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Updated integration docs (#3392)

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This commit is contained in:
Parshva Daftari
2025-09-02 04:26:43 +05:30
committed by GitHub
parent 97fd320bbf
commit c8f9f20dff
10 changed files with 163 additions and 95 deletions
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docs/integrations/agentops.mdx
+3 -1
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@@ -17,7 +17,7 @@ Before setting up Mem0 with AgentOps, ensure you have:
1. Installed the required packages:
```bash
pip install mem0ai agentops
pip install mem0ai agentops python-dotenv
```
2. Valid API keys:
@@ -37,7 +37,9 @@ import asyncio
import logging
from dotenv import load_dotenv
import agentops
import openai
load_dotenv()
#Set up environment variables for API keys
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
docs/integrations/agno.mdx
+9 -8
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@@ -18,7 +18,7 @@ Before setting up Mem0 with Agno, ensure you have:
1. Installed the required packages:
```bash
pip install agno mem0ai
pip install agno mem0ai python-dotenv
```
2. Valid API keys:
@@ -81,7 +81,7 @@ agent = Agent(
def chat_user(
user_input: Optional[str] = None,
user_id: str = "user_123",
user_id: str = "alex",
image_path: Optional[str] = None
) -> str:
"""
@@ -120,13 +120,13 @@ def chat_user(
})
# Store messages in memory
client.add(messages, user_id=user_id)
client.add(messages, user_id=user_id, output_format='v1.1')
print("✅ Image and text stored in memory.")
if user_input:
# Search for relevant memories
memories = client.search(user_input, user_id=user_id)
memory_context = "\n".join(f"- {m['memory']}" for m in memories.get('results', []))
memories = client.search(user_input, user_id=user_id, output_format='v1.1')
memory_context = "\n".join(f"- {m['memory']}" for m in memories['results'])
# Construct the prompt
prompt = f"""
@@ -150,7 +150,8 @@ User question:
response = agent.run(prompt)
# Store the interaction in memory
client.add(f"User: {user_input}\nAssistant: {response.content}", user_id=user_id)
interaction_message = [{"role": "user", "content": f"User: {user_input}\nAssistant: {response.content}"}]
client.add(interaction_message, user_id=user_id, output_format='v1.1')
return response.content
return "No user input or image provided."
@@ -159,9 +160,9 @@ User question:
# Example Usage
if __name__ == "__main__":
response = chat_user(
"This is the picture of what I brought with me in the trip to Bahamas",
"I like to travel and my favorite destination is London",
image_path="travel_items.jpeg",
user_id="user_123"
user_id="alex"
)
print(response)
```
docs/integrations/autogen.mdx
+16 -9
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@@ -1,3 +1,7 @@
---
title: AutoGen
---
Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
## Overview
@@ -10,7 +14,7 @@ In this guide, we'll explore an example of creating a conversational AI system w
Install necessary libraries:
```bash
pip install pyautogen mem0ai openai
pip install autogen mem0ai openai python-dotenv
```
First, we'll import the necessary libraries and set up our configurations.
@@ -22,15 +26,18 @@ import os
from autogen import ConversableAgent
from mem0 import MemoryClient
from openai import OpenAI
from dotenv import load_dotenv
load_dotenv()
# Configuration
OPENAI_API_KEY = 'sk-xxx' # Replace with your actual OpenAI API key
MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key from https://app.mem0.ai
USER_ID = "customer_service_bot"
# OPENAI_API_KEY = 'sk-xxx' # Replace with your actual OpenAI API key
# MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key from https://app.mem0.ai
USER_ID = "alice"
# Set up OpenAI API key
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
os.environ['MEM0_API_KEY'] = MEM0_API_KEY
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
# os.environ['MEM0_API_KEY'] = MEM0_API_KEY
# Initialize Mem0 and AutoGen agents
memory_client = MemoryClient()
@@ -55,7 +62,7 @@ conversation = [
{"role": "assistant", "content": "Thank you for the information. Let's troubleshoot this issue..."}
]
memory_client.add(messages=conversation, user_id=USER_ID)
memory_client.add(messages=conversation, user_id=USER_ID, output_format="v1.1")
print("Conversation added to memory.")
