CLI — deepcrew-ai

Infrastructure

CLI — deepcrew run

Run declarative YAML workflow files from the terminal. No Python code required for simple workflows.

Installation check

shell

pip install deepcrew-ai
deepcrew --version
# deepcrew-ai 0.4.0

Write a workflow YAML

workflow.yaml

agents:
  - name: researcher
    model: openai/gpt-4o-mini
    system_prompt: Research the topic thoroughly using all available information.
    tools:
      - web_search   # built-in skill by name

  - name: analyst
    model: anthropic/claude-haiku-4-5-20251001
    system_prompt: Critically analyze the research findings. Identify gaps and strengths.

  - name: writer
    model: openai/gpt-4o
    system_prompt: Write a clear, well-structured executive summary report.
    tools:
      - summarize   # built-in summarize skill

workflow:
  - step: research
    agent: researcher
    task: "{input}"

  - step: analysis
    agent: analyst
    task: |
      Analyze this research:
      {research}
    depends_on:
      - research

  - step: report
    agent: writer
    task: |
      Write an executive summary based on:

      Research: {research}
      Analysis: {analysis}
    depends_on:
      - research
      - analysis

Run it

shell

# Stream output to terminal
deepcrew run workflow.yaml --input "The future of autonomous vehicles"

# Non-streaming (prints only final result)
deepcrew run workflow.yaml --input "Quantum computing in 2026" --no-stream

# List all agents in a config
deepcrew agents list --config workflow.yaml

YAML schema

agents[].name*

str

Agent identifier, referenced in workflow steps.

agents[].model*

str

LiteLLM model string.

agents[].system_prompt

str

Agent's system prompt.

agents[].tools

list[str]

Built-in skill names: web_search, summarize, code_exec. Any name not in that set is silently ignored — it is dropped, not an error, so a typo in a tool name fails silently rather than raising at load time.

agents[].max_turns

int = 10

Max inner loop turns for this agent.

agents[].temperature

float | null

Sampling temperature.

agents[].max_tokens

int | null

Maximum output tokens per LLM call.

workflow[].step*

str

Step name — used as a variable {step_name} in subsequent task templates.

workflow[].agent*

str

References an agent by name. Referencing a name not defined in agents: raises a ValueError at load time, before anything runs.

workflow[].task

str = "{input}"

Task template. {input} is the CLI --input value (or the top-level input: YAML field if --input is omitted). {step_name} is the text output of that step.

workflow[].depends_on

list[str] = []

Step names this step depends on. Steps without overlapping dependencies run in parallel.

input

str | null

Top-level fallback for {input} when --input isn't passed on the command line.

router_model

str = "openai/gpt-4o-mini"

Parsed from the YAML but currently unused by deepcrew run — the CLI always builds an explicit WorkflowBuilder DAG from your workflow: steps, never an Orchestrator, so there's no router LLM call for this to configure. Setting it has no effect today.

Supported built-in tool names

YAML name	Skill class	Description
`web_search`	`WebSearchSkill`	DuckDuckGo search
`summarize`	`SummarizeSkill`	LLM-backed summarization
`code_exec`	`CodeExecutionSkill`	Python subprocess execution

For advanced workflows with custom Python tools, memory providers, or observability, use the Python API directly. The CLI is designed for simple, shareable workflows that don't need custom code — there is no YAML way to attach a custom @tool function, an MCP server, a MemoryProvider, or an ObservabilityConfig; only the three built-in skills above are reachable from YAML.

Common pitfalls

Unknown tool names fail silently. A typo like web_serach in agents[].tools is simply dropped — the agent runs with one fewer tool than you intended, no warning.
router_model does nothing yet. The CLI never routes — every workflow you write in YAML is an explicit DAG.
Only three skills are reachable from YAML. There's no config surface for custom tools, MCP servers, memory, or observability — reach for the Python API once you need any of those.
A bad agent reference in workflow: fails before any LLM call. This is a fast, cheap validation error — check your step's agent: field spelling against your agents[].name list first if you see it.

CLI — deepcrew run

Installation check

Write a workflow YAML

Run it

YAML schema

Supported built-in tool names

Common pitfalls

See also