Save up to 50% of tokenson your 
Claude Code.
Every decision, dead end, and fix from every past session — recalled in-agent, with the date it happened. One memory across Claude Code, Cursor, and Codex. 38% fewer tokens, measured.
❯ the /orders endpoint times out under load
⏺ Let me bump the connection pool size…
⏺ conare · recall
Dead end (Aug 28): pool size wasn't it. Root cause was a missing index on orders.user_id.
⏺ Skipping the pool. Adding the index instead.
❯
02 / Compatibility
One memory. Every agent.
Switch from Cursor to Claude Code mid-project and the context follows. One layer, every tool — not a memory siloed inside each one.
Claude Code
Ready
Cursor
Ready
Codex
Ready
Windsurf
Ready
OpenCode
Ready
Grok
Ready
Pi
Ready
Any MCP client
Open protocol
→ Zero per-tool setup. Install once, recall from any client over MCP.
03 / The payoff
About to re-walk a dead end you already hit?
“Let's bump the connection pool to fix the timeout.”
A fresh agent, confidently about to burn the same afternoon you already lost.
dead end · session Aug 28 · 7 weeks ago
Already tried — pool size wasn't it. Root cause was an unindexed FK join. Fixed with a composite index.
Pulled from your own history. With the date it happened.
Straight to the real fix.
No re-debugging the thing you already ruled out. The dead end you found, remembered.
04 / Token efficiency
Spend tokens on code, not on searching.
~70%
fewer tokens — recall the few that matter, don't dump the whole history.
Memory-layer benchmarks ¹
83%
win rate over a cold agent. Zero losses.
Our A/B · 30 real sessions ²
Fewer round-trips
No open-ended search loop.
Fewer corrections
Right context, first try.
Stop hitting limits
Spend caps on code, not search.
¹ Memory-layer benchmarks (Mem0, Headroom). ² 30 real sessions · blind LLM judge.
05 / What you get
Memory native agents can't give you.
Ingests your backlog
Months of sessions already on your disk, recallable from day one.
Dated receipts
Every recall carries the session and date it came from.
Outlives every agent
Stays yours when you switch tools, machines, or models.
Pricing that scales with your memory, not your headcount.
Start free. Every tier unlocks more of your history — flat price, no metered surprises.
Free
Enough to feel the memory click.
- 200 memories
- 20 deep searches a month
- Auto-indexes Claude Code, Cursor, Codex
- Community support
Pro
Never start a session from zero again.
- 10,000 memories
- 100 deep searches a month
- Your full session history, indexed
- Email support
Max
For people who live in their agents.
- 100,000 memories
- 300 deep searches a month
- Rate limits you'll never hit
- Priority support
Team
Solve it once. The whole team remembers.
- Shared team memory
- 10 seats included
- Repo-scoped sharing, per-dev consent
- 500 deep searches a month
Enterprise
For teams larger than 10.
Frequently asked.
No. Conare indexes your past AI chats — Claude Code sessions, Codex conversations, Cursor threads. Your source code never leaves your machine.
Each workspace's memory lives in its own isolated store — never pooled with other customers, never used to train models. Everything is encrypted in transit, and you can delete any memory (or all of them) instantly.
Team sharing is repo-scoped and consent-based. Admins allowlist repositories by git remote, and every developer controls what their machine shares. Personal chats stay personal.
CLAUDE.md is a flat file on one machine. Conare gives you semantic search across every past session, works across Claude Code, Cursor, and Codex at once, and syncs between devices and teammates.
Any MCP-compatible client — Claude Code, Cursor, Codex, Windsurf, and more. One install works across all of them simultaneously.
No. Memory lookups take ~200ms. Your agent decides when to recall context, so there's zero overhead on prompts that don't need it.
Teams larger than 10 get unlimited usage, admin-managed repository allowlists, sharing audit logs, and self-hosted deployment on request — with a direct line to the founders at artem@conare.ai.