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Agency Context is a centralized data store accessible by all tools and agents within an agency. It allows you to share data between agents, control execution flow, and maintain state across tool calls without passing large data structures in messages.
Agency context is available when tools are deployed together with agents. If tools are deployed as separate APIs, they won’t share the same context, and you’ll need to implement your own state management solution.

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Understanding Agency Context

Agency context is particularly useful when your agents interact with multiple tools that need to exchange information. Here’s why:
  • Without Agency Context: Suppose Tool A collects data that Tool B needs. The agent must explicitly pass this data as a parameter to Tool B, consuming tokens and potentially hitting message limits.
Without Agency Context
  • With Agency Context: Tool A can store the required data in the agency context, and Tool B can retrieve it without needing direct parameter passing. This approach reduces complexity, saves tokens, and enables additional workflows.
With Agency Context

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Using Agency Context

The agency context is accessible in both FunctionTools and BaseTools using .get and .set, which let you access and modify the MasterContext passed during execution. Below is an example of how it can be used across tools. In this example, calling the Query Database tool stores database context, which is later retrieved by the Answer Question tool.
  • BaseTool
  • Function Tool
Originally, in v0.x, shared data was stored in the special _shared_state variable, which was carried over into v1.x. You can access it using the following methods:
  • Setting a value in the shared state: _shared_state.set('key', value)
  • Getting a value from the shared state: _shared_state.get('key')
Starting with v1.x, _shared_state simply holds a pointer to the agency context, allowing you to share data between FunctionTools and BaseTools.
You can also access the same context within the BaseTool by using self.context variable same way it’s done in FunctionTools.
from agency_swarm.tools import BaseTool
from pydantic import Field

class QueryDatabase(BaseTool):
    """
    Retrieves data from the database and stores it in the agency context.
    """
    question: str = Field(..., description="The query to execute.")

    async def run(self):
        # Fetch data based on the question
        context = query_database_api(self.question)

        # Store the context in the agency context
        self._shared_state.set('database_context', context)
        # Same as
        # self.context.set('database_context', context)
        self._shared_state.set('last_query', self.question)

        return "Database context has been retrieved and stored successfully."

class AnswerQuestion(BaseTool):
    """
    Provides answers based on the context stored in the agency context.
    """
    async def run(self):
        # Access the stored context
        context = self._shared_state.get('database_context')
        # Same as
        # context = self.context.get('database_context')

        if not context:
            return "Database context is missing. Please call the query_database tool first."

        # Generate an answer using the context
        answer = f"Answer derived from context: {context}"
        return answer

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Advanced Agency Context Patterns

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Complex Data Structures

Agency context can store any Python object, making it perfect for complex workflows: 
@function_tool
async def analyze_market_data(ctx: RunContextWrapper[MasterContext], symbols: str) -> str:
    """Analyzes multiple stocks and stores data."""
    symbol_list = symbols.split(',')

    market_analysis = {
        'timestamp': datetime.now().isoformat(),
        'symbols': {},
        'summary': {},
        'analyst': ctx.context.current_agent_name
    }

    for symbol in symbol_list:
        # Simulate market data fetch
        market_analysis['symbols'][symbol] = {
            'price': 150.0,
            'volume': 1000000,
            'trend': 'bullish'
        }

    # Store in agency context
    ctx.context.set('market_analysis', market_analysis)
    ctx.context.set('analysis_complete', True)

    return f"Market analysis complete for {len(symbol_list)} symbols"

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Workflow Coordination

Use agency context to coordinate multi-step workflows: 
@function_tool
async def step_1_data_collection(ctx: RunContextWrapper[MasterContext], params: str) -> str:
    """First step in a multi-step workflow."""
    # Perform step 1
    result = collect_data(params)

    # Store progress in agency context
    ctx.context.set('workflow_step_1', result)
    ctx.context.set('workflow_status', 'step_1_complete')

    return "Step 1 complete. Ready for step 2."

@function_tool
async def step_2_data_processing(ctx: RunContextWrapper[MasterContext]) -> str:
    """Second step that depends on first step."""
    # Check if step 1 is complete
    if ctx.context.get('workflow_status') != 'step_1_complete':
        return "Error: Step 1 must be completed first"

    # Get data from step 1
    step_1_data = ctx.context.get('workflow_step_1')

    # Process the data
    result = process_data(step_1_data)

    # Update progress
    ctx.context.set('workflow_step_2', result)
    ctx.context.set('workflow_status', 'step_2_complete')

    return "Step 2 complete. Workflow finished."

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Session Management

Agency context is perfect for maintaining session state: 
# Initialize agency with session context
agency = Agency(
    entry_agent,
    communication_flows=[(entry_agent, worker_agent)],
    user_context={
        'session_id': 'user_123',
        'user_preferences': {
            'language': 'en',
            'timezone': 'UTC',
            'risk_tolerance': 'moderate'
        },
        'session_start': datetime.now().isoformat()
    }
)

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Best Practices

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Use Descriptive Keys

Use clear, descriptive keys to avoid conflicts between different agents and workflows: 
# Good: Descriptive keys
ctx.context.set('user_portfolio_analysis_2024', data)
ctx.context.set('market_data_AAPL_realtime', market_data)

# Avoid: Generic keys that might conflict
ctx.context.set('data', data)
ctx.context.set('result', result)

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Provide Default Values

Always provide sensible defaults when retrieving data: 
# Good: Provides default
user_prefs = ctx.context.get('user_preferences', {})
risk_level = ctx.context.get('risk_tolerance', 'moderate')

# Risky: No default, might return None
risk_level = ctx.context.get('risk_tolerance')

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Clean Up Unneeded Data

For long-running sessions, clean up temporary data to avoid memory issues: 
@function_tool
async def cleanup_temporary_data(ctx: RunContextWrapper[MasterContext]) -> str:
    """Cleans up temporary analysis data."""
    temp_keys = ['temp_calculation_1', 'temp_calculation_2', 'scratch_data']

    for key in temp_keys:
        if key in ctx.context.user_context:
            del ctx.context.user_context[key]

    return "Temporary data cleaned up successfully"

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Migrating tools

If you’re migrating from Agency Swarm v0.x, and want to use new FunctionTool instead of BaseTool, here’s how you can do that: BaseTool Pattern: 
class MyTool(BaseTool):
    async def run(self):
        self._shared_state.set("key", "value")
        data = self._shared_state.get("key", "default")
        return "Done"
FunctionTool Pattern: 
@function_tool
async def my_tool(ctx: RunContextWrapper[MasterContext], param: str) -> str:
    """Updated tool using agency context."""
    ctx.context.set("key", "value")
    data = ctx.context.get("key", "default")
    return "Done"

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Example: Complete Workflow

For a full example showing agency context in action, see the Agency Context Workflow Example which demonstrates:
  • Multi-step data collection and analysis
  • Cross-agent data sharing
  • Session management
  • Workflow coordination
  • Context monitoring and debugging
Agency context eliminates the need for complex parameter passing and enables multi-agent workflows while maintaining clean separation of concerns.
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