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Phase 4: Advanced Features

This document describes the advanced features implemented in Phase 4 of AgentMind development.

Overview

Phase 4 introduces powerful capabilities for agent self-improvement, team templates, evaluation, visualization, and advanced orchestration patterns.

1. Self-Improvement Mechanisms

Agents can now improve their own performance through multiple mechanisms.

Prompt Optimization

Agents can generate and optimize their own role prompts based on performance feedback.

from agentmind.improvement import PromptOptimizer

optimizer = PromptOptimizer(llm_provider)

# Optimize a prompt based on feedback
optimized = await optimizer.optimize_prompt(
    current_prompt="You are a helpful assistant",
    task_examples=[...],
    feedback=["Be more technical", "Add examples"],
    performance_metrics={"accuracy": 0.75}
)

# Generate a new role prompt from scratch
new_prompt = await optimizer.generate_role_prompt(
    role="data_analyst",
    capabilities=["statistical analysis", "visualization", "reporting"],
    constraints=["focus on business metrics"]
)

Debate-Based Improvement

Multiple agents debate to refine outputs through structured argumentation.

from agentmind.improvement import DebateImprover

improver = DebateImprover(llm_provider)

# Run a debate
result = await improver.debate(
    topic="Should we implement feature X?",
    agents=[agent1, agent2, agent3],
    rounds=3,
    judge_agent=judge
)

# Improve output through iterative criticism
improved = await improver.improve_output(
    original_output="Initial draft...",
    critic_agents=[critic1, critic2],
    improvement_rounds=2
)

Feedback Loops

Track performance metrics and automatically adjust agent behavior.

from agentmind.improvement import FeedbackLoop

loop = FeedbackLoop()
loop.add_agent(agent)

# Record interactions
loop.record_interaction(
    agent.name,
    task="Explain quantum computing",
    response="...",
    rating=4.5,
    success=True,
    response_time=2.3
)

# Get performance metrics
metrics = loop.get_performance_metrics(agent.name)
# {'avg_rating': 4.2, 'success_rate': 0.85, ...}

# Get improvement suggestions
suggestions = loop.get_improvement_suggestions(agent.name)

2. Template Marketplace

20+ pre-configured agent team templates for common scenarios.

Available Templates

  1. research - Deep research team
  2. code-generation - Software development team
  3. startup-validator - Startup idea validation
  4. content-creation - Content writing team
  5. data-analysis - Data analysis team
  6. customer-support - Customer support team
  7. product-design - Product design team
  8. marketing-campaign - Marketing team
  9. legal-review - Legal review team
  10. education - Educational content team
  11. crisis-management - Crisis response team
  12. scientific-research - Scientific research team
  13. investment-analysis - Investment analysis team
  14. game-development - Game development team
  15. healthcare-consultation - Healthcare insights team
  16. debate - Structured debate team
  17. translation - Translation team
  18. security-audit - Security audit team
  19. creative-writing - Fiction writing team
  20. devops - DevOps team

Usage

from agentmind.templates import load_template, TemplateLoader

# Quick load
mind = load_template("research", llm_provider)
result = await mind.collaborate("Research quantum computing")

# Advanced usage
loader = TemplateLoader(llm_provider)

# List available templates
templates = loader.list_templates()

# Get template details
info = loader.get_template_info("research")

# Load with custom config
mind = loader.load(
    "code-generation",
    config_overrides={
        "architect": {"system_prompt": "Custom prompt..."}
    }
)

3. Evaluation Suite

Comprehensive benchmarking and evaluation capabilities.

Benchmark Suites

  • GAIA Subset - General AI Assistant benchmarks
  • AgentBench Subset - Agent capability benchmarks
  • Custom Suite - AgentMind-specific benchmarks

Usage

from agentmind.evaluation import Evaluator, MarkdownReporter
from agentmind.evaluation.benchmark import (
    create_gaia_subset,
    create_agent_bench_subset,
    create_custom_suite
)

# Create evaluator
evaluator = Evaluator()

# Add benchmark suites
evaluator.add_suite(create_gaia_subset())
evaluator.add_suite(create_agent_bench_subset())
evaluator.add_suite(create_custom_suite())

# Run evaluation
results = await evaluator.evaluate(mind, max_rounds=3)

# Print summary
evaluator.print_summary()

# Generate Markdown report
reporter = MarkdownReporter()
for suite_name, suite_results in evaluator.get_results().items():
    reporter.add_results(suite_results, suite_name)

reporter.generate_report("benchmarks/report.md")

Custom Benchmarks

from agentmind.evaluation import Benchmark, BenchmarkSuite

# Create custom benchmark
benchmark = Benchmark(
    name="custom_test",
    task="Solve this problem...",
    expected_output="expected result",
    evaluation_fn=lambda response, expected: custom_eval(response),
    timeout=30.0
)

# Create suite
suite = BenchmarkSuite("My Suite", "Custom benchmarks")
suite.add_benchmark(benchmark)

# Run
results = await suite.run_all(mind)

4. Visualization Dashboard

Interactive Gradio-based dashboard for monitoring and debugging.

