Relevant context
is not always true.
Lint-AI turns scattered project history, including sessions, documents, decisions, and traces, into current, evidence-backed context at the moment an agent needs it.
“Increase retries to recover from the intermittent timeout.”
Topically relevant · no longer current“Retries amplify load. Cap attempts and fix the token clock skew.”
Source-linked · time-aware · current- 01 Relevance
- 02 Recency
- 03 Supersession
- 04 Evidence
Ordinary retrieval can surface an old recommendation as current because it is still topically relevant.
The current state ranks first. Older guidance remains useful as history without quietly becoming today’s answer.
Move from relevant retrieval to current understanding.
Search can find the right topic. Lint-AI helps agents distinguish what is relevant from what is still true, while preserving the sources, timestamps, and history behind each answer.
cargo install --git https://github.com/RooAGI/Lint-AI Heuristic release backend · LongMemEval-S, 500 scoped questions, fair comparison track using any-hit recall · HTTP figure uses 23,366 records
Reproducible performance comparison¶
Lint-AI's retrieval and server-load measurements are published with the exact scripts, payloads, corpus sizes, and caveats needed to reproduce them. In the normalized 23,366-record HTTP run, Lint-AI sustained 952 req/s at concurrency 10, compared with 171 req/s for AgentMemory in keyless BM25 mode. This is a service-load comparison, not a claim that the two systems have identical retrieval semantics.
View the comparison methodology and results
Prevent confident staleness¶
Agent context is not a pile of text. Decisions supersede older decisions. Terms drift. Ownership changes. The right answer often depends on when something was true and where the evidence came from.
Know what is current
Rank current evidence ahead of older guidance and preserve historical answers when a question depends on the past.
Show why it is current
Return source, time, and relationship signals with the context so an agent or reviewer can inspect the basis for an answer.
Make drift visible
Surface contradictions, stale claims, terminology drift, orphan pages, and missing links before they become confident answers.
Keep your sources. Add a current-state layer.¶
Lint-AI does not ask you to discard your existing project knowledge. It indexes the sessions, notes, documents, and decisions you already have, then makes their relationships and history usable at retrieval time. Each provider gets isolated, project-scoped memory, lifecycle capture, and shared MCP controls.
From corpus to grounded context¶
Start with a local corpus. Keep the evidence.