Multi-Agent Memory

Share memory context across agents in multi-agent workflows

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"},
))

Option	Type	Default	Description
user_id	str | None	None	Scope memory to a specific user or project
embedder	Any | None	None	Embedder configuration (e.g., {"provider": "openai"})

How It Works

Phase	What happens
1. Store	First agent saves discoveries to shared memory
2. Retrieve	Subsequent agents access all stored context
3. Build	Later agents build on earlier agents’ work

---

Configuration Options

Full list of options, types, and defaults — MultiAgentMemoryConfig

Option	Type	Default	Description
user_id	str | None	None	Shared user/project identifier for scoped memory
embedder	Any | None	None	Embedder configuration for semantic memory
config	dict | None	None	Memory 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

Always set user_id for project isolation

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.

Use semantic memory for related topics

Configure the embedder when agents need to find semantically related information (not just exact matches). OpenAI’s text-embedding-3-small is a good default — fast and cost-effective.

Order tasks to build context

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.

---

Isolating collections per agent

When several agents share one rag_db_path, give each a distinct collection_name so they coexist and can be reset independently.

from praisonaiagents import Agent

researcher = Agent(
    name="Researcher",
    memory={
        "provider": "rag",
        "rag_db_path": "./chroma_db",
        "collection_name": "researcher_ltm",
    },
)

writer = Agent(
    name="Writer",
    memory={
        "provider": "rag",
        "rag_db_path": "./chroma_db",
        "collection_name": "writer_ltm",
    },
)

# researcher.memory.reset_long_term() no longer wipes writer's memory.

collection_name defaults to "memory_store". See Memory Configuration.

---

Related

Advanced Memory

Single-agent memory configuration

Multi-Agent Planning

Plan tasks before executing them

Multi-Agent Hooks

Intercept task lifecycle events

Learn

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