How Memora compares

Catch your AI citing sources that don't say what it claims.

This page is deliberately honest about where Memora leads and where it is behind. It is an architectural comparison, not a published head-to-head benchmark.

The one claim no funded vendor makes

Memora performs post-generation, hash-reverified citation rejection: after the model answers, Memora re-reads the verbatim source span behind each cited claim, recomputes its blake3 fingerprint, and strips any citation the source does not actually contain before the answer is returned. The contract is enforced by Rust types and span hashes, not by prompting.

No funded agent-memory vendor does this. Mem0, Zep, Letta, and Cognee retrieve and store memory well, but they do not re-read the source and reject citations. Anthropic's Citations API returns offsets the model asserts — it does not independently re-read and re-hash them.

Two of these rows matter more than the rest, and they are the honest ones: the "provenance only, by design" row (we tell you exactly where our guarantee ends) and the "behind on scale/ecosystem" row (we are not pretending to out-scale a funded team). Those two admissions are what make the top row believable.

What Memora guarantees — and what it does not

• Provenance integrity (guaranteed): the cited source span verbatim exists, is unmodified (hash-proven), and contains any quoted text. This is strictly stronger than model-asserted citations.
• Entailment (optional, not guaranteed): whether the source actually supports the assertion (rather than just containing the quote) is a separate, harder question. memora verify --entailment runs an opt-in, LLM-judged check and flags citations the source does not support. It is best-effort, kept clearly separate from the hash-proven provenance above, and never sends secret content to a cloud model.

The landscape, by category

For each system the question is narrow: when an LLM answer must be grounded in your content, how is the citation verified?

Agent-memory layers — Mem0, Zep / Graphiti, Letta, Cognee

The category leaders. Strong on storing, recalling, and (Zep/Graphiti) temporally modeling memory across sessions, with broad framework integrations and hosted offerings. Citations, where present, are model- or prompt-level — the system does not re-read the original source span and reject a citation whose bytes don't match. Memora is behind these on scale, integrations, and ecosystem, and overlaps them on temporal validity and contradiction handling (so Memora does not market those as unique). Memora's divergence is the hash-reverified rejection step and per-claim privacy redaction at the wire boundary.

Grounded-citation APIs — Anthropic Citations

Returns source locations the model asserts for a generated answer. Useful and first-party, but the offsets are not independently re-read and re-hashed against the source after generation. Memora's divergence: it treats the model's citation as a claim to be re-proven, not as ground truth — and rejects it on a hash mismatch.

PKM-AI over notes — Obsidian Copilot, Smart Connections, Khoj, Notion AI

Excellent zero-config retrieval and chat over personal notes, often free and local. Citations are typically prompt-level: the model is asked to reference notes, with no strict post-generation verification of each cited identifier against an immutable span. Memora is slower and higher-friction here (it indexes claims and needs an LLM for extraction), and diverges by enforcing the citation contract structurally rather than trusting the model to quote faithfully.

Traditional RAG over Obsidian / markdown

Index chunks, retrieve top-k, pass to the model with citation guidance in the prompt. Temporal reasoning is usually recency-biased; privacy is folder/note-level; synthesis staleness is rarely tracked. Memora diverges by retrieving atomic claims with byte-range provenance, verifying every cited id against its source span, modeling validity windows and supersession, and tracking stale derived claims over a provenance DAG.

Summary matrix (architectural)

Final note on benchmarking

This is an architectural comparison. Memora has not been run in published head-to-head retrieval benchmarks against these systems, and this page makes no such numeric claims. The one number Memora does publish — its citation-rejection rate over a labeled fixture — is reproducible locally with make bench and is described in the README. Vendor capabilities evolve; verify current feature sets against each vendor's own docs.