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Camel AI

Welcome Mem0 Platform Open Source Cookbooks Integrations API Reference Release Notes

• Documentation

Overview

• Overview

Agent Frameworks

• Langchain
• LangGraph
• LlamaIndex
• CrewAI
• AutoGen
• Agno
• Camel AI
• OpenClaw
• OpenAI Agents SDK
• Google ADK
• Mastra
• Vercel AI SDK

Voice & Real-time

• Livekit
• Pipecat
• ElevenLabs

Cloud & Infrastructure

• AWS Bedrock

Developer Tools

• Dify
• Flowise
• Langchain Tools
• AgentOps
• Keywords AI
• Raycast Extension

On this page

• Camel AI integration
• Configure credentials
• Wire Mem0 into a Camel agent
• Verify the integration
• Troubleshooting

​ 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

• Camel

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Export your API key

export MEM0_API_KEY="sk-..."

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(Self-host) Point to your Mem0 API

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

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Install Camel with Mem0 dependency

pip install "camel-ai>=0.2.0" mem0ai

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(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

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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"},
)

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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",
)

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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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