Chat History Restore

Save an Agent conversation and reopen it later with tool context intact

Save a conversation with getHistory() and reopen it later with setHistory() — the model regains its memory, tool calls included.

Quick Start

1. Save a chat

   import { Agent } from 'praisonai';
   
   const agent = new Agent({ instructions: 'You are helpful' });
   await agent.chat('My name is Alice');
   
   const saved = agent.getHistory();  // persist to disk / your store

2. Reopen the chat

   import { Agent } from 'praisonai';
   
   const restored = new Agent({ instructions: 'You are helpful' });
   restored.setHistory(saved);
   await restored.chat('What is my name?');  // "Your name is Alice"

How It Works

getHistory() returns a copy of the conversation; setHistory() validates it and replaces the agent’s history, so the next chat() replays the full conversation to the model.

AgentMessage

Each saved message is an AgentMessage. Persist the array getHistory() returns and pass it back to setHistory().

import { Agent, type AgentMessage } from 'praisonai';

const agent = new Agent({ instructions: 'You are helpful' });
await agent.chat('Hello');

const saved: AgentMessage[] = agent.getHistory();

Validation Rules

setHistory() validates at load time and throws a descriptive Error before the next model call. The messages are copied on write, so mutating the array you pass in never changes the agent’s state.

setHistory() also clears the internal response cache, so a repeat prompt after a restore is re-evaluated against the restored history rather than served from the pre-restore prompt-keyed cache.

Common Patterns

Save to disk (Node) and reopen

import { Agent } from 'praisonai';
import fs from 'node:fs';

const agent = new Agent({ instructions: 'You are helpful' });
await agent.chat('My name is Alice');
fs.writeFileSync('chat.json', JSON.stringify(agent.getHistory()));

// … later, in a fresh process …
const restored = new Agent({ instructions: 'You are helpful' });
restored.setHistory(JSON.parse(fs.readFileSync('chat.json', 'utf8')));
await restored.chat('What is my name?');  // "Your name is Alice"

Mobile / Tauri key-value store

Mirror the file example using the platform’s key-value store — this is the scenario the SDK was designed for (reopening a chat in a mobile app).

import { Agent } from 'praisonai/mobile';

const agent = new Agent({ instructions: 'You are helpful' });
await agent.chat('My name is Alice');
await store.set('chat', JSON.stringify(agent.getHistory()));

// … after the app reopens …
const restored = new Agent({ instructions: 'You are helpful' });
restored.setHistory(JSON.parse(await store.get('chat')));
await restored.chat('What is my name?');  // "Your name is Alice"

Tool-calling conversation round-trip

Restored tool results carry over, so the model does not re-run the tool — it already “remembers” the result.

import { Agent } from 'praisonai';

const getWeather = (city: string) => `Weather in ${city}: 22°C, Sunny`;

const agent = new Agent({ instructions: 'You are helpful', tools: [getWeather] });
await agent.chat("What's the weather in Paris?");  // calls getWeather

const restored = new Agent({ instructions: 'You are helpful', tools: [getWeather] });
restored.setHistory(agent.getHistory());
await restored.chat('Is that warm?');  // no re-run; answers from the saved result

Best Practices

Related