Having trouble doing >5 tool calls in a row?
Consistently chain together tool calls by helping your AI agents memorize execution-proven code.
Make vertical AI agents more consistent with code
Drop custom tools, go from 80% to 99% by only running code your agent generated that previously satisfied users.
How Code Scoring Works
Raysurfer surfaces the best code LLMs need, the moment they need it.
Inputs
User Satisfaction ScoringManaged Codegen vs Tool Calling
LLM agents repeat the same patterns constantly. Raysurfer retrieves proven code and runs it with new inputs—no regeneration needed.
Raysurfer{
"user": "Update our Q3 revenue ($1.02B) in the quarterly report and sync it with the investor deck."
}Understanding task: update Q3 revenue in report and deck.
Searching for cached solution...> Match: update_financials_and_sync.py
> Similarity: 0.96 | Verdict: +52 | Runs: 8,431> Running cached code with params:
revenue=$1.02B, files=[report, deck, board, briefing, config, warehouse]✓ Updated all 6 files and synced systems in 6sYour code is your moat
For LLMs that generate code and execute it live-cache what works, skip what doesn't.
Code files generated by LLMs are cached. Retrieve and run proven code instead of regenerating.
Learn from History
Track which code executions succeeded or failed. Future agents retrieve successful code and avoid patterns that didn't work.
Low Variance Outputs
B2B vertical AI code is predictable. The same report generator, the same API client-perfect for caching and reuse.
Two calls. That's the whole API.
.search() to retrieve cached snippets. .upload() to cache new ones.
Search by task description
Call .search() with a natural language query. Hybrid search finds the most relevant cached code from prior agent runs.
Upload what works
Call .upload() with the task, file, and result. Raysurfer indexes it with semantic embeddings for future retrieval.
Vote on results
Code that works gets thumbs up, code that fails gets thumbs down. Verdict-aware scoring improves retrieval over time.
from raysurfer import AsyncRaySurfer
rs = AsyncRaySurfer()
# Retrieve cached snippets
results = await rs.search("Update quarterly report")
# Cache new code after execution
await rs.upload(task, file, succeeded)Also available as a drop-in replacement for Claude Agent SDK that handles caching automatically via RaysurferClient.
The Token-Industrial Complex
Why are we all paying to regenerate the same tokens?
The Current Reality
Every time your agent runs, you wait for tokens to generate. The same patterns. The same outputs. Every. Single. Time.
You're paying for tokens. You're waiting for generation. For code that's already been generated somewhere else.
The Raysurfer Way
Raysurfer retrieves and runs proven code from previous executions. No waiting. No regenerating. Just execute.
Stop watching your agent think. Get instant results from code that already works.
Perfect for long-running tasks: Dynamic code generation becomes trivial when your generated code is already context-managed. No more iteration loops. No more regeneration cycles. The code exists, it's been validated, it just needs to be executed.
“More tokens output causes a decrease in accuracy causes even more token output.”
Break the cycle. The median LLM agent activity for B2B SaaS is surprisingly low variance. Everything eventually viewed by a human is just HTML, PDF, or docs.
Code Reputation Management
Track the user satisfaction rate of running different pieces of code
Code Snippet Search
Estimate from the code and logs what user queries this code would best solve
Simple API
.search() and .upload() — two calls, that's it
Frequently Asked Questions
Start caching your agent's code today
Free tier includes 500 API requests. No credit card required.