08-advanced-features.md

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default	Chapter 8: Advanced Features	SuperAGI Tutorial	8

Chapter 8: Advanced Features

Welcome to Chapter 8: Advanced Features. In this part of SuperAGI Tutorial: Production-Ready Autonomous AI Agents, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

Master custom agent development, plugin architecture, enterprise integrations, and advanced SuperAGI capabilities.

Overview

This chapter covers advanced SuperAGI features including custom agent architectures, plugin development, enterprise integrations, security considerations, and cutting-edge capabilities for building sophisticated AI agent systems.

Custom Agent Architectures

Domain-Specific Agent

from superagi import Agent, Tool, Memory
from typing import Dict, Any, List
from pydantic import BaseModel, Field

class FinancialAnalysisAgent(Agent):
    """Specialized agent for financial analysis tasks."""

    class Config(BaseModel):
        risk_tolerance: str = Field(default="moderate")
        market_focus: List[str] = Field(default=["stocks", "bonds"])
        analysis_depth: str = Field(default="detailed")

    def __init__(self, config: Config = None):
        super().__init__()
        self.config = config or self.Config()
        self._setup_financial_tools()
        self._setup_financial_memory()

    def _setup_financial_tools(self):
        """Register financial analysis tools."""
        self.register_tool(MarketDataTool())
        self.register_tool(TechnicalAnalysisTool())
        self.register_tool(FundamentalAnalysisTool())
        self.register_tool(RiskAssessmentTool())
        self.register_tool(PortfolioOptimizerTool())

    def _setup_financial_memory(self):
        """Configure financial-specific memory."""
        self.memory = FinancialMemory(
            market_data_retention_days=90,
            analysis_cache_size=1000
        )

    async def analyze_investment(
        self,
        symbol: str,
        investment_amount: float
    ) -> Dict[str, Any]:
        """Comprehensive investment analysis."""
        # Technical analysis
        technical = await self.use_tool(
            "technical_analysis",
            {"symbol": symbol, "indicators": ["RSI", "MACD", "BB"]}
        )

        # Fundamental analysis
        fundamental = await self.use_tool(
            "fundamental_analysis",
            {"symbol": symbol, "metrics": ["PE", "PB", "ROE", "DebtEquity"]}
        )

        # Risk assessment
        risk = await self.use_tool(
            "risk_assessment",
            {
                "symbol": symbol,
                "amount": investment_amount,
                "risk_tolerance": self.config.risk_tolerance
            }
        )

        # Generate recommendation
        recommendation = await self._generate_recommendation(
            technical, fundamental, risk
        )

        return {
            "symbol": symbol,
            "technical_analysis": technical,
            "fundamental_analysis": fundamental,
            "risk_assessment": risk,
            "recommendation": recommendation
        }

    async def _generate_recommendation(
        self,
        technical: Dict,
        fundamental: Dict,
        risk: Dict
    ) -> Dict[str, Any]:
        """Generate investment recommendation using LLM reasoning."""
        prompt = f"""
        Based on the following analysis, provide an investment recommendation:

        Technical Analysis: {technical}
        Fundamental Analysis: {fundamental}
        Risk Assessment: {risk}
        Risk Tolerance: {self.config.risk_tolerance}

        Provide:
        1. Overall recommendation (BUY/HOLD/SELL)
        2. Confidence level (0-100%)
        3. Key factors supporting the recommendation
        4. Risk warnings
        5. Suggested position size
        """

        return await self.reason(prompt)

Reasoning Chain Agent

from enum import Enum
from typing import Optional

class ReasoningStrategy(Enum):
    CHAIN_OF_THOUGHT = "cot"
    TREE_OF_THOUGHT = "tot"
    REFLEXION = "reflexion"
    REACT = "react"

class AdvancedReasoningAgent(Agent):
    """Agent with multiple reasoning strategies."""

    def __init__(self, default_strategy: ReasoningStrategy = ReasoningStrategy.REACT):
        super().__init__()
        self.default_strategy = default_strategy
        self.reasoning_history = []

    async def solve(
        self,
        problem: str,
        strategy: Optional[ReasoningStrategy] = None
    ) -> Dict[str, Any]:
        """Solve problem using specified reasoning strategy."""
        strategy = strategy or self.default_strategy

        if strategy == ReasoningStrategy.CHAIN_OF_THOUGHT:
            return await self._chain_of_thought(problem)
        elif strategy == ReasoningStrategy.TREE_OF_THOUGHT:
            return await self._tree_of_thought(problem)
        elif strategy == ReasoningStrategy.REFLEXION:
            return await self._reflexion(problem)
        else:
            return await self._react(problem)

    async def _chain_of_thought(self, problem: str) -> Dict[str, Any]:
        """Chain-of-thought reasoning."""
        prompt = f"""
        Solve this problem step by step:

