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statewave-multi-agent-shared-context
by smaramwbc
Shared-state pattern preventing parallel-agent context collisions
Python
Updated Jul 28, 2026
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Summary
Demonstrates coordinating multiple agents (Planner, Coder, Reviewer) around a single shared Statewave subject to prevent context collisions. Uses a central shared-state subject so parallel agents read/write a single source of truth and avoid diverging context windows. Includes a small Python demo showing how state updates, locks, or versioning can serialize agent intent. See shared-state coordination and single source of truth.
The Value Proposition
As agents run in parallel, conflicting context and race conditions silently degrade outcomes and make agent behavior hard to evaluate. This demo makes shared-state patterns explicit so teams can reason about agent-to-agent interactions and reproduce failures. Understanding and enforcing a single source of truth is a concrete step toward reliable agent delegation and trackable agent behavior. It also highlights how teams manage agent-to-agent interactions.
Ideal For
Researchers and engineers prototyping multi-agent workflows who need to avoid context races and experiment with shared-state coordination.
Use Cases
- Avoiding context collisions when multiple agents update the same task state
- Prototyping shared-state coordination patterns for agent pipelines
- Reproducing and debugging multi-agent race conditions in development
Works With
python
Topics
agent-coordinationai-agentsllmmcpmulti-agentpythonshared-contextstatewave
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Keywords
multi-agent orchestrationshared-contextagent delegationmulti-agent trust