FeatureKnowledgeMemory
When UsedPre-loaded before agent executionCreated and updated during runtime
PurposeProvide static reference informationStore dynamic context and interactions
StorageRead-only knowledge baseRead-write memory store
PersistencePermanent until explicitly changedCan be temporary (STM) or persistent (LTM)
UpdatesManual updates through knowledge filesAutomatic updates during agent execution

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Quick Start

1

Install Package

First, install the PraisonAI Agents package:

pip install "praisonaiagents[memory]" duckduckgo_search

duckduckgo_search is a tool that allows agents to search the web. It is required for the multiple agents example shown below.

2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY=your_api_key_here
3

Create a file

Create a new file app.py with the basic setup:

4

Start Agents

Type this in your terminal to run your agents:

python app.py

Requirements

  • Python 3.10 or higher
  • OpenAI API key. Generate OpenAI API key here. Use Other models using this guide.
  • Basic understanding of Python

​
Understanding Memory

What is Agent Memory?

Memory in AI agents enables them to:

  • Maintain context across multiple tasks
  • Remember previous interactions and findings
  • Build upon past knowledge
  • Share information between agents
  • Create more coherent and contextual responses

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Features

Context Retention

Maintain information across multiple interactions.

Information Sharing

Share knowledge between multiple agents.

Long-term Storage

Store and retrieve information over extended periods.

Memory Types

Support for different memory types (short-term, long-term).

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Multi-Agent Memory

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import duckduckgo

# Create first agent for research
researcher = Agent(
    role="Research Analyst",
    goal="Research and analyze market trends",
    backstory="Expert in market research and data analysis",
    tools=[duckduckgo],
    verbose=True
)

# Create second agent for report writing
writer = Agent(
    role="Report Writer",
    goal="Create comprehensive market reports",
    backstory="Expert in technical writing and report creation",
    verbose=True
)

# Create research task
research_task = Task(
    description="Research current market trends",
    expected_output="Detailed market analysis",
    agent=researcher
)

# Create writing task
report_task = Task(
    description="Create a market report based on research",
    expected_output="Comprehensive market report",
    agent=writer
)

# Create and start the agents with memory
agents = PraisonAIAgents(
    agents=[researcher, writer],
    tasks=[research_task, report_task],
    memory=True,
    process="sequential"
)

# Start execution
result = agents.start()

​
Configuration Options

# Create an agent with memory configuration
agent = Agent(
    role="Research Analyst",
    goal="Research and retain information",
    backstory="Expert in research and analysis",
    tools=[duckduckgo],
    verbose=True,  # Enable detailed logging
    llm="gpt-4o"  # Language model to use
)

# Create agents with memory options
agents = PraisonAIAgents(
    agents=[agent],
    tasks=[task],
    memory=True,  # Enable memory
    memory_config={
        "provider": "rag",  # Use RAG for semantic search
        "use_embedding": True,  # Enable embeddings for better search
        "short_db": ".praison/short_term.db",  # Path for short-term memory
        "long_db": ".praison/long_term.db",    # Path for long-term memory
        "rag_db_path": ".praison/chroma_db"    # Path for vector database
    }
)

​
Troubleshooting

Memory Issues

If memory isn’t working as expected:

  • Check memory configuration
  • Enable verbose mode for debugging
  • Verify memory provider settings

Context Flow

If context isn’t being maintained:

  • Review task dependencies
  • Check memory configuration
  • Verify agent communication

​
Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

Explore lightweight, focused AI agents

For optimal results, configure memory settings based on your specific use case requirements and expected interaction patterns.

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Memory Configuration Options

The memory system in PraisonAI supports various configuration options to customize how agents store and retrieve information:

memory_config = {
    # Memory Provider
    "provider": "rag",        # Options: "rag", "mem0", "none"
    "use_embedding": True,    # Enable semantic search with embeddings
    
    # Storage Paths
    "short_db": ".praison/short_term.db",  # Short-term memory SQLite DB
    "long_db": ".praison/long_term.db",    # Long-term memory SQLite DB
    "rag_db_path": ".praison/chroma_db",   # Vector database path
    
    # Memory Settings
    "ttl": 3600,             # Time to live for memory items (in seconds)
    
    # Optional Mem0 Config (if using mem0 provider)
    "config": {
        "api_key": "...",    # Mem0 API key
        "org_id": "...",     # Organization ID
        "project_id": "..."  # Project ID
    }
}

# Create agents with memory configuration
agents = PraisonAIAgents(
    agents=[agent],
    tasks=[task],
    memory=True,
    memory_config=memory_config
)

​
Memory Types

PraisonAI’s memory system includes several types of memory:

Short-term Memory

  • Temporary storage for current context
  • Automatically cleared between sessions
  • Fast access for immediate task context

Long-term Memory

  • Persistent storage for important information
  • Semantic search capabilities with RAG
  • Quality-based storage decisions

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Memory Quality Control

PraisonAI includes built-in quality control for memory storage:

# Example of storing with quality metrics
agents.memory.store_long_term(
    text="Important information to remember",
    metadata={
        "task_id": "task_123",
        "agent": "research_agent"
    },
    completeness=0.9,    # How complete is the information
    relevance=0.85,      # How relevant to the task
    clarity=0.95,        # How clear and well-structured
    accuracy=0.9,        # How accurate is the information
    weights={            # Custom weights for quality score
        "completeness": 0.3,
        "relevance": 0.3,
        "clarity": 0.2,
        "accuracy": 0.2
    }
)

# Search with quality filter
results = agents.memory.search_long_term(
    query="search query",
    min_quality=0.8,     # Only return high-quality matches
    limit=5              # Maximum number of results
)

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