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RAGeval

CI PyPI License: AGPL v3

Drop-in LLMOps observability for RAG pipelines. Self-hosted. SQLite-default. Persona-aware. Multi-judge consensus.

Version: v0.1.23 | pip install omnismart-rageval

🔗 Live demo: https://rageval.ysiddo-ai-projects.app/ — browser dashboard (score a query + view metrics). Also fully scriptable — API: /health, /eval/* via curl/HTTPie. Self-hosting: see SELF_HOSTING.md.

The 60-Second Pitch

from rageval import track

@track(model="anthropic/claude-sonnet-4-6", persona="cfo")
async def answer_question(query: str, context_chunks: list[str]) -> str:
    ...

That's it. Open the dashboard at localhost:8003.

What It Measures

Metric Definition
Retrieval relevance Cosine similarity between query and retrieved chunks (BGE-large by default)
Groundedness consensus Multi-judge LLM scoring across your configured JUDGE_MODELS (min. 2 — no single-judge fallback), flags disagreement
Faithfulness Per-sentence max-similarity to any chunk (NLI proxy)
Cost USD per interaction, tracked by model
Latency End-to-end wall-clock

See RESEARCH.md for the reasoning behind an LLM-judge, multi-judge-consensus design, and eval/JUDGE_BENCHMARK.md for measured accuracy against a hallucination benchmark.

Comparison vs Alternatives

Feature RAGeval Phoenix Langfuse TruLens
Self-hosted ✅ ✅ ✅ ✅
SQLite default ✅ ❌ ❌ ❌
Drop-in decorator ✅ partial ❌ partial
Persona-aware ✅ ❌ ❌ ❌
Multi-judge consensus ✅ ❌ ❌ ❌
Cost tracking ✅ ✅ ✅ partial
Setup time 60 sec 10 min 15 min 10 min

As of 2026, based on each project's public documentation. Feature sets change fast in this space — worth re-checking before you decide.

Quick Start

pip install omnismart-rageval   # v0.1.23 — distribution name; CLI + import remain `rageval`
rageval init                    # creates ~/.rageval/rageval.db
rageval serve --port 8003

Integration

FastAPI

from rageval import track

@app.post("/ask")
@track(model="anthropic/claude-sonnet-4-6", persona="cfo")
async def ask(query: str):
    chunks = await retriever.search(query)
    return await llm.generate(query, chunks=chunks)

LangChain

@track(model="groq/openai/gpt-oss-120b")
def chain_invoke(query: str, context_chunks: list[str]):
    return chain.invoke({"query": query, "context": context_chunks})

Endpoints

Method Path Purpose
GET /health Liveness
POST /eval/log Score + store
POST /eval/score Score only (no storage)
GET /eval/metrics?days=7 Aggregate dashboard data
GET /eval/queries Query log (filter by needs_review)
GET /eval/cost-report?days=30 Cost breakdown by day + model
GET /eval/alerts Recent flagged queries
GET /eval/events Recent evaluation-pipeline telemetry
GET /eval/config Judge/embedding/threshold configuration
POST /eval/retrieval-bench A/B compare retrieval strategies
POST /eval/embedding-comparison Compare embedding models
WS /eval/live Real-time event feed

Full reference with request/response shapes: the in-app API Docs page (/api-docs in the dashboard).

Tests

70+ test functions across smoke, API, evaluator, decorator, DSPy integration, store, and e2e:

pytest tests/ -q

License & Commercial Use

This project is open-source under AGPL-3.0 — free for researchers, students, and open-source use.

The AGPLv3 requires that any proprietary network service (SaaS, internal corporate tooling) that uses or modifies this code also open-source its entire backend. If you need to use RAGeval in a closed-source commercial environment, or need enterprise features (SSO, custom RBAC, etc.), see COMMERCIAL.md for a commercial license.

Anonymous Telemetry

RAGeval sends a single anonymous startup ping (a timestamp + a random, non-hardware-derived install ID — no API keys, prompts, or application data) so the maintainer can gauge usage. Disable it by setting TELEMETRY_OPT_OUT=true in your .env. See TELEMETRY.md for details.

Metadata

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