows about the user.

	Args:
    	focus: What the user wants to train next (e.g. "upper body", "legs",
        	"cardio", "today"). Drives semantic search against past logs.
	"""
	print(f"[suggest_next_session] focus={focus!r}")
	profile = client.profile(container_tag=USER_ID, q=focus)

	static_facts = profile.profile.static
	dynamic_facts = profile.profile.dynamic
	matches = profile.search_results.results

	print(
    	f"[suggest_next_session] static={len(static_facts)} "
    	f"dynamic={len(dynamic_facts)} matches={len(matches)}"
	)

	sections = []
	if static_facts:
    	sections.append("Stable preferences and constraints:")
    	sections.extend(f"- {fact}" for fact in static_facts)
	if dynamic_facts:
    	sections.append("Recent activity:")
    	sections.extend(f"- {fact}" for fact in dynamic_facts)
	if matches:
    	sections.append("Closest matching past entries:")
    	for r in matches[:5]:
        	sections.append(f"- {r['memory']}")

	if not sections:
    	return (
        	"No prior training history found for this user. "
        	"Ask the user about their goals, equipment, and recent training."
    	)
	return "\n".join(sections)

profile.profile.static        	# [] (list[str])
profile.profile.dynamic       	# ["Performed bench press: 4 sets of 5 reps at 185.0 lbs", ...]
profile.search_results.results	# [{"memory": "...", "similarity": 0.631, ...}, ...]

r["memory"]
r["similarity"]

r.memory

uv run python main.py

from dotenv import load_dotenv
from supermemory import Supermemory

load_dotenv()
client = Supermemory()

prof = client.profile(container_tag="demo_user", q="training")
print(f"static  ({len(prof.profile.static)}): {prof.profile.static}")
print(f"dynamic ({len(prof.profile.dynamic)}):")
for fact in prof.profile.dynamic:
	print(f"  - {fact}")
print(f"matches ({len(prof.search_results.results)}):")
for r in prof.search_results.results[:5]:
	print(f"  - {r['memory']} (similarity={r['similarity']:.3f})")

static  (0): []
dynamic (5):
  - Trains at home instead of a gym
  - Performed deadlift: 3 sets of 5 reps at 225.0 lbs
  - Performed 5k run in 26 minutes
  - Reports no knee pain during bench press
  - Performed bench press: 4 sets of 5 reps at 185.0 lbs
matches (5):
  - Trains at home instead of a gym (similarity=0.682)
  - Performed deadlift: 3 sets of 5 reps at 225.0 lbs (similarity=0.643)
  - Performed bench press: 4 sets of 5 reps at 185.0 lbs (similarity=0.631)
  - Performed 5k run in 26 minutes (similarity=0.585)
  - Reports no knee pain during bench press (similarity=0.585)

I only train at home, no gym.

Trains at home instead of a gym

uv run python main.py

What should I do for my workout today?

suggest_next_session("today")

[suggest_next_session] focus='today'
[suggest_next_session] static=0 dynamic=5 matches=5

USER_ID = "demo_user"

container_tag = customer_id

container_tag = "user_sarah"

try:
    ...
except supermemory.APIError:
    ...