> ## 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.

# 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.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Shared Memory"
        A1["🤖 Agent 1\n(Researcher)"] --> MEM["🧠 Shared\nMemory"]
        A2["🤖 Agent 2\n(Analyst)"] --> MEM
        A3["🤖 Agent 3\n(Writer)"] --> MEM
        MEM --> A1
        MEM --> A2
        MEM --> A3
    end

    classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef memory fill:#6366F1,stroke:#7C90A0,color:#fff

    class A1,A2,A3 agent
    class MEM memory
```

## Quick Start

<Steps>
  <Step title="Simple Usage">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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()
    ```
  </Step>

  <Step title="With Custom Embedder">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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()
    ```
  </Step>
</Steps>

***

## Configuration Options

<Card title="MultiAgentMemoryConfig SDK Reference" icon="code" href="/docs/sdk/reference/python/classes/MultiAgentMemoryConfig">
  Full parameter reference for MultiAgentMemoryConfig
</Card>

**Precedence ladder:**

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Workflow
    participant Agent1
    participant Agent2
    participant Memory

    User->>Workflow: Start workflow
    Workflow->>Agent1: Execute task
    Agent1->>Memory: Store findings
    Agent1-->>Workflow: Task 1 complete
    Workflow->>Agent2: Execute task
    Agent2->>Memory: Retrieve Agent1's findings
    Memory-->>Agent2: Shared context
    Agent2-->>Workflow: Task 2 complete (with context)
    Workflow-->>User: Combined output
```

| 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

<Card icon="code" href="/docs/sdk/reference/python/MultiAgentMemoryConfig">
  Full list of options, types, and defaults — `MultiAgentMemoryConfig`
</Card>

| 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

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

<AccordionGroup>
  <Accordion title="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.
  </Accordion>

  <Accordion title="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.
  </Accordion>

  <Accordion title="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.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="Advanced Memory" icon="database" href="/docs/features/advanced-memory">
    Single-agent memory configuration
  </Card>

  <Card title="Multi-Agent Planning" icon="list-check" href="/docs/features/multi-agent-planning">
    Plan tasks before executing them
  </Card>

  <Card title="Multi-Agent Hooks" icon="webhook" href="/docs/features/multi-agent-hooks">
    Intercept task lifecycle events
  </Card>

  <Card title="Learn" icon="graduation-cap" href="/docs/features/learn">
    Continuous learning from conversations
  </Card>
</CardGroup>