        Problem: {problem}

        Let's think through this step by step:
        1. First, let's understand what we're asked to do...
        2. Next, let's identify the key information...
        3. Now, let's work through the solution...
        4. Finally, let's verify our answer...
        """

        response = await self.llm.generate(prompt)
        return {"strategy": "cot", "reasoning": response, "problem": problem}

    async def _tree_of_thought(self, problem: str) -> Dict[str, Any]:
        """Tree-of-thought reasoning with branching exploration."""
        # Generate multiple initial approaches
        approaches_prompt = f"""
        Problem: {problem}

        Generate 3 different approaches to solve this problem.
        For each approach, provide:
        - Approach name
        - Key steps
        - Potential challenges
        """

        approaches = await self.llm.generate(approaches_prompt)

        # Evaluate each approach
        evaluations = []
        for approach in self._parse_approaches(approaches):
            eval_prompt = f"""
            Evaluate this approach for solving: {problem}

            Approach: {approach}

            Score from 1-10 on:
            - Feasibility
            - Completeness
            - Efficiency
            """
            evaluation = await self.llm.generate(eval_prompt)
            evaluations.append({"approach": approach, "evaluation": evaluation})

        # Select best approach and execute
        best = max(evaluations, key=lambda x: self._extract_score(x["evaluation"]))

        solution_prompt = f"""
        Execute this approach to solve: {problem}

        Approach: {best['approach']}

        Provide the complete solution.
        """

        solution = await self.llm.generate(solution_prompt)

        return {
            "strategy": "tot",
            "approaches_considered": len(evaluations),
            "selected_approach": best["approach"],
            "solution": solution
        }

    async def _reflexion(self, problem: str, max_iterations: int = 3) -> Dict[str, Any]:
        """Reflexion reasoning with self-critique and improvement."""
        attempts = []

        for iteration in range(max_iterations):
            # Generate solution attempt
            if iteration == 0:
                attempt_prompt = f"Solve this problem: {problem}"
            else:
                attempt_prompt = f"""
                Solve this problem: {problem}

                Previous attempts and feedback:
                {self._format_attempts(attempts)}

                Improve upon the previous attempts based on the feedback.
                """

            solution = await self.llm.generate(attempt_prompt)

            # Self-critique
            critique_prompt = f"""
            Evaluate this solution attempt:

            Problem: {problem}
            Solution: {solution}

            Provide:
            1. Is the solution correct? (yes/no)
            2. What are the strengths?
            3. What are the weaknesses or errors?
            4. What specific improvements are needed?
            """

            critique = await self.llm.generate(critique_prompt)
            attempts.append({"solution": solution, "critique": critique})

            # Check if solution is satisfactory
            if "yes" in critique.lower()[:50]:
                break

        return {
            "strategy": "reflexion",
            "iterations": len(attempts),
            "final_solution": attempts[-1]["solution"],
            "reasoning_trace": attempts
        }

    async def _react(self, problem: str) -> Dict[str, Any]:
        """ReAct reasoning with interleaved thinking and acting."""
        steps = []
        context = f"Problem: {problem}\n\n"
        max_steps = 10

        for step_num in range(max_steps):
            # Think
            think_prompt = f"""
            {context}

            Step {step_num + 1} - Think:
            What should I do next to solve this problem?
            What information do I need?
            """

            thought = await self.llm.generate(think_prompt)
            steps.append({"type": "thought", "content": thought})

            # Decide on action
            action_prompt = f"""
            Based on this thought: {thought}

            What action should I take?
            Options:
            - SEARCH: Look up information
            - CALCULATE: Perform computation
            - ANALYZE: Analyze data
            - CONCLUDE: Provide final answer

            Respond with: ACTION_TYPE: action_details
            """

            action = await self.llm.generate(action_prompt)
            steps.append({"type": "action", "content": action})

