honcho-memory

# Honcho Memory Skill This skill provides three tools for storing and retrieving AI memory using [Honcho](https://honcho.dev). ## Setup 1. Get a Honcho API key at [honcho.dev](https://honcho.dev). 2. Set environment variables: ``` HONCHO_API_KEY=your-api-key HONCHO_WORKSPACE_ID=default # optional, defaults to "default" ``` 3. Install dependencies: ``` pip install honcho-ai python-dotenv ``` ## Tools ### `save_memory` Saves a conversation turn (user or assistant message) to Honcho. **When to use:** After every message exchange to build up the user's memory. ```python from tools.save_memory import save_memory save_memory( user_id="alice", # unique user identifier content="I love hiking", # message text role="user", # "user" or "assistant" session_id="chat-1", # conversation session ID assistant_id="assistant" # optional: assistant peer ID (default: "assistant") ) ``` ### `query_memory` Asks a natural language question against stored memory using Honcho's Dialectic API. **When to use:** When the user asks "do you remember...?", or when you need to recall facts about the user before responding. ```python from tools.query_memory import query_memory answer = query_memory( user_id="alice", query="What are Alice's hobbies?", session_id="chat-1" # optional: scope to a session ) # Returns: "Alice enjoys hiking." ``` ### `get_context` Retrieves recent conversation history formatted for direct use in an LLM API call. **When to use:** At the start of each LLM call to inject relevant context from past conversations. ```python from tools.get_context import get_context messages = get_context( user_id="alice", session_id="chat-1", assistant_id="assistant", tokens=4000 # max tokens to include ) # Returns: [{"role": "user", "content": "..."}, ...] ``` ## Concept Mapping | Zo Computer | Honcho | |---|---| | Account | Workspace | | User | Peer | | Conversation | Session | | Message | Message | ## Example: Full Conversation Flow ```python from tools.save_memory import save_memory from tools.query_memory import query_memory from tools.get_context import get_context user_id = "alice" session_id = "session-1" # 1. Save user message save_memory(user_id, "I'm learning Rust and love rock climbing", "user", session_id) # 2. Save assistant reply save_memory(user_id, "That's great! Both require patience.", "assistant", session_id) # 3. In a later session, recall what you know print(query_memory(user_id, "What does Alice do in her free time?")) # → "Alice is learning Rust and enjoys rock climbing." # 4. Get context window for next LLM call messages = get_context(user_id, session_id, "assistant", tokens=4000) ```