AI Developer Tools

PSI KV Governor

A reference implementation using Linux Pressure Stall Information to trim LLM KV cache under memory pressure.

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PSI KV Governor

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What is PSI KV Governor?

PSI KV Governor is a small reference implementation that leverages Linux Pressure Stall Information (PSI) to automatically trim the key-value (KV) cache of large language models when the system is under memory pressure, helping to prevent out-of-memory issues and improve stability during LLM inference on Linux systems.

PSI KV Governor vs Similar AI Tools

Pricing ModelFreeFreeFreeFree
Free Credits
Key Features
  • PSI-based KV cache trimming
  • Reference implementation for LLM inference
  • Integration with llama.cpp
  • Seven breakable boxes covering OWASP Agentic Top-10 vulnerabilities
  • Three guided simulations for cascading failures, human-agent trust, and rogue agents
  • Network-isolated Docker containers for safe execution
  • Code-to-runtime reasoning across cloud, Git, and Kubernetes
  • Action-gate enforces read-only policy on every API call
  • Sandboxed JavaScript execution for concurrent research
  • Append-only, SHA-256-addressed event history through Jaybase
  • AES-256-GCM encryption for stored node payloads
  • Unified RBAC for ledger, notes, snapshots, and audit reads
Pros
  • Leverages Linux kernel PSI for accurate memory pressure detection
  • Lightweight and easy to integrate with existing LLM runtimes
  • Covers full OWASP Agentic Top-10 in a realistic manner
  • Docker isolation prevents accidental damage
  • Read-only by construction prevents accidental writes
  • Evidence-backed verification cross-checks every finding
  • Opinionated and secure accounting CLI with immutable audit trail
  • Designed for AI agent integration with JSON output
Cons
  • Linux-only due to PSI dependency
  • Experimental, not production-ready
  • Requires Docker and technical setup
  • Not for production use; only for lab environments
  • Requires an LLM API key, incurring token costs
  • Limited to read-only operations, cannot remediate
  • Pre-1.0, limited feature set
  • No native QuickBooks import (agents must normalize data)
Best For
  • Developers optimizing LLM inference on Linux
  • Users of llama.cpp on memory-constrained systems
  • Security researchers focusing on AI agent vulnerabilities
  • Developers building MCP-based applications
  • Security engineers
  • DevOps teams
  • Small teams needing secure, auditable accounting with AI agent support
  • Developers integrating automated bookkeeping workflows

How to use PSI KV Governor?

  1. 1Check PSI availability: cat /proc/pressure/memory
  2. 2Run reference simulator: PYTHONPATH=src python -m psi_kv_governor.cli simulate
  3. 3Build llama.cpp runner: scripts/build_llama_runner.sh
  4. 4Download demo model: python scripts/download_demo_model.py
  5. 5Run PSI benchmark: PYTHONPATH=src python benchmarks/pressure_bench.py [options]

PSI KV Governor Key Features

  • PSI-based KV cache trimming
  • Reference implementation for LLM inference
  • Integration with llama.cpp
  • Benchmark scripts for evaluating performance
  • Configurable trimming parameters

PSI KV Governor Use Cases

  • Preventing out-of-memory errors during LLM inference on Linux
  • Optimizing memory usage for serving LLMs on constrained systems
  • Testing PSI-aware cache management strategies

PSI KV Governor Pricing & Free Credits

PSI KV Governor currently operates on a Free model.

This tool is completely free to use

Free

$0

Open-source project available on GitHub

PSI KV Governor Pros & Cons

Pros

  • Leverages Linux kernel PSI for accurate memory pressure detection
  • Lightweight and easy to integrate with existing LLM runtimes
  • Open-source and customizable

Cons

  • Linux-only due to PSI dependency
  • Experimental, not production-ready
  • Limited documentation and examples

What is PSI KV Governor best for?

  • Developers optimizing LLM inference on Linux
  • Users of llama.cpp on memory-constrained systems
  • Researchers exploring memory-efficient LLM serving

PSI KV Governor FAQ

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