            # Execute action
            if "CONCLUDE" in action:
                break

            observation = await self._execute_action(action)
            steps.append({"type": "observation", "content": observation})
            context += f"\nThought: {thought}\nAction: {action}\nObservation: {observation}"

        return {
            "strategy": "react",
            "steps": steps,
            "final_answer": steps[-1]["content"] if steps else None
        }

Plugin Architecture

Plugin System

from abc import ABC, abstractmethod
from typing import Dict, Any, List, Type
import importlib
import pkgutil

class Plugin(ABC):
    """Base class for SuperAGI plugins."""

    name: str
    version: str
    description: str

    @abstractmethod
    def initialize(self, agent: Agent) -> None:
        """Initialize plugin with agent context."""
        pass

    @abstractmethod
    def get_tools(self) -> List[Tool]:
        """Return tools provided by this plugin."""
        pass

    @abstractmethod
    def get_capabilities(self) -> List[str]:
        """Return capabilities provided by this plugin."""
        pass

    def on_agent_start(self, task: str) -> None:
        """Hook called when agent starts execution."""
        pass

    def on_agent_complete(self, result: Dict[str, Any]) -> None:
        """Hook called when agent completes execution."""
        pass

    def on_tool_use(self, tool_name: str, args: Dict[str, Any]) -> None:
        """Hook called when agent uses a tool."""
        pass

class PluginManager:
    """Manages plugin lifecycle and discovery."""

    def __init__(self):
        self.plugins: Dict[str, Plugin] = {}
        self.hooks: Dict[str, List[callable]] = {
            "agent_start": [],
            "agent_complete": [],
            "tool_use": []
        }

    def discover_plugins(self, package_path: str) -> List[Type[Plugin]]:
        """Discover plugins in a package."""
        discovered = []

        package = importlib.import_module(package_path)
        for importer, modname, ispkg in pkgutil.iter_modules(package.__path__):
            module = importlib.import_module(f"{package_path}.{modname}")

            for item_name in dir(module):
                item = getattr(module, item_name)
                if (isinstance(item, type) and
                    issubclass(item, Plugin) and
                    item is not Plugin):
                    discovered.append(item)

        return discovered

    def register_plugin(self, plugin: Plugin) -> None:
        """Register a plugin."""
        self.plugins[plugin.name] = plugin

        # Register hooks
        self.hooks["agent_start"].append(plugin.on_agent_start)
        self.hooks["agent_complete"].append(plugin.on_agent_complete)
        self.hooks["tool_use"].append(plugin.on_tool_use)

    def initialize_all(self, agent: Agent) -> None:
        """Initialize all plugins with agent context."""
        for plugin in self.plugins.values():
            plugin.initialize(agent)

    def get_all_tools(self) -> List[Tool]:
        """Get tools from all plugins."""
        tools = []
        for plugin in self.plugins.values():
            tools.extend(plugin.get_tools())
        return tools

    def emit(self, hook_name: str, *args, **kwargs) -> None:
        """Emit event to all registered hooks."""
        for hook in self.hooks.get(hook_name, []):
            try:
                hook(*args, **kwargs)
            except Exception as e:
                logging.error(f"Plugin hook error: {e}")

Example Plugin Implementation

class SlackNotificationPlugin(Plugin):
    """Plugin for Slack notifications."""

    name = "slack_notifications"
    version = "1.0.0"
    description = "Send notifications to Slack channels"

    def __init__(self, webhook_url: str, channel: str = "#agents"):
        self.webhook_url = webhook_url
        self.channel = channel
        self.agent = None

    def initialize(self, agent: Agent) -> None:
        self.agent = agent

    def get_tools(self) -> List[Tool]:
        return [SlackMessageTool(self.webhook_url)]

    def get_capabilities(self) -> List[str]:
        return ["send_slack_message", "create_slack_thread"]

    def on_agent_start(self, task: str) -> None:
        self._send_notification(
            f"🚀 Agent started: {task[:100]}..."
        )

    def on_agent_complete(self, result: Dict[str, Any]) -> None:
        status = "✅" if result.get("success") else "❌"
        self._send_notification(
            f"{status} Agent completed: {result.get('summary', 'No summary')}"
        )

    def _send_notification(self, message: str) -> None:
        import requests
        requests.post(self.webhook_url, json={
            "channel": self.channel,
            "text": message
        })


class DatabasePlugin(Plugin):
    """Plugin for database operations."""

