honcho-memory

Files (11)

• README.md
• SKILL.md
• pyproject.toml
• test_basic.py · tests/
• test_tools.py · tests/
• __init__.py · tools/
• client.py · tools/
• get_context.py · tools/
• query_memory.py · tools/
• save_memory.py · tools/
• uv.lock

SKILL.md (first lines)

# 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)
```

From clawdsolana/OpenClawd, AGPL-3.0.

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