AgentEvo — Self-hosted AI Evaluation Platform

Systematically and quantitatively evaluate the response quality of LLMs and Agents — model configs, dataset management, pre-evaluation scripts, 9 evaluation types with AI judges, result analytics and A/B testing. Built with FastAPI + Vue 3 + Celery, deployable as a single Docker Compose stack.

中文文档

What it does

Preparation → Evaluation → Analysis, in one platform:

Module	Capabilities
Model configs	OpenAI / Anthropic compatible endpoints, custom HTTP (import from curl), runtime variable injection, streaming connectivity test
Datasets	Excel import (dynamic fields), Agent session file import, media fields, field-level validation
Script library	Pre-evaluation scripts (zip / folder packages), model.py contract declaration, online file-tree editing, scaffold templates
Evaluation tasks	AI judge / Agent long-session / consistency / success-rate / RAG retrieval / binary classification, batch runs, real-time progress over WebSocket
Judge engines	Claude Code judge, DeepSeek Harness judge, generic LLM judge graph; per-task evaluation dimensions
Results & analytics	Score details, run-to-run history, Excel/JSON/CSV export, trend statistics, side-by-side A/B comparison
Administration	Teams and user enable/disable (super admin), audit log, built-in user guide

First registered user automatically becomes the super admin; sessions are JWT (Authorization: Bearer), passwords stored as PBKDF2-HMAC-SHA256 hashes.

Quick start (local development)

Prerequisites: Python 3.12+, Node.js 18+ with pnpm, Docker (for PostgreSQL and Redis).

# 1. Start PostgreSQL + Redis (dev containers)
docker compose -f docker-compose.dev.yml up -d

# 2. Backend dependencies (use a virtualenv)
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 3. Configure environment (.env is not committed)
cp .env.example .env
#   set DATABASE_URL / REDIS_URL / AUTH_JWT_SECRET

# 4. Run database migrations
export AIEVO_DATABASE_URL="$DATABASE_URL"
alembic upgrade head

# 5. Start the backend (port 8100)
python scripts/run_local_dev.py

# 6. Start the frontend (another terminal, /api proxies to 8100)
cd frontend && pnpm install && pnpm dev   # https://localhost:3000

Open https://localhost:3000 → register (first user becomes super admin) → add a target model and a judge model under Model Configs → import a dataset from Excel → create and run an evaluation task.

Deploy with Docker Compose

The image is built online (Dockerfile.online: Node stage builds the frontend, Python stage installs dependencies):

cp .env.example .env   # set DATABASE_URL / REDIS_URL / AUTH_JWT_SECRET / IMAGE_TAG
docker compose up -d --build

• api serves both the REST API and the built frontend on port 8100 (AIEVO_PORT to change)
• worker runs Celery evaluation tasks; logs/ and uploads/ are persisted via volumes
• set COMPOSE_PROFILES=bundled-db in .env to also start a bundled PostgreSQL

The full offline/CI pipeline (locked wheels, bundled Claude Code CLI) is described in docs/build-pipeline.md.

Evaluation types

Type	One-liner	Needs a judge model
AI judge	LLM judge scores model outputs on configured dimensions	Yes
Agent (long session)	Judge scores imported agent session transcripts	Yes
Consistency	Same input called N times, measures output stability	Optional
Success rate	Share of successful calls	No
RAG retrieval	Precision / recall and 4 more retrieval metrics	Partial
Binary classification	Label comparison: accuracy / sensitivity / specificity	No

Documentation

• User guide (中文, with screenshots)
• Quick start (中文)
• API docs are served by the backend at /api-docs once running

Security notes

• Set a strong AUTH_JWT_SECRET in production (missing values fall back to a derived key, local development only)
• anc-hub endpoints use strict TLS verification by default; self-signed internal endpoints can opt out with AIEVO_HUB_TLS_INSECURE=1
• This platform intentionally calls user-configured model endpoints (including internal gateways) — deploy it inside a trust boundary you control

License

Apache-2.0