    name = "database"
    version = "1.0.0"
    description = "Execute database queries and operations"

    def __init__(self, connection_string: str, allowed_operations: List[str] = None):
        self.connection_string = connection_string
        self.allowed_operations = allowed_operations or ["SELECT"]
        self.connection = None

    def initialize(self, agent: Agent) -> None:
        import sqlalchemy
        self.engine = sqlalchemy.create_engine(self.connection_string)

    def get_tools(self) -> List[Tool]:
        return [
            SQLQueryTool(self.engine, self.allowed_operations),
            SchemaExplorerTool(self.engine),
            DataExportTool(self.engine)
        ]

    def get_capabilities(self) -> List[str]:
        return ["sql_query", "explore_schema", "export_data"]

Enterprise Integrations

SSO and Authentication

from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import OAuth2AuthorizationCodeBearer
import jwt
from typing import Optional

class EnterpriseAuth:
    """Enterprise authentication integration."""

    def __init__(
        self,
        issuer: str,
        client_id: str,
        client_secret: str,
        audience: str
    ):
        self.issuer = issuer
        self.client_id = client_id
        self.client_secret = client_secret
        self.audience = audience

        self.oauth2_scheme = OAuth2AuthorizationCodeBearer(
            authorizationUrl=f"{issuer}/authorize",
            tokenUrl=f"{issuer}/oauth/token"
        )

    async def verify_token(self, token: str) -> Dict[str, Any]:
        """Verify JWT token."""
        try:
            # Get JWKS
            jwks_client = jwt.PyJWKClient(f"{self.issuer}/.well-known/jwks.json")
            signing_key = jwks_client.get_signing_key_from_jwt(token)

            payload = jwt.decode(
                token,
                signing_key.key,
                algorithms=["RS256"],
                audience=self.audience,
                issuer=self.issuer
            )

            return payload
        except jwt.exceptions.InvalidTokenError as e:
            raise HTTPException(status_code=401, detail=str(e))

    def require_permissions(self, required: List[str]):
        """Decorator to require specific permissions."""
        async def dependency(token: str = Depends(self.oauth2_scheme)):
            payload = await self.verify_token(token)
            user_permissions = payload.get("permissions", [])

            missing = set(required) - set(user_permissions)
            if missing:
                raise HTTPException(
                    status_code=403,
                    detail=f"Missing permissions: {missing}"
                )

            return payload
        return dependency


# Usage
auth = EnterpriseAuth(
    issuer="https://auth.company.com",
    client_id="superagi-app",
    client_secret="secret",
    audience="superagi-api"
)

app = FastAPI()

@app.post("/agents/execute")
async def execute_agent(
    request: AgentRequest,
    user: Dict = Depends(auth.require_permissions(["agents:execute"]))
):
    """Execute agent with authorization."""
    return await agent_service.execute(request, user_id=user["sub"])

Audit Logging

from datetime import datetime
from typing import Dict, Any, Optional
import json

class AuditLogger:
    """Enterprise audit logging for compliance."""

    def __init__(self, storage_backend):
        self.storage = storage_backend

    async def log_event(
        self,
        event_type: str,
        actor: str,
        resource: str,
        action: str,
        details: Dict[str, Any],
        outcome: str = "success"
    ):
        """Log an audit event."""
        event = {
            "id": str(uuid.uuid4()),
            "timestamp": datetime.utcnow().isoformat(),
            "event_type": event_type,
            "actor": {
                "id": actor,
                "type": "user"
            },
            "resource": {
                "type": resource.split(":")[0],
                "id": resource.split(":")[1] if ":" in resource else resource
            },
            "action": action,
            "outcome": outcome,
            "details": details,
            "metadata": {
                "service": "superagi",
                "version": "1.0.0"
            }
        }

        await self.storage.store(event)
        return event

class AgentAuditMiddleware:
    """Middleware for automatic agent audit logging."""

    def __init__(self, audit_logger: AuditLogger):
        self.audit = audit_logger

    async def __call__(self, agent: Agent, task: str, user_id: str):
        """Wrap agent execution with audit logging."""
        execution_id = str(uuid.uuid4())

        # Log start
        await self.audit.log_event(
            event_type="agent.execution.started",
            actor=user_id,
            resource=f"agent:{agent.id}",
            action="execute",
            details={
                "execution_id": execution_id,
                "task_preview": task[:200],
                "agent_type": agent.__class__.__name__
            }
        )

        try:
            result = await agent.run(task)

