[mmabrouk]

Context

Agent sends waited about 15 s in /api/workflows/revisions/resolve when the agent had large inline skills. While it ran, the API event loop stalled, and Redis 0.5 s socket timeouts fired. On a real revision (670 KB JSON, 7 embedded skills, one skill 1.39 MB), one embed scan took 2.9 s on the revision and 7.8 s on the skills.

The cause is _find_embed_tokens. At every character it ran text[i:].startswith("@ag.embed["). The slice copies the rest of the string each time, so the scan is O(n^2) and holds the event loop.

Changes

_find_embed_tokens now jumps between candidates with text.find("@ag.embed[", i) and does not slice. Matching is unchanged: balanced brackets, incomplete tokens skipped, same order in the returned list. A str.find scan of the same inputs takes under 1 ms.

I checked the rest of embeds/utils.py. _find_snippet_tokens slices only 2 to 3 characters per step, so it is linear. I left it alone.

Tests

• New TestFindEmbedTokens in api/oss/tests/pytest/unit/embeds/test_utils.py: no token, one token, nested brackets, two tokens, incomplete token at the end, and a 2 MB input that must finish in under 1 s.
• pytest oss/tests/pytest/unit/embeds: 106 passed.
• ruff format and ruff check are clean.

[coderabbitai]

No actionable comments were generated in the recent review.

Recent review info

Run configuration

• Source mode: Auto
• Repository: Agenta-AI/agenta
• Branch: fix/api-embed-scan-quadratic
• File: .coderabbit.yaml
• Commit: b2ef534
• Layers: Repository YAML (base), Organization UI (inherited)
• Review profile: CHILL
• Plan: Advanced
• Run ID: 10c6e6e5-35f0-4d62-af0f-e699ae6a1dd7

Commits

Reviewing files that changed from the base of the PR and between 696f908 and b2ef534.

Files selected for processing: 2

• api/oss/src/core/embeds/utils.py
• api/oss/tests/pytest/unit/embeds/test_utils.py

Included review availability: This review used your included allowance. Your plan provides up to 8 included reviews per hour; 1 remain after this review.

📝 Summary

Summary by CodeRabbit

• Bug Fixes
  • Embedded content tokens are now detected more reliably, including when they contain nested brackets or appear multiple times in the same text.
  • Incomplete trailing tokens are ignored, and scanning large inputs is more efficient.

The embed-token scanner now searches directly for token prefixes, retains nested-bracket matching, and skips incomplete candidates. Tests cover token detection and scanning a large input.

Changes: Embed Token Scanning

Layer / File(s)	Summary
Prefix scanning and validation api/oss/src/core/embeds/utils.py, api/oss/tests/pytest/unit/embeds/test_utils.py	_find_embed_tokens searches for token prefixes and returns complete tokens with nested brackets. Tests cover absent, multiple, nested, and incomplete tokens, and check runtime on a large input.

Priority: High

Estimated code review effort: 2 (Simple) | ~8 minutes

Merge Risk: Minimal · up to b2ef5

No actionable merge-blocking risk remains.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 25.00% which is insufficient. The required threshold is 60.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 8 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly and concisely states the main change: making the embed token scan linear.
Description check		The description directly explains the performance issue, implementation change, preserved behavior, and validation for the embed token scan.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Finishing Touches: 💡 1

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[mmabrouk]

🤖 The AI agent says:

I timed the old and new _find_embed_tokens on the real data from the slow agent, on the dev box under load. Both versions return the same tokens.

Input	Size	Old	New
Agent revision JSON	0.67 MB	6,698 ms	0.3 ms
Embedded skills	1.49 MB	32,212 ms	0.6 ms

[github-actions]

Railway Preview Environment

Item	Value
Status	stopped
Commit	b2ef534b79c7786aff99fde8a85cc74c21e6e702
Manual expiry (UTC)	Not granted
CI deadline (UTC)	Not granted
Workflow	https://github.com/Agenta-AI/agenta/actions/runs/40387078635

PR closed or converted to draft. Environment absence verified; retained storage requires separate provider accounting.

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