Evaluation note: the session trace, benchmark values, usage counts, and API output shown on this page are illustrative fixtures, not measured production results.
Every WebFetch is your session bleeding out.
You've watched it happen: links, raw pages, 50,000 tokens of soup — then the compaction notice. WebBrief runs the research on our servers and hands your agent the answer. Same question, both panes:
❯ Has anything replaced plain vector stores for agent memory this year?
⏺ WebSearch("agent memory 2026 vector store alternatives")
⎿ 10 links · 2,940 tokens
⏺ Fetch(https://blog.langchain.dev/agent-memory-patterns/)
⎿ Received 52.3KB · 12,410 tokens
⏺ Fetch(https://reddit.com/r/LocalLLaMA/comments/…)
⎿ Received 41.7KB · 9,860 tokens
⋮ two more fetches, 47k tokens total
⚠ Context low — compacting conversation…
❯ Has anything replaced plain vector stores for agent memory this year?
bottom line
Partly — pure vector retrieval is now the fallback, not the default.
Production agent stacks have largely moved to layered memory: a structured working set, episodic summaries, and vector search only as recall of last resort [1].
Two widely-cited postmortems this spring attribute agent drift to unfiltered vector recall; both teams replaced it with scoped, time-decayed retrieval [2].
Sources disagree on graph memory: one benchmark shows wins on multi-hop tasks, maintainers of two major frameworks call it premature [3].
⎿ 512 tokens · 9 sources, all dated · context intact
Works with Claude Code out of the box · free tier, no card
Tools give your agent reach. WebBrief gives it a research contract.
Search can return pages. Connector bundles can reach richer sources. Both can leave the host agent running the research loop inside the context it is trying to protect. WebBrief owns that loop and returns one cited, dated brief with a hard boundary.
See the evidence-backed comparison with Click →
640
hard cap, tokens
400–550
typical brief
100%
cited & dated
0
raw pages, ever
The receipts
Table 1 — tokens per research task, answer parity judged blind
| Task type | Search + fetch | WebBrief | Parity |
|---|---|---|---|
| Library upgrade check | 38,400 | 610 | same answer |
| Compare 3 frameworks | 52,100 | 632 | same answer |
| Debug a fresh API change | 24,900 | 540 | same answer |
| State of a technique | 61,300 | 640 | brief more current |
Illustrative fixture data — do not publish or cite as measured. Production benchmark numbers must be reproducible from the public evals repo. Latency: a brief takes seconds, not milliseconds — it's doing the reading you didn't.
How do you know the brief isn't confidently wrong?
A claim that can't be traced to a dated source doesn't ship. When sources disagree, the brief says so instead of picking a side. Every citation is one deep_dive away from audit. And public briefs carry a corrections policy: when a brief is wrong, we say so, on the brief, with a date. The full methodology is public.
The response, verbatim — POST /api/brief
{
"id": "brf_7f2k1",
"query": "Has anything replaced plain vector stores for agent memory?",
"mode": "quick",
"bottom_line": "Partly — pure vector retrieval is now the fallback, not the default. …",
"brief_md": "…(512 tokens, inline [n] citation markers)…",
"tokens": 512,
"citations": [
{ "n": 1, "title": "Agent memory patterns", "url": "https://…",
"source_type": "github_discussion", "published": "2026-06-30" }
],
"generated_at": "2026-07-12T09:14:03Z"
}Your agent is brilliant. Its knowledge is frozen.
Even a current model generates from its statistical center — it knows a million ways to build this, and defaults to average. A brief in context aims it at the frontier: what's current, what's contested, what actually works now.
Knowledge gaps close as models improve. Direction is needed forever. This gets more valuable as models get smarter, not less.
The difference between an agent that knows and an agent that finds out.
Nothing to lose but the soup
Free
$0
25 briefs/mo
no card
Pro
$19
500 briefs/mo
per month
Max
$49
2,000 briefs/mo
per month
Deep briefs meter higher · credit packs for overage · never unlimited · full pricing
Questions skeptics ask
- My model can synthesize search results itself — why pay twice?
- The cost was never the synthesis. It's the 50,000 tokens of raw input your model must hold to synthesize. You're not buying intelligence; you're buying your context back.
- Tavily already returns cited answers.
- It does — with lengths that are, in their own words, “targets, not hard caps,” shaped for humans reading reports. A brief is shaped for an agent's context budget: the cap is the contract.
- Click already gives my agent live sources.
- That breadth is useful, especially for raw professional, social, travel, or financial records. WebBrief is designed for a different deliverable: one bounded, cited research artifact with server-owned synthesis and a reproducible trail.
- How fast is a brief?
- Seconds, not milliseconds — it's doing the reading you didn't. We publish exact p50/p95 numbers only when the evaluation harness can reproduce them.
- What happens at the 640-token cap?
- The cap is enforced at generation, not truncation. If a question genuinely needs more, the brief says so — deep_dive exists for escalation.
- What do you keep?
- The query, the brief, and the citation trail — that's what makes briefs auditable. Nothing from your session's context ever reaches us.
Sixty seconds. Your agent can even do it for you.
Paste the agent setup into Claude Code and it installs itself.
Briefs, not pages.