            # Log success
            await self.audit.log_event(
                event_type="agent.execution.completed",
                actor=user_id,
                resource=f"agent:{agent.id}",
                action="execute",
                details={
                    "execution_id": execution_id,
                    "tools_used": result.get("tools_used", []),
                    "iterations": result.get("iterations", 0)
                },
                outcome="success"
            )

            return result

        except Exception as e:
            # Log failure
            await self.audit.log_event(
                event_type="agent.execution.failed",
                actor=user_id,
                resource=f"agent:{agent.id}",
                action="execute",
                details={
                    "execution_id": execution_id,
                    "error": str(e),
                    "error_type": type(e).__name__
                },
                outcome="failure"
            )
            raise

Data Governance

from enum import Enum
from typing import Set

class DataClassification(Enum):
    PUBLIC = "public"
    INTERNAL = "internal"
    CONFIDENTIAL = "confidential"
    RESTRICTED = "restricted"

class DataGovernancePolicy:
    """Data governance and classification policies."""

    def __init__(self):
        self.classification_rules = {}
        self.retention_policies = {}
        self.access_controls = {}

    def classify_data(self, data: Dict[str, Any]) -> DataClassification:
        """Classify data based on content."""
        # Check for sensitive patterns
        sensitive_patterns = [
            r'\b\d{3}-\d{2}-\d{4}\b',  # SSN
            r'\b\d{16}\b',  # Credit card
            r'password|secret|api.?key',  # Secrets
        ]

        import re
        data_str = json.dumps(data).lower()

        for pattern in sensitive_patterns:
            if re.search(pattern, data_str):
                return DataClassification.RESTRICTED

        # Check for PII
        pii_fields = {'email', 'phone', 'address', 'name', 'ssn', 'dob'}
        if pii_fields & set(data.keys()):
            return DataClassification.CONFIDENTIAL

        return DataClassification.INTERNAL

    def can_access(
        self,
        user_clearance: DataClassification,
        data_classification: DataClassification
    ) -> bool:
        """Check if user can access data."""
        clearance_levels = {
            DataClassification.PUBLIC: 0,
            DataClassification.INTERNAL: 1,
            DataClassification.CONFIDENTIAL: 2,
            DataClassification.RESTRICTED: 3
        }

        return clearance_levels[user_clearance] >= clearance_levels[data_classification]

class SecureAgentWrapper:
    """Agent wrapper with data governance enforcement."""

    def __init__(self, agent: Agent, policy: DataGovernancePolicy, user_clearance: DataClassification):
        self.agent = agent
        self.policy = policy
        self.user_clearance = user_clearance

    async def run(self, task: str) -> Dict[str, Any]:
        """Run agent with data governance checks."""
        result = await self.agent.run(task)

        # Classify output
        classification = self.policy.classify_data(result)

        # Check access
        if not self.policy.can_access(self.user_clearance, classification):
            return {
                "error": "Access denied",
                "classification": classification.value,
                "required_clearance": classification.value
            }

        # Redact sensitive data if needed
        if classification == DataClassification.RESTRICTED:
            result = self._redact_sensitive(result)

        return result

    def _redact_sensitive(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Redact sensitive information."""
        import re

        data_str = json.dumps(data)

        # Redact SSN
        data_str = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '***-**-****', data_str)

        # Redact credit cards
        data_str = re.sub(r'\b\d{16}\b', '****-****-****-****', data_str)

        return json.loads(data_str)

Advanced Capabilities

Self-Improving Agent

class SelfImprovingAgent(Agent):
    """Agent that learns and improves from experience."""

    def __init__(self):
        super().__init__()
        self.performance_history = []
        self.learned_patterns = {}
        self.improvement_threshold = 0.7

    async def run_with_learning(self, task: str) -> Dict[str, Any]:
        """Execute task with learning enabled."""
        # Check for similar past tasks
        similar = self._find_similar_task(task)

        if similar and similar["success_rate"] > 0.8:
            # Use learned approach
            result = await self._apply_learned_approach(task, similar)
        else:
            # Standard execution with learning
            result = await self._execute_and_learn(task)

        return result

    async def _execute_and_learn(self, task: str) -> Dict[str, Any]:
        """Execute task and learn from the experience."""
        # Execute task
        start_time = time.time()
        result = await self.run(task)
        execution_time = time.time() - start_time

