Bring Ci Cd
to CrewAI
Learn how to connect Dagger (Programmable CI) to CrewAI and start using 10 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Dagger (Programmable CI) MCP Server?
Connect to the Dagger Engine to orchestrate your delivery pipelines using a powerful, programmable GraphQL API. This server allows your AI agent to interact directly with Dagger's Directed Acyclic Graph (DAG) of operations.
What you can do
- Container Orchestration — Initialize scratch containers, pull images, and manage OCI-compatible states.
- GraphQL Workflows — Execute raw GraphQL queries to compose complex build and test logic dynamically.
- Source Control — Query Git repositories and host environments to pull source code into your pipelines.
- Resource Management — Handle secrets securely, manage persistent cache volumes, and fetch remote files via HTTP.
- Module Inspection — Query the current module state and engine version to ensure environment consistency.
How it works
- Ensure a Dagger Engine is running in your environment.
- Provide the session port and token generated by the Dagger CLI.
- Start building and deploying through natural language commands.
Who is this for?
- DevOps Engineers — Automate pipeline debugging and execution without leaving the chat interface.
- Software Developers — Run builds, tests, and container operations directly from the code editor.
- SREs — Inspect engine states and orchestrate infrastructure tasks using programmable CI logic.
Built-in capabilities (10)
You can chain fields to create a Directed Acyclic Graph (DAG) of operations. Execute a raw GraphQL query against the Dagger engine
Constructs a cache volume
Creates a scratch container and returns its ID
Queries the current module
Creates an empty directory and returns its ID
Queries a Git repository
Queries the host environment
Returns a file from a URL
g., env://VAR_NAME, file://PATH, cmd://COMMAND). Creates a new secret
Get the Dagger Engine version
Why CrewAI?
When paired with CrewAI, Dagger (Programmable CI) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Dagger (Programmable CI) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
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
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter 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
Dagger (Programmable CI) in CrewAI
Dagger (Programmable CI) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Dagger (Programmable CI) to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Dagger (Programmable CI) in CrewAI
The Dagger (Programmable CI) 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. All 10 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Dagger (Programmable CI) for CrewAI
Every tool call from CrewAI to the Dagger (Programmable CI) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I run a specific build command using Dagger?
You can use the execute_graphql_query tool to send a raw GraphQL query to the Dagger engine, allowing you to chain container operations like from, withExec, and stdout.
Can I access files from my local machine in the pipeline?
Yes, the query_host tool allows you to retrieve the state ID for your host environment, which can then be used to mount local directories into your Dagger containers.
How are secrets handled in this integration?
Use the query_secret tool to load secrets from URIs (like env:// or file://). This returns a secret ID that can be safely passed to other Dagger operations without exposing the plaintext value.
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.
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.
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