Use case · Research agents

Agents that stop
repeating 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.

The problem

What it costs you today.

01

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.

02

Answers without receipts

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

03

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.

Shipped today

The capabilities that do the work.

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

Your tools LeanCTX Model
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
Quickstart

From zero to first gain.

# 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")
FAQ

Questions teams ask before adopting.

How does LeanCTX keep research citable?

The EvidenceLedger stores verbatim quotes with their source URL or file. Answers can reference evidence IDs, so every claim traces back to an original you can open.

Does memory survive restarts and new sessions?

Yes. Findings live in a local knowledge store with categories (discovery, decision, blocker…) and are restored into every new session automatically.

Can it handle large corpora?

Collected sources are deduplicated, compressed and indexed with BM25 plus a knowledge graph. Recall returns ranked, budgeted context, never the whole corpus at once.

Take back control of your context.

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