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Workspace context lets platform-hosted agents load settings and instructions scoped to a workspace. 
from praisonaiagents import Agent
from praisonaiagents.auth import WorkspaceContextProtocol

class PlatformWorkspaceContext:
    async def get_workspace_context(self, workspace_id: str) -> str | None:
        # Load workspace instructions from your database
        return "You support the Acme Corp workspace."

    async def get_agent_config(self, workspace_id: str, agent_id: str) -> dict | None:
        return {
            "system_prompt": "Be concise and professional.",
            "model": "gpt-4o-mini",
            "tools": ["web_search"],
            "max_concurrent_tasks": 3,
        }

agent = Agent(
    name="workspace-assistant",
    instructions="Help users in their workspace.",
)
The user opens a workspace thread; the agent loads scoped instructions from platform context.

​
Quick Start

1

Simple Usage

Implement WorkspaceContextProtocol with workspace-level instructions:
from praisonaiagents.auth import WorkspaceContextProtocol

class PlatformWorkspaceContext:
    def __init__(self, db):
        self.db = db

    async def get_workspace_context(self, workspace_id: str) -> str | None:
        workspace = await self.db.get_workspace(workspace_id)
        if not workspace:
            return None
        return workspace.instructions

    async def get_agent_config(self, workspace_id: str, agent_id: str) -> dict | None:
        agent = await self.db.get_agent(workspace_id, agent_id)
        if not agent:
            return None
        return {
            "system_prompt": agent.system_prompt,
            "model": agent.model,
            "tools": agent.tools,
            "max_concurrent_tasks": agent.max_concurrent_tasks,
        }
2

With Configuration

Merge workspace and agent settings before starting the agent:
from praisonaiagents import Agent

async def start_platform_agent(ctx: PlatformWorkspaceContext, workspace_id: str, agent_id: str):
    workspace_text = await ctx.get_workspace_context(workspace_id)
    config = await ctx.get_agent_config(workspace_id, agent_id) or {}

    instructions = config.get("system_prompt", "You are a helpful assistant.")
    if workspace_text:
        instructions = f"{workspace_text}\n\n{instructions}"

    agent = Agent(
        name=config.get("name", "assistant"),
        instructions=instructions,
        llm=config.get("model"),
    )
    return agent

​
How It Works

MethodReturns
get_workspace_context(workspace_id)Workspace instructions string, or None
get_agent_config(workspace_id, agent_id)Config dict with system_prompt, model, tools, max_concurrent_tasks

​
Configuration Options

Protocol fieldTypeDescription
get_workspace_contextasync (workspace_id) → str | NoneWorkspace-level instructions
get_agent_configasync (workspace_id, agent_id) → dict | NonePer-agent runtime settings
Expected keys in the agent config dict: system_prompt, model, tools, max_concurrent_tasks.

​
Best Practices

Filter by both agent_id and workspace_id — never fetch agents globally.
Missing workspace or agent records should return None, not raise.
Pass AsyncSession via constructor — do not create sessions inside the context provider.
Convert UUIDs and datetimes to strings before returning config dicts.

Platform Auth Configuration

Authentication and authorisation setup

Platform Agents

Register and manage platform-hosted agents