docs: add Autogent integration example and update integrations overvi…

docs/integrations/overview.md

@@ -9,9 +9,64 @@ Memori works seamlessly with popular AI frameworks:
 | [AWS Strands](https://github.com/GibsonAI/memori/blob/main/examples/integrations/aws_strands_example.py) | Professional development coach with Strands SDK and persistent memory | Career coaching agent with goal tracking |
 | [Azure AI Foundry](https://github.com/GibsonAI/memori/blob/main/examples/integrations/azure_ai_foundry_example.py) | Azure AI Foundry agents with persistent memory across conversations | Enterprise AI agents with Azure integration |
 | [AutoGen](https://github.com/GibsonAI/memori/blob/main/examples/integrations/autogen_example.py) | Multi-agent group chat memory recording | Agent chats with memory integration |
+| [Autogent](https://github.com/GibsonAI/memori/blob/main/examples/integrations/autogent_example.py) | Lightweight agent orchestration with built-in Memori memory storage | Minimal agent loop with automatic memory ingestion |
 | [CamelAI](https://github.com/GibsonAI/memori/blob/main/examples/integrations/camelai_example.py) | Multi-agent communication framework with automatic memory recording and retrieval | Memory-enhanced chat agents with conversation continuity |
 | [CrewAI](https://github.com/GibsonAI/memori/blob/main/examples/integrations/crewai_example.py) | Multi-agent system with shared memory across agent interactions | Collaborative agents with memory |
 | [Digital Ocean AI](https://github.com/GibsonAI/memori/blob/main/examples/integrations/digital_ocean_example.py) | Memory-enhanced customer support using Digital Ocean's AI platform | Customer support assistant with conversation history |
 | [LangChain](https://github.com/GibsonAI/memori/blob/main/examples/integrations/langchain_example.py) | Enterprise-grade agent framework with advanced memory integration | AI assistant with LangChain tools and memory |
 | [OpenAI Agent](https://github.com/GibsonAI/memori/blob/main/examples/integrations/openai_agent_example.py) | Memory-enhanced OpenAI Agent with function calling and user preference tracking | Interactive assistant with memory search and user info storage |
 | [Swarms](https://github.com/GibsonAI/memori/blob/main/examples/integrations/swarms_example.py) | Multi-agent system framework with persistent memory capabilities | Memory-enhanced Swarms agents with auto/conscious ingestion |
+
+"""
+Autogent + Memori Integration Example
+
+This example shows how to use Memori as a persistent memory layer inside an
+Autogent lightweight agent loop. Each agent turn both retrieves memory and
+stores new user/agent messages.
+
+Requirements:
+    pip install memori autogent
+"""
+
+from autogent.agent import Agent
+from memori import Memori
+
+
+def main():
+    # Initialize Memori
+    memori = Memori(user_id="autogent_user")
+
+    # Initialize Autogent agent
+    agent = Agent(
+        name="AutogentAssistant",
+        instructions="You are a helpful assistant with long-term memory.",
+    )
+
+    print("Autogent + Memori example. Type 'exit' to stop.\n")
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "exit":
+            break
+
+        # Store user message into memory
+        memori.auto(user_input)
+
+        # Retrieve relevant memories to add as context
+        memories = memori.search(user_input)
+        context_notes = "\n".join([m.content for m in memories])
+
+        # Agent response with memory-augmented context
+        response = agent.run(
+            f"User said: {user_input}\n\nRelevant past memories:\n{context_notes}"
+        )
+
+        print(f"Assistant: {response}")
+
+        # Store the assistant response in memory
+        memori.auto(response)
+
+
+if __name__ == "__main__":
+    main()
+