Observability and Evaluation · Public

quivr

Observability and evaluation project for turning logs, quality metrics, drift, or experiment results into reviewable signals.

ObservabilityEvaluationQuality metricsData driftExperiment tracking

Best fitDevelopers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.

Check whether this project matches your task before installing it.

What it can doObservability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot39k stars

3.7k forks · 119 contributors

Official first step Read manual preview Source repository

Publication status · 2026-05-25

What is quivr?

quivr helps developers observe, evaluate, or monitor AI/data application behavior and quality.
Best fit: Developers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.
Not for: Not for users without logs/sample data, privacy boundaries, or those who only need a chat UI.
Capability added to an AI workflow: Observability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage
First safe verification step: Verify collection, metric interpretation, export, and deletion paths with redacted sample data first.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: The main risk is sending sensitive logs, user data, or misleading metrics into production observability.
Evidence base: https://github.com/QuivrHQ/quivr, https://github.com/QuivrHQ/quivr#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.

Match the project to your task before installing it.

CapabilityObservability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage

Observability and evaluation project for turning logs, quality metrics, drift, or experiment results into reviewable signals.

RepositoryQuivrHQ/quivr

39k stars · 3.7k forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Introduction to Quivr

Related topics: Getting Started, System Architecture Overview

Source: https://github.com/QuivrHQ/quivr / Human Manual

Getting Started

Related topics: Introduction to Quivr, Installation

Source: https://github.com/QuivrHQ/quivr / Human Manual

Installation

Related topics: Getting Started, LLM Integration

Source: https://github.com/QuivrHQ/quivr / Human Manual

System Architecture Overview

Related topics: Core Components, Brain Class, RAG Implementation

Source: https://github.com/QuivrHQ/quivr / Human Manual

Core Components

Related topics: System Architecture Overview, Brain Class, LLM Integration

Source: https://github.com/QuivrHQ/quivr / Human Manual

Sources: https://github.com/QuivrHQ/quivr, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars39k stars

Forks3.7k forks

Contributors119 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using quivr with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
[Bug]:
github / github_issue
02
Integration idea: Screenpipe for screen/audio context
github / github_issue
03
[Bug]: RuntimeError: There is no current event loop in thread 'MainThrea
github / github_issue
04
EU AI Act Compliance Scan Results — Sharing Findings for Feedback
github / github_issue
05
The garbage collector is trying to clean up non-checked-in connection <A
github / github_issue
06
core: v0.0.33
github / github_release
07
core: v0.0.29
github / github_release
08
core: v0.0.27
github / github_release
09
core: v0.0.26
github / github_release
10
core: v0.0.25
github / github_release
11
core: v0.0.24
github / github_release
12
README/documentation is current enough for a first validation pass.
GitHub / issue

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install quivr-core

Official start command · https://github.com/QuivrHQ/quivr#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

quivr Manual

Quivr follows a modular architecture with the quivr-core package as its central component. The architecture is designed around a workflow-based system where different processing nodes are ...

Open the full manual

https://github.com/QuivrHQ/quivr Project Manual
Table of Contents
Introduction to Quivr
Related Pages
Key Features
Architecture Overview
Package Structure
Getting Started

Introduction to Quivr

Related topics: Getting Started, System Architecture Overview

Source: https://github.com/QuivrHQ/quivr / Human Manual

Getting Started

Related topics: Introduction to Quivr, Installation

Source: https://github.com/QuivrHQ/quivr / Human Manual

Installation

Related topics: Getting Started, LLM Integration

Source: https://github.com/QuivrHQ/quivr / Human Manual

System Architecture Overview

Related topics: Core Components, Brain Class, RAG Implementation

Source: https://github.com/QuivrHQ/quivr / Human Manual

Core Components

Related topics: System Architecture Overview, Brain Class, LLM Integration

Source: https://github.com/QuivrHQ/quivr / Human Manual

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start ManifestPROJECT_PACK.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

high

Review upstream issue

The source signal needs review before production use.

high

Review upstream issue

The source signal needs review before production use.

medium

Review upstream issue

The source signal needs review before production use.

medium

Review upstream issue

The source signal needs review before production use.

medium

Review upstream issue

README/documentation is current enough for a first validation pass.

medium

Review upstream issue

The source signal needs review before production use.

medium

Review upstream issue

The source signal needs review before production use.

medium

Review upstream issue

The source signal needs review before production use.