kyegomez/swarms

[feat][graph_workflow][parallel branch execution]

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#1,559 opened on Apr 21, 2026

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FEATenhancementgood first issue

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Description

Problem

GraphWorkflow currently walks the topology sequentially even when the DAG has independent branches. If the graph is A -> B, A -> C, B -> D, C -> D, today B and C execute one after the other despite having no data dependency on each other.

Proposed feature

Detect independent branches at run time and execute them concurrently using ThreadPoolExecutor. A correctness-preserving pure latency win.

Design sketch

  • Topological sort groups nodes into "layers": layer N contains all nodes whose dependencies are in layer < N.
  • Each layer is executed concurrently with ThreadPoolExecutor(max_workers=len(layer)).
  • Per-run concurrency cap exposed as max_parallel_nodes on the constructor (default: CPU count).
  • Same deep-copy isolation pattern used in the recent AgentRearrange.batch_run refactor to prevent shared-state bugs.

Files

  • swarms/structs/graph_workflow.py

Why

Pure latency improvement with no behavior change for serial graphs. DAGs with any width benefit immediately.

Contributor guide

Research direction
Investigate the current sequential execution in GraphWorkflow and implement concurrent execution using ThreadPoolExecutor for independent branches in the DAG.
Tech stack
python
Domain
backend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonConcurrency
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50