LangChain

LangChain adapters are available for both the TypeScript and Python SDKs.

Before you start

Choose service mode or direct Postgres mode.
Read How MemexAI works for the tool-call-to-Postgres flow.
Read Prompt block to understand why memory must be included in the system prompt.
Read Memory tools to choose agentic tools or raw file tools.
Read Memory scopes before writing paths like user/profile.md.

TypeScript

Install

npm install @memexai/sdk langchain @langchain/core

For direct Postgres mode:

npm install @memexai/core langchain @langchain/core

Service mode

import { MemexAI } from '@memexai/sdk'
import { createLangChainTools } from '@memexai/sdk/adapters/langchain'
import { AgentExecutor, createToolCallingAgent } from 'langchain/agents'
import { ChatGoogleGenerativeAI } from '@langchain/google-genai'
import { ChatPromptTemplate } from '@langchain/core/prompts'

const memex = new MemexAI({ url: 'http://localhost:8080', apiKey: process.env.MEMEX_API_KEY! })
const memory = memex.forUser({ userId: 'user_123', actor: 'assistant' })
const tools = createLangChainTools(memory)
const system = await memory.getSystemPrompt('You are a helpful assistant with durable user memory.')

const llm = new ChatGoogleGenerativeAI({ model: 'gemini-2.5-flash' })
const prompt = ChatPromptTemplate.fromMessages([
  ['system', system],
  ['human', '{input}'],
  ['placeholder', '{agent_scratchpad}'],
])

const agent = await createToolCallingAgent({ llm, tools, prompt })
const executor = new AgentExecutor({ agent, tools })

const result = await executor.invoke({ input: 'Remember I prefer quiet neighborhoods.' })
console.log(result.output)

Direct Postgres mode

import { createMemex } from '@memexai/core'
import { createLangChainTools } from '@memexai/core/adapters/langchain'

const memex = createMemex({ databaseUrl: process.env.DATABASE_URL! })
await memex.migrate()

const memory = memex.forUser({ userId: 'user_123', actor: 'assistant' })
const tools = createLangChainTools(memory)
const system = await memory.getSystemPrompt('You are a helpful assistant with durable user memory.')

Use system in the LangChain prompt template alongside the tools.

Python

Install

pip install memexai langchain langchain-google-genai

Usage

from memexai import MemexAI
from memexai.adapters.langchain import get_langchain_tools
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate

memex = MemexAI(url="http://localhost:8080", api_key="dev-agent-key")
user = memex.for_user("user_123", actor="assistant")
tools = get_langchain_tools(user)
system = await user.get_system_prompt("You are a helpful assistant with durable user memory.")

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
prompt = ChatPromptTemplate.from_messages([
    ("system", system),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = await executor.ainvoke({"input": "Remember I prefer quiet neighborhoods."})
print(result["output"])

await memex.close()

The Python adapter exposes all seven memory tools: memory_list, memory_read, memory_write, memory_patch, memory_smart_read, memory_search, and memory_memorize. For most assistants, start with memory_memorize and memory_search; see Memory tools for the tradeoff.

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