Honcho Memory
Honcho is an AI-native memory platform that gives Mona deep, persistent memory across sessions. Unlike simple file-based memory, Honcho uses dialectic reasoning to build a rich model of each user.
What Honcho adds
- User modeling — Honcho builds a psychological and preference model of each user
- Dialectic memory — Memory is refined through questioning and contradiction
- Multi-agent support — Multiple agents can share the same user memory
- Semantic search — Retrieve relevant memories by meaning, not just keywords
Installation
The Honcho extra is included in the [all] install. If you installed the minimal bundle:
cd ~/.monoclaw/monoclaw-runtime
uv pip install -e ".[honcho]"
Setup
- Sign up at honcho.dev
- Create an app and copy your API key
- Configure MonoClaw:
monoclaw config set HONCHO_API_KEY "your-key"
monoclaw config set memory.provider honcho
Or in config.yaml:
memory:
provider: honcho
honcho:
api_key: "${HONCHO_API_KEY}"
app_id: "your-app-id"
How it works
When Honcho is enabled:
- Every user message is analyzed for new facts, preferences, and context
- Honcho stores these as structured memory documents
- Before replying, Mona queries Honcho for relevant memories about the user
- Memories are surfaced naturally in conversation — no explicit commands needed
Memory vs file-based memory
File-based (MEMORY.md) | Honcho | |
|---|---|---|
| Storage | Local markdown files | Cloud API |
| Search | Keyword + regex | Semantic vector search |
| User modeling | Basic preference list | Dialectic reasoning |
| Multi-agent | Shared file | Per-user shared memory |
| Privacy | Fully local | Encrypted cloud storage |
Privacy considerations
Honcho stores memory in the cloud. If you require fully local data residency, use the built-in file-based memory instead.
Troubleshooting
| Problem | Fix |
|---|---|
| "Honcho API key not set" | Run monoclaw config set HONCHO_API_KEY |
| Memory not retrieving | Check your app_id matches the Honcho dashboard |
| Slow responses | Honcho adds ~200–500ms latency per turn |