Features

  • Real-time collaboration monitoring
  • Message flow visualization
  • Memory inspection
  • Interactive task execution
  • Performance statistics

Usage

from agentmind.visualization import launch_dashboard

# Launch dashboard
launch_dashboard(mind, share=False)

# Or use Dashboard class directly
from agentmind.visualization import Dashboard

dashboard = Dashboard(mind)
result, flow, memory = await dashboard.run_collaboration(
    "Analyze this data",
    max_rounds=3
)

Dashboard Tabs

  1. Collaboration - Run tasks and view results
  2. Agents - View agent information and status
  3. History - Browse collaboration history
  4. Statistics - Detailed performance metrics

5. Advanced Orchestration Patterns

Sophisticated coordination mechanisms for complex multi-agent scenarios.

Consensus Mechanisms

Agents vote and reach consensus through various mechanisms.

from agentmind.orchestration.advanced import (
    ConsensusOrchestrator,
    VotingMechanism
)

orchestrator = ConsensusOrchestrator(agents)

# Majority vote
result = await orchestrator.reach_consensus(
    "Should we implement feature X?",
    mechanism=VotingMechanism.MAJORITY,
    threshold=0.6
)

# Weighted vote
result = await orchestrator.reach_consensus(
    proposal,
    mechanism=VotingMechanism.WEIGHTED,
    weights={"expert": 2.0, "junior": 1.0}
)

# Multi-round consensus with discussion
result = await orchestrator.multi_round_consensus(
    proposal,
    max_rounds=3
)

Dynamic Agent Spawning

Automatically create agents based on task complexity.

from agentmind.orchestration.advanced import DynamicAgentSpawner

spawner = DynamicAgentSpawner(llm_provider)

# Spawn agents for a task
agents = await spawner.spawn_for_task(
    "Build a web application with authentication",
    max_agents=5
)

# Spawn on-demand and add to AgentMind
new_agents = await spawner.spawn_on_demand(
    mind,
    task="Complex task requiring specialized agents"
)

Parallel Task Decomposition

Break complex tasks into parallel subtasks.

from agentmind.orchestration.advanced import ParallelTaskDecomposer

decomposer = ParallelTaskDecomposer(llm_provider)

# Decompose task
subtasks = await decomposer.decompose(
    "Research and write a comprehensive report",
    max_subtasks=5
)

# Execute in parallel
results = await decomposer.execute_parallel(
    subtasks,
    agents,
    timeout=60.0
)

# Or do both in one call
results = await decomposer.decompose_and_execute(
    task,
    agents
)

Agent Specialization

Track agent skills and match them to tasks.

from agentmind.orchestration.advanced import (
    SpecializationEngine,
    SkillMatcher
)

# Set up specialization
engine = SpecializationEngine()

# Add skills to agents
engine.add_agent_skill(agent, "python", proficiency=0.9)
engine.add_agent_skill(agent, "testing", proficiency=0.7)

# Improve skills over time
engine.improve_skill(agent, "python", improvement=0.1)

# Match agents to tasks
matcher = SkillMatcher(engine)

best_agent = matcher.find_best_agent(
    agents,
    required_skills=["python", "testing"],
    min_proficiency=0.6
)

# Analyze skill coverage
coverage = matcher.get_skill_coverage(agents, required_skills)

# Get training recommendations
recommendations = matcher.recommend_training(agents, required_skills)

Installation

Install Phase 4 features:

# Visualization dashboard
pip install -e ".[visualization]"

# Evaluation suite
pip install -e ".[evaluation]"

# All Phase 4 features
pip install -e ".[visualization,evaluation]"

Examples

See the examples/ directory for complete examples:

  • examples/self_improvement.py - Self-improvement mechanisms
  • examples/template_marketplace.py - Template usage
  • examples/run_benchmarks.py - Evaluation suite
  • examples/visualization_dashboard.py - Dashboard
  • examples/advanced_orchestration.py - Advanced patterns

Performance Considerations

Caching

Phase 4 features can benefit from caching:

# Enable response caching (future feature)
mind = AgentMind(llm_provider=llm, enable_cache=True)

Parallel Execution

Use parallel execution for better performance:

# Run benchmarks in parallel
results = await suite.run_all(mind, parallel=True)

# Execute subtasks in parallel
results = await decomposer.execute_parallel(subtasks, agents)

Resource Management

Monitor resource usage:

from agentmind.utils.observability import CostTracker

tracker = CostTracker()
# Track costs during evaluation

Future Enhancements

Planned improvements for Phase 4:

  1. Integration Examples

    • LangChain integration
    • LlamaIndex integration
    • Haystack integration
    • AutoGen interop

  2. Performance Optimizations

    • LLM response caching
    • Batch processing
    • Streaming improvements
    • Memory optimization

  3. Enhanced Visualization

    • Real-time message flow graphs
    • Performance dashboards
    • Agent interaction networks
    • Cost tracking visualization

  4. Advanced Evaluation

    • More benchmark suites
    • Custom evaluation metrics
    • A/B testing framework
    • Regression testing

Contributing

We welcome contributions to Phase 4 features! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.