Skip to main content
Multi-Agent Memory gives all agents in a workflow access to shared memory, so knowledge discovered by one agent is available to others. 
from praisonaiagents import Agent, Task, PraisonAIAgents, MultiAgentMemoryConfig

researcher = Agent(name="Researcher", instructions="Research topics and share findings.")
writer = Agent(name="Writer", instructions="Write based on shared research.")

tasks = [
    Task(description="Research climate change impacts on agriculture", agent=researcher),
    Task(description="Write a policy brief based on the research", agent=writer),
]

workflow = PraisonAIAgents(
    agents=[researcher, writer],
    tasks=tasks,
    memory=MultiAgentMemoryConfig(user_id="project_climate", config={"provider": "rag"}),
)
workflow.start()
The user runs a multi-agent workflow; all agents read and write shared memory under one user id.

​
Quick Start

1

Simple Usage

from praisonaiagents import Agent, Task, PraisonAIAgents, MultiAgentMemoryConfig

agent1 = Agent(name="Gatherer", instructions="Gather and remember information.")
agent2 = Agent(name="Synthesizer", instructions="Synthesize remembered information.")

tasks = [
    Task(description="Gather key facts about solar energy", agent=agent1),
    Task(description="Create a summary from gathered facts", agent=agent2),
]

workflow = PraisonAIAgents(
    agents=[agent1, agent2],
    tasks=tasks,
    memory=MultiAgentMemoryConfig(user_id="solar_project"),
)
workflow.start()
2

With Custom Embedder

from praisonaiagents import Agent, Task, PraisonAIAgents, MultiAgentMemoryConfig

agents = [
    Agent(name="Expert", instructions="Provide domain expertise."),
    Agent(name="Summarizer", instructions="Summarize expert findings."),
]

tasks = [
    Task(description="Analyze cybersecurity vulnerabilities in cloud systems", agent=agents[0]),
    Task(description="Create an executive summary for non-technical stakeholders", agent=agents[1]),
]

workflow = PraisonAIAgents(
    agents=agents,
    tasks=tasks,
    memory=MultiAgentMemoryConfig(
        user_id="security_review",
        embedder={"provider": "openai", "config": {"model": "text-embedding-3-small"}},
        config={"provider": "rag"},
    ),
)
workflow.start()

​
Configuration Options

MultiAgentMemoryConfig SDK Reference

Full parameter reference for MultiAgentMemoryConfig
Precedence ladder: 
# Level 1: Bool (enable with defaults)
team = PraisonAIAgents(memory=True)

# Level 2: MultiAgentMemoryConfig (full control)
team = PraisonAIAgents(memory=MultiAgentMemoryConfig(
    user_id="user-123",
    embedder={"provider": "openai"},
))
OptionTypeDefaultDescription
user_idstr | NoneNoneScope memory to a specific user or project
embedderAny | NoneNoneEmbedder configuration (e.g., {"provider": "openai"})

​
How It Works

PhaseWhat happens
1. StoreFirst agent saves discoveries to shared memory
2. RetrieveSubsequent agents access all stored context
3. BuildLater agents build on earlier agents’ work

​
Configuration Options

Full list of options, types, and defaults — MultiAgentMemoryConfig
OptionTypeDefaultDescription
user_idstr | NoneNoneShared user/project identifier for scoped memory
embedderAny | NoneNoneEmbedder configuration for semantic memory
configdict | NoneNoneMemory provider configuration

​
Common Patterns

​
Pattern 1 — Project-scoped research memory

from praisonaiagents import Agent, Task, PraisonAIAgents, MultiAgentMemoryConfig

agents = [
    Agent(name="Researcher", instructions="Research and document findings."),
    Agent(name="Analyst", instructions="Analyze documented findings."),
    Agent(name="Reporter", instructions="Create final reports."),
]

tasks = [
    Task(description="Research market trends in renewable energy", agent=agents[0]),
    Task(description="Identify key investment opportunities from research", agent=agents[1]),
    Task(description="Write an investment recommendation report", agent=agents[2]),
]

result = PraisonAIAgents(
    agents=agents,
    tasks=tasks,
    memory=MultiAgentMemoryConfig(user_id="renewable_energy_2025"),
).start()
print(result)

​
Best Practices

Set user_id to a unique project or session identifier to prevent memory from bleeding between unrelated workflows. Without user_id, all workflows share the same memory namespace.
Tasks run in order by default. Place information-gathering tasks first and synthesis/writing tasks later so downstream agents have full context from upstream agents.

Advanced Memory

Single-agent memory configuration

Multi-Agent Planning

Plan tasks before executing them

Multi-Agent Hooks

Intercept task lifecycle events

Learn

Continuous learning from conversations