memseek
Memory layer for coding agents

Your agent keeps re-suggesting what the team rejected in March. Memseek remembers what's true now.

One layer between your records and the model. Current, dated, source-linked context — not similar-looking chunks. Your data stays where it is.

Join the waitlist → See the benchmarks Runs on cheap, self-hostable models
agent asks: "what should I know before the Acme call?"
the full history~101k tok
CRM · Feb
Budget approved: $40k
call notes · May
Budget cut to $25k this year
email · Jun
Wants SSO before they sign
note
Prefers email over calls
support
Billing complaint — resolved
marketing
Opened the newsletter
served to the agent
Budget is now $25k this year
cites call notes · May
Blocker: needs SSO before signing
cites email · Jun
Reach out by email, not calls
supersedes the old $40k
~101,000tokens served
Plugs into what you already have Databases Vector stores Code repos Chat & conversation logs Documents Business apps
why it isn't RAG

Retrieval finds what looks similar.
Memory knows what's true now.

Reliable memory is about context selection, not context volume. That's the part a vector search was never built to do.

plain RAG vector search

  • ✕
    Returns chunks that look similar — no idea which is current.
  • ✕
    Contradictions surface side by side; the model guesses.
  • ✕
    Dumps the whole history into the prompt to be safe.
  • ✕
    Re-derives a different answer on every call.
  • ✕
    Sources and dates fall off along the way.

memseek context harness

  • ✓
    Selects the few records that matter for this question.
  • ✓
    Knows what changed and what's superseded — serves the current truth.
  • ✓
    Resolves conflicts across sources into one coherent picture.
  • ✓
    Compact context — ~30× fewer tokens than the full history.
  • ✓
    Every item stays dated and linked to its source.
what's in the kit

One layer. The jobs a vector store can't do.

Memseek is a composable harness, not a single trick. The same layer handles direct recall, evolving profiles, temporal reasoning, and multi-document retrieval.

↯
recall

Find the needle

Surface the right record in a long, messy history — where the answer is one line buried under thousands.

⟲
temporal

Know what changed

Track what was superseded and what still holds, so the agent gets the latest valid fact — not last year's decision.

⚡
conflict

Resolve disagreements

When two sources say different things, reconcile to the current truth instead of handing the model a contradiction.

⇄
cross-source

Connect the evidence

Link related facts across repos, docs, chats, and apps into one picture — multi-hop, not single-shot.

⌖
provenance

Keep the receipts

Every piece of served context stays attached to a dated, verbatim source you can open and verify.

◨
outputs

Any shape you need

Return raw context, a direct answer, an update-ready profile, or a skill digest — from the same layer.

watch the hard parts work

The intelligence, not the plumbing.

Distilling patterns, catching contradictions, reasoning across time, and improving a skill from real feedback are the parts teams get wrong. Here's each one, live.

digest

Distills patterns into insight

Reads many small signals and writes one higher-level, cited takeaway.

digest · cites 4 signals
"Prefers small, reviewed PRs over big drops."
Split a 900-line PR on request
Asked for smaller commits twice
Reverted a large refactor
Praised an incremental migration
conflict

Catches beliefs that collide

When new evidence contradicts the old, it's flagged — and the current, cited fact wins.

Auth: JWT in gatewayJan
Auth: httpOnly cookiesMar
⚡ superseded — resolved
served nowhttpOnly cookies · cites PR #482
temporal

Knows what held, and when

Ask what was true last quarter — the history is preserved, not overwritten.

Jan
Apr
Jul
auth pattern, as of then
Session cookies (trial)
as of Apr · source PR #451
maintain

Improves the skill from feedback

Turns what real work taught into a cited draft — and leaves the decision to ship it with you.

exception · INC-2417Paged 18 min late — the "wait for monitoring" rule blocked it
pitfalls · draft
− wait for monitoring to confirm before paging
+ two matching customer reports = confirmation; page now
your callpromote →nothing goes live until you do
public benchmarks

Better context. A fraction of the tokens.

