Skill Introduction

Overview

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.

Core value

Turns reusable Development know-how into an installable skill, helping users complete github, community, agent-memory, ai work faster.

Target users

Developers, testers, and maintainers who handle Development tasks in Focus Code.
Teams that already trust workflows or content from plastic-labs.
Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

Read the skill content first to confirm required inputs, expected outputs, and dependencies.
Try it on a small task before relying on it for critical work.
Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

Tasks related to github, community, agent-memory, ai that need a reusable execution flow.
Converting a community repo, team convention, or personal workflow into day-to-day assistance.
Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

Results depend on the quality of the original skill content and may need human correction.
It does not replace code review, tests, security review, or professional judgment.
External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

Structured around Development, making it easier to discover and reuse than loose prompt snippets.
Marked as GitHub, which helps users judge trust and maintenance expectations.
Keeps the original source link available for repository, documentation, or discussion follow-up.
Tagged with github, community, agent-memory, ai, so it can be filtered by concrete task intent.

Install and use

Install

Copy Install Command

focus install zo-292eb6

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SKILL.md

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name: honcho-memory description: 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. license: AGPL-3.0 compatibility: Requires Python 3.9+, honcho-ai>=2.1.0, and a Honcho API key from honcho.dev. Set HONCHO_API_KEY and optionally HONCHO_WORKSPACE_ID in your environment. metadata: author: plastic-labs version: "0.1.0" honcho-sdk: "2.1.0"

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)

zo