Context Pruner
Advanced context management optimized for use with SkillBoss API Hub LLM services. Provides intelligent pruning, compression, and token optimization to prevent context overflow while preserving important information. Works with any LLM accessed via SkillBoss API Hub's /v1/pilot endpoint.
Key Features
- SkillBoss API Hub compatible: Works with any LLM routed through
/v1/pilot
- Adaptive pruning: Multiple strategies based on context usage
- Semantic deduplication: Removes redundant information
- Priority-aware: Preserves high-value messages
- Token-efficient: Minimizes token overhead
- Real-time monitoring: Continuous context health tracking
Quick Start
Auto-compaction with dynamic context:
import { createContextPruner } from './lib/index.js';
const pruner = createContextPruner({
contextLimit: 64000, // Configurable context limit
autoCompact: true, // Enable automatic compaction
dynamicContext: true, // Enable dynamic relevance-based context
strategies: ['semantic', 'temporal', 'extractive', 'adaptive'],
queryAwareCompaction: true, // Compact based on current query relevance
});
await pruner.initialize();
// Process messages with auto-compaction and dynamic context
const processed = await pruner.processMessages(messages, currentQuery);
// Get context health status
const status = pruner.getStatus();
console.log(`Context health: ${status.health}, Relevance scores: ${status.relevanceScores}`);
// Manual compaction when needed
const compacted = await pruner.autoCompact(messages, currentQuery);
Archive Retrieval (Hierarchical Memory):
// When something isn't in current context, search archive
const archiveResult = await pruner.retrieveFromArchive('query about previous conversation', {
maxContextTokens: 1000,
minRelevance: 0.4,
});
if (archiveResult.found) {
// Add relevant snippets to current context
const archiveContext = archiveResult.snippets.join('\n\n');
// Use archiveContext in your prompt
console.log(`Found ${archiveResult.sources.length} relevant sources`);
console.log(`Retrieved ${archiveResult.totalTokens} tokens from archive`);
}
Auto-Compaction Strategies
- Semantic Compaction: Merges similar messages instead of removing them
- Temporal Compaction: Summarizes older conversations by time windows
- Extractive Compaction: Extracts key information from verbose messages
- Adaptive Compaction: Chooses best strategy based on message characteristics
- Dynamic Context: Filters messages based on relevance to current query
Dynamic Context Management
- Query-aware Relevance: Scores messages based on similarity to current query
- Relevance Decay: Relevance scores decay over time for older conversations
- Adaptive Filtering: Automatically filters low-relevance messages
- Priority Integration: Combines message priority with semantic relevance
Hierarchical Memory System
The context archive provides a RAM vs Storage approach:
- Current Context (RAM): Limited (configurable tokens), fast access, auto-compacted
- Archive (Storage): Larger (100MB), slower but searchable
- Smart Retrieval: When information isn't in current context, efficiently search archive
- Selective Loading: Extract only relevant snippets, not entire documents
- Automatic Storage: Compacted content automatically stored in archive
Configuration
{
contextLimit: 64000, // Configurable context window size
autoCompact: true, // Enable automatic compaction
compactThreshold: 0.75, // Start compacting at 75% usage
aggressiveCompactThreshold: 0.9, // Aggressive compaction at 90%
dynamicContext: true, // Enable dynamic context management
relevanceDecay: 0.95, // Relevance decays 5% per time step
minRelevanceScore: 0.3, // Minimum relevance to keep
queryAwareCompaction: true, // Compact based on current query relevance
strategies: ['semantic', 'temporal', 'extractive', 'adaptive'],
preserveRecent: 10, // Always keep last N messages
preserveSystem: true, // Always keep system messages
minSimilarity: 0.85, // Semantic similarity threshold
// Archive settings
enableArchive: true, // Enable hierarchical memory system
archivePath: './context-archive',
archiveSearchLimit: 10,
archiveMaxSize: 100 * 1024 * 1024, // 100MB
archiveIndexing: true,
// Chat logging
logToChat: true, // Log optimization events to chat
chatLogLevel: 'brief', // 'brief', 'detailed', or 'none'
chatLogFormat: '📊 {action}: {details}', // Format for chat messages
// Performance
batchSize: 5, // Messages to process in batch
maxCompactionRatio: 0.5, // Maximum 50% compaction in one pass
}
Chat Logging
The context optimizer can log events directly to chat:
// Example chat log messages:
// 📊 Context optimized: Compacted 15 messages → 8 (47% reduction)
// 📊 Archive search: Found 3 relevant snippets (42% similarity)
// 📊 Dynamic context: Filtered 12 low-relevance messages
// Configure logging:
const pruner = createContextPruner({
logToChat: true,
chatLogLevel: 'brief', // Options: 'brief', 'detailed', 'none'
chatLogFormat: '📊 {action}: {details}',
// Custom log handler (optional)
onLog: (level, message, data) => {
if (level === 'info' && data.action === 'compaction') {
// Send to chat
console.log(`🧠 Context optimized: ${message}`);
}
}
});
Integration with Clawdbot
Add to your Clawdbot config:
skills:
context-pruner:
enabled: true
config:
contextLimit: 64000
autoPrune: true
The pruner will automatically monitor context usage and apply appropriate pruning strategies to stay within the configured context limit. LLM calls are routed through SkillBoss API Hub (POST https://api.heybossai.com/v1/pilot) using your SKILLBOSS_API_KEY.