These test exactly the failures agents hit today: finding the right thing in a long history, handling updates and contradictions, and connecting evidence across sources.

0%
recall & reasoning accuracy
LongMemEval-S · long-conversation memory
0%
picks the latest valid fact
LongMemEval-S · after facts change over time
+0 pts
exact match vs. dense retrieval
MuSiQue · multi-hop QA
+0 pts
exact match vs. dense retrieval
2WikiMultiHopQA · multi-hop QA

Context per question

≈ 32× fewer tokens
Full history
~101,000
Memseek
~3,200

On inexpensive, self-hostable models — no indexing-time LLM calls, no frontier-model crutch. On your own data, measure correction rate and downstream task success from the first integration.

flexible outputs

Ask once. Get the shape you need.

The same query-time layer returns whatever your application is built to consume — no second pipeline.

context

Raw evidence

The relevant, current records with dates and sources attached.

[ {id, text, date, src},
  … ]
answer

Direct answer

A resolved answer to the question, with its citations.

"httpOnly cookies"
cites: [pr#482, auth.md]
profile

Update-ready profile

A current, keyed view you can drop into a prompt or store.

role: VP Eng
prefers: small PRs
skill

Skill digest

A distilled how-we-do-it for a recurring task.

steps: […]
pitfalls: […]
first product

Living memory for coding agents.

Keep an accurate, evolving understanding of how a team actually builds — so the agent stops re-suggesting what was already rejected.

  • ›Developer preferences
  • ›Repo conventions
  • ›Architecture decisions
  • ›Accepted & rejected approaches
  • ›Recurring mistakes
  • ›Review feedback
agent.py
# point it at the sources you already have
mem = Memseek(sources=[repo, chat, docs])

ctx = mem.context(
    query="what's our auth pattern?",
    shape="answer",   # context | answer | profile | skill
)

ctx.answer      # "httpOnly session cookies…"
ctx.citations   # [pr#482, auth.md]
ctx.tokens      3200            # not 101,000
buy vs. build

The same reason you don't hand-roll your own Postgres.

A real context layer is substantial engineering most teams don't solve well. The question isn't whether you could build it — it's whether that's the best use of your team.

◐

Value on day one

Better context out of the box, faster than an internal build, over your existing storage — no months of research and evaluation.

◔

It compounds

The learning loop tunes ranking, routing, and temporal rules to your data — until replacing it means rebuilding how your app reads its own history.

✓

You can see it working

Built-in evaluation shows exactly what context was served and whether it improved the outcome. No black box.

straight answers

The questions engineers actually ask.

Isn't this just RAG?+
No. RAG retrieves text that looks similar. Memseek selects the right, current context, resolves contradictions, keeps every source and date, and serves ~30× fewer tokens. On public benchmarks it beats standard dense retrieval by +7.5 to +10.8 points — and it does it without indexing-time LLM calls.
Is it a database? Do I move my data into it?+
No, and no. Your data stays exactly where it is. Memseek builds a lightweight access structure over your existing sources — databases, vector stores, repos, chat logs, docs, business apps — and decides what your agent should see.
Does it need an expensive frontier model?+
No. The results above are on inexpensive, self-hostable models, with no indexing-time LLM calls. Performance and low cost — not one at the expense of the other.
What do I actually get back?+
Whatever shape your app consumes: raw context, a direct answer, an update-ready profile, or a skill digest — all from the same query-time layer.
Can I run it privately / on my own infra?+
Yes. Private and local deployment is a first-class path for teams where data control, latency, or infrastructure constraints matter.
How do I know it's actually helping?+
Evaluation is built in: it shows what context was served for every request and whether it improved the outcome, so you can prove lift on correction rate and task success on your own data.
early access

Give your agents memory they can trust.

Join the waitlist for early access to Memseek and be among the first teams to give agents current, source-linked context.

Join the waitlist → Early access for coding-agent teams