```
@@ -65,7 +72,7 @@ Create a function to get context-aware responses based on user's question and pr
```python
def get_context_aware_response(question):
relevant_memories = memory_client.search(question, user_id=USER_ID)
relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
prompt = f"""Answer the user question considering the previous interactions:
@@ -97,7 +104,7 @@ manager = ConversableAgent(
)
def escalate_to_manager(question):
relevant_memories = memory_client.search(question, user_id=USER_ID)
relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
prompt = f"""
docs/integrations/elevenlabs.mdx
+1 -1
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@@ -16,7 +16,7 @@ In this guide, we'll build a voice agent that:
Install necessary libraries:
```bash
pip install elevenlabs mem0 python-dotenv
pip install elevenlabs mem0ai python-dotenv
```
Configure your environment variables:
docs/integrations/google-ai-adk.mdx
+16 -11
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@@ -17,7 +17,7 @@ Before setting up Mem0 with Google ADK, ensure you have:
1. Installed the required packages:
```bash
pip install google-adk mem0ai
pip install google-adk mem0ai python-dotenv
```
2. Valid API keys:
@@ -30,15 +30,19 @@ The following example demonstrates how to create a Google ADK agent with Mem0 me
```python
import os
import asyncio
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
from mem0 import MemoryClient
from dotenv import load_dotenv
load_dotenv()
# Set up environment variables
os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
# os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
# Initialize Mem0 client
mem0 = MemoryClient()
@@ -46,17 +50,18 @@ mem0 = MemoryClient()
# Define memory function tools
def search_memory(query: str, user_id: str) -> dict:
"""Search through past conversations and memories"""
memories = mem0.search(query, user_id=user_id)
memories = mem0.search(query, user_id=user_id, output_format='v1.1')
if memories.get('results', []):
memory_context = "\n".join([f"- {mem['memory']}" for mem in memories.get('results', [])])
memory_list = memories['results']
memory_context = "\n".join([f"- {mem['memory']}" for mem in memory_list])
return {"status": "success", "memories": memory_context}
return {"status": "no_memories", "message": "No relevant memories found"}
def save_memory(content: str, user_id: str) -> dict:
"""Save important information to memory"""
try:
mem0.add([{"role": "user", "content": content}], user_id=user_id)
return {"status": "success", "message": "Information saved to memory"}
result = mem0.add([{"role": "user", "content": content}], user_id=user_id, output_format='v1.1')
return {"status": "success", "message": "Information saved to memory", "result": result}
except Exception as e:
return {"status": "error", "message": f"Failed to save memory: {str(e)}"}
@@ -72,7 +77,7 @@ personal_assistant = Agent(
tools=[search_memory, save_memory]
)
def chat_with_agent(user_input: str, user_id: str) -> str:
async def chat_with_agent(user_input: str, user_id: str) -> str:
"""
Handle user input with automatic memory integration.
