How to Build a Persistent AI Agent with Hermes in 15 Minutes

# via Docker
docker pull nousresearch/hermes-agent
docker run -p 11434:11434 nousresearch/hermes-agent

curl http://localhost:11434/health
# {"status":"ok"}

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="hermes",
)

SESSION_ID = "my-first-agent"

def chat(message: str) -> str:
    response = client.chat.completions.create(
        model="hermes",
        messages=[{"role": "user", "content": message}],
        extra_headers={"X-Hermes-Session-Id": SESSION_ID},
    )
    return response.choices[0].message.content

print(chat("My name is Alex and I'm building a todo app in Go."))
# "Nice to meet you, Alex! ..."

print(chat("What language am I using?"))
# "You're using Go, as you mentioned earlier."

events = [
    "Decision: Switched auth from JWT to session cookies. Reason: race condition in token refresh under concurrent requests caused 2% of users to be logged out.",
    "Decision: Removed Redis cache layer. Reason: cache invalidation bugs caused stale data in production. Replaced with direct DB reads.",
    "Decision: Added rate limiting to /api/search. Reason: one customer was generating 40% of total API load.",
]

for event in events:
    chat(event)

# Now ask about the accumulated history
print(chat("What are the biggest reliability concerns in this codebase?"))
# Hermes synthesizes across all three events

import httpx

httpx.post(
    "http://localhost:11434/api/jobs",
    headers={"Authorization": "Bearer hermes"},
    json={
        "name": "daily-standup",
        "schedule": "0 9 * * 1-5",
        "prompt": (
            "You are the project memory agent. Using what you remember "
            "from recent activity, generate a concise standup summary: "
            "what changed, why, and what to watch."
        ),
    },
)

# Per-user memory
session_id = f"user:{user_id}"

# Per-repository institutional memory
session_id = f"repo:{owner}/{repo_name}"

# Per-customer support history
session_id = f"support:{customer_id}"