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Adk Career Coach Agent Memory
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Multi LLM Memory
About this app
This Streamlit application demonstrates a multi-LLM system with a shared memory layer, allowing users to interact with different language models while maintaining conversation history and context across sessions.
- Complete open-source code
- Step-by-step tutorial
Technical sheet
- Category
- Apps with Memory
- Difficulty
- Intermediate
- Models
- OpenAI, Claude
- Frameworks
- -
- Runs locally?
- Needs cloud APIs
- Required keys
ANTHROPIC_API_KEY
License Apache-2.0 · content curated and maintained by Doutor AI.
Full tutorial
🧠 Multi-LLM App with Shared Memory
This Streamlit application demonstrates a multi-LLM system with a shared memory layer, allowing users to interact with different language models while maintaining conversation history and context across sessions.
Features
- Support for multiple LLMs:
- OpenAI's GPT-4o
- Anthropic's Claude 3.5 Sonnet
- Persistent memory using Qdrant vector store
- User-specific conversation history
- Memory retrieval for contextual responses
- User-friendly interface with LLM selection
How to get Started?
- Clone the GitHub repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/llm_apps_with_memory_tutorials/multi_llm_memory- Install the required dependencies:
pip install -r requirements.txt- Ensure Qdrant is running:
The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different.
docker pull qdrant/qdrant
docker run -p 6333:6333 qdrant/qdrant- Run the Streamlit App
streamlit run multi_llm_memory.pyFrom the same category
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