From 50ba384f3fa36675f3904e02a4f1ca991a0c94e9 Mon Sep 17 00:00:00 2001 From: Sayee092931 Date: Sat, 15 Nov 2025 20:13:41 +0530 Subject: [PATCH] docs: add Autogent integration example and update integrations overview * Added new Autogent example at examples/integrations/autogent_example.py * Updated framework integrations table to include Autogent * Ensured consistency with existing integration examples MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary This PR adds an official Autogent integration example and updates the Framework Integrations documentation to include Autogent. This provides developers with a lightweight agent orchestration example using Memori as a persistent memory layer. ## Changes ### ๐Ÿ†• Added - New example: `examples/integrations/autogent_example.py` - Demonstrates: - Persistent memory ingestion with `memori.auto()` - Memory retrieval with `memori.search()` - Autogent agent loop augmented with memory context ### ๐Ÿงพ Updated - Framework Integrations table in the documentation - Added entry for Autogent with description and example link ## Why this PR? - Autogent is widely used for simple, minimalist agent loops. - Developers have requested a lightweight integration example similar to AutoGen and CamelAI. - This example clarifies how Memori can be embedded into small standalone agents. ## Testing - Manually tested Autogent + Memori run loop - Verified memory storage, retrieval, and loop stability - Confirmed documentation renders correctly --- docs/integrations/overview.md | 55 +++++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) diff --git a/docs/integrations/overview.md b/docs/integrations/overview.md index a6fa856e..74258376 100644 --- a/docs/integrations/overview.md +++ b/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() +