empirica 1.6.2

Genuine AI epistemic self-assessment framework - Universal interface for single AI tracking

Empirica

We Gave AI a Mirror. Now It Measures What It Believes.

Empirica is an epistemic measurement system that makes AI agents measurably more reliable — tracking what they know, preventing action before understanding, and compounding learning across sessions.

Training & Guides | CLI Reference | Architecture

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The Problem

AI coding agents today have no self-awareness about what they know:

• Forgets between sessions — same questions, same dead ends, every time
• Acts before understanding — edits your code without knowing the architecture
• Can't tell you when it's guessing — no distinction between knowledge and confabulation
• No audit trail — reasoning evaporates with the context window

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What Empirica Does

Capability	What You Experience
Measures before acting	AI investigates your codebase before touching it. The Sentinel gate blocks edits until understanding is demonstrated
Remembers across sessions	Findings, dead-ends, and learnings persist in a 4-layer memory system. Session 3 starts where Session 2 left off
Prevents confident mistakes	The CHECK gate requires know >= 0.70 and uncertainty <= 0.35 before allowing action
Shows confidence in real-time	Live statusline in your terminal: [empirica] ⚡94% │ 🎯3 │ POSTFLIGHT │ K:95% U:5%
Calibrates against reality	Dual-track verification compares AI self-assessment against objective evidence — tests, git metrics, goal completion
Works through natural language	You describe tasks normally. The AI operates the measurement system automatically

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How You Use It

You talk to your AI normally. Empirica works in the background:

You:      "Fix the authentication bug in the login flow"

Empirica: [AI investigates → logs findings → passes Sentinel gate → implements fix → measures learning]

You see:  ⚡87% │ 🎯1 │ POSTFLIGHT │ K:88% U:12% │ ✓ stable

You direct. The AI measures.

Empirica's CLI has 150+ commands spanning investigation, measurement, calibration, and memory — like a cockpit instrument panel. You don't need to learn any of them. The AI reads the instruments, operates the controls, and reports back in natural language. The statusline gives you the flight data at a glance.

For power users, direct CLI access is always available: empirica goals-list, empirica calibration-report, empirica project-search --task "...", and more.

Learn the full workflow: getempirica.com has interactive training, guides, and deep explanations of every concept.

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Quick Start

Install + Claude Code (Recommended)

pip install empirica
empirica setup-claude-code

Then just start working. The hooks, Sentinel, system prompt, statusline, and MCP server are all configured automatically. See Claude Code Setup for details.

Alternative Installation Methods

One-Line Installer

# Linux / macOS
curl -fsSL https://raw.githubusercontent.com/Nubaeon/empirica/main/scripts/install.py | python3 -

# Windows (PowerShell)
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/Nubaeon/empirica/main/scripts/install.py" -OutFile "install.py"
python install.py

Homebrew (macOS)

brew tap nubaeon/tap
brew install empirica
empirica setup-claude-code

Docker

# Security-hardened Alpine image (~276MB, recommended)
docker pull nubaeon/empirica:1.6.2-alpine

# Standard image (Debian slim, ~414MB)
docker pull nubaeon/empirica:1.6.2

# Run
docker run -it -v $(pwd)/.empirica:/data/.empirica nubaeon/empirica:1.6.2 /bin/bash

Manual / Other AI Platforms

pip install empirica
pip install empirica-mcp        # MCP Server (for Cursor, Cline, etc.)
cd your-project && empirica project-init

The CLI works standalone on any platform. The full epistemic workflow (CASCADE, Sentinel, calibration) requires loading the system prompt into your AI. See System Prompts for Claude, Copilot, Gemini, Qwen, and Roo Code.

First Session

empirica onboard   # Interactive walkthrough of the full workflow

Or just start working — with Claude Code hooks active, the AI manages the epistemic workflow automatically.

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The Measurement Architecture

Empirica works through nested abstraction layers:

Plan
 └── Transaction 1 (Goal A)
      ├── NOETIC: investigate, search, read → findings, unknowns, dead-ends
      ├── CHECK: Sentinel gate → proceed / investigate more
      ├── PRAXIC: implement, write, commit → goals completed
      └── POSTFLIGHT: measure learning delta → persists to memory
 └── Transaction 2 (Goal B, informed by T1's findings)
      └── ...

Plans decompose into transactions — one per goal or Claude Code task. Each transaction is a noetic-praxic loop: investigate first (noetic), then act (praxic), with the Sentinel gating the transition. Along the way, the AI collects and reads artifacts (findings, unknowns, assumptions, dead-ends, decisions) while using semantic search to surface relevant epistemic patterns and anti-patterns from the project's history. Top artifacts are ranked by confidence and fed into each project's MEMORY.md as a hot cache.

The CASCADE Cycle

PREFLIGHT ────────► CHECK ────────► POSTFLIGHT
    │                 │                  │
 Baseline         Sentinel           Learning
 Assessment        Gate               Delta
    │                 │                  │
 "What do I      "Am I ready      "What did I
  know now?"      to act?"         learn?"

