📢 Announcing our research paper: Zentry achieves 26% higher accuracy than OpenAI Memory, 91% lower latency, and 90% token savings! Read the paper to learn how we're revolutionizing AI agent memory.
Overview
Build a multi-user collaborative chat or task management system with zentry. Each message is attributed to its author, and all messages are stored in a shared project space. zentry makes it easy to track contributions, sort and group messages, and collaborate in real time.
Setup
Install the required packages:
pip install openai zentryai
Full Code Example
from openai import OpenAI
from zentry import Memory
import os
from datetime import datetime
from collections import defaultdict
# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "sk-your-key"
# Shared project context
RUN_ID = "project-demo"
# Initialize zentry
mem = Memory()
class CollaborativeAgent:
def __init__(self, run_id):
self.run_id = run_id
self.mem = mem
def add_message(self, role, name, content):
msg = {"role": role, "name": name, "content": content}
self.mem.add([msg], run_id=self.run_id, infer=False)
def brainstorm(self, prompt):
# Get recent messages for context
memories = self.mem.search(prompt, run_id=self.run_id, limit=5)["results"]
context = "\n".join(f"- {m['memory']} (by {m.get('actor_id', 'Unknown')})" for m in memories)
client = OpenAI()
messages = [
{"role": "system", "content": "You are a helpful project assistant."},
{"role": "user", "content": f"Prompt: {prompt}\nContext:\n{context}"}
]
reply = client.chat.completions.create(
model="gpt-4o-mini",
messages=messages
).choices[0].message.content.strip()
self.add_message("assistant", "assistant", reply)
return reply
def get_all_messages(self):
return self.mem.get_all(run_id=self.run_id)["results"]
def print_sorted_by_time(self):
messages = self.get_all_messages()
messages.sort(key=lambda m: m.get('created_at', ''))
print("\n--- Messages (sorted by time) ---")
for m in messages:
who = m.get("actor_id") or "Unknown"
ts = m.get('created_at', 'Timestamp N/A')
try:
dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
ts_fmt = dt.strftime('%Y-%m-%d %H:%M:%S')
except Exception:
ts_fmt = ts
print(f"[{ts_fmt}] [{who}] {m['memory']}")
def print_grouped_by_actor(self):
messages = self.get_all_messages()
grouped = defaultdict(list)
for m in messages:
grouped[m.get("actor_id") or "Unknown"].append(m)
print("\n--- Messages (grouped by actor) ---")
for actor, mems in grouped.items():
print(f"\n=== {actor} ===")
for m in mems:
ts = m.get('created_at', 'Timestamp N/A')
try:
dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
ts_fmt = dt.strftime('%Y-%m-%d %H:%M:%S')
except Exception:
ts_fmt = ts
print(f"[{ts_fmt}] {m['memory']}")
Usage
# Example usage
agent = CollaborativeAgent(RUN_ID)
agent.add_message("user", "alice", "Let's list tasks for the new landing page.")
agent.add_message("user", "bob", "I'll own the hero section copy.")
agent.add_message("user", "carol", "I'll choose product screenshots.")
# Brainstorm with context
print("\nAssistant reply:\n", agent.brainstorm("What are the current open tasks?"))
# Print all messages sorted by time
agent.print_sorted_by_time()
# Print all messages grouped by actor
agent.print_grouped_by_actor()
Key Points
- Each message is attributed to a user or agent (actor)
- All messages are stored in a shared project space (
run_id
)
- You can sort messages by time, group by actor, and format timestamps for clarity
- zentry makes it easy to build collaborative, attributed chat/task systems
Conclusion
zentry enables fast, transparent collaboration for teams and agents, with full attribution, flexible memory search, and easy message organization.