I built an open-source Rust memory engine that cuts AI agent tokens by 96% across 1,000+ turns

IdeaSniper — Wed, 16 Sep 2026 12:56:10 +0000

If you use autonomous coding agents (Cursor, Claude Code, Antigravity, OpenClaw, Hermes, Codex), you've likely hit these long-sprint pain points:

The "Turn 200" Amnesia: You set a database or security rule at step 10. By turn 250, the agent has completely forgotten it and breaks your build.
The "Alert Storm" Doom Loop: A test fails and dumps 100 lines of error logs. Recency bias drowns out the real root cause that happened 50 turns ago.
Runaway Token Costs: Re-sending full conversation history on turn 500+ burns 100k+ input tokens per prompt, quickly draining your wallet.

To solve this, I built and open-sourced Continuum — an ultra-lightweight, zero-dependency continuous temporal memory engine written in 100% pure standard library Rust.

Real Hardware Benchmarks (Apple M4)

Instead of stuffing 100k+ histories or spinning up heavy 2GB vector databases, Continuum maintains a strictly bounded physical memory manifold (750 slots, < 75 KB RAM).

We benchmarked Continuum using OpenAI tiktoken (cl100k_base) on an Apple M4 across real coding sessions:

Metric	Full Context Appending	Standard Sliding Window (10 turns)	Continuum (Native Rust Engine)
100-Turn Cumulative Tokens	148,522 tokens	26,450 tokens	5,896 tokens (96.03% slash)
Multi-Depth Needle Recall	5/5 (100%)	0/5 (0% - Forgotten)	5/5 (100% at Rank #1)
Rule Override & Contradiction	Ambiguous prompt conflict	❌ 0% (Forgotten)	✅ 100% (Latest override at Rank #1)
Task Success / Build Pass	100% (High cost)	❌ 0% (Broken config)	✅ 100% (Tests pass, 96% token cut)
Engine Retrieval Overhead	1.2 ~ 2.5s (full history scan)	N/A (truncated)	60.46 µs (< 0.0001s, 16,540 QPS)
End-to-End Prompt Latency	12 ~ 18s (100k token load)	1.1s (shallow window)	1.2s (compact 256-token prompt)
100,000-Step Stress Test	Process Crash (OOM)	Memory leaks	Flat 750 slots (< 75 KB RAM, 0 leaks)

The Takeaway: Sliding windows save tokens but destroy outcomes (0% task success). Continuum cuts token usage by 96%~99% while guaranteeing 100% multi-depth recall and contradiction resolution.

How It Works in 4 Bullets

Physical $O(K)$ Bounded Memory: Exactly 750 slots (< 75 KB contiguous RAM). Memory usage stays a flat line forever across 100,000 steps.
Subspace Diversity Deduplication: Eliminates alert storms without naive FIFO eviction. 500 repetitive errors collapse into minimal slots, protecting ancient root causes.
Retrospective Causal Revision & Supersession: Bypasses decay for genuine anchors, while actively suppressing stale predecessors when rules are updated/contradicted.
Zero External Dependencies: 100% pure Rust std — single standalone binary, zero GC pauses, microsecond startup.

1-Minute Quickstart (MCP)

Continuum comes with a built-in Model Context Protocol (MCP) server for Cursor, Claude Desktop, and Antigravity:


bash
# 1. Install standalone CLI
curl -fsSL https://raw.githubusercontent.com/reacherwu/continuum/main/install.sh | bash

# 2. Init in any repo (< 75 KB memory manifold)
continuum-cli init .

DEV Community: IdeaSniper

I built an open-source Rust memory engine that cuts AI agent tokens by 96% across 1,000+ turns

Real Hardware Benchmarks (Apple M4)

How It Works in 4 Bullets

1-Minute Quickstart (MCP)