Cognition Labs launched a Code Graph Indexer for its autonomous engineer Devin. By maintaining incremental AST dependency topologies across repositories, Devin reduces symbol and call-chain retrieval times in 1M+ line monorepos from 8s to under 600ms, while cutting irrelevant context token overhead by 65%.

Key Takeaways

  • ✓Incremental AST and dependency graph topology drastically speeds up agent comprehension in huge repos.
  • ✓Symbol retrieval in million-line monorepos drops to 600ms with a 65% token saving.
  • ✓Live for all Devin Enterprise users with support for self-hosted code repositories.
⚡

Key Decision Metrics at a Glance

TL;DR Verdict
🏆 Benchmark RankRank
Symbol retrieval in million-line monorepos drops to 600ms with a 65% token saving.
Full Leaderboard→
💰 Cost & PlansCompare
API & Subscription
Compare Plans↗
🛠️ Ecosystem & IDERules
Devin 2.0 (Cognition)
Setup IDE Rules↗
🧭

Turn your technical choice into a development budget

Compare 40 dev plans & simulate token costs vs $20/mo subscriptions

⚔️ Compare Model→💰 Compare plans & limits↗
Action HubReady to adopt this in production?

Benchmark side-by-side against alternatives, or calculate monthly token cost vs subscription break-even.

⚔️ Launch VS Compare→💳 View Plans↗
Up NextDevin now works with any personal ChatGPT plan: Go, Plus or Pro subscriptions cover GPT model usage instead of Devin quota
Read Now→