Use case · Research agents

Agents that stoprepeating themselves.

LeanCTX gives research agents persistent, citable memory: findings land in a knowledge store with provenance, the EvidenceLedger keeps verbatim quotes with sources, and cached re-reads cost ~13 tokens. Your agent collects once, remembers across sessions, and answers with citations instead of re-crawling.

Same engine. Different workflow. Always the right context.

What it costs you today.

Every session starts from zero

Yesterday’s findings are gone. The agent re-reads the same sources, re-derives the same conclusions, and bills you twice.

Answers without receipts

A summary without sources is a liability. Research output needs verbatim quotes with provenance.

Sources drown the window

Twenty open PDFs do not fit in a context window. Agents need ranked recall of what matters, not everything at once.

The capabilities that do the work.

Everything below ships in the open-source binary today. No roadmap items, no waitlists.

Your toolsLeanCTXModel

EvidenceLedger

verbatim quotes with source attribution, ready to cite

Knowledge store

findings, decisions and blockers persist across sessions

facts / quotes / transcript modes

sources collapse into attributable units

Semantic + BM25 recall

meaning-based retrieval over everything collected

~13-token re-reads

revisiting a known source is nearly free

From zero to first gain.

terminal
# collect a source as quotable evidence$ ctx_url_read("https://arxiv.org/abs/2310.08560", mode="quotes")
# remember a finding$ lean-ctx knowledge remember "accuracy falls 98→64% with window noise" --category discovery --key context-rot
# recall across sessions$ lean-ctx knowledge recall "context rot"
# semantic search over the corpus$ ctx_semantic_search("why does accuracy fall with window size")

Questions teams ask before adopting.

Take back control of your context.

Free for local use, forever. CI enforces it. One binary, ten minutes to the first measured gain.

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