ReddRadar
Agent skill
honcho-memory
Gives AI agents persistent memory across conversations using Honcho. Automatically saves and retrieves user context so the AI remembers preferences, history, and facts between sessions. Use when you need the AI to remember past conversations, recall what a user has told it, inject relevant context into prompts, or manage separate memory spaces for different topics.
Install this agent skill to your Project
npx add-skill https://github.com/plastic-labs/honcho/tree/main/examples/zo
Metadata
Additional technical details for this skill
- author
- plastic-labs
- version
- 0.1.0
- honcho sdk
- 2.1.0
SKILL.md
Honcho Memory Skill
This skill provides three tools for storing and retrieving AI memory using Honcho.
Setup
-
Get a Honcho API key at honcho.dev.
-
Set environment variables:
HONCHO_API_KEY=your-api-key HONCHO_WORKSPACE_ID=default # optional, defaults to "default" -
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.
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.
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.
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
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)
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