langchain-prefid 0.2.0
LangChain integration for PrefID - Preference-aware AI agents
License: MIT
Requires Python: >=3.9
Keywords: langchain, prefid, preferences, ai, agents, llm

# langchain-prefid

> **Official LangChain integration for PrefID** - Build preference-aware AI agents that adapt to user preferences

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[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## What is PrefID?

PrefID is identity-aware AI memory infrastructure that helps AI agents understand:
- **WHAT users like** (food, music, travel preferences)
- **HOW users want responses** (thinking preferences - brevity, detail, structure)

## Installation

```bash
pip install langchain-prefid
```

## Quick Start

```python
from langchain_anthropic import ChatAnthropic
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_prefid import create_prefid_tools

# Create PrefID tools
tools = create_prefid_tools(
    client_id="your-prefid-client-id",
    access_token="user-access-token",
    user_id="user_123"
)

# Create agent
llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Use the preference tools to personalize responses."),
    ("placeholder", "{chat_history}"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

# Use it!
result = executor.invoke({
    "input": "Recommend a restaurant for tonight"
})
```

## Available Tools

### 1. `get_user_preferences`
Get user preferences for any domain (food, music, travel, coding, etc.)

```python
# Agent will call this tool
"Get user's food preferences to recommend restaurants"
```

### 2. `get_thinking_preferences`
Get HOW the user wants AI to respond (Atom of Thought)

```python
# Returns:
# - Reasoning style (stepwise, summary_first)
# - Decision style (recommend, tradeoffs, options)
# - Verbosity (concise, detailed, examples)
# - Autonomy (proactive, confirm_first)
```

### 3. `learn_thinking_preference`
Learn and remember how the user wants responses

```python
# When user says: "I prefer brief answers"
# Agent calls this tool to remember it
```

### 4. `explain_response_style`
Introspection - explain why AI is responding a certain way

```python
# When user asks: "Why are you responding like this?"
# Agent explains active preferences
```

## Example Agent

```python
from langchain_anthropic import ChatAnthropic
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_prefid import create_prefid_tools

# Setup
tools = create_prefid_tools(
    client_id="...",
    access_token="...",
    user_id="user_123"
)

llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")

system_prompt = """You are a restaurant recommendation assistant.

BEFORE recommending:
1. Call get_thinking_preferences() to learn HOW to respond
2. Call get_user_preferences('food_profile') to learn WHAT they like
3. Structure response according to their preferences

If user asks why you responded that way, call explain_response_style().
If user tells you how they prefer responses, call learn_thinking_preference().
"""

prompt = ChatPromptTemplate.from_messages([
    ("system", system_prompt),
    ("placeholder", "{chat_history}"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run
response = executor.invoke({
    "input": "Where should I eat tonight?"
})
```

## Individual Tools

You can also use tools individually:

```python
from langchain_prefid import (
    PrefIDPreferenceTool,
    PrefIDThinkingTool,
    PrefIDLearnTool,
    PrefIDWhyTool
)
from prefid import PrefID

prefid = PrefID(client_id="...", access_token="...")

# Create individual tools
pref_tool = PrefIDPreferenceTool(prefid_client=prefid)
thinking_tool = PrefIDThinkingTool(prefid_client=prefid)
learn_tool = PrefIDLearnTool(prefid_client=prefid)
why_tool = PrefIDWhyTool(prefid_client=prefid)

# Use them
food_prefs = pref_tool.invoke({"domain": "food_profile"})
thinking = thinking_tool.invoke({"user_id": "user_123"})
```

## Key Concepts

### Atom of Thought (AoT)
PrefID's unique feature - learns HOW users want AI to respond:
- "I prefer step-by-step explanations" → Reasoning: stepwise
- "Just give me one recommendation" → Decision: recommend
- "Keep it brief" → Verbosity: concise

This goes beyond WHAT users like to HOW they think.

### Thinking Preferences vs Content Preferences
- **Content**: What music/food/travel they like
- **Thinking**: How they want information structured

Both are important for truly personalized AI.

## Examples

See `/examples` for complete working examples:
- `restaurant_recommender.py` - Full agent with all tools
- `basic_agent.py` - Simple preference-aware agent

## LangSmith Hub

Standard prompts for PrefID agents are available on LangSmith Hub:

| Prompt | Description |
|--------|-------------|
| **[talentxmdu/restaurant-recommender](https://smith.langchain.com/hub/talentxmdu/restaurant-recommender)** | Full agent prompt with AoT + Preference logic |
| **[talentxmdu/basic-agent](https://smith.langchain.com/hub/talentxmdu/basic-agent)** | Minimal prompt for preference aware agents |

Usage:
```python
from langchain import hub
prompt = hub.pull("talentxmdu/restaurant-recommender")
```

## Get PrefID Credentials

1. Sign up at [prefid.in](https://prefid.in)
2. Create an OAuth client in Dashboard → Connected Apps
3. Get your `client_id` and `client_secret`

## Links

- **Documentation**: https://docs.prefid.in
- **PrefID SDK**: https://github.com/Talentxmdu/PrefID
- **LangChain**: https://python.langchain.com/
- **Support**: hello@prefid.in

## License

MIT © PrefID Team