Persistent Memory Backend for LangChain Agents
LangChain's built-in memory stores are ephemeral — they live in RAM and die when your process restarts. AgentMind gives your LangChain agents durable, cross-session, cross-agent memory for $0.01 per operation.
Why LangChain's default memory isn't enough
ConversationBufferMemory❌ Grows unbounded. Sends entire history every call. Token costs explode.
✓ AgentMind stores history externally. Retrieve only what you need.
ConversationSummaryMemory❌ Summarises context, losing important details. Breaks on restart.
✓ Store full context permanently. Retrieve by key in under 50ms.
VectorStoreRetrieverMemory❌ Requires a separate vector DB. Expensive to run and maintain.
✓ AgentMind is a single HTTP API. No infrastructure to manage.
Multi-agent context sharing❌ No native way for separate LangChain agents to share memory.
✓ Same agent_key = shared memory. Multiple agents, one store.
Drop-in replacement — Python example
No new dependencies required. Just HTTP requests.
from langchain_core.messages import HumanMessage, AIMessage
import requests
AGENT_KEY = "your_agentmind_key"
BASE = "https://agentmind.dev"
HEADERS = {"X-API-Key": AGENT_KEY}
class AgentMindMemory:
"""Drop-in memory backend for LangChain agents."""
def save_context(self, session_id: str, messages: list) -> None:
"""Persist conversation history to AgentMind."""
requests.post(f"{BASE}/api/ac/memory", headers=HEADERS, json={
"agent_key": AGENT_KEY,
"memory_key": f"session_{session_id}",
"memory_value": str(messages),
"memory_type": "context"
})
def load_memory(self, session_id: str) -> list:
"""Load previous context for this session."""
res = requests.get(
f"{BASE}/api/ac/memory",
headers=HEADERS,
params={"agent_key": AGENT_KEY, "memory_key": f"session_{session_id}"}
)
if res.ok and res.json().get("memory"):
return eval(res.json()["memory"]["memory_value"])
return []
# Usage with any LangChain agent
memory = AgentMindMemory()
history = memory.load_memory("user_42") # Loads previous session
# ... run your agent ...
memory.save_context("user_42", history) # Persists for next timeEverything LangChain memory should be
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