Can I search for failed workflows across my entire history?

Yes. The search tool supports Elasticsearch query syntax — search by status (FAILED, TIMED_OUT), workflow type, date ranges, or correlation IDs. Ask your agent 'show me all failed workflows from the last 24 hours' and it returns matching executions with their IDs, failure reasons, and timestamps.

What's the difference between workflow definitions and running instances?

Definitions are the blueprints — the graph schema with tasks, operators, and branching logic. Running instances are actual executions of those definitions, each with their own input data, current state, and task-by-task progress. Think of definitions as classes and instances as objects.

Does this integration support triggering new workflow executions?

Currently, this integration focuses on observability — listing definitions, monitoring running instances, and searching execution history. It does not trigger new workflow executions. For launching workflows, use the Orkes Conductor UI or direct API calls.

Orkes Conductor MCP Server for CrewAI 6 tools — connect in under 2 minutes

Name: Orkes Conductor
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 6 Tools Framework

Connect your CrewAI agents to Orkes Conductor through Vinkius, pass the Edge URL in the `mcps` parameter and every Orkes Conductor tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Orkes Conductor Specialist",
    goal="Help users interact with Orkes Conductor effectively",
    backstory=(
        "You are an expert at leveraging Orkes Conductor tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Orkes Conductor "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 6 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Orkes Conductor MCP Server

Connect your Orkes Conductor cluster to any AI agent and get full visibility into your workflow orchestration layer — definitions, running instances, task states, and execution history.

When paired with CrewAI, Orkes Conductor becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Orkes Conductor tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Workflow Definitions — List all registered workflow definitions with versions and descriptions, or inspect a specific workflow's graph schema with tasks, operators, and branching logic
Task Definitions — List all registered task definitions available for orchestration within your workflows
Running Instances — List actively running workflow instances filtered by workflow name to monitor what's currently executing
Execution Details — Get deep state details for any workflow execution including input/output mappings, task-by-task trace histories, and exceptions
Workflow Search — Search across all workflow executions using Elasticsearch queries, filtering by status, correlation ID, or workflow type

The Orkes Conductor MCP Server exposes 6 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Orkes Conductor to CrewAI via MCP

Follow these steps to integrate the Orkes Conductor MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 6 tools from Orkes Conductor

Why Use CrewAI with the Orkes Conductor MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Orkes Conductor through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Orkes Conductor + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Orkes Conductor MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Orkes Conductor for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Orkes Conductor, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Orkes Conductor tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Orkes Conductor against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Orkes Conductor MCP Tools for CrewAI (6)

These 6 tools become available when you connect Orkes Conductor to CrewAI via MCP:

get_execution

Get deep state details of a specific Workflow Execution

get_workflow_def

Get a specific Workflow Definition explicitly by name

list_running

List active, running workflow instances by explicit workflow name

list_task_defs

List all explicitly registered Task Definitions via Conductor API

list_workflow_defs

List all registered overarching Workflow Definitions via Orkes API

search_workflows

Perform an elastic Search across all Workflow executions

Example Prompts for Orkes Conductor in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Orkes Conductor immediately.

"Show me all registered workflow definitions."

"Are there any failed workflows in the last 24 hours?"

"How many instances of the order-processing workflow are currently running?"

Troubleshooting Orkes Conductor MCP Server with CrewAI

Common issues when connecting Orkes Conductor to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Orkes Conductor + CrewAI FAQ

Common questions about integrating Orkes Conductor MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.