Omneum

An open-source context evaluator for AI agents.

Agents routinely assemble context from many different sources — a RAG pipeline, web search, APIs, databases, files, memory systems, other agents — but stuffing dozens of raw chunks into a single context window can leave the model working through a mess of information that was never organized for it.

Most agents focus on retrieving more context. Omneum is focused on making sure the context reaching the model is clean and reliable.

Why Omneum and how it works

Modern agent pipelines use several different techniques to decide what context should reach a model:

Semantic similarity (Vector DBs). The piece of text that looks closest to the prompt wins.

Semantic reranking. Retrieved context is reordered based on how relevant it is to the query.

LLM-as-a-Judge. An LLM reads the retrieved context and is instructed to evaluate it against a set of instructions.

These techniques can be highly effective at selecting context, but relevance is only one dimension of context quality.

Chunks can still hold conflicting or stale information. Multiple retrieved chunks might not be deduplicated or appear independent despite sharing an upstream source. This contributes to context bloat: low-quality information consumes context-window capacity that could otherwise be used for higher quality information.

Omneum addresses many of these limitations: it uses a graph-based mathematical analysis to model the relationships between claims and their supporting sources. Importantly, this analysis does not naively assume every source has derived its information independently. The same information often propagates across multiple sources. Without dependency tracking, an agent cannot distinguish five independent observations from the same information repeated five times.

Agent pipelines already handle structured metadata alongside retrieved context. Omneum uses available provenance and lineage metadata to estimate source dependencies, then incorporates those estimates into its calculation of source reliability and claim support scores.

These scores are computed without interpreting the semantic meaning of the context. For instance, when considering a claim such as:

“Acme reported $4.2 billion in revenue in 2025.”

Its support score depends on the reliability of the sources supporting it. In turn, those sources receive reliability from the claims they support.

This definition seems circular, and it is. Nonetheless, it yields a well-defined notion of reliability that helps distinguish strongly supported context from weakly supported context.

Omneum uses BRAID, its open-source algorithm, to compute these reliability and support scores. Explore the algorithm and research →

Where Omneum fits

Omneum runs locally as a Model Context Protocol (MCP) server. It adds a lightweight post-retrieval evaluation layer to your existing retrieval or ranking stack; no inference request, no extra model call.

Retrieval / tools → Reranking (optional) → Omneum evaluation → agent control flow → LLM

Omneum returns context-quality signals; your agent makes evidence-aware decisions throughout its workflow.

Architecture and design →

Quick Start

Note: This tutorial assumes Python 3.11–3.14. SDKs for other languages are not currently supported.