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Orchestrate multi-agent workflows with DAG execution, conflict handling, and event tracking.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "AllBrain MCP" yet — see the docs or source repo.
Design a multi-agent workflow for a new feature release, including requirement breakdown, code generation, test execution, and result summarization. Represent dependencies as a DAG and specify each node’s inputs and outputs.
A structured workflow plan with node responsibilities, dependencies, execution order, and outputs.
When two agents modify the same task status and execution result, design conflict detection and resolution rules, and explain how to preserve audit logs using the global event store.
A conflict resolution strategy covering detection conditions, priority rules, rollback or merge methods, and auditing.
From the user request, 'Analyze abnormal alerts from the past week and provide remediation suggestions,' extract the intent, break it into executable steps, and generate a plan suitable for a multi-agent system.
A plan including extracted intent, task breakdown, agent roles, and execution dependencies.
Orchestrate AI agent workflows with dependencies, parallel execution, and failure policies.
Orchestrate multi-agent workflows with durable state and replayable execution traces.
Orchestrate intelligent AI workflows with memory, integrations, and unified usage credits.
Enforce structured AI workflows with dependencies, quality gates, and validated outputs.
Coordinate multiple AI agents in parallel for debate, review, and synthesis.
Orchestrate complex AI agent workflows with dependency handling, retries, and progress tracking.