community: add Valthera integration (#30105)

```markdown
**Description:**  
This PR integrates Valthera into LangChain, introducing an framework designed to send highly personalized nudges by an LLM agent. This is modeled after Dr. BJ Fogg's Behavior Model. This integration includes:

- Custom data connectors for HubSpot, PostHog, and Snowflake.
- A unified data aggregator that consolidates user data.
- Scoring configurations to compute motivation and ability scores.
- A reasoning engine that determines the appropriate user action.
- A trigger generator to create personalized messages for user engagement.

**Issue:**  
N/A

**Dependencies:**  
N/A

**Twitter handle:**  
- `@vselvarajijay`

**Tests and Docs:**  
- `docs/docs/integrations/tools/valthera` 
- `https://github.com/valthera/langchain-valthera/tree/main/tests`

```

---------

Co-authored-by: Chester Curme <[email protected]>

docs/docs/integrations/providers/valthera.mdx

@@ -0,0 +1,63 @@
+# Valthera
+
+> [Valthera](https://github.com/valthera/valthera) is an open-source framework that empowers LLM Agents to drive meaningful, context-aware user engagement. It evaluates user motivation and ability in real time, ensuring that notifications and actions are triggered only when users are most receptive.
+> 
+> **langchain-valthera** integrates Valthera with LangChain, enabling developers to build smarter, behavior-driven engagement systems that deliver personalized interactions.
+
+## Installation and Setup
+
+### Install langchain-valthera
+
+Install the LangChain Valthera package via pip:
+
+```bash
+pip install -U langchain-valthera
+```
+
+Import the ValtheraTool:
+
+```python
+from langchain_valthera.tools import ValtheraTool
+```
+
+### Example: Initializing the ValtheraTool for LangChain
+
+This example shows how to initialize the ValtheraTool using a `DataAggregator` and configuration for motivation and ability scoring.
+
+```python
+import os
+from langchain_openai import ChatOpenAI
+from valthera.aggregator import DataAggregator
+from mocks import hubspot, posthog, snowflake  # Replace these with your actual connector implementations
+from langchain_valthera.tools import ValtheraTool
+
+# Initialize the DataAggregator with your data connectors
+data_aggregator = DataAggregator(
+    connectors={
+        "hubspot": hubspot(),
+        "posthog": posthog(),
+        "app_db": snowflake()
+    }
+)
+
+# Initialize the ValtheraTool with your scoring configurations
+valthera_tool = ValtheraTool(
+    data_aggregator=data_aggregator,
+    motivation_config=[
+        {"key": "hubspot_lead_score", "weight": 0.30, "transform": lambda x: min(x, 100) / 100.0},
+        {"key": "posthog_events_count_past_30days", "weight": 0.30, "transform": lambda x: min(x, 50) / 50.0},
+        {"key": "hubspot_marketing_emails_opened", "weight": 0.20, "transform": lambda x: min(x / 10.0, 1.0)},
+        {"key": "posthog_session_count", "weight": 0.20, "transform": lambda x: min(x / 5.0, 1.0)}
+    ],
+    ability_config=[
+        {"key": "posthog_onboarding_steps_completed", "weight": 0.30, "transform": lambda x: min(x / 5.0, 1.0)},
+        {"key": "posthog_session_count", "weight": 0.30, "transform": lambda x: min(x / 10.0, 1.0)},
+        {"key": "behavior_complexity", "weight": 0.40, "transform": lambda x: 1 - (min(x, 5) / 5.0)}
+    ]
+)
+
+print("✅ ValtheraTool successfully initialized for LangChain integration!")
+```
+
+
+The langchain-valthera integration allows you to assess user behavior and decide on the best course of action for engagement, ensuring that interactions are both timely and relevant within your LangChain applications.