CrewAI

Track usage, control costs, and add guardrails to your CrewAI multi-agent crews

What is CrewAI?

CrewAI is a leading open-source framework for orchestrating role-playing, autonomous AI agents. You define agents with roles, goals, and backstories, assign them tasks, and CrewAI coordinates them as a "crew" to accomplish multi-step workflows.

Multi-agent crews generate a high volume of LLM calls, which makes cost visibility and control critical. By routing CrewAI through FastRouter, you get:

• 100+ models from OpenAI, Anthropic, Google, xAI, Meta, Groq, Mistral, and more through one endpoint—assign different providers to different agents with one key

• Observability for every request: cost, tokens, latency, and model selection tracked in real time

• Reliability through automatic failover across providers, response caching, and intelligent routing

• Governance with per-key budgets, rate limits, model restrictions, role-based access, and project isolation

This guide covers connecting CrewAI (1.x) to FastRouter, running a crew, and assigning different models to different agents.

Prerequisites

• A FastRouter.ai account (sign up)

• Python 3.10 or higher

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Quick Start

Step 1: Create a Project and Virtual Environment

You'll only need to do this once. CrewAI requires Python 3.10–3.13:

mkdir my_project
cd my_project
python -m venv .venv

Activate the virtual environment. Do this every time you start a new terminal session.

On macOS or Linux:

source .venv/bin/activate

On Windows:

.venv\Scripts\activate

Step 2: Install CrewAI

pip install crewai

Step 3: Get Your FastRouter API Key

1. Sign up or log in at fastrouter.ai

2. Navigate to your project's Keys page

3. Click Create User Key

4. Copy the key immediately. FastRouter does not display the key again after creation.

Export it in your terminal:

export FASTROUTER_API_KEY=sk-add-your-key-here

Step 4: Configure the CrewAI LLM to Use FastRouter

Set provider="openai" so CrewAI uses its OpenAI-compatible client and passes the FastRouter model slug through verbatim. Save this as crew_example.py:

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

# provider="openai" makes CrewAI use the OpenAI-compatible protocol and
# pass the FastRouter model slug through verbatim
llm = LLM(
    model="openai/gpt-5.2",
    provider="openai",
    base_url="https://api.fastrouter.ai/api/v1",
    api_key=os.environ["FASTROUTER_API_KEY"],
)

researcher = Agent(
    role="Tech Researcher",
    goal="Summarize technical topics clearly",
    backstory="You are an experienced technology analyst.",
    llm=llm,
    verbose=True,
)

task = Task(
    description="Write a two-sentence summary of what an LLM gateway does.",
    expected_output="A two-sentence summary.",
    agent=researcher,
)

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

Note: provider="openai" is required. Without it, CrewAI parses the model string itself and strips the provider prefix (sending gpt-5.2 instead of openai/gpt-5.2), or—for non-OpenAI slugs—tries to use that provider's native SDK instead of FastRouter.

Step 5: Run the Crew

python crew_example.py

CrewAI prints the agent's progress (because verbose=True) followed by the final answer:

Every LLM call from every agent appears in your FastRouter Dashboard with token usage and cost.

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Use CrewAI with 100+ Models

With provider="openai" set, any FastRouter slug works as-is—including non-OpenAI providers. A common pattern is to give cheap, fast models to high-volume worker agents and a frontier model to the planner, with no extra credentials:

fast_llm = LLM(
    model="openai/gpt-4.1-nano",
    provider="openai",
    base_url="https://api.fastrouter.ai/api/v1",
    api_key=os.environ["FASTROUTER_API_KEY"],
)

smart_llm = LLM(
    model="anthropic/claude-4.5-sonnet",
    provider="openai",
    base_url="https://api.fastrouter.ai/api/v1",
    api_key=os.environ["FASTROUTER_API_KEY"],
)

worker = Agent(role="Researcher", goal="...", backstory="...", llm=fast_llm)
planner = Agent(role="Lead Strategist", goal="...", backstory="...", llm=smart_llm)

Explore the full model catalog

Automatic Model Selection

Let FastRouter pick the best model for each request based on query complexity, domain, and cost:

llm = LLM(
    model="fastrouter/auto",
    provider="openai",
    base_url="https://api.fastrouter.ai/api/v1",
    api_key=os.environ["FASTROUTER_API_KEY"],
)

Explore automatic model selection

Flex Pricing for Unattended Crews

Crews that run unattended—research pipelines, content generation, scheduled jobs—tolerate higher latency. Append :flex to any model to access up to 50% lower token costs:

model="openai/gpt-5.2:flex"

Note: Flex is not recommended for crews where you are waiting on the output interactively.

Compare flex pricing options

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FAQs

Configuration & Setup

I get ImportError: Unable to initialize LLM with model '...' mentioning LiteLLM. Do I need LiteLLM?

No. This error means the model string didn't match a provider CrewAI recognizes natively. Add provider="openai" to your LLM(...) call and pass the FastRouter slug in model—no LiteLLM install needed.

I get 400 - There is no available model provider that meets your routing requirements. What's wrong?

The model slug reaching FastRouter is malformed. Ensure provider="openai" is set and model is an exact FastRouter catalog slug (for example, openai/gpt-5.2).

Can I use multiple models with the same API key?

Yes. The API key controls access and budget. Each agent can have its own LLM instance with a different model, all sharing one key.

Costs & Budgeting

What happens when a key exceeds its budget?

FastRouter blocks further requests until the budget resets (if a reset interval is configured) or an admin increases the limit. The crew receives an error response indicating the budget has been exceeded.

How do I track spending per crew or per team?

Create separate projects for each team, issue project-scoped API keys, and use Dynamic Tags for finer-grained attribution. The Dashboard breaks down costs by project, key, model, and tag.

Performance & Reliability

Does FastRouter add latency to crew runs?

FastRouter adds near-zero gateway overhead. For multi-step crew executions, this is negligible compared to model inference time.

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Next Steps

• Explore the full model catalog

• Set up Fallback Models for high availability

• Configure Alerts for spend and performance monitoring

• Run a Free Audit on your existing LLM traffic to identify savings

• Join the Discord community