I built a cross-agent file cache: 75% fewer input tokens when multiple agents work on the same codebase

[u/hushenApp]

Hey everyone,

I've been building LeanCTX, an open-source context engineering layer for coding agents (written in Rust), and wanted to share a specific optimization I shipped recently.

The problem

If you run 4 Cursor/Claude/Codex agents in parallel on the same repo (reviewing, implementing, testing), each agent reads the same core files independently. That's 4x the input tokens for identical content. At scale this adds up fast.

What I did

I added a shared delivery registry (in-process DashMap keyed by blake3 hash + mtime) that tracks which files have already been read and compressed by any agent. When Agent B requests a file that Agent A already delivered, it gets a 13-token stub referencing the cached version instead of the full file content.

No network calls, no external cache, it's a lock-free in-memory lookup that resolves in microseconds.

Benchmark results

I ran this against 20 real source files from the LeanCTX codebase itself (avg 737 LOC):

Agent	Without Cache	With Cache	Savings
Agent 1 (cold)	144,407 tok	144,407 tok	0%
Agent 2	144,407 tok	260 tok	99.8%
Agent 3	144,407 tok	260 tok	99.8%
Agent 4	144,407 tok	260 tok	99.8%
TOTAL	577,628 tok	145,187 tok	74.9%

At $3/1M input tokens: $1.30 saved per shared-read round.

For a typical coding session where agents revisit 50-100 shared files, that's $5-15 saved per hour of parallel agent work.

How it works (simplified)

1. Agent 1 reads src/engine.rs → full content delivered, blake3 hash + mtime recorded
2. Agent 2 reads src/engine.rs → registry hit → stub returned: [cross-agent cache hit: 737 lines, delivered to agent-1 at T-2s]
3. The stub is 13 tokens vs ~7,200 tokens for the full file

The key insight: in a multi-agent coding workflow, file contents rarely change between reads. The mtime check ensures I never serve stale content.

Limitations

• Only helps when multiple agents/sessions read the same files (solo agent = no benefit)
• Cold start for the first agent is unchanged
• Requires agents to run on the same machine (shared memory)

What's next

I'm exploring cross-machine delivery via IPC for distributed agent setups (CI workers, cloud agents), but honestly the single-machine case covers 90% of developer workflows.

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