One task in.
A swarm of agents out.
SpawnLoop decomposes complex business tasks into autonomous agent swarms. Each agent spawns in its own sandboxed environment, works in parallel, and reports results back to the orchestrator.
Define once. SpawnLoop handles the rest.
Define the task
Describe what you need done in plain language. "Research 50 companies and score them by fit." "Process these invoices and flag anomalies." SpawnLoop understands scope.
Agents spawn
The orchestrator decomposes your task and spawns specialized sub-agents. Each runs in its own isolated sandbox. They work simultaneously, not sequentially.
Results converge
Sub-agents report back. The orchestrator aggregates, deduplicates, and synthesizes. You get one clean output. Not 50 tabs.
Frameworks require engineers.
SpawnLoop requires a goal.
Parallel, not sequential
SpawnLoop agents work simultaneously across sandboxed environments. A task that takes one agent 4 hours takes a swarm 20 minutes.
No code required
CrewAI, LangChain, AutoGen all assume you write code. SpawnLoop is a managed platform. Describe what you want. Watch agents work.
Sandboxed isolation
Each spawned agent operates in its own environment with its own context window. No cross-contamination. Clean results every time.
Self-scaling swarms
SpawnLoop dynamically adjusts the number of sub-agents based on task complexity. Small job? Two agents. Massive dataset? Fifty.
The future of work isn't one AI doing everything. It's many, working together.
SpawnLoop is building the orchestration layer for the multi-agent era.