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Documentation Index

Fetch the complete documentation index at: https://docs.meshai.dev/llms.txt

Use this file to discover all available pages before exploring further.

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Installation

pip install meshai-sdk[crewai]

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Usage

from meshai import MeshAI
from meshai.integrations.crewai import track_crewai

client = MeshAI(api_key="msh_...", agent_name="my-crew")
client.register(framework="crewai")

# Enable global tracking — applies to ALL crews
track_crewai(client)

# Run your crew as normal
from crewai import Agent, Task, Crew

researcher = Agent(role="Researcher", llm="gpt-4o", ...)
writer = Agent(role="Writer", llm="claude-sonnet-4-6", ...)

crew = Crew(agents=[researcher, writer], tasks=[...])
result = crew.kickoff()
# Each agent's LLM calls tracked with their specific model

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How It Works

MeshAI registers a global after_llm_call hook with CrewAI. After every LLM interaction, the hook:
  1. Extracts the model name from the LLM context
  2. Extracts token counts from the response
  3. Infers the provider from the model name
  4. Sends the usage event to MeshAI (buffered, batched)
This means each agent in your crew is tracked with its own model — no hardcoding needed.

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Multi-Model Crews

CrewAI crews often use different models per agent. MeshAI tracks each one separately: 
Dashboard shows:
  researcher (gpt-4o)      → 15,000 tokens, $0.45
  writer (claude-sonnet)   → 8,000 tokens, $0.24
  reviewer (gpt-4o-mini)   → 3,000 tokens, $0.01