Memory and Context — Agents That Remember

import anthropic, json, os
from datetime import datetime
from pathlib import Path

client = anthropic.Anthropic()
MEMORY_FILE = "agent_memory.json"
MAX_MESSAGES = 20  # summarize when history exceeds this

# ── Long-term memory ───────────────────────────────
def load_memory() -> dict: if Path(MEMORY_FILE).exists(): return json.loads(Path(MEMORY_FILE).read_text()) return {"facts": [], "preferences": {}, "task_history": []}

def save_memory(memory: dict): Path(MEMORY_FILE).write_text(json.dumps(memory, indent=2))

def remember_fact(fact: str, category: str = "general") -> str: memory = load_memory() entry = { "fact": fact, "category": category, "timestamp": datetime.now().isoformat() } memory["facts"].append(entry) save_memory(memory) return f"Remembered: '{fact}' (category: {category})"

def recall_facts(query: str = "") -> str: memory = load_memory() facts = memory["facts"] if query: facts = [f for f in facts if query.lower() in f["fact"].lower() or query.lower() in f["category"].lower()] if not facts: return "No facts found." return json.dumps(facts, indent=2)

# ── Context window management ──────────────────────
def summarize_old_messages(messages: list) -> list: """When history is long, summarize old messages into a single system message.""" if len(messages) <= MAX_MESSAGES: return messages # Keep the most recent 10 messages recent = messages[-10:] old = messages[:-10] # Ask Claude to summarize the old messages summary_text = "\n".join( f"{m['role']}: {str(m['content'])[:200]}" for m in old ) summary_resp = client.messages.create( model="claude-haiku-4-5",  # cheap model for summarization max_tokens=512, messages=[{ "role": "user", "content": f"Summarize this conversation history in 3-5 sentences:\n{summary_text}" }] ) summary = summary_resp.content[0].text # Replace old messages with a summary summary_message = { "role": "user", "content": f"[Earlier conversation summary]: {summary}" } return [summary_message] + recent

# ── Tools (extends Day 2) ─────────────────────────
TOOLS = [ {"name":"remember_fact","description":"Store a fact in long-term memory for future sessions. Use for important info the user shares.", "input_schema":{"type":"object","properties":{"fact":{"type":"string"},"category":{"type":"string"}},"required":["fact"]}}, {"name":"recall_facts","description":"Retrieve facts from long-term memory. Use at session start or when you need to recall past information.", "input_schema":{"type":"object","properties":{"query":{"type":"string"}},"required":[]}},
]

def execute_tool(name, inp): if name == "remember_fact": return remember_fact(inp["fact"], inp.get("category","general")) elif name == "recall_facts": return recall_facts(inp.get("query","")) raise ValueError(f"Unknown tool: {name}")

# ── Agent with memory ─────────────────────────────
class MemoryAgent: def __init__(self): self.messages = [] self.memory = load_memory() # Inject existing long-term memory as context if self.memory["facts"]: facts_str = "\n".join(f"- {f['fact']}" for f in self.memory["facts"]) print(f"Loaded {len(self.memory['facts'])} facts from long-term memory.") self._system_context = f"You know these facts from previous sessions:\n{facts_str}" else: self._system_context = "No previous session memory." def chat(self, user_input: str, max_steps=10): self.messages.append({"role":"user","content":user_input}) self.messages = summarize_old_messages(self.messages) for _ in range(max_steps): resp = client.messages.create( model="claude-sonnet-4-5", max_tokens=2048, system=self._system_context, tools=TOOLS, messages=self.messages ) if resp.stop_reason == "end_turn": answer = resp.content[0].text self.messages.append({"role":"assistant","content":answer}) return answer results = [] for b in resp.content: if b.type == "tool_use": result = execute_tool(b.name, b.input) results.append({"type":"tool_result","tool_use_id":b.id,"content":str(result)}) self.messages += [ {"role":"assistant","content":resp.content}, {"role":"user","content":results} ] return "Max steps reached."

# ── Demo: multi-session memory ─────────────────────
if __name__ == "__main__": agent = MemoryAgent() # Session 1: tell agent something r1 = agent.chat("My name is Alex and I prefer Python over JavaScript.") print("Response 1:", r1) # Session 2 (new agent instance simulates new session) agent2 = MemoryAgent() r2 = agent2.chat("What's my name and language preference?") print("Response 2:", r2) # Agent should answer correctly from long-term memory!