@@ -85,7 +90,7 @@ def chat_with_agent(user_input: str, user_id: str) -> str:
"""
# Set up session and runner
session_service = InMemorySessionService()
session = session_service.create_session(
session = await session_service.create_session(
app_name="memory_assistant",
user_id=user_id,
session_id=f"session_{user_id}"
@@ -107,10 +112,10 @@ def chat_with_agent(user_input: str, user_id: str) -> str:
# Example usage
if __name__ == "__main__":
response = chat_with_agent(
response = asyncio.run(chat_with_agent(
"I love Italian food and I'm planning a trip to Rome next month",
user_id="alice"
)
))
print(response)
```
docs/integrations/langchain.mdx
+42 -28
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@@ -16,7 +16,7 @@ In this guide, we'll create a Travel Agent AI that:
Install necessary libraries:
```bash
pip install langchain langchain_openai mem0ai
pip install langchain langchain_openai mem0ai python-dotenv
```
Import required modules and set up configurations:
@@ -30,10 +30,13 @@ from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from mem0 import MemoryClient
from dotenv import load_dotenv
load_dotenv()
# Configuration
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
# os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
# Initialize LangChain and Mem0
llm = ChatOpenAI(model="gpt-4o-mini")
@@ -61,19 +64,26 @@ Create functions to handle context retrieval, response generation, and addition
```python
def retrieve_context(query: str, user_id: str) -> List[Dict]:
"""Retrieve relevant context from Mem0"""
memories = mem0.search(query, user_id=user_id)
serialized_memories = ' '.join([mem["memory"] for mem in memories.get('results', [])])
context = [
{
"role": "system",
"content": f"Relevant information: {serialized_memories}"
},
{
"role": "user",
"content": query
}
]
return context
try:
memories = mem0.search(query, user_id=user_id, output_format='v1.1')
memory_list = memories['results']
serialized_memories = ' '.join([mem["memory"] for mem in memory_list])
context = [
{
"role": "system",
"content": f"Relevant information: {serialized_memories}"
},
{
"role": "user",
"content": query
}
]
return context
except Exception as e:
print(f"Error retrieving memories: {e}")
# Return empty context if there's an error
return [{"role": "user", "content": query}]
def generate_response(input: str, context: List[Dict]) -> str:
"""Generate a response using the language model"""
@@ -86,17 +96,21 @@ def generate_response(input: str, context: List[Dict]) -> str:
def save_interaction(user_id: str, user_input: str, assistant_response: str):
"""Save the interaction to Mem0"""
interaction = [
{
"role": "user",
"content": user_input
},
{
"role": "assistant",
"content": assistant_response
}
]
mem0.add(interaction, user_id=user_id)
try:
interaction = [
{
"role": "user",
"content": user_input
},
{
"role": "assistant",
"content": assistant_response
}
]
result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
print(f"Memory saved successfully: {len(result.get('results', []))} memories added")
except Exception as e:
print(f"Error saving interaction: {e}")
```
## Create Chat Turn Function
@@ -124,7 +138,7 @@ Set up the main program loop for user interaction:
```python
if __name__ == "__main__":
print("Welcome to your personal Travel Agent Planner! How can I assist you with your travel plans today?")
user_id = "john"
user_id = "alice"
while True:
user_input = input("You: ")
docs/integrations/langgraph.mdx
+45 -17
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@@ -16,7 +16,7 @@ In this guide, we'll create a Customer Support AI Agent that:
Install necessary libraries:
```bash
pip install langgraph langchain-openai mem0ai
pip install langgraph langchain-openai mem0ai python-dotenv
```
@@ -31,14 +31,17 @@ from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI
from mem0 import MemoryClient
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from dotenv import load_dotenv
load_dotenv()
# Configuration
OPENAI_API_KEY = 'sk-xxx' # Replace with your actual OpenAI API key
MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key
# OPENAI_API_KEY = 'sk-xxx' # Replace with your actual OpenAI API key
# MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key
# Initialize LangChain and Mem0
llm = ChatOpenAI(model="gpt-4", api_key=OPENAI_API_KEY)
mem0 = MemoryClient(api_key=MEM0_API_KEY)
llm = ChatOpenAI(model="gpt-4")
mem0 = MemoryClient()
```
## Define State and Graph
@@ -62,22 +65,47 @@ def chatbot(state: State):
messages = state["messages"]
user_id = state["mem0_user_id"]
# Retrieve relevant memories
memories = mem0.search(messages[-1].content, user_id=user_id)
try:
# Retrieve relevant memories
memories = mem0.search(messages[-1].content, user_id=user_id, output_format='v1.1')
# Handle dict response format
memory_list = memories['results']
context = "Relevant information from previous conversations:\n"
for memory in memories.get('results', []):
context += f"- {memory['memory']}\n"
context = "Relevant information from previous conversations:\n"
for memory in memory_list:
context += f"- {memory['memory']}\n"
system_message = SystemMessage(content=f"""You are a helpful customer support assistant. Use the provided context to personalize your responses and remember user preferences and past interactions.
system_message = SystemMessage(content=f"""You are a helpful customer support assistant. Use the provided context to personalize your responses and remember user preferences and past interactions.
