Which Yi model is best for complex reasoning?

For complex reasoning and high-quality outputs, `yi-large` is recommended. For faster response times and cost efficiency, `yi-medium` or `yi-spark` are excellent alternatives.

Can I automatically retrieve my remaining account balance?

Yes! Use the `get_balance` tool. Your agent will connect to the Lingyi Wanwu billing service and return your current remaining credits.

How do I list all the technical specs for the Yi models?

Use the `list_models` tool. Your agent will retrieve a list of all models currently available on the platform, along with their IDs and capabilities.

Lingyi Wanwu MCP Server for CrewAI 5 tools — connect in under 2 minutes

Name: Lingyi Wanwu
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 5 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Lingyi Wanwu Specialist",
    goal="Help users interact with Lingyi Wanwu effectively",
    backstory=(
        "You are an expert at leveraging Lingyi Wanwu 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 Lingyi Wanwu "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 5 available tools "
        "and what they can do."
    ),
)

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

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Lingyi Wanwu MCP Server

Connect your AI agents to Lingyi Wanwu (01.AI), the high-performance AI lab founded by Dr. Kai-Fu Lee. This MCP provides 10 tools to automate interactions with the Yi series of large language models, including state-of-the-art chat completions, semantic embeddings, and account usage monitoring.

When paired with CrewAI, Lingyi Wanwu becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Lingyi Wanwu 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

Yi Model Interaction — Trigger chat completions with Yi-34B, Yi-Large, and other optimized models using persistent context
Vector Embeddings — Generate high-dimensional semantic embeddings to power advanced RAG and search workflows
Model Intelligence — List all available models and retrieve granular technical specifications for each version
Account Management — Monitor your token consumption and balance programmatically to optimize costs

The Lingyi Wanwu MCP Server exposes 5 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 Lingyi Wanwu to CrewAI via MCP

Follow these steps to integrate the Lingyi Wanwu 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 5 tools from Lingyi Wanwu

Why Use CrewAI with the Lingyi Wanwu MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Lingyi Wanwu 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

Lingyi Wanwu + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Lingyi Wanwu MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Lingyi Wanwu 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 Lingyi Wanwu, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Lingyi Wanwu 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 Lingyi Wanwu against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Lingyi Wanwu MCP Tools for CrewAI (5)

These 5 tools become available when you connect Lingyi Wanwu to CrewAI via MCP:

chat_completions

Send a message to a Yi model

check_moderation

Check content for policy violations

get_embeddings

Generate text embeddings

get_usage

Retrieve account usage statistics

list_models

List available Yi models

Example Prompts for Lingyi Wanwu in CrewAI

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

"Chat with the Yi-Large model and ask 'Explain the impact of AI on the future of work'."

"Generate embeddings for my company's mission statement."

"Check my current account balance in Lingyi Wanwu."

Troubleshooting Lingyi Wanwu MCP Server with CrewAI

Common issues when connecting Lingyi Wanwu 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.

Lingyi Wanwu + CrewAI FAQ

Common questions about integrating Lingyi Wanwu 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.