Mem0 -> Honcho migration (#281)
* docs: First draft of mem0 honcho migration * docs: updating migration guide comparisons * docs: simplifying structure and suggestions * docs: Simplifying API comparsion * docs: verifying code and simplfying * docs: emphasizing the quality of honcho * docs: minor textual changes * docs: adding observation endpoint * docs: coderabbit fixes and textual changes.
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"v2/guides/overview"
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]
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},
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{
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"group": "Migrations",
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"pages": [
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"v2/migrations/from-mem0"
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]
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},
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{
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"group": "Integrations",
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"pages": [
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---
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title: 'Migrating from Mem0'
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description: 'A guide to migrate from Mem0 to Honcho'
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icon: 'arrow-right-arrow-left'
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---
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Interested in transferring your data from Mem0 to Honcho? This guide covers why to switch, how to migrate your data, and differences between the two products.
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## Why Honcho?
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Mem0 & Honcho both store your data. Only Honcho reasons about it. [Read more about our approach](https://blog.plasticlabs.ai/blog/Memory-as-Reasoning).
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**Compounding Insights** - Honcho extracts insights that build on each other over time. The more your users interact, the richer and more accurate their profiles become.
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**Superior Performance** - Higher accuracy on memory retrieval benchmarks with faster inference times (more details soon!).
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**Competitive Pricing** - Mem0 charges for retrieval, not ingestion. Meaning you pay to access your own data. Honcho offers straightforward pricing with a generous free tier.
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**Advanced Multi-Peer Sessions** - Honcho offers configurable observation settings (who builds memories about whom), representation-based queries between participants, and first-class peer objects.
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<Note>
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We would love to support the transfer and cost—just [book a call!](https://cal.com/team/plasticlabs/migration-to-honcho)
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</Note>
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## Quick Migration
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For the best results, we recommend importing your raw messages directly into Honcho. This gives Honcho the full context to build rich, accurate representations and enables features like session summaries.
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However, if you'd like to get started quickly, you can migrate your existing Mem0 memories directly as **observations**.
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<Info>
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Get your API key at [app.honcho.dev/api-keys](https://app.honcho.dev/api-keys). New accounts start with $100 credits.
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</Info>
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<CodeGroup>
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```python Python
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# pip install mem0ai honcho-ai
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from mem0 import MemoryClient
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from honcho import Honcho
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# Export from Mem0
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mem0 = MemoryClient(api_key="your-mem0-api-key")
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memories = mem0.get_all(filters={"user_id": "user123"}, page_size=100)
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# Initialize Honcho
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honcho = Honcho(api_key="your-honcho-api-key")
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user = honcho.peer("user123")
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session = honcho.session("imported")
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session.add_peers([user])
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# Import memories directly as observations
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observations = []
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for memory in memories['results']:
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content = memory.get("memory") or memory.get("messages", [{}])[0].get("content", "")
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if content:
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observations.append({"content": content, "session_id": "imported"})
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# Batch create observations (up to 100 at a time)
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if observations:
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user.observations.create(observations)
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print(f"Migrated {len(observations)} memories as observations!")
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```
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```typescript TypeScript
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// npm install mem0ai @honcho-ai/sdk
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import MemoryClient from "mem0ai";
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import { Honcho } from "@honcho-ai/sdk";
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// Export from Mem0
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const mem0 = new MemoryClient({ apiKey: "your-mem0-api-key" });
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const memories = await mem0.getAll({ filters: { user_id: "user123" }, page_size: 100 });
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// Initialize Honcho
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const honcho = new Honcho({ apiKey: "your-honcho-api-key" });
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const user = await honcho.peer("user123");
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const session = await honcho.session("imported");
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await session.addPeers([user]);
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// Import memories directly as observations
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const observations = memories.results
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.map(memory => ({
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content: memory.memory || memory.messages?.[0]?.content || "",
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session_id: "imported"
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}))
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.filter(obs => obs.content);
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// Batch create observations (up to 100 at a time)
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if (observations.length > 0) {
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await user.observations.create(observations);
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}
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console.log(`Migrated ${observations.length} memories as observations!`);
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```
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</CodeGroup>
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That's it! The user's Mem0 memories are now searchable in Honcho as observations. For richer representations with deductive reasoning and session summaries, consider importing your raw messages as described in the [Step-by-Step Migration](#step-by-step-migration) section.
