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Build with Hyphen

CrewAI

CrewAI builds crews of role-playing agents that hand work to each other. Point its LLM class at Hyphen and every agent in the crew uses it.

Code
pip install crewai
export HYPHEN_API_KEY="sk-..."

Setup guide: this config comes from CrewAI's official documentation and has not been run end to end against the gateway. Corrections to support@hyphen-solution.com.

The model

Code
import os
from crewai import LLM

llm = LLM(
    model="openai/minimax-m3",
    custom_openai=True,
    base_url="https://api.hyphen-solution.com/v1",
    api_key=os.environ["HYPHEN_API_KEY"],
    max_tokens=4000,
)

A crew

Code
import os
from crewai import LLM, Agent, Task, Crew

llm = LLM(
    model="openai/minimax-m3",
    custom_openai=True,
    base_url="https://api.hyphen-solution.com/v1",
    api_key=os.environ["HYPHEN_API_KEY"],
    max_tokens=4000,
)

researcher = Agent(
    role="Researcher",
    goal="Find concise, verifiable facts about {topic}",
    backstory="You are a meticulous research analyst who hates padding.",
    llm=llm,
    verbose=True,
)

writer = Agent(
    role="Writer",
    goal="Turn research notes into tight prose",
    backstory="You write like a developer. Short sentences. No filler.",
    llm=llm,
    verbose=True,
)

research_task = Task(
    description="Write three key facts about {topic}.",
    expected_output="A bullet list of exactly three facts.",
    agent=researcher,
)

write_task = Task(
    description="Turn the research into a single paragraph a developer would read.",
    expected_output="One paragraph, at most five sentences.",
    agent=writer,
    context=[research_task],
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task], verbose=True)
print(crew.kickoff(inputs={"topic": "the Voyager probes"}))

Mixing models in one crew

Crews are where per-agent model choice actually pays off. Put the flagship on the agent that plans, and the fast model on the agents that do bulk work.

Code
flagship = LLM(
    model="openai/minimax-m3",
    custom_openai=True,
    base_url="https://api.hyphen-solution.com/v1",
    api_key=os.environ["HYPHEN_API_KEY"],
    max_tokens=4000,
)

fast = LLM(
    model="openai/minimax-m2.5",
    custom_openai=True,
    base_url="https://api.hyphen-solution.com/v1",
    api_key=os.environ["HYPHEN_API_KEY"],
    max_tokens=4000,
)

planner = Agent(role="Planner", goal="...", backstory="...", llm=flagship)
summariser = Agent(role="Summariser", goal="...", backstory="...", llm=fast)

Both meter at the same per-token rate. The saving is that minimax-m2.5 is terser, not that it is cheaper per token. See Choosing a model.

Watch the budget

Crews are the easiest way to spend a monthly cap by accident. Every agent handoff is a fresh request carrying the accumulated context, so a five-agent crew on a long task can burn through tokens fast.

Two things help. Set max_iter on agents so a stuck agent cannot loop forever, and test with minimax-m2.5 before switching the crew to minimax-m3.

Code
researcher = Agent(
    role="Researcher",
    goal="...",
    backstory="...",
    llm=llm,
    max_iter=8,
)

The hard cap is still the backstop. A runaway crew returns 429 rather than an invoice. See Rate limits & caps.

Gotchas

• max_tokens of 4000. CrewAI agents use tools heavily and the M-series reason before every call. See Choosing a model.
• No embedding models. CrewAI's memory features need an embedder. Configure a local or third-party one, since the Hyphen catalog has none.
• 429 means the monthly budget is spent. See Handling the 429 cap.

Related

• CrewAI LLM docs
• CrewAI LLM connections
• Choosing a model: which agent gets which model.