{context}""")
full_messages = [system_message] + messages
response = llm.invoke(full_messages)
full_messages = [system_message] + messages
response = llm.invoke(full_messages)
# Store the interaction in Mem0
mem0.add(f"User: {messages[-1].content}\nAssistant: {response.content}", user_id=user_id)
return {"messages": [response]}
# Store the interaction in Mem0
try:
interaction = [
{
"role": "user",
"content": messages[-1].content
},
{
"role": "assistant",
"content": response.content
}
]
result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
print(f"Memory saved: {len(result.get('results', []))} memories added")
except Exception as e:
print(f"Error saving memory: {e}")
return {"messages": [response]}
except Exception as e:
print(f"Error in chatbot: {e}")
# Fallback response without memory context
response = llm.invoke(messages)
return {"messages": [response]}
```
## Set Up Graph Structure
@@ -115,7 +143,7 @@ Set up the main program loop for user interaction:
```python
if __name__ == "__main__":
print("Welcome to Customer Support! How can I assist you today?")
mem0_user_id = "customer_123" # You can generate or retrieve this based on your user management system
mem0_user_id = "alice" # You can generate or retrieve this based on your user management system
while True:
user_input = input("You: ")
if user_input.lower() in ['quit', 'exit', 'bye']:
docs/integrations/llama-index.mdx
+20 -12
View File
@@ -13,7 +13,7 @@ LlamaIndex supports Mem0 as a [memory store](https://llamahub.ai/l/memory/llama-
To install the required package, run:
```bash
pip install llama-index-core llama-index-memory-mem0
pip install llama-index-core llama-index-memory-mem0 python-dotenv
```
### Setup with Mem0 Platform
@@ -25,18 +25,23 @@ Set your Mem0 Platform API key as an environment variable. You can replace `<you
</Note>
```python
os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
from dotenv import load_dotenv
import os
load_dotenv()
# os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
```
Import the necessary modules and create a Mem0Memory instance:
```python
from llama_index.memory.mem0 import Mem0Memory
context = {"user_id": "user_1"}
context = {"user_id": "alice"}
memory_from_client = Mem0Memory.from_client(
context=context,
api_key="<your-mem0-api-key>",
search_msg_limit=4, # optional, default is 5
output_format='v1.1', # Remove deprecation warnings
)
```
@@ -44,8 +49,8 @@ Context is used to identify the user, agent or the conversation in the Mem0. It
```python
context = {
"user_id": "user_1",
"agent_id": "agent_1",
"user_id": "alice",
"agent_id": "llama_agent_1",
"run_id": "run_1",
}
```
@@ -98,17 +103,20 @@ memory_from_config = Mem0Memory.from_config(
context=context,
config=config,
search_msg_limit=4, # optional, default is 5
output_format='v1.1', # Remove deprecation warnings
)
```
Initialize the LLM
```python
import os
from llama_index.llms.openai import OpenAI
from dotenv import load_dotenv
os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
llm = OpenAI(model="gpt-4o")
load_dotenv()
# os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
llm = OpenAI(model="gpt-4o-mini")
```
### SimpleChatEngine
@@ -122,7 +130,7 @@ agent = SimpleChatEngine.from_defaults(
)
# Start the chat
response = agent.chat("Hi, My name is Mayank")
response = agent.chat("Hi, My name is Alice")
print(response)
```
Now we will learn how to use Mem0 with FunctionCalling and ReAct agents.
@@ -165,7 +173,7 @@ agent = FunctionCallingAgent.from_tools(
)
# Start the chat
response = agent.chat("Hi, My name is Mayank")
response = agent.chat("Hi, My name is Alice")
print(response)
```
@@ -182,7 +190,7 @@ agent = ReActAgent.from_tools(
)
# Start the chat
response = agent.chat("Hi, My name is Mayank")
response = agent.chat("Hi, My name is Alice")
print(response)
```
docs/integrations/pipecat.mdx
+1 -1
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@@ -92,7 +92,7 @@ async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
# Basic setup with minimal configuration
user_id = "user123"
user_id = "alice"
# WebSocket transport
transport = FastAPIWebsocketTransport(