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LangChain Adapter

Use a2p profiles as memory in LangChain applications.

Installation

npm install @a2p/langchain

pip install a2p-langchain

Quick Start

import { A2PLangChainMemory } from '@a2p/langchain';
import { ChatOpenAI } from '@langchain/openai';
import { ConversationChain } from 'langchain/chains';

const memory = new A2PLangChainMemory({
  userDid: 'did:a2p:user:local:alice',
  agentDid: 'did:a2p:agent:local:my-agent',
  scopes: ['a2p:preferences', 'a2p:interests']
});

const chain = new ConversationChain({
  llm: new ChatOpenAI(),
  memory
});

const response = await chain.call({
  input: 'What should I work on today?'
});

from a2p_langchain import A2PLangChainMemory
from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain

memory = A2PLangChainMemory(
    user_did="did:a2p:user:local:alice",
    agent_did="did:a2p:agent:local:my-agent",
    scopes=["a2p:preferences", "a2p:interests"]
)

chain = ConversationChain(
    llm=ChatOpenAI(),
    memory=memory
)

response = chain.invoke({
    "input": "What should I work on today?"
})

Configuration

Option Type Description
userDid string User's DID
agentDid string Your agent's DID
scopes string[] Scopes to request
purpose Purpose Access purpose (auto-generated if not provided)
autoPropose boolean Auto-propose memories from conversation

Memory Variables

The adapter provides these memory variables:

Variable Content
a2p_profile User preferences and context
a2p_memories Episodic memories
history Conversation history

Proposing Memories

// Enable auto-propose
const memory = new A2PLangChainMemory({
  // ...
  autoPropose: true,
  proposeThreshold: 0.8  // Minimum confidence
});

// Or manually
await memory.proposeMemory(
  'User prefers detailed explanations',
  { category: 'a2p:preferences.communication', confidence: 0.9 }
);

Next Steps