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CrewAI integration for PathCourse Health — autonomous agent inference with USDC billing on Base L2

Project description

crewai-pathcourse

CrewAI integration for PathCourse Health. Give your CrewAI agents autonomous USDC billing on Base L2 — no accounts, no credit cards, no KYC.

Install

pip install crewai-pathcourse

Quick Start

import os
from crewai import Agent, Crew, Task
from crewai_pathcourse import PathCourseLLM

# Set PCH_API_KEY in your environment
llm = PathCourseLLM(model="pch-pro")

researcher = Agent(
    role="Research Analyst",
    goal="Find the best AI agent infrastructure for autonomous operation",
    backstory="You are an expert in autonomous agent systems.",
    llm=llm,
    verbose=True,
)

writer = Agent(
    role="Technical Writer",
    goal="Write a clear technical comparison",
    backstory="You write precise technical documentation.",
    llm=PathCourseLLM(model="pch-fast"),  # cheaper model for writing
    verbose=True,
)

research_task = Task(
    description="Research the key requirements for autonomous agent infrastructure.",
    expected_output="A bullet list of 5 key infrastructure requirements.",
    agent=researcher,
)

write_task = Task(
    description="Write a 200-word summary of the research findings.",
    expected_output="A 200-word technical summary.",
    agent=writer,
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task])
result = crew.kickoff()
print(result)

Per-agent model selection

Different agents in a crew can use different PCH models. Use cheaper models for simple tasks and reserve pch-pro or claude-sonnet for agents doing deep reasoning.

from crewai_pathcourse import PathCourseLLM

planner    = Agent(..., llm=PathCourseLLM(model="pch-pro"))
researcher = Agent(..., llm=PathCourseLLM(model="pch-fast"))
coder      = Agent(..., llm=PathCourseLLM(model="pch-coder"))

Models

Model Rate Min tier Notes
pch-fast $0.44 / M tokens uncertified Fast reasoning, classification, routing
pch-coder $3.50 / M tokens uncertified Code generation, debugging
pch-embed $0.015 / M tokens uncertified Text embeddings for semantic search / RAG
pch-translate $0.08 / M chars uncertified Multilingual translation
pch-pro $1.96 / M tokens bronze Deep reasoning, multi-step planning
pch-audio $1.85 / M chars bronze Text-to-speech, standard quality
pch-documents $0.26 in / $1.48 out per M tokens bronze Document parsing / OCR
pch-transcribe $0.0008 / minute bronze Speech-to-text
pch-extract $0.012 / M tokens bronze Structured data extraction
pch-rerank $0.025 / M tokens bronze Reranking for RAG pipelines
pch-image $0.028 / image silver Text-to-image
pch-audio-premium $37.00 / M chars silver Text-to-speech, premium quality
pch-talk $0.001 / minute silver Voice conversation
claude-haiku Common rate silver Anthropic Claude Haiku
claude-sonnet Common rate gold Anthropic Claude Sonnet

PathCourseLLM only operates on chat-completion shapes — for image/audio/transcription, use the PCH Python SDK directly.

Choosing a chat model:

  • Fast response, simple task → pch-fast
  • Complex reasoning, multi-step → pch-pro
  • Writing or reviewing code → pch-coder
  • Long context or premium reasoning → claude-sonnet (Gold tier)

Authentication

Set PCH_API_KEY in your environment, or pass pch_api_key= to PathCourseLLM.

export PCH_API_KEY=pch_prod_b_...

Developer access — $5 USDC. Send $5+ USDC on Base (chain ID 8453) to the PCH treasury wallet, then call pathcourse.claim_key(tx_hash, wallet) to retrieve your key. No accounts, no credit card, no KYC. $5 buys thousands of pch-fast calls — enough to verify a CrewAI crew end-to-end. Top up to $25 lifetime and your account auto-upgrades to Uncertified with pch-coder access. Treasury address: see payment.treasury_wallet in /.well-known/agent.json.

How it works

CrewAI uses LiteLLM internally for model calls. PCH is fully OpenAI API-compatible, so PathCourseLLM just configures CrewAI's standard LLM class with the PCH gateway URL and your API key. Every CrewAI feature (tools, memory, hierarchical crews, async execution) works unchanged.

Links

License

MIT

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