Build an AI Agent with
Persistent Memory

export ITERA_KEY="it-XXXX-XXXX-XXXX" # get yours at iteratools.com/keys

curl -X POST https://api.iteratools.com/memory/upsert \ -H "Authorization: Bearer $ITERA_KEY" \ -H "Content-Type: application/json" \ -d '{ "namespace": "my-agent", "key": "user-pref-1", "text": "User prefers concise answers under 3 sentences. Dislikes bullet points.", "metadata": { "category": "preferences", "user_id": "u_123" } }'

curl -X POST https://api.iteratools.com/memory/upsert \ -H "Authorization: Bearer $ITERA_KEY" \ -H "Content-Type: application/json" \ -d '{ "namespace": "my-agent", "key": "user-context-2", "text": "User is building a SaaS product for restaurant owners in Brazil.", "metadata": { "category": "context" } }' # Add as many memories as you need — each is instantly searchable

curl -X POST https://api.iteratools.com/memory/search \ -H "Authorization: Bearer $ITERA_KEY" \ -H "Content-Type: application/json" \ -d '{ "namespace": "my-agent", "query": "How should I format my response?", "limit": 3 }'

USER_MSG="How should I explain our pricing?" # 1. Search for relevant memories CONTEXT=$(curl -sX POST https://api.iteratools.com/memory/search \ -H "Authorization: Bearer $ITERA_KEY" \ -H "Content-Type: application/json" \ -d "{\"namespace\":\"my-agent\",\"query\":\"$USER_MSG\",\"limit\":3}" \ | python3 -c "import sys,json; r=json.load(sys.stdin)['results']; print('\n'.join(x['text'] for x in r))") # 2. Call AI with memory context injected curl -X POST https://api.iteratools.com/ai/chat \ -H "Authorization: Bearer $ITERA_KEY" \ -H "Content-Type: application/json" \ -d "{ \"model\": \"gpt-4o-mini\", \"messages\": [ { \"role\": \"system\", \"content\": \"You are a helpful assistant.\n\nContext from memory:\n$CONTEXT\" }, { \"role\": \"user\", \"content\": \"$USER_MSG\" } ] }"

import requests API_KEY = "it-XXXX-XXXX-XXXX" BASE = "https://api.iteratools.com" HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"} NS = "my-agent" def remember(key, text, metadata=None): """Store a fact in agent memory.""" requests.post(f"{BASE}/memory/upsert", headers=HEADERS, json={ "namespace": NS, "key": key, "text": text, "metadata": metadata or {} }) def recall(query, limit=3): """Retrieve semantically similar memories.""" r = requests.post(f"{BASE}/memory/search", headers=HEADERS, json={"namespace": NS, "query": query, "limit": limit}) return [m["text"] for m in r.json().get("results", [])] def chat(user_msg): """Answer user question with memory context.""" memories = recall(user_msg) context = "\n".join(f"- {m}" for m in memories) system = f"You are a helpful assistant.\n\nWhat you know about this user:\n{context}" r = requests.post(f"{BASE}/ai/chat", headers=HEADERS, json={ "model": "gpt-4o-mini", "messages": [ {"role": "system", "content": system}, {"role": "user", "content": user_msg} ] }) return r.json()["reply"] # --- Seed some memories --- remember("pref-format", "User wants concise answers, max 3 sentences, no bullet points.") remember("context-biz", "User builds SaaS for restaurant owners in Brazil.") remember("context-lang", "User writes code in Python and JavaScript.") # --- Chat loop --- while True: msg = input("You: ") if msg.lower() in ("quit", "exit"): break print("Agent:", chat(msg))

# Each user gets their own isolated memory space remember("pref-1", "Likes formal tone", namespace="user-alice") remember("pref-1", "Prefers casual language", namespace="user-bob") # Searching is always scoped to a namespace — no cross-contamination recall("communication style", namespace="user-alice") # → ["Likes formal tone"]

# Delete a specific key curl -X DELETE https://api.iteratools.com/memory/clear \ -H "Authorization: Bearer $ITERA_KEY" \ -H "Content-Type: application/json" \ -d '{"namespace": "my-agent", "key": "user-pref-1"}' # Or wipe the entire namespace curl -X DELETE https://api.iteratools.com/memory/clear \ -H "Authorization: Bearer $ITERA_KEY" \ -H "Content-Type: application/json" \ -d '{"namespace": "my-agent"}'