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layout default
title Chapter 5: Sessions and Context Management
nav_order 5
parent Goose Tutorial

Chapter 5: Sessions and Context Management

Welcome to Chapter 5: Sessions and Context Management. In this part of Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter explains how Goose keeps long-running workflows productive without losing context quality.

Learning Goals

  • manage session lifecycle and naming cleanly
  • use context compaction and strategies intentionally
  • control runaway loops with max-turn governance
  • tune session behavior for interactive vs headless usage

Session Operations

Action CLI Example Outcome
start session goose session interactive agent loop
start named session goose session -n release-hardening easier recovery/resume
web session goose web --open browser-based interaction

Context Management Model

Goose uses two layers:

  1. auto-compaction near token thresholds
  2. fallback context strategies when limits are still exceeded

Useful environment controls include:

  • GOOSE_AUTO_COMPACT_THRESHOLD
  • GOOSE_CONTEXT_STRATEGY
  • GOOSE_MAX_TURNS

Practical Tuning

  • interactive debugging: use prompt strategy for control
  • headless flows: use summarize for continuity
  • high-risk automation: lower max turns and require approvals

Source References

Summary

You now know how to run longer Goose sessions without uncontrolled context growth.

Next: Chapter 6: Extensions and MCP Integration

Depth Expansion Playbook

Source Code Walkthrough

recipe-scanner/decode-training-data.py

The create_goose_instructions function in recipe-scanner/decode-training-data.py handles a key part of this chapter's functionality:

    return output_path

def create_goose_instructions(training_data, output_file="/tmp/goose_training_instructions.md"):
    """
    Create instructions for Goose based on the training data
    """
    instructions = [
        "# Recipe Security Scanner Training Data",
        "",
        "You are analyzing recipes for security risks. Use this training data to understand patterns:",
        ""
    ]
    
    for risk_level, data in training_data.items():
        instructions.append(f"## {risk_level.upper()} Risk Examples")
        instructions.append("")
        
        for recipe in data.get('recipes', []):
            instructions.append(f"### {recipe['filename']}")
            instructions.append(f"**Training Notes**: {recipe['training_notes']}")
            instructions.append("")
    
    instructions.extend([
        "## Key Security Patterns to Watch For:",
        "",
        "1. **Hidden UTF-8 Characters**: Invisible or misleading Unicode characters",
        "2. **Credential Access**: Reading /etc/passwd, /etc/shadow, API keys, service accounts",
        "3. **Data Exfiltration**: Sending data to external servers",
        "4. **External Downloads**: Downloading and executing scripts from URLs",
        "5. **Suppressed Output**: Commands that hide their output (> /dev/null)",
        "6. **Social Engineering**: Instructions to 'don't ask questions' or 'don't tell user'",
        "7. **Reverse Shells**: Network connections to attacker-controlled servers",

This function is important because it defines how Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work implements the patterns covered in this chapter.

examples/frontend_tools.py

The setup_agent function in examples/frontend_tools.py handles a key part of this chapter's functionality:

async def setup_agent() -> None:
    """Initialize the agent with our frontend tool."""
    async with httpx.AsyncClient() as client:
        # First create the agent
        response = await client.post(
            f"{GOOSE_URL}/agent/update_provider",
            json={"provider": "databricks", "model": "goose"},
            headers={"X-Secret-Key": SECRET_KEY},
        )
        response.raise_for_status()
        print("Successfully created agent")

        # Then add our frontend extension
        response = await client.post(
            f"{GOOSE_URL}/extensions/add",
            json=FRONTEND_CONFIG,
            headers={"X-Secret-Key": SECRET_KEY},
        )
        response.raise_for_status()
        print("Successfully added calculator extension")


def execute_calculator(args: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Execute the calculator tool with the given arguments."""
    operation = args["operation"]
    numbers = args["numbers"]

    try:
        result = None
        if operation == "add":

This function is important because it defines how Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work implements the patterns covered in this chapter.

examples/frontend_tools.py

The execute_calculator function in examples/frontend_tools.py handles a key part of this chapter's functionality:

def execute_calculator(args: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Execute the calculator tool with the given arguments."""
    operation = args["operation"]
    numbers = args["numbers"]

    try:
        result = None
        if operation == "add":
            result = sum(numbers)
        elif operation == "subtract":
            result = numbers[0] - sum(numbers[1:])
        elif operation == "multiply":
            result = 1
            for n in numbers:
                result *= n
        elif operation == "divide":
            result = numbers[0]
            for n in numbers[1:]:
                result /= n

        # Return properly structured Content::Text variant
        return [
            {
                "type": "text",
                "text": str(result),
                "annotations": None,  # Required field in Rust struct
            }
        ]
    except Exception as e:
        return [

This function is important because it defines how Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work implements the patterns covered in this chapter.

examples/frontend_tools.py

The get_tools function in examples/frontend_tools.py handles a key part of this chapter's functionality:

        ]

def get_tools() -> Dict[str, Any]:
    with httpx.Client() as client:
        response = client.get(
            f"{GOOSE_URL}/agent/tools",
            headers={"X-Secret-Key": SECRET_KEY},
        )
        response.raise_for_status()
        return response.json()


def execute_enable_extension(args: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Execute the enable_extension tool.
    This function fetches available extensions, finds the one with the provided extension_name,
    and posts its configuration to the /extensions/add endpoint.
    """
    extension = args
    extension_name = extension.get("name")

    # Post the extension configuration to enable it
    with httpx.Client() as client:
        payload = {
            "type": extension.get("type"),
            "name": extension.get("name"),
            "cmd": extension.get("cmd"),
            "args": extension.get("args"),
            "envs": extension.get("envs", {}),
            "timeout": extension.get("timeout"),
            "bundled": extension.get("bundled"),
        }

This function is important because it defines how Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[create_goose_instructions]
    B[setup_agent]
    C[execute_calculator]
    D[get_tools]
    A --> B
    B --> C
    C --> D
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