        # Evaluate performance
        evaluation = await self._evaluate_performance(task, result)

        # Store experience
        experience = {
            "task": task,
            "task_embedding": await self._embed_task(task),
            "approach": result.get("approach"),
            "tools_used": result.get("tools_used", []),
            "execution_time": execution_time,
            "success": evaluation["success"],
            "quality_score": evaluation["quality_score"]
        }

        self.performance_history.append(experience)

        # Learn patterns if successful
        if evaluation["quality_score"] > self.improvement_threshold:
            await self._extract_patterns(experience)

        return result

    async def _evaluate_performance(
        self,
        task: str,
        result: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Evaluate execution performance."""
        prompt = f"""
        Evaluate this task execution:

        Task: {task}
        Result: {result}

        Score the following (0-1):
        1. Task completion (did it achieve the goal?)
        2. Efficiency (was it done optimally?)
        3. Quality (is the output high quality?)

        Respond with JSON: {{"completion": 0.X, "efficiency": 0.X, "quality": 0.X}}
        """

        evaluation = await self.llm.generate(prompt)
        scores = json.loads(evaluation)

        return {
            "success": scores["completion"] > 0.8,
            "quality_score": (scores["completion"] + scores["efficiency"] + scores["quality"]) / 3,
            "details": scores
        }

    async def _extract_patterns(self, experience: Dict[str, Any]):
        """Extract reusable patterns from successful experiences."""
        prompt = f"""
        Extract a reusable pattern from this successful task execution:

        Task: {experience['task']}
        Approach: {experience['approach']}
        Tools used: {experience['tools_used']}

        Create a pattern that can be applied to similar tasks.
        Include:
        1. Task category
        2. Key steps
        3. Recommended tools
        4. Success factors
        """

        pattern = await self.llm.generate(prompt)
        pattern_key = await self._categorize_task(experience['task'])

        if pattern_key not in self.learned_patterns:
            self.learned_patterns[pattern_key] = []

        self.learned_patterns[pattern_key].append({
            "pattern": pattern,
            "success_rate": experience["quality_score"],
            "timestamp": datetime.utcnow().isoformat()
        })

Ensemble Agent System

class EnsembleAgentSystem:
    """Ensemble of agents with voting and aggregation."""

    def __init__(self, agents: List[Agent], strategy: str = "majority_vote"):
        self.agents = agents
        self.strategy = strategy

    async def execute(self, task: str) -> Dict[str, Any]:
        """Execute task across ensemble and aggregate results."""
        # Run all agents in parallel
        results = await asyncio.gather(*[
            agent.run(task) for agent in self.agents
        ], return_exceptions=True)

        # Filter successful results
        successful = [r for r in results if not isinstance(r, Exception)]

        if not successful:
            raise Exception("All agents failed")

        # Aggregate based on strategy
        if self.strategy == "majority_vote":
            return self._majority_vote(successful)
        elif self.strategy == "weighted_average":
            return self._weighted_average(successful)
        elif self.strategy == "best_of":
            return await self._best_of(task, successful)
        else:
            return successful[0]  # First successful

    def _majority_vote(self, results: List[Dict]) -> Dict[str, Any]:
        """Aggregate by majority vote on key decisions."""
        from collections import Counter

        # Extract key decisions/answers
        answers = [r.get("answer") or r.get("result") for r in results]
        vote_counts = Counter(answers)

        winner, count = vote_counts.most_common(1)[0]

        return {
            "answer": winner,
            "confidence": count / len(results),
            "vote_distribution": dict(vote_counts),
            "strategy": "majority_vote"
        }

    async def _best_of(
        self,
        task: str,
        results: List[Dict]
    ) -> Dict[str, Any]:
        """Select best result using LLM evaluation."""
        evaluation_prompt = f"""
        Task: {task}

        Evaluate these solutions and select the best one:

        {json.dumps(results, indent=2)}

        Respond with the index (0-based) of the best solution and explain why.
        """

        evaluation = await self.agents[0].llm.generate(evaluation_prompt)
        best_index = int(evaluation.split()[0])

        return {
            **results[best_index],
            "strategy": "best_of",
            "selection_reason": evaluation
        }

Agent Marketplace

class AgentMarketplace:
    """Marketplace for sharing and discovering agents."""

    def __init__(self, storage_backend):
        self.storage = storage_backend

    async def publish_agent(
        self,
        agent: Agent,
        metadata: Dict[str, Any],
        publisher_id: str
    ) -> str:
        """Publish agent to marketplace."""
        agent_id = str(uuid.uuid4())

