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Agent-Owned Repos

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What This Means

An agent-owned repo is the idea that agents can build up a library of proven code over time, based on execution results rather than one-off code generation. Instead of generating from scratch every run, agents:
  1. Search for proven code that already solved similar tasks
  2. Execute the code in an isolated sandbox environment
  3. Score the results — code that produces useful output gets promoted
  4. Build a library — over time, the agent accumulates a growing collection of reliable code

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The Core Loop

Search proven code → Execute → Score → Promote top patterns
This makes agent behavior more stable over time. Agents start from high-signal code paths and only generate new code when necessary.

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How Raysurfer Supports This

Raysurfer combines three things to make agent-owned repos work:
  • Proven code retrieval — semantic search finds code that already worked for similar tasks, so agents reuse instead of regenerate
  • Reputation scoring — every snippet earns a score through execution results and user feedback (thumbs_up / thumbs_down), so the best code rises to the top
  • Reuse-first workflows — SDKs and integrations search the cache before generating, making retrieval the default path

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Sandbox Execution

When agents execute code through Raysurfer, it runs in an isolated environment. This means:
  • Code runs safely without affecting your production systems
  • Execution results are captured and used for reputation scoring
  • Failed code is automatically excluded from future retrieval

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Persistent Code Library

Published functions stay available across sessions. When you publish functions via publish_function_registry (Python) or publishFunctionRegistry (TypeScript), they’re stored and accessible for future agent sessions within the same org/workspace scope. This means agents don’t need to re-upload functions each session — proven code persists and is available across runs. See the Function Registry for setup details.
  1. Add Raysurfer to one high-frequency workflow first.
  2. Track which snippets are repeatedly reused with positive votes.
  3. Expand to additional workflows once retrieval quality is stable.
Keep values parameterized in snippets. Hardcode only values you expect to be reused verbatim.

Code Reputation Registry

Inspect publicly ranked snippets and quality signals that feed reuse decisions.

How It Works

Learn about proven code retrieval and reputation scoring.

Integrations

Add the same loop through Python/TypeScript SDKs, CLI, MCP, skill, or plugin.

Results

See real-world accuracy and speed improvements.