langchain-kalibr 0.1.0

The first autonomous routing system for AI agents. Routes to the optimal execution path to prevent failures, degradations, and cost spikes before they impact users.

langchain-kalibr

Kalibr is the first autonomous routing system for AI agents — routing to the optimal execution path (model + tool + parameters) to prevent failures, degradations, and cost spikes before they impact users. This package drops Kalibr into any LangChain chain, agent, or LangGraph workflow. Works with CrewAI natively.

What is this?

langchain-kalibr is a LangChain integration that gives your chains and agents adaptive model routing. Instead of hardcoding a single LLM provider, you define multiple models (paths) and Kalibr learns which one works best for each task — then routes traffic accordingly.

• Drop-in replacement for ChatOpenAI, ChatAnthropic, etc.
• Works with LangChain chains, agents, and LangGraph
• Works with CrewAI (accepts any LangChain LLM)
• Automatic fallback — if one model fails, tries the next
• Outcome learning — report success/failure and Kalibr improves routing over time

Installation

pip install langchain-kalibr

With specific provider support:

pip install langchain-kalibr[openai]      # OpenAI models
pip install langchain-kalibr[anthropic]   # Anthropic models
pip install langchain-kalibr[all]         # All providers

Setup

Get your credentials from dashboard.kalibr.systems/settings:

export KALIBR_API_KEY="your-api-key"
export KALIBR_TENANT_ID="your-tenant-id"
export OPENAI_API_KEY="sk-..."            # for OpenAI models
export ANTHROPIC_API_KEY="sk-ant-..."     # for Anthropic models
export GOOGLE_API_KEY=...                # for Gemini models

Quick Start

from langchain_kalibr import ChatKalibr

# Define models to route between
llm = ChatKalibr(
    goal="summarize",
    paths=["gpt-4o", "claude-sonnet-4-20250514", "gemini-2.0-flash"],
)

# Use like any LangChain chat model
response = llm.invoke("Summarize the key benefits of adaptive routing.")
print(response.content)

# Report outcome to improve future routing
llm.report(success=True)

Use in LangChain Chains

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_kalibr import ChatKalibr

llm = ChatKalibr(
    goal="answer_questions",
    paths=["gpt-4o", "claude-sonnet-4-20250514", "gpt-4o-mini"],
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Answer concisely."),
    ("human", "{question}"),
])

chain = prompt | llm | StrOutputParser()
answer = chain.invoke({"question": "What is Thompson Sampling?"})

Use with LangGraph Agents

from langgraph.prebuilt import create_react_agent
from langchain_kalibr import ChatKalibr

llm = ChatKalibr(
    goal="agent_tasks",
    paths=["gpt-4o", "claude-sonnet-4-20250514"],
)

agent = create_react_agent(llm, tools=[...])
result = agent.invoke({"messages": [("human", "Search for recent AI news")]})

Use with CrewAI

CrewAI accepts any LangChain LLM natively:

from crewai import Agent, Task, Crew
from langchain_kalibr import ChatKalibr

llm = ChatKalibr(
    goal="research",
    paths=["gpt-4o", "claude-sonnet-4-20250514"],
)

researcher = Agent(
    role="Research Analyst",
    goal="Find and summarize key information",
    llm=llm,
)

task = Task(
    description="Research the latest developments in adaptive AI routing.",
    agent=researcher,
)

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

Outcome Reporting

Outcome reporting is what makes Kalibr learn. After each call, tell Kalibr whether it worked:

Manual Reporting

response = llm.invoke("Extract the email from: Contact us at [e-mail]")

# Check if the task succeeded
has_email = "@" in response.content
llm.report(success=has_email)

Auto-Reporting with success_when

llm = ChatKalibr(
    goal="extract_email",
    paths=["gpt-4o", "claude-sonnet-4-20250514"],
    success_when=lambda output: "@" in output,
)

# Outcome reported automatically after each call
response = llm.invoke("Extract the email from: Contact us at [e-mail]")

Advanced: Routing Between Configurations

Route between different parameter configurations, not just models:

llm = ChatKalibr(
    goal="creative_writing",
    paths=[
        {"model": "gpt-4o", "params": {"temperature": 0.3}},
        {"model": "gpt-4o", "params": {"temperature": 0.9}},
        {"model": "claude-sonnet-4-20250514", "params": {"temperature": 0.7}},
    ],
)

Route between different tool configurations:

llm = ChatKalibr(
    goal="research",
    paths=[
        {"model": "gpt-4o", "tools": ["web_search"]},
        {"model": "gpt-4o", "tools": ["code_interpreter"]},
        {"model": "claude-sonnet-4-20250514"},
    ],
)

How Routing Works

Routing is outcome-aware. Kalibr captures step-level telemetry and success signals, canaries traffic across the paths you define, and keeps your agents on the best-performing path at all times.

Observability shows you what went wrong. Kalibr prevents it from happening. It actively adapts to changing conditions as your agents run in production.

Trust invariant: Success rate always dominates. Cost and latency only break ties between paths with comparable success rates. Kalibr never sacrifices quality for cost savings.

1. You define paths — models, tools, and parameter configurations that can handle your task
2. Kalibr picks — uses Thompson Sampling to balance trying new options vs. using what works
3. You report outcomes — tell Kalibr if the task succeeded
4. Kalibr learns — routes more traffic to what works, automatically routes around degradation

API Reference

ChatKalibr

Parameter	Type	Default	Description
goal	str	required	Task name for routing
paths	list	["gpt-4o"]	Models or configs to route between
api_key	str	env var	Kalibr API key
tenant_id	str	env var	Kalibr tenant ID
success_when	callable	None	Auto-evaluate success
exploration_rate	float	None	Override exploration rate (0.0-1.0)

Methods

Method	Description
invoke(messages)	Send messages, get routed response
report(success, reason, score)	Report outcome for routing improvement
last_trace_id	Get trace ID from last call
last_model_id	Get which model was used last

Environment Variables

Variable	Description	Required
KALIBR_API_KEY	API key from dashboard	Yes
KALIBR_TENANT_ID	Tenant ID from dashboard	Yes
OPENAI_API_KEY	OpenAI API key	If using OpenAI models
ANTHROPIC_API_KEY	Anthropic API key	If using Anthropic models
GOOGLE_API_KEY	Google API key	If using Google models

Links

• Kalibr Dashboard
• Kalibr Docs
• Kalibr Python SDK
• GitHub
• LangChain Docs

License

MIT