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For more details on replacing Mem0 API calls with Honcho equivalents go to [API Comparison](#api-comparison).
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## Step-by-Step Migration
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Prefer a more detailed walkthrough? Follow these steps:
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### 1. Export User Messages
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Importing raw user messages gives Honcho the full conversational context to build the most accurate representations. We recommend using a data structure that preserves the session and peer structure.
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<Note>
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If you need any help with this transfer or have any questions, please reach out at hello@plasticlabs.ai or [book a call!](https://cal.com/team/plasticlabs/migration-to-honcho)
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</Note>
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Alternatively, if you want to import the Mem0 memories, follow the example above and find more info in Mem0's [export API documentation](https://docs.mem0.ai/cookbooks/essentials/exporting-memories).
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### 2. Install the Honcho SDK
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<CodeGroup>
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```bash Python (uv)
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uv add honcho-ai
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```
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```bash Python (pip)
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pip install honcho-ai
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```
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```bash TypeScript (npm)
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npm install @honcho-ai/sdk
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```
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```bash TypeScript (yarn)
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yarn add @honcho-ai/sdk
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```
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```bash TypeScript (pnpm)
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pnpm add @honcho-ai/sdk
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```
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</CodeGroup>
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### 3. Initialize the Honcho Client
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<Info>
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Get your API key at [app.honcho.dev/api-keys](https://app.honcho.dev/api-keys). New accounts start with $100 credits.
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</Info>
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<CodeGroup>
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```python Python
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from honcho import Honcho
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honcho = Honcho( api_key="your-api-key" )
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```
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```typescript TypeScript
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import { Honcho } from '@honcho-ai/sdk';
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const honcho = new Honcho({apiKey: process.env.HONCHO_API_KEY!});
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```
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</CodeGroup>
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### 4. Import Your Data
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This is a possible implementation using raw user messages. Adapt the data structure to match your exported format.
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<CodeGroup>
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```python Python
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# Example data structure (preserving message history with timestamps):
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exported_data = {
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"session-1": {
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"user123": [
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{"content": "I prefer dark mode", "timestamp": "2024-01-15T10:30:00Z"},
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{"content": "My name is Alex", "timestamp": "2024-01-15T10:31:00Z"},
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],
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"user456": [
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{"content": "I work in finance", "timestamp": "2024-01-15T11:00:00Z"},
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{"content": "I like concise responses", "timestamp": "2024-01-15T11:02:00Z"},
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],
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},
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"session-2": {
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"user123": [
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{"content": "Meeting notes from last week...", "timestamp": "2024-01-16T09:00:00Z"},
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],
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}
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}
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# Import into Honcho
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for session_name, users in exported_data.items():
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session = honcho.session(session_name)
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for user_id, messages in users.items():
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peer = honcho.peer(user_id)
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session.add_peers([peer])
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# Sort by timestamp to preserve message order
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sorted_messages = sorted(messages, key=lambda m: m["timestamp"])
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session.add_messages([peer.message(m["content"]) for m in sorted_messages])
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```
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```typescript TypeScript
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// Example data structure (preserving message history with timestamps):
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interface Message {
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content: string;
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timestamp: string;
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}
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const exportedData: Record<string, Record<string, Message[]>> = {
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"session-1": {
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"user123": [
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{ content: "I prefer dark mode", timestamp: "2024-01-15T10:30:00Z" },
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{ content: "My name is Alex", timestamp: "2024-01-15T10:31:00Z" },
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],
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"user456": [
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{ content: "I work in finance", timestamp: "2024-01-15T11:00:00Z" },
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{ content: "I like concise responses", timestamp: "2024-01-15T11:02:00Z" },
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],
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},
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"session-2": {
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"user123": [
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{ content: "Meeting notes from last week...", timestamp: "2024-01-16T09:00:00Z" },
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],
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}
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};
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// Import into Honcho
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for (const [sessionName, users] of Object.entries(exportedData)) {
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const session = await honcho.session(sessionName);
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for (const [userId, messages] of Object.entries(users)) {
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const peer = await honcho.peer(userId);
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await session.addPeers([peer]);
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// Sort by timestamp to preserve message order
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const sortedMessages = messages.sort((a, b) =>
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new Date(a.timestamp).getTime() - new Date(b.timestamp).getTime()
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);
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await session.addMessages(sortedMessages.map((m) => peer.message(m.content)));
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}
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}
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```
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</CodeGroup>
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### 5. Update Your Application Code
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Reference the [API Comparison](#api-comparison) to replace your Mem0 API calls with the Honcho equivalents.
