TALA Open-Source Release: New Orthogonal Auto‑Layout Engine for D2

TL;DR – What happened and why it matters

TALA (Terrastruct's AutoLayout Algorithm) has been released as open‑source software under the MPL‑2.0 license and is included in D2 v0.9.0 (--layout=tala). It provides an orthogonal, whiteboard‑style layout engine that produces more aesthetically balanced architecture diagrams than D2’s previous DAG‑centric engines, while also supporting custom node positioning for AI‑assisted and hybrid layouts.

What is TALA?

TALA is a novel auto‑layout algorithm designed specifically for software‑architecture diagrams. Unlike traditional DAG‑based engines (e.g., Dagre, ELK) that grow in a single direction, TALA produces orthogonal layouts that better resemble hand‑drawn whiteboard sketches. The engine blends techniques from several graph‑drawing research papers (cited in the source code) and adds original heuristics to optimize multiple aesthetic criteria:

Symmetry and median distance
Flow direction and clustering of related nodes
Edge routing that avoids label collisions
Support for user‑specified node coordinates (top/left) to lock parts of a diagram

How to try TALA

Install D2 v0.9.0 or later.
Render a diagram with d2 --layout=tala <script.d2>.
Or use the client‑side playground at https://play.d2lang.com with layout=tala pre‑selected.

Key Features Demonstrated in the Blog Post

1. Comparative quality

The author posted a batch of real‑world D2 files rendered with three engines: Dagre, ELK, and TALA. In most cases TALA produced cleaner, more symmetric diagrams that matched the visual style of hand‑drawn architecture sketches. The only exception noted was the Go Queue worker example, where TALA’s layout appeared more tangled than the left‑to‑right flow of Dagre/ELK.

2. Custom positioning for AI‑generated diagrams

TALA allows nodes to be placed explicitly using top and left coordinates. This enables workflows where a language model generates a rough diagram (e.g., a signal‑chain or data‑platform) and TALA handles edge routing while preserving the user‑defined layout. The blog includes several AI‑generated examples (Signal House, Atlas/Data Platform, Night‑Shift Mission Control, etc.) that showcase this hybrid approach.

3. Partial positioning (mixed manual and automatic)

A third batch demonstrates "partial positioning": some nodes are pinned (e.g., CMYK stations in a printing‑room diagram) while the engine automatically arranges the remaining nodes. This flexibility is useful for diagrams that have a fixed physical layout but variable logical components.

Trade‑offs and Performance

"It has randomness in the algorithm. It finds the best layout by using a default of 3 seeds and choosing the one scored the best. Given the same seeds and same input, it'll produce the same diagram. But let's say you just add one more node. The diagram could look completely different. In Dagre and ELK, it looks mostly the same as prior, with the extra node accommodated for. This is sometimes desirable."

Randomness – TALA runs multiple seeded attempts and selects the highest‑scoring layout. Adding a node can cause a global re‑arrangement, which may be beneficial for aesthetic freshness but can break visual continuity.
DAG handling – TALA is not optimized for deep directed‑acyclic graphs; users often prefer Dagre/ELK for long, flowing pipelines.
Scalability – Runtime grows non‑linearly with diagram size. Benchmarks are available in the d2-benchmarks repository (https://github.com/d2lang/d2-benchmarks).

Community Reaction on Hacker News

The post attracted 278 up‑votes and 23 comments. Highlights include:

Positive impressions – Users praised the tidier output, especially compared to D2’s default engine. One comment noted that the Go queue diagram looked more complicated with TALA, indicating that the engine may not always be the best fit.
Cost concerns – A commenter mentioned that the commercial price of TALA was above their “fun‑money” budget, though the open‑source release removes that barrier.
Desire for integration – Several participants asked whether TALA could be merged into other graph tools like Graphviz or yEd, reflecting demand for broader ecosystem support.
Use‑case enthusiasm – Developers planning to add TALA to their own projects (e.g., Daedalus) expressed excitement about the new layout capabilities.
Calls for tooling – One user requested a Graphviz‑to‑TALA converter, indicating interest in migration paths.

Practical Guidance for New Users

Start with the playground – The web UI runs entirely client‑side and lets you experiment with --layout=tala without installing anything.
Pin critical nodes – Use top/left to lock elements that must stay in a specific geometry (e.g., hardware racks, UI panels).
Control randomness – Pass a deterministic seed via --seed=<int> if you need reproducible layouts across CI runs.
Benchmark large diagrams – For diagrams with >200 nodes, test runtime against Dagre/ELK to decide whether TALA’s aesthetic benefits outweigh the performance cost.
Contribute – The repository is open‑source; contributions to heuristics, performance optimizations, or additional shape libraries are welcomed.

Conclusion

TALA’s open‑source release adds a powerful, orthogonal auto‑layout option to the D2 ecosystem, filling a long‑standing gap for architecture‑focused diagramming. While it excels at producing balanced, whiteboard‑style visuals and supports hybrid manual/automatic positioning, users should be aware of its randomness, limited DAG performance, and higher computational cost on large graphs. Community feedback on Hacker News confirms strong interest and a desire for broader integration, suggesting that TALA will quickly become a staple for developers and architects who need high‑quality, programmable diagrams.