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Smart Compression
Reduce AI prompt sizes by up to 60% while preserving semantic meaning using state-of-the-art compression algorithms optimized for LLM contexts.
Prompt Compression
Prompt Compression for LLMs, Reduce Token Usage, Save Costs, Build Faster
prompt-piper
$ cat large_context.txt | prompt-piper compress --model claude-3
✓ Loading IPFS model: QmX7k9...
✓ Tokenizing input: 12,847 tokens detected
✓ Applying semantic compression...
✓ Output: 4,931 tokens (62% reduction)
$ prompt-piper analyze --cost-estimate
📊 Compression Stats:
• Original cost: $0.385 | Compressed: $0.148
• Monthly savings (1000 calls): $237.00
• Context window usage: 38% → 15%
✓ Loading IPFS model: QmX7k9...
✓ Tokenizing input: 12,847 tokens detected
✓ Applying semantic compression...
✓ Output: 4,931 tokens (62% reduction)
$ prompt-piper analyze --cost-estimate
📊 Compression Stats:
• Original cost: $0.385 | Compressed: $0.148
• Monthly savings (1000 calls): $237.00
• Context window usage: 38% → 15%
Compression Models: Powered by IPFS
IPFS Integration
Decentralized storage of compression models on IPFS ensures availability, versioning, and censorship resistance for your compression pipelines.
Token Optimization
Intelligent token analysis and optimization reduces API costs by minimizing token usage while maintaining prompt effectiveness and clarity.
Stream Processing
Real-time stream processing with Unix pipe compatibility allows seamless integration into existing workflows and CI/CD pipelines.
Multi-Model Support
Compatible with GPT-4, Claude, Gemini, and other major LLMs with model-specific optimization strategies for maximum efficiency.
Docker Ready
Fully containerized with Docker support for consistent deployment across environments and easy integration with Kubernetes orchestration.
Expand Context Window
Maximize your LLM's potential by compressing prompts efficiently. Fit more context, preserve meaning, and reduce API costs with intelligent compression.
prompt-piper
$ echo "Your long prompt here..." | prompt-piper compress
✓ Analyzing tokens...
✓ Applying compression model...
✓ Compressed: 8,192 → 3,276 tokens
✓ Saved 60% tokens | Cost reduced by $0.42 per prompt
✓ Analyzing tokens...
✓ Applying compression model...
✓ Compressed: 8,192 → 3,276 tokens
✓ Saved 60% tokens | Cost reduced by $0.42 per prompt
Context Optimization
Intelligently compress and restructure prompts to fit more information within LLM context windows while maintaining semantic integrity.
Batch Processing
Process multiple prompts simultaneously with configurable batch sizes and parallel execution for high-throughput compression workflows.
Custom Strategies
Define custom compression strategies with configurable parameters for domain-specific optimization and fine-tuned performance.
