Platform Workspace Context

Provide workspace-level context and agent configuration from the platform database

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
   

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How It Works

Method	Returns
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 field	Type	Description
get_workspace_context	async (workspace_id) → str | None	Workspace-level instructions
get_agent_config	async (workspace_id, agent_id) → dict | None	Per-agent runtime settings

Expected keys in the agent config dict: system_prompt, model, tools, max_concurrent_tasks.

---

Best Practices

Always scope agent queries

Filter by both agent_id and workspace_id — never fetch agents globally.

Return None for missing resources

Missing workspace or agent records should return None, not raise.

Inject database sessions

Pass AsyncSession via constructor — do not create sessions inside the context provider.

Keep return values JSON-serialisable

Convert UUIDs and datetimes to strings before returning config dicts.

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Related

Platform Auth Configuration

Authentication and authorisation setup

Platform Agents

Register and manage platform-hosted agents