        # Serialize agent
        agent_data = {
            "id": agent_id,
            "name": metadata["name"],
            "description": metadata["description"],
            "version": metadata["version"],
            "capabilities": metadata.get("capabilities", []),
            "tools_required": [t.name for t in agent.tools],
            "config_schema": agent.Config.schema() if hasattr(agent, 'Config') else {},
            "publisher_id": publisher_id,
            "published_at": datetime.utcnow().isoformat(),
            "serialized_agent": agent.serialize()
        }

        await self.storage.store(f"agents/{agent_id}", agent_data)
        return agent_id

    async def search_agents(
        self,
        query: str = None,
        capabilities: List[str] = None,
        limit: int = 10
    ) -> List[Dict[str, Any]]:
        """Search marketplace for agents."""
        filters = {}

        if capabilities:
            filters["capabilities"] = {"$all": capabilities}

        results = await self.storage.search(
            collection="agents",
            query=query,
            filters=filters,
            limit=limit
        )

        return results

    async def install_agent(
        self,
        agent_id: str,
        config: Dict[str, Any] = None
    ) -> Agent:
        """Install agent from marketplace."""
        agent_data = await self.storage.get(f"agents/{agent_id}")

        if not agent_data:
            raise ValueError(f"Agent {agent_id} not found")

        # Deserialize and configure
        agent = Agent.deserialize(agent_data["serialized_agent"])

        if config:
            agent.configure(config)

        return agent

Summary

In this chapter, you've learned:

• Custom Agents: Domain-specific and advanced reasoning architectures
• Plugin System: Extensible plugin architecture for custom functionality
• Enterprise: SSO, audit logging, and data governance
• Advanced: Self-improving agents, ensemble systems, and marketplaces
• Security: Authentication, authorization, and compliance patterns

Key Takeaways

1. Specialize Wisely: Build domain-specific agents for complex tasks
2. Extend with Plugins: Use plugin architecture for maintainability
3. Enterprise Ready: Implement auth, audit, and governance from the start
4. Continuous Learning: Enable agents to improve from experience
5. Ensemble Power: Combine multiple agents for better results

Tutorial Complete

Congratulations! You've completed the SuperAGI tutorial. You now have the knowledge to:

• Build sophisticated autonomous agents
• Implement multi-agent systems
• Deploy to production with monitoring
• Extend functionality through plugins
• Meet enterprise requirements

Further Resources

• SuperAGI Documentation
• SuperAGI GitHub
• Community Discord

---

Generated for Awesome Code Docs

What Problem Does This Solve?

Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for self, agent, Dict so behavior stays predictable as complexity grows.

In practical terms, this chapter helps you avoid three common failures:

• coupling core logic too tightly to one implementation path
• missing the handoff boundaries between setup, execution, and validation
• shipping changes without clear rollback or observability strategy

After working through this chapter, you should be able to reason about Chapter 8: Advanced Features as an operating subsystem inside SuperAGI Tutorial: Production-Ready Autonomous AI Agents, with explicit contracts for inputs, state transitions, and outputs.

Use the implementation notes around task, problem, result as your checklist when adapting these patterns to your own repository.

How it Works Under the Hood

Under the hood, Chapter 8: Advanced Features usually follows a repeatable control path:

1. Context bootstrap: initialize runtime config and prerequisites for self.
2. Input normalization: shape incoming data so agent receives stable contracts.
3. Core execution: run the main logic branch and propagate intermediate state through Dict.
4. Policy and safety checks: enforce limits, auth scopes, and failure boundaries.
5. Output composition: return canonical result payloads for downstream consumers.
6. Operational telemetry: emit logs/metrics needed for debugging and performance tuning.

When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.

Source Walkthrough

Use the following upstream sources to verify implementation details while reading this chapter:

• View Repo Why it matters: authoritative reference on View Repo (github.com).
• AI Codebase Knowledge Builder Why it matters: authoritative reference on AI Codebase Knowledge Builder (github.com).

Suggested trace strategy:

• search upstream code for self and agent to map concrete implementation paths
• compare docs claims against actual runtime/config code before reusing patterns in production

Chapter Connections

• Tutorial Index
• Previous Chapter: Chapter 7: Deployment & Scaling
• Main Catalog
• A-Z Tutorial Directory