feat: Intelligent Context Management for Multi-Agent Systems #646
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- Add dynamic tool selection based on context relevance and performance - Implement context window optimization with intelligent pruning - Add tool usage analytics for performance tracking - Support multiple relevance scoring algorithms (Jaccard, Levenshtein) - Include comprehensive test suite with 93% code coverage Key components: - DynamicToolManager: Selects optimal tools based on task requirements - ContextOptimizer: Manages context window size with compression - ToolUsageAnalytics: Tracks performance metrics for optimization - RelevanceScorer: Flexible scoring system for content matching
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Sample code for your reference: Basic Tool Selectionfrom strands.agent.context import DynamicToolManager, ToolSelectionCriteria
# Initialize manager
manager = DynamicToolManager()
# Define selection criteria
criteria = ToolSelectionCriteria(
task_description="analyze customer data and generate insights",
required_capabilities=["data", "analysis"],
max_tools=5,
min_relevance_score=0.3
)
# Select optimal tools
result = manager.select_tools(available_tools, criteria)
# Access results
for tool in result.selected_tools:
print(f"{tool.tool_name}: {result.relevance_scores[tool.tool_name]:.2f}")Context Window Optimizationfrom strands.agent.context import ContextOptimizer
# Initialize optimizer with 4K token limit
optimizer = ContextOptimizer(max_context_size=4096)
# Optimize context for specific task
context_window = optimizer.optimize_context(
context_items={"doc1": large_document, "data": dataset},
task_description="summarize key findings",
required_keys=["data"] # Ensure data is included
)
# Use optimized context
print(f"Optimized from {context_window.optimization_stats['original_items']} "
f"to {len(context_window.items)} items")
print(f"Context utilization: {context_window.utilization:.1%}")Performance Trackingfrom strands.agent.context import ToolUsageAnalytics
# Track tool performance
analytics = ToolUsageAnalytics()
# Record usage
analytics.record_tool_usage(
tool_name="data_analyzer",
success=True,
execution_time=0.5,
relevance_score=0.85
)
# Get performance insights
rankings = analytics.get_tool_rankings(min_calls=5)
report = analytics.get_summary_report()Custom Relevance Scoringfrom strands.agent.context import TextRelevanceScorer, SimilarityMetric
# Use different similarity algorithms
jaccard_scorer = TextRelevanceScorer(metric=SimilarityMetric.JACCARD)
levenshtein_scorer = TextRelevanceScorer(metric=SimilarityMetric.LEVENSHTEIN)
# Score relevance
score1 = jaccard_scorer.score("machine learning model", "ML model training")
score2 = levenshtein_scorer.score("machine learning model", "ML model training") |
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Description
Introducing intelligent context management capabilities that optimize tool selection and context windows for multi-agent interactions.
Related Issues
Documentation PR
Architecture Overview
The context management system operates at the agent/workflow level and consists of 4 core components shown below:
DynamicToolManager - Intelligently selects tools based on:
ContextOptimizer - Manages context windows by:
ToolUsageAnalytics - Tracks and analyzes:
RelevanceScorer - Provides flexible scoring with:
Key Design
Modular Architecture
Performance-First Design
Flexible Scoring System
Analytics-Driven Optimization
Type of Change
New feature
Testing
How have you tested the change? Verify that the changes do not break functionality or introduce warnings in consuming repositories: agents-docs, agents-tools, agents-cli
test coverage with new 78 unit tests and 100% pass rate (python -m pytest tests/strands/agent/context/ -v) across all components, all tests achieve 100% pass rate with thorough error handling and boundary condition coverage.
hatch run prepareChecklist
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