> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Mem0 and PraisonAI Integration
> Guide for integrating Mem0 (formerly EmbedChain) memory management system with PraisonAI, including tools for storing, retrieving, and managing memories
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="Memory Assistant",
instructions="Remember user preferences across turns.",
memory=True,
)
agent.start("Remember that I prefer concise bullet answers")
```
The user shares a preference; the agent stores it with Mem0-backed memory for later sessions.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
U[Input] --> A[Agent]
A --> T[Tool]
T --> O[Output]
classDef agent fill:#8B0000,color:#fff
classDef tool fill:#189AB4,color:#fff
class A agent
class U,O tool
class T tool
```
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
participant User
participant Agent
participant Feature as Mem0 and PraisonAI Integration
User->>Agent: Request
Agent->>Feature: Process request
Feature-->>Agent: Result
Agent-->>User: Response
```
# Mem0 and PaisonAI Integration
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install "praisonaiagents[memory]"
```
Mem0 is a tools to store, updated, delete and retrieve memories.
[https://github.com/mem0ai/mem0](https://github.com/mem0ai/mem0)
**Vector store backend choice matters.** mem0's MongoDB vector store has an upstream bug ([mem0ai/mem0#3185](https://github.com/mem0ai/mem0/issues/3185)) that causes searches to silently return empty. PraisonAI catches this specific `TypeError` and logs a warning, but recovery requires switching to **Qdrant** or **Chroma** as mem0's vector store backend. If you need MongoDB itself, use PraisonAI's built-in [MongoDB Memory](/docs/features/mongodb-memory) adapter instead — it is separate from mem0 and unaffected by this bug. See [Memory Troubleshooting](/docs/features/memory-troubleshooting#why-is-memory-mem0-returning-empty-search-results-mongodb-vector-store) for the fix.
## Quick Start
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents[memory]
```
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="MemoryAgent",
instructions="Remember user preferences across conversations.",
memory=True,
)
agent.start("My favorite color is blue")
agent.start("What is my favorite color?")
```
## Mem0 previously called EmbedChain
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from mem0 import Memory
from praisonai_tools import BaseTool
class AddMemoryTool(BaseTool):
name: str = "Add Memory Tool"
description: str = ("This tool allows storing a new memory with user ID and optional metadata.\n"
"Example:\n"
" - Input: text='I am working on improving my tennis skills. Suggest some online courses.', user_id='alice', metadata={'category': 'hobbies'}\n"
" - Output: Memory added with summary 'Improving her tennis skills. Looking for online suggestions.'")
def _run(self, text: str, user_id: str, metadata: dict = None):
m = Memory()
result = m.add(text, user_id=user_id, metadata=metadata)
return result
class GetAllMemoriesTool(BaseTool):
name: str = "Get All Memories Tool"
description: str = ("This tool retrieves all stored memories.\n"
"Example:\n"
" - Input: action='get_all'\n"
" - Output: List of all stored memories.")
def _run(self):
m = Memory()
result = m.get_all()
return result
class SearchMemoryTool(BaseTool):
name: str = "Search Memory Tool"
description: str = ("This tool searches for specific memories based on a query and user ID.\n"
"Example:\n"
" - Input: query='What are Alice's hobbies?', user_id='alice'\n"
" - Output: Search results related to Alice's hobbies.")
def _run(self, query: str, user_id: str):
m = Memory()
result = m.search(query=query, user_id=user_id)
return result
class UpdateMemoryTool(BaseTool):
name: str = "Update Memory Tool"
description: str = ("This tool updates an existing memory by memory ID and new data.\n"
"Example:\n"
" - Input: memory_id='cb032b42-0703-4b9c-954d-77c36abdd660', data='Likes to play tennis on weekends'\n"
" - Output: Memory updated to 'Likes to play tennis on weekends.'")
def _run(self, memory_id: str, data: str):
m = Memory()
result = m.update(memory_id=memory_id, data=data)
return result
class MemoryHistoryTool(BaseTool):
name: str = "Memory History Tool"
description: str = ("This tool gets the history of changes made to a specific memory by memory ID.\n"
"Example:\n"
" - Input: memory_id='cb032b42-0703-4b9c-954d-77c36abdd660'\n"
" - Output: History of the specified memory.")
def _run(self, memory_id: str):
m = Memory()
result = m.history(memory_id=memory_id)
return result
```
## Best Practices
Always pass the same `user_id` for the same user across sessions to retrieve their memories correctly.
Store memories at the end of a session to avoid storing intermediate or incorrect information.
Search existing memories before adding new ones to avoid duplicates.
Clean up outdated memories periodically to keep retrieval accurate over time.
## Related
Build your own agent tools
Browse PraisonAI tool documentation