Agent Amplifier

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Runtime Amplification Layer for AI Coding Agents — Claude Code Hooks That Don't Just Block, They Amplify

Agent Amplifier is the runtime amplification layer for AI coding agents — the missing half of the hook layer. Modern coding agents (Claude Code, Cursor, Copilot, LangGraph, CrewAI, AgentScope, LangChain) all expose lifecycle hooks. The community filled them with guardrails — products whose job is to BLOCK dangerous actions. That work is necessary, but it's only half the job. Hooks can also amplify: dynamically shape WHAT the agent does next based on prompt complexity, drift signals, convergence, and budget. Agent Amplifier fills that empty slot with five deterministic Python hooks — effort router (5-tier complexity classifier), goal anchor (anti-drift re-injection), convergence detector (LTI stability), persona escalation (audit pressure per iteration), and token budget controller. Zero extra LLM calls, zero network, fail-open, AGPL-3.0. Dogfooded across 1.71 billion tokens of real coding sessions before v1.0.0 shipped on 2026-05-13. 1,741 tests, 100% branch coverage, mypy --strict.

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$pip install agent-amplifier

GitHub →Website →

1,741

Tests

100%

Branch Coverage

Host Adapters

1.71B

Tokens Dogfooded

Features

Effort Router (5-Tier Classifier)

Deterministic complexity classifier routes each user prompt to one of five effort tiers (minimal, low, medium, high, ultra). Shapes phase budget + model tier suggestion without an extra LLM call.

Goal Anchor (Anti-Drift)

Re-injects the original goal every N tool calls to prevent multi-turn drift. Solves the 486M-token rabbit-hole problem at the hook layer.

Convergence Detection (LTI Stability)

Detects when agent output is substantially similar to the prior iteration. Stops the loop. Saves tokens.

Persona Escalation

Each iteration ramps audit pressure: senior dev → adversarial reviewer → red-team auditor. Catches what single-pass review misses.

Token Budget Controller

Cost-bounded amplification. Set a session budget; AA shapes phase prompts to stay within it without truncating mid-task.

7 Host Adapters

Claude Code, Cursor, GitHub Copilot, LangGraph, CrewAI, AgentScope, LangChain. One install command per host: agent-amp install <host>.

SLM Memory Provider

Built-in integration with SuperLocalMemory. recall() before each prompt, remember() at session end. Local-first, zero cloud.

Streamlit Dashboard

Live telemetry: token bar, classification distribution, persona escalation timeline. agent-amp dashboard to launch.

Use Cases

Reduce token usage in long Claude Code sessions

Stop multi-turn drift on complex refactors

Auto-escalate audit pressure on critical PRs

Cross-host amplification (one hook layer, 7 agents)

Cost-bounded agent runs with hard token budgets

Convergence-aware iteration (no infinite loops)

Licensed under AGPL-3.0-or-later