Why Honcho?
Mem0 & Honcho both store your data. Only Honcho reasons about it. Read more about our approach. 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. Superior Performance - Higher accuracy on memory retrieval benchmarks with faster inference times (more details soon!). 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. Advanced Multi-Peer Sessions - Honcho offers configurable observation settings (who builds memories about whom), representation-based queries between participants, and first-class peer objects.We would love to support the transfer and cost—just book a call!
Quick Migration
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. However, if you’d like to get started quickly, you can migrate your existing Mem0 memories directly as observations.Get your API key at app.honcho.dev/api-keys. New accounts start with $100 credits.
Step-by-Step Migration
Prefer a more detailed walkthrough? Follow these steps:1. Export User Messages
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.If you need any help with this transfer or have any questions, please reach out at [email protected] or book a call!
2. Install the Honcho SDK
3. Initialize the Honcho Client
Get your API key at app.honcho.dev/api-keys. New accounts start with $100 credits.
4. Import Your Data
This is a possible implementation using raw user messages. Adapt the data structure to match your exported format.5. Update Your Application Code
Reference the API Comparison to replace your Mem0 API calls with the Honcho equivalents.API Comparison
Core Operations
| Operation | Mem0 | Honcho | Notes |
|---|---|---|---|
| Initialize | MemoryClient(api_key=...) | Honcho(api_key=...) | |
| Identity | user_id string param | peer = honcho.peer("id") | Peers can be users or AI agents |
| Add messages | client.add(messages, user_id=...) | session.add_messages([peer.message(...)]) | Session-scoped, triggers reasoning |
| Add observations | peer.observations.create([...]) | Direct observation or “memory” import, no processing | |
| Search | client.search(query, filters={"user_id": ...}) | peer.search(query) or peer.observations.query(...) | Scoped to peer or session |
| List all | client.get_all(filters={"user_id": ...}) | session.get_messages() or peer.observations.list() | Messages or observations |
| Update | client.update(memory_id, data=...) | honcho.update_message(message, metadata=...) | Metadata updates only |
| Delete | client.delete(memory_id) | peer.observations.delete(id) or session.delete() | Observation or session-level |
Honcho-Only Capabilities
Mem0 requires manual assembly of context fromsearch() 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()).
Get Context
Learn more about token-optimized context retrieval
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.
Dialectic Endpoint
Learn more about inference-powered queries
| Honcho Method | Description | Use Case |
|---|---|---|
peer.card() | Stable biographical facts (name, preferences, background) | User profiles, personalization |
session.working_rep(peer) | Cached psychological analysis (mental state, intentions) | Real-time adaptation |
session.get_summaries() | Auto-generated short/long session summaries | Conversation continuity |
SessionPeerConfig | Configure observation settings (who learns about whom) | Privacy controls, role-based learning |