PREFLIGHT: AI assesses its knowledge state before starting work. CHECK: Sentinel gate validates readiness before allowing code edits. POSTFLIGHT: AI measures what it learned, creating a delta that persists.

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Live Statusline

With Claude Code hooks enabled, you see the AI's epistemic state in real-time:

[empirica] ⚡94% │ 🎯3 ❓12/5 │ POSTFLIGHT │ K:95% U:5% C:92% │ ✓ │ ✓ stable

Signal	Meaning
⚡94%	Overall epistemic confidence
🎯3 ❓12/5	Open goals (3), unknowns (12 total, 5 blocking)
POSTFLIGHT	Current CASCADE phase
K:95% U:5% C:92%	Knowledge, Uncertainty, Context
✓ stable	Drift indicator

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The 13 Epistemic Vectors

These vectors emerged from 600+ real working sessions across multiple AI systems. They measure the dimensions that consistently predict success or failure in complex tasks.

Tier	Vector	What It Measures
Gate	engagement	Is the AI actively processing or disengaged?
Foundation	know	Domain knowledge depth
	do	Execution capability
	context	Access to relevant information
Comprehension	clarity	How clear is the understanding?
	coherence	Do the pieces fit together?
	signal	Signal-to-noise in available information
	density	Information richness
Execution	state	Current working state
	change	Rate of progress/change
	completion	Task completion level
	impact	Significance of the work
Meta	uncertainty	Explicit doubt tracking

Deep dive: Epistemic Vectors Explained

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How It Works With Claude Code

Empirica doesn't replace or reinvent anything Claude Code already does. Claude Code owns tasks, plans, memory, and projects. Empirica adds the measurement layer on top:

Claude Code Does	Empirica Adds
Task management	Epistemic goals with measurable completion
Plan mode	Investigation phase with Sentinel gating — no edits until understanding is verified
MEMORY.md	Auto-curated hot cache ranked by epistemic confidence
Context window	4-layer memory that survives compaction and persists across sessions
Code editing	Grounded calibration — was the AI's confidence justified by test results?
Subagent spawning	Bounded autonomy with delegated work counting and budget tracking

The result: Claude Code's native capabilities, enhanced with measurement, gating, and calibration feedback that compounds over time.

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Platform Support

Platform	Integration Level	What You Get
Claude Code	Full (production)	Hooks, Sentinel gate, skills, agents, statusline, MCP
Cursor, Cline	MCP server	CASCADE workflow, memory, calibration via MCP tools
Gemini CLI, Copilot	Experimental	System prompt + CLI
Any AI	CLI + prompt	Full measurement via CLI commands and system prompt

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Documentation & Training

Resource	What It Covers
getempirica.com	Training course, interactive guides, deep explanations
Natural Language Guide	How to collaborate with AI using Empirica
Getting Started	First-time setup and concepts
CLI Reference	All 150+ commands documented
Architecture	Technical reference for contributors
System Prompts	AI prompts for Claude, Copilot, Gemini, Qwen, Roo

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The Empirica Ecosystem

Project	Description	Status
Empirica	Core measurement system — CASCADE, Sentinel, calibration, 13 vectors	Open source
Empirica Iris	Epistemic browser automation with SVG spatial indexing — Sentinel gating for visual interactions	Open source
Docpistemic	Epistemic documentation coverage assessment — know what your docs know	Open source
Breadcrumbs	Survive context compacts with git notes — dead simple session continuity	Open source
Empirica Workspace	Entity Knowledge Graph, Epistemic Prompt Engine, CRM, portfolio dashboard	Proprietary

Building something with Empirica? Open an issue to get listed.

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What's New in 1.6.2

• Code Quality Evidence — Grounded calibration includes ruff, radon, and pyright metrics as objective evidence
• docs-assess Ignore Patterns — [tool.empirica.docs-assess] in pyproject.toml with fnmatch patterns
• API Reference Expansion — 15+ class entries added, docs-assess coverage 71.8% to 84.0%
• Claude Code Symbiosis — Architecture docs for MEMORY.md hot cache, task-goal bridge, session lifecycle hooks
• Security Updates — flask >= 3.1.3, werkzeug >= 3.1.6, pillow >= 12.1.1

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Privacy & Data

Your data stays local:

• .empirica/ — Local SQLite database (gitignored by default)
• .git/refs/notes/empirica/* — Epistemic checkpoints (local unless you push)
• Qdrant runs locally if enabled

No cloud dependencies. No telemetry. Your epistemic data is yours.

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Community & Support

• Website: getempirica.com
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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License

MIT License — see LICENSE for details.

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Author: David S. L. Van Assche Version: 1.6.2

Turtles all the way down — built with its own epistemic framework, measuring what it knows at every step.