REMBR
AI Context Infrastructure
for Agents and Assistants
Up to 80%+ token cost reduction with improved accuracy.
Powered by Rembr and the Recursive Language Model pattern proven by MIT.
AI Cost Savings Calculator
See how much Rembr can save your team with AI Context Infrastructure
Your Usage Profile
$0.41 per query
No cross-session memory
$0.17 per query
⚡How RLM + Rembr Reduces Costs
What Is Recursive Language Model?
A smarter way to handle massive contexts without breaking the bank
The Problem: Context Overload
Traditional AI agents stuff everything into one massive context window:
- •Your entire codebase (100K+ tokens)
- •Documentation and web results (50K+ tokens)
- •Chat history and tool outputs (50K+ tokens)
Result: 200K tokens × $0.01/1K = $2 per request
Performance degrades as context grows (context rot)
The RLM Solution: Smart Delegation
Instead of loading everything at once, the AI breaks down work and delegates to sub-agents:
Break task into smaller sub-problems
Spawn fresh sub-agents for each piece
Combine results into final answer
Example: "Refactor auth system across 50 files"
Main agent: Analyzes structure (5K tokens)
Sub-agents: Each handles 1 file (10 × 3K = 30K tokens)
Total: 35K tokens instead of 200K+ = 80%+ savings
How Rembr Enables RLM
RLM's power comes from not having to re-analyze everything. That requires infrastructure:
Store analyzed context once, retrieve instantly. Main agent doesn't re-read 100K tokens every session.
Sub-agents retrieve only relevant memories (500 tokens) instead of full context (100K+ tokens).
All sub-agents access the same memory pool. No redundant analysis across parallel tasks.
Memories persist between sessions. Your agent gets smarter over time, not amnesic.
Fast enough to enable aggressive decomposition. Spawn 10 sub-agents in parallel without latency penalty.
Works with Claude, Cursor, Windsurf, and any MCP client. No custom integration needed.
The Bottom Line:
RLM is the strategy (smart delegation).
Rembr is the infrastructure (persistent, searchable context).
Together: 80%+ cost savings + better accuracy.
The Research Behind Rembr
MIT's Recursive Language Model paper demonstrated that intelligent context management:
Reduces token usage by 80%
Up to 2× better performance vs context stuffing
Handles 10M+ token contexts
REMBR Pricing
Scale from side project to enterprise.
All plans include the full RLM context layer.
- ✓25K memories
- ✓1K searches/day
- ✓10 projects
- ✓Priority support
- ✓250K memories
- ✓10K searches/day
- ✓50 projects
- ✓Team collaboration
- ✓2M memories
- ✓100K searches/day
- ✓Unlimited projects
- ✓SLA + SSO
Unlimited everything • SLA • SSO • Dedicated support
