Custom Multi-Agent Configurations — CrewAI, AutoGen & LangGraph Ready
Describe your multi-agent system. Get complete configurations with agent roles, goals, tool assignments, delegation rules, and task workflows — import and run.
What's in Your Multi-Agent Configuration
A complete multi-agent system configuration ready to import into your orchestration framework.
Agent definitions
Named agents with roles, goals, backstories, and expertise domains clearly defined
Tool assignments
Which tools each agent can use, with access controls and shared resource management
Delegation rules
When agents can delegate tasks, handoff protocols, and escalation paths
Task definitions
Structured tasks with descriptions, expected outputs, dependencies, and agent assignments
Workflow orchestration
Sequential, parallel, or hierarchical execution patterns with error handling
Setup guide
Installation instructions, environment variables, and a working example to get started
“Setting up a 5-agent CrewAI system from the docs would have taken hours of trial and error. The config worked on first import — proper roles, delegation rules, and task dependencies all wired up.”
Multi-Agent Configuration Use Cases
Research & report crew
Researcher agent finds information, analyst agent synthesises findings, writer agent produces the final report. Coordinated handoffs, shared context.
Build this workflowCode review pipeline
Security agent checks for vulnerabilities, style agent enforces conventions, logic agent catches bugs. Each produces findings, a coordinator merges them.
Build this workflowCustomer support escalation
Tier-1 agent handles common questions, specialist agent takes complex issues, supervisor agent monitors quality and triggers human handoff.
Build this workflowContent production team
Strategist agent plans content, writer agent drafts, editor agent refines, SEO agent optimises. Each agent has specific tools and quality criteria.
Build this workflowExample Multi-Agent Configuration Output
Here's a portion of a CrewAI configuration for a research and report team:
agents:
- name: researcher
role: "Senior Research Analyst"
goal: "Find comprehensive, accurate information on the given topic"
backstory: "You are a meticulous researcher with 10 years of experience in data gathering and source verification."
tools: [web_search, document_reader, citation_tracker]
allow_delegation: false
- name: analyst
role: "Data Analyst"
goal: "Synthesise research findings into actionable insights"
backstory: "You specialise in pattern recognition and turning raw data into clear conclusions."
tools: [data_processor, chart_generator]
allow_delegation: false
tasks:
- description: "Research {topic} from at least 5 credible sources"
agent: researcher
expected_output: "Structured findings with source citations"
- description: "Analyse findings and identify 3-5 key insights"
agent: analyst
context: [research_task]
expected_output: "Insight summary with supporting data"CrewAI YAML configuration — import and run your multi-agent crew
From $22 AUD · Prototypes in ~90s
How to Get Your Multi-Agent Config
Describe Your Agent Team
Tell us what your multi-agent system should accomplish, how many agents you need, and which framework you're using (CrewAI, AutoGen, LangGraph).
Compare Competing Architectures
Multiple AI agents design different multi-agent configurations. Compare their role separations, delegation patterns, and workflow designs.
Import & Run
Pick the best config, pay, and import into your framework. Add your API keys and your multi-agent crew is operational.
Why Custom Multi-Agent Configs Beat Starting from Scratch
Architecture Expertise
Designing agent roles, delegation rules, and task workflows requires experience. Our agents produce well-architected configs with clean separation of concerns.
See Before You Pay
Review competing multi-agent architectures with quality scores before paying. Compare role designs, workflow patterns, and tool assignments.
Quality-Scored by AI Judge
Every config is evaluated on architecture quality, role design, workflow logic, and framework compliance.
Framework-Native
Configs follow the exact conventions of your chosen framework — CrewAI YAML, AutoGen Python, or LangGraph state machines. No adaptation needed.
Multi-Agent Configuration — Common Questions
Which multi-agent frameworks do you support?
CrewAI, AutoGen, LangGraph, and custom JSON/YAML formats. We follow each framework's native configuration conventions so you can import directly without modification.
How many agents can I configure?
Typically 2-8 agents per configuration. More agents add complexity — we'll design clean delegation rules and handoff protocols to keep the system manageable.
Do you include tool definitions for each agent?
Yes. Each agent's tool assignments are specified in the config. For complex tool definitions, consider pairing with our Tool Definitions task type for complete JSON schemas.
Can agents delegate tasks to each other?
Yes. We configure delegation rules, hierarchical management, and handoff protocols. The config specifies which agents can delegate, to whom, and under what conditions.
What about shared memory and context?
We configure shared memory stores, context passing between agents, and task dependency chains. Each framework handles this differently and we follow the native patterns.
How do I test the multi-agent system?
The config includes example tasks you can run immediately. For comprehensive testing, pair with our Eval Dataset task type to build a test suite for the full agent team.
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