Camel AI

Plug Mem0 cloud memory into Camel’s agents with the built‑in Mem0Storage.

Camel AI integration

Connect Camel’s agent framework to Mem0 so every agent can persist and recall conversation context across sessions with minimal setup.

Prerequisites

• Mem0: MEM0_API_KEY (or self-hosted endpoint), pip install mem0ai
• Camel AI: pip install camel-ai (requires Python 3.9+)
• Optional: OpenAI API key if you run LLM-backed agents

Camel provides a Python SDK today. A TypeScript path is not available yet.

Configure credentials

Mem0

1. Export your API key

   export MEM0_API_KEY="sk-..."
   
2. (Self-host) Point to your Mem0 API

   export MEM0_BASE_URL="https://your-mem0-domain"
   

Camel

1. Install Camel with Mem0 dependency

   pip install "camel-ai>=0.2.0" mem0ai
   
2. (Optional) Add your model credentials

   export OPENAI_API_KEY="sk-openai..."
   

Mem0Storage reads MEM0_API_KEY automatically. Pass api_key explicitly only when you need to override the environment.

Wire Mem0 into a Camel agent

1. Create a Mem0-backed memory store

   import os
   from camel.storages import Mem0Storage
   
   mem0_store = Mem0Storage(
       api_key=os.environ.get("MEM0_API_KEY"),
       agent_id="travel_agent",
       user_id="alice",
       metadata={"source": "camel-demo"},
   )
   
2. Attach it to Camel memory

   from camel.memories import ChatHistoryMemory, ScoreBasedContextCreator
   from camel.utils import OpenAITokenCounter
   from camel.types import ModelType
   
   memory = ChatHistoryMemory(
       context_creator=ScoreBasedContextCreator(
           token_counter=OpenAITokenCounter(ModelType.GPT_4O_MINI),
           token_limit=1024,
       ),
       storage=mem0_store,
       agent_id="travel_agent",
   )
   
3. Let your agent read and write Mem0

   from camel.agents import ChatAgent
   from camel.messages import BaseMessage
   
   agent = ChatAgent(
       system_message=BaseMessage.make_assistant_message(
           role_name="Agent",
           content="You are a helpful travel assistant. Reuse stored memories."
       )
   )
   
   agent.memory = memory
   
   response = agent.step(
       BaseMessage.make_user_message(
           role_name="User",
           content="I prefer boutique hotels in Paris."
       )
   )
   
   print(response.msgs[0].content)
   

Run python camel_mem0_demo.py (or the snippet above in a REPL). You should see the agent respond and the memory persisted to Mem0. Re-running with a new prompt should include the stored preference.

Verify the integration

• Mem0 dashboard shows new memories under agent_id=travel_agent and user_id=alice.
• mem0_store.load() returns the records you just wrote.
• Camel agent replies reference prior user preferences on subsequent runs.

Troubleshooting

• Missing MEM0_API_KEY: set export MEM0_API_KEY="sk-..." or pass api_key into Mem0Storage.
• No memories returned: ensure agent_id/user_id in your query match what you used when writing.
• Network errors to Mem0: if self-hosting, set MEM0_BASE_URL to your deployment URL.

Memory types in Mem0

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Using Mem0? Star us on GitHub to help more developers discover memory for AI apps.