update

Author: sansyrox

docs_src/src/pages/documentation/en/api_reference/agents.mdx

@@ -37,14 +37,12 @@ app.start()
 ## Features
 
 ### 🧠 **Memory System**
-- Multiple provider support (InMemory, Mem0)
+- InMemory provider support
 - Persistent conversation history
-- Semantic search capabilities
 - Context-aware responses
 
 ### 🤖 **Agent Runners**
 - Simple runner with OpenAI integration
-- LangGraph runner for complex workflows
 - Custom runner support
 - Async execution
 
@@ -81,30 +79,6 @@ messages = await mem.get()
 await mem.clear()
 ```
 
-#### Mem0 Provider
-
-Integration with Mem0 for advanced memory capabilities including semantic search and persistence.
-
-```python
-from robyn.ai import memory
-
-# Create Mem0 memory instance
-mem = memory(
-    provider="mem0",
-    user_id="user123",
-    # Mem0 configuration
-    vector_store={
-        "provider": "chroma",
-        "config": {"collection_name": "robyn_memory"}
-    }
-)
-
-# Add messages with metadata
-await mem.add("I love pizza", metadata={"category": "food_preference"})
-
-# Search with query
-results = await mem.get(query="food preferences")
-```
 
 ### Memory API
 
@@ -138,25 +112,6 @@ result = await simple_agent.run("What's the weather like?")
 # Returns structured response with AI-generated content
 ```
 
-#### LangGraph Runner
-
-Integration with LangGraph for complex workflow execution:
-
-```python
-from robyn.ai import agent, memory
-
-# Create memory
-mem = memory(provider="inmemory", user_id="user123")
-
-# Create LangGraph agent with custom workflow
-graph_agent = agent(
-    runner="langgraph:path/to/workflow.json",
-    memory=mem
-)
-
-# Execute with context
-result = await graph_agent.run("Process this request", history=True)
-```
 
 #### Custom Runners
 
@@ -199,11 +154,6 @@ export AI_MODEL="gpt-4o"
 export AI_TEMPERATURE="0.7"
 export AI_MAX_TOKENS="1000"
 
-# Mem0 Configuration
-export MEM0_API_KEY="your-mem0-key"
-
-# LangGraph Configuration
-export LANGGRAPH_API_KEY="your-langgraph-key"
 ```
 
 ### Programmatic Configuration
@@ -224,9 +174,8 @@ chat_agent = agent(runner="simple", config=config)
 
 # Or configure memory
 mem = memory(
-    provider="mem0",
-    user_id="user123",
-    vector_store={"provider": "chroma"}
+    provider="inmemory",
+    user_id="user123"
 )
 ```
 
@@ -247,11 +196,10 @@ config = configure(
     temperature=0.7
 )
 
-# Create memory with Mem0 provider
+# Create memory with InMemory provider
 mem = memory(
-    provider="mem0",
-    user_id="guest",
-    vector_store={"provider": "chroma"}
+    provider="inmemory",
+    user_id="guest"
 )
 
 # Create agent with memory and config
@@ -337,10 +285,10 @@ if __name__ == "__main__":
 
 **ImportError for required packages**: Install the required packages:
 ```bash
-pip install openai mem0ai langgraph
+pip install openai
 ```
 
-**Memory not persisting**: Ensure you're using a persistent provider like Mem0, not the in-memory provider for production.
+**Memory not persisting**: Note that the in-memory provider loses data when the application restarts. Consider implementing a custom persistent provider for production use.
 
 **Agent timeouts**: Complex workflows may take time. Consider implementing timeout handling in your endpoints.
 

docs_src/src/pages/documentation/en/api_reference/ai.mdx

@@ -13,15 +13,6 @@ The AI features are included with the base Robyn installation:
 pip install robyn
 ```
 
-For additional providers, you may need to install their respective packages:
-
-```bash
-# For Mem0 memory provider
-pip install mem0ai
-
-# For LangGraph agent runner
-pip install langgraph
-```
 
 ## Quick Start
 
@@ -77,30 +68,6 @@ messages = await mem.get()
 await mem.clear()
 ```
 
-#### Mem0 Provider
-
-Integration with Mem0 for advanced memory capabilities including semantic search and persistence.
-
-```python
-from robyn.ai import memory
-
-# Create Mem0 memory instance
-mem = memory(
-    provider="mem0",
-    user_id="user123",
-    # Mem0 configuration
-    vector_store={
-        "provider": "chroma",
-        "config": {"collection_name": "robyn_memory"}
-    }
-)
-
-# Add messages with metadata
-await mem.add("I love pizza", metadata={"category": "food_preference"})
-
-# Search with query
-results = await mem.get(query="food preferences")
-```
 
 ### Memory API
 
@@ -118,38 +85,22 @@ Agents provide the execution layer for AI functionality. They can use different
 
 #### Simple Runner
 
-A basic echo runner useful for testing and development:
+A runner with OpenAI integration that provides intelligent responses:
 
 ```python
 from robyn.ai import agent
 
-# Create simple agent
-simple_agent = agent(runner="simple")
+# Create simple agent with OpenAI
+from robyn.ai import configure
+
+config = configure(openai_api_key="your-openai-key")
+simple_agent = agent(runner="simple", config=config)
 
 # Use the agent
-result = await simple_agent.run("Hello world")
-# Returns: {"response": "Echo: Hello world", "query": "Hello world", "kwargs": {}}
+result = await simple_agent.run("What's the weather like?")
+# Returns structured response with AI-generated content
 ```
 
-#### LangGraph Runner
-
-Integration with LangGraph for complex workflow execution:
-
-```python
-from robyn.ai import agent, memory
-
-# Create memory
-mem = memory(provider="inmemory", user_id="user123")
-
-# Create LangGraph agent
-graph_agent = agent(
-    runner="langgraph:path/to/workflow.json",
-    memory=mem
-)
-
-# Execute with context
-result = await graph_agent.run("Process this request", history=True)
-```
 
 ### Agent API
 
@@ -169,11 +120,10 @@ from robyn.ai import agent, memory
 
 app = Robyn(__file__)
 
-# Create memory with Mem0 provider
+# Create memory with InMemory provider
 mem = memory(
-    provider="mem0",
-    user_id="guest",
-    vector_store={"provider": "chroma"}
+    provider="inmemory",
+    user_id="guest"
 )
 
 # Create agent with memory
@@ -302,13 +252,13 @@ custom_agent = Agent(runner=CustomAgentRunner())
 
 ### Common Issues
 
-**ImportError for mem0ai/langgraph**: Install the required packages:
+**ImportError for openai**: Install the required package:
 ```bash
-pip install mem0ai langgraph
+pip install openai
 ```
 
-**Memory not persisting**: Ensure you're using a persistent provider like Mem0, not the in-memory provider for production.
+**Memory not persisting**: Note that the in-memory provider loses data when the application restarts. Consider implementing a custom persistent provider for production use.
 
-**Agent timeouts**: Complex LangGraph workflows may take time. Consider implementing timeout handling in your endpoints.
+**Agent timeouts**: Complex operations may take time. Consider implementing timeout handling in your endpoints.
 
 **Memory growing too large**: Implement periodic cleanup or use providers with built-in retention policies.