Framework Integrations

CrewAI, LangChain, OpenAI Agents SDK, HuggingFace, OpenClaw, and Hermes.

Overview

Kalibr integrates with popular agent frameworks and supports any model across any modality. Use router.as_langchain() for LangChain, or auto-instrument HuggingFace's InferenceClient for 17 task types.

CrewAI

Install

pip install kalibr[crewai] crewai openai anthropic

Code (Python only)

from kalibr import Router
from crewai import Agent, Task, Crew

# Create Kalibr router
router = Router(
    goal="research_task",
    paths=["gpt-4o-mini", "claude-sonnet-4-20250514"]
)

# Get LangChain-compatible LLM
llm = router.as_langchain()

# Use with CrewAI
researcher = Agent(
    role="Researcher",
    goal="Find accurate information",
    backstory="You are a research assistant.",
    llm=llm,  # Kalibr handles model selection
    verbose=True
)

task = Task(
    description="Research the history of Python programming language.",
    expected_output="A summary of Python's history.",
    agent=researcher
)

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

# Report outcome to Kalibr
router.report(success=len(str(result)) > 100)

Note: CrewAI requires Python 3.10+.

LangChain

Install

pip install kalibr[langchain] langchain langchain-openai langchain-anthropic

Code (Python only)

from kalibr import Router
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

router = Router(
    goal="summarize_text",
    paths=["gpt-4o-mini", "claude-sonnet-4-20250514"]
)

llm = router.as_langchain()

prompt = ChatPromptTemplate.from_template("Summarize this: {text}")
chain = prompt | llm | StrOutputParser()

result = chain.invoke({"text": "Long article text here..."})

router.report(success=len(result) > 50)

OpenAI Agents SDK

Install

pip install kalibr[openai-agents] openai-agents

Code (Python only)

from kalibr import Router
router = Router(
    goal="agent_task",
    paths=["gpt-4o", "gpt-4o-mini"]
)

# Get routing decision
from kalibr import get_policy
policy = get_policy(goal="agent_task")
model = policy["recommended_model"]

# Use with OpenAI Agents SDK
from agents import Agent, Runner

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    model=model,
)
result = Runner.run_sync(agent, "Your task here")

# Report outcome
router.report(success=len(result.final_output) > 0)

HuggingFace

Install

pip install kalibr huggingface_hub

Code (Python only)

import kalibr  # instruments InferenceClient automatically
from huggingface_hub import InferenceClient

client = InferenceClient()
# All 17 task methods are now traced with cost tracking

# Route between HuggingFace models
from kalibr import Router
router = Router(
    goal="transcribe_call",
    paths=["openai/whisper-large-v3", "facebook/seamless-m4t-v2-large"],
    success_when=lambda output: len(output) > 50
)
result = router.execute(task="automatic_speech_recognition", input_data=audio_bytes)

Note: HuggingFace instrumentation covers 17 task types across text, audio, image, embedding, and classification. Set HF_API_TOKEN or HUGGING_FACE_HUB_TOKEN to access private models or avoid free-tier rate limits.

Supported task types

Pass any of these as the task argument to router.execute():

DeepSeek

DeepSeek uses the OpenAI Python SDK with a different endpoint. No separate SDK needed — just set your API key and use deepseek-* model names in your paths.

Install

pip install kalibr openai

Setup

export DEEPSEEK_API_KEY=sk-...   # from platform.deepseek.com

Code (Python only)

import kalibr
from kalibr import Router

# Route between DeepSeek models
router = Router(
    goal="classify_icp",
    paths=["deepseek-chat", "deepseek-reasoner"],
    success_when=lambda output: len(output) > 0
)
response = router.completion(messages=[{"role": "user", "content": "Is this company a good ICP fit?"}])

# Mix DeepSeek with other providers — Thompson Sampling learns which wins
router = Router(
    goal="classify_icp",
    paths=["gpt-4o-mini", "deepseek-chat", "claude-sonnet-4-20250514"],
)
response = router.completion(messages=[...])

Models: deepseek-chat (DeepSeek-V3, fast and cheap), deepseek-reasoner (DeepSeek-R1, for complex reasoning), deepseek-coder (code tasks). Kalibr tracks cost and attributes spans correctly for each.

When to Use as_langchain() vs get_policy()

Claude Code Plugin

The Kalibr Claude Code plugin instruments your project automatically from within Claude Code.

Install

/plugin add kalibr

Instrument an existing project

/kalibrize

This scans all Python files for bare OpenAI, Anthropic, LangChain, and CrewAI calls, shows each call with a proposed Router replacement, and applies changes with your approval.

What it does automatically

• Injects Kalibr context so Claude Code generates Router-based code when building agents
• Adds kalibr to requirements.txt
• Adds KALIBR_API_KEY and KALIBR_TENANT_ID to .env.example

Next

• API Reference - Full Router API
• Production Guide - Error handling, monitoring