Empirica

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

Epistemic infrastructure for AI — measurement, memory, and calibration across sessions.

Empirica tracks what AI knows, gates what it does, and compounds learning across session boundaries. It measures the gap between what AI predicts and what's true — making AI agents measurably more reliable.

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 uses thresholds computed dynamically from calibration data before allowing action
Shows confidence in real-time	Live statusline in your terminal: [empirica] ⚡94% ↕70% │ 🎯3 │ POST 🔍92% │ K:95% C:92%
Calibrates against reality	Dual-track verification compares AI self-assessment against objective evidence — tests, git metrics, goal completion
Tracks your codebase	Temporal entity model auto-extracts functions, classes, and imports from every file edit — the AI knows what's alive and what's stale
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% ↕70% │ 🎯1 │ POST 🔍85% │ K:88% C:82% │ Δ +K

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.

Already have Claude Code configured? Use --force to replace your default Claude Code settings with Empirica's epistemic hooks. Without --force, setup only writes files that don't already exist — so if you've already used Claude Code, the default internals stay in place and Empirica's hooks won't activate.

empirica setup-claude-code --force

--force replaces hooks in settings.json but only removes Empirica's own hooks — hooks from other plugins (Railway, Superpowers, etc.) are preserved.

Alternative Installation Methods

Homebrew (macOS)

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

Docker

# Security-hardened Alpine image (~276MB, recommended)
docker pull nubaeon/empirica:1.7.7-alpine

# Standard image (Debian slim, ~414MB)
docker pull nubaeon/empirica:1.7.7

# Run
docker run -it -v $(pwd)/.empirica:/data/.empirica nubaeon/empirica:1.7.7 /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 (epistemic transactions, 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 Epistemic Transaction 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% ↕70% │ 🎯3 ❓12/5 │ POST 🔍92% │ K:95% C:92% │ Δ +K +C

Signal	Meaning
⚡94%	Overall epistemic confidence
↕70%	Sentinel threshold (know gate) — user-facing only
🎯3 ❓12/5	Open goals (3), unknowns (12 total, 5 blocking)
POST 🔍92%	Transaction phase + work state (🔍 investigating / 🔨 acting) with composite score
K:95% C:92%	Knowledge and Context vectors (color-coded by gap to threshold)
Δ +K +C	Learning delta (POSTFLIGHT only) — which vectors improved

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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	Epistemic transaction 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 — epistemic transactions, 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 Cortex	Cross-project intelligence layer — serves verified predictions and accumulated learnings to condition future work	Proprietary
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 1.7.7

• Intelligence search kind — New kind='intelligence' search mode with collection-type boost weights for cross-domain knowledge discovery. Filters code_api from eidetic queries to reduce noise. Renamed from initial kind='cross-domain' for clarity
• Workflow Pattern Mining — Detect repeated tool sequences across transactions via sequential pattern analysis. New workflow-patterns CLI command and MCP tool
• Workflow Suggestion Engine — Epistemic-correlated pattern analysis surfaces workflow suggestions based on historical transaction data
• Sentinel noetic allow list — Added ToolSearch (deferred tool discovery), intelligence layer MCP tools, and git notes show/git notes list as always-allowed noetic tools
• Sentinel closed-transaction noetic check — Closed transactions now correctly allow noetic tools and safe Bash commands without requiring a new PREFLIGHT. Praxic tools with closed transactions get the correct error message
• PreCompact hook schema — hookSpecificOutput is not supported by PreCompact events (only PreToolUse, UserPromptSubmit, PostToolUse). Switched to systemMessage for compact guidance output
• Docker token-gen removed from safe commands — docker token-gen no longer classified as a safe noetic command in Sentinel
• POSTFLIGHT intelligence layer auth — Session-end POSTFLIGHT push now includes Authorization: Bearer header when CORTEX_API_KEY is set

Previous Highlights (1.6.11–1.7.0)

• Empirica Constitution — 12-section governance framework routing situations to mechanisms
• Epistemic Persistence Protocol (EPP) — Calibrated position-holding under pushback, replacing AAP
• Lean Core Prompt — 81% reduction in always-loaded context. setup-claude-code --lean
• Cross-Project Search — --global searches ALL projects' Qdrant collections
• Cross-Project Artifact Writing — finding-log --project-id <name> writes to another project
• Plugin Renamed — empirica-integration → empirica. Run setup-claude-code --force
• Brier Score Calibration — Proper scoring rule with dynamic thresholds
• Profile Management — profile-sync, profile-prune, profile-status

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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: 1.7.7

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