Benchmark Results: Claude Agent Swarm with Memory System Shows 30-43% Token Cost Savings

Memory System Benchmark for Claude Agent Swarms
A developer has been building a memory system called Stompy for nine months, evolving from file-based to SQLite to PostgreSQL. The goal was to minimize token usage when running Claude agent swarms. They conducted a benchmark comparing performance with and without the memory system.
Test Setup
The benchmark used a 40-point coding task requiring a full booking feature with backend, frontend, and tests. A 6-agent swarm was tested with three different Claude models as lead: Sonnet 4.6, Opus 4.6, and Haiku 4.5. All tests used the same codebase, same teammates, and same scoring system. Teammate agents always ran Opus regardless of the lead model.
Benchmark Results
- Sonnet 4.6 + memory: 40/40, $3.98, 6.5min, 2 turns
- Sonnet 4.6 no memory: 40/40, $7.04, 9.6min, 4 turns
- Opus 4.6 + memory: 40/40, $4.34, 9.6min, 29 turns
- Opus 4.6 no memory: 40/40, $7.65, 10.0min, 70 turns
- Haiku 4.5 + memory: 39/40, $4.95, 7.5min, 2 turns
- Haiku 4.5 no memory: 0/40, $3.97, 5.8min, 3 turns
Key Findings
Opus and Sonnet with memory saved about 43% on cost compared to running without memory. The developer notes that these models are smart enough to complete the task without memory, but they burn tokens on codebase exploration that the memory system eliminates.
The Haiku result was unexpected: it scored 0/40 without memory but 39/40 with memory. The developer observed that Haiku couldn't coordinate the Opus teammate agents without understanding the project structure, but became a competent lead with memory access.
Sonnet with memory was the best overall configuration, beating memoryless Opus on every metric at roughly half the cost. The takeaway is that making project knowledge available to the model matters more than using expensive models.
Technical Details
The memory system is called Stompy and is MCP/API/CLI-based, working with Claude Code. The benchmark setup is available on GitHub for others to use or improve. The developer notes this is n=1 per condition so far, with more runs planned.
📖 Read the full source: r/ClaudeAI
👀 See Also

MuninnDB adds Dream Engine for LLM memory consolidation with vault isolation
MuninnDB, a Go-based cognitive memory database, now includes a Dream Engine that performs LLM-driven memory consolidation between sessions using deduplication thresholds and semantic review. The system features vault trust tiers for data isolation and runs locally with Ollama.

Coasts: Containerized Hosts for Running Multiple Localhost Environments
Coasts is a Docker-in-Docker solution that solves the problem of running multiple localhost environments simultaneously, handling port conflicts, secrets, and volume topologies without requiring complex scripting.

ClawPort: Open Source Orchestration for AI Agent Workflows with Self-Healing Cron
ClawPort is an open source orchestration layer for AI agent workflows that auto-configures cron pipelines, self-heals on failures, and lets you test agents directly before they run on schedule.

Claudlytics: Self-Hosted Dashboard for Tracking Claude Code Token Usage and Costs
Claudlytics is a Node.js web server that reads Claude Code's local .jsonl session files to provide real-time tracking of token usage and costs. It runs locally on 127.0.0.1 and can be accessed via SSH tunnel for remote servers.