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## API Comparison
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### Core Operations
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| Operation | Mem0 | Honcho | Notes |
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|-----------|------|--------|-------|
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| **Initialize** | `MemoryClient(api_key=...)` | `Honcho(api_key=...)` | |
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| **Identity** | `user_id` string param | `peer = honcho.peer("id")` | Peers can be users or AI agents |
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| **Add messages** | `client.add(messages, user_id=...)` | `session.add_messages([peer.message(...)])` | Session-scoped, triggers reasoning |
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| **Add observations** | | `peer.observations.create([...])` | Direct observation or "memory" import, no processing |
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| **Search** | `client.search(query, filters={"user_id": ...})` | `peer.search(query)` or `peer.observations.query(...)` | Scoped to peer or session |
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| **List all** | `client.get_all(filters={"user_id": ...})` | `session.get_messages()` or `peer.observations.list()` | Messages or observations |
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| **Update** | `client.update(memory_id, data=...)` | `honcho.update_message(message, metadata=...)` | Metadata updates only |
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| **Delete** | `client.delete(memory_id)` | `peer.observations.delete(id)` or `session.delete()` | Observation or session-level |
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### Honcho-Only Capabilities
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Mem0 requires manual assembly of context from `search()` results. Honcho's `session.get_context()` returns a ready-to-use `SessionContext` object with built-in token limits, auto-included summaries, and format helpers (`.to_openai()`, `.to_anthropic()`).
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<Card title="Get Context" icon="window-restore" href="../../documentation/core-concepts/features/get-context">
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Learn more about token-optimized context retrieval
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</Card>
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Mem0's `search()` returns basic vector, semantic, or raw memory matches. Honcho's `peer.chat()` enables your agent to *reason* about what it knows—returning synthesized natural language insights with streaming support and scoped queries.
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<Card title="Dialectic Endpoint" icon="brain" href="../../documentation/core-concepts/features/dialectic-endpoint">
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Learn more about inference-powered queries
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</Card>
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Additional features with **no Mem0 equivalent**:
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| Honcho Method | Description | Use Case |
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|---------------|-------------|----------|
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| `peer.card()` | Stable biographical facts (name, preferences, background) | User profiles, personalization |
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| `session.working_rep(peer)` | Cached psychological analysis (mental state, intentions) | Real-time adaptation |
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| `session.get_summaries()` | Auto-generated short/long session summaries | Conversation continuity |
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| `SessionPeerConfig` | Configure observation settings (who learns about whom) | Privacy controls, role-based learning |
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## Next Steps
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<CardGroup cols={3}>
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<Card title="Architecture" icon="rocket" href="../../documentation/core-concepts/architecture">
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Understand peers and sessions
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</Card>
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<Card title="Dialectic API" icon="brain" href="../../documentation/core-concepts/features/dialectic-endpoint">
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Inference responses
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</Card>
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<Card title="Guides" icon="book" href="../../guides/overview">
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Integration examples
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</Card>
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</CardGroup>
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Questions? Join our [Discord](https://discord.gg/honcho) or open an issue on [GitHub](https://github.com/plastic-labs/honcho/issues).
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