10 best LLM evaluation tools with superior integrations in 2025

The wave of AI applications flooding production environments has created an exciting new challenge: how do you ensure your LLM-powered features actually work as intended? While building an AI chatbot or agent might seem straightforward in a demo, production-grade AI systems require rigorous evaluation and LLM observability capabilities. The secret weapon that separates reliable AI applications from experimental prototypes? Seamless integrations with your existing tech stack.

Integrations with your development workflow, from OpenTelemetry tracing to framework-specific SDKs, have become the difference between AI teams that ship fast and those that get bogged down in evaluation overhead. When your evaluation platform connects natively to tools like the Vercel AI SDK, LangChain, or Instructor, you gain instant visibility into model performance without rewriting your application code.

Why integration capabilities matter

Modern AI application development happens across diverse LLM platforms and technology stacks. Many frameworks for running AI agents and wrapping LLM calls have becoming popular with developers. Tools like LangChain, the Vercel AI SDK, OpenTelemetry and Instructor are now part of many developers tech stacks. The last thing you want is an evaluation tool that forces you to rewrite your application logic or maintain separate instrumentation code. Robust integrations are a must for AI evaluation platforms.

The integration advantage:

• Reduced time-to-value: Get evaluation running in minutes, not days
• Lower maintenance overhead: No separate instrumentation to maintain
• Better adoption: Teams actually use tools that fit their workflow
• Comprehensive coverage: Trace your entire AI application stack seamlessly

Integration-focused evaluation framework analysis

We evaluated platforms based on their integration ecosystem breadth, ease of implementation, and framework-specific support quality.

1. Braintrust

Integration Ecosystem: ⭐⭐⭐⭐⭐

Braintrust sets the industry standard for LLM evaluation integrations as an end-to-end platform for building AI applications, offering the most comprehensive ecosystem with native support for 9+ major frameworks. Trusted by leading AI teams at Notion, Stripe, Zapier, Vercel, among others. What distinguishes Braintrust is not just the breadth of integrations but the depth of each implementation. Each integration is purpose-built for production AI applications. Braintrust integrates with all of the major AI frameworks. OpenTelemetry, Vercel AI SDK, OpenAI Agent SDK, Instructor, Langchain, Langraph, Google ADK, Mastra, and Pydantic AI are all supported by Braintrust as integrations.

Complete integration suite:

OpenTelemetry integration

Braintrust provides industry-leading OpenTelemetry support with native exporter functionality, automatic LLM tracing, and automatic LLM span conversion. The platform supports multiple configuration approaches including SDK-based setup, pure OTLP configuration, and integration with popular libraries like OpenLLMetry.

Advanced configuration options:

• Python SDK: Uses BraintrustSpanProcessor with configurable parameters including filter_ai_spans for selective logging and custom_filter functions for fine-grained control
• TypeScript SDK: Provides BraintrustSpanProcessor with NodeSDK integration and manual tracer provider configuration
• OTLP Configuration: Direct exporter setup with endpoint https://api.braintrust.dev/otel/v1/traces and support for custom headers including x-bt-parent for trace hierarchy

# Python OpenTelemetry Configuration
from braintrust.otel import BraintrustSpanProcessor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
 
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BraintrustSpanProcessor(
        parent="project_name:my-project", filter_ai_spans=True, custom_filter=lambda span: span.name.startswith("llm")
    )
)

Vercel AI SDK integration

Braintrust provides dedicated support for both Vercel AI SDK v4 and v5 with specialized wrapper functions. The v5 integration uses wrapAISDK for top-level functions while v4 uses wrapAISDKModel for individual model instances.

V5 implementation (latest):

import { initLogger, wrapAISDK } from "braintrust";
import * as ai from "ai";
import { openai } from "@ai-sdk/openai";
 
initLogger({
  projectName: "My AI Project",
});
 
const { generateText } = wrapAISDK(ai);
 
async function main() {
  // Automatic tracing for all AI SDK functions
  const result = await generateText({
    model: openai("gpt-4"),
    prompt: "Hello world",
  });
 
  console.log(result.text);
}
 
main().catch(console.error);

Tool call tracing: The integration automatically traces both LLM tool call suggestions and actual tool executions, supporting both array-based and object-based tools formats.

OpenAI Agents SDK integration

Braintrust provides specialized trace processors for the OpenAI Agents SDK with comprehensive monitoring and evaluation capabilities. The integration supports TypeScript environments with the @braintrust/openai-agents package.

import { initLogger } from "braintrust";
import { OpenAIAgentsTraceProcessor } from "@braintrust/openai-agents";
import { Agent, run, addTraceProcessor } from "@openai/agents";
 
// Initialize Braintrust logger
const logger = initLogger({
  projectName: "agent-project",
});
 
// Create the tracing processor
const processor = new OpenAIAgentsTraceProcessor({ logger });
 
// Add the processor to OpenAI Agents
addTraceProcessor(processor);
 
const agent = new Agent({
  name: "Assistant",
  model: "gpt-4o-mini",
  instructions: "You are a helpful assistant.",
});

Instructor integration

For structured output generation, Braintrust integrates with Instructor by wrapping the OpenAI client with both frameworks. The implementation requires wrapping with Braintrust first to capture low-level usage information and headers.

import instructor
from braintrust import wrap_openai
from openai import OpenAI
 
# Wrap OpenAI client first, then apply Instructor
client = instructor.patch(wrap_openai(OpenAI()))

LangChain integration

LangChain applications integrate through callback handlers, providing comprehensive tracing for chain workflows and evaluation metrics.

LangGraph integration

LangGraph applications use global LangChain callback handlers with the BraintrustCallbackHandler and setGlobalHandler functions.

import {
  BraintrustCallbackHandler,
  setGlobalHandler,
} from "@braintrust/langchain-js";
import { StateGraph } from "@langchain/langgraph";
import { initLogger } from "braintrust";
 
const logger = initLogger({ projectName: "My Project" });
const handler = new BraintrustCallbackHandler({ logger });
setGlobalHandler(handler);
 
// Define channels and nodes
const graphStateChannels = {
  messages: { value: "append" },
};
 
function sayHello(state: any) {
  return { messages: "Hello from LangGraph!" };
}
 
// All LangGraph operations automatically logged
const graph = new StateGraph({ channels: graphStateChannels })
  .addNode("sayHello", sayHello)
  .compile();

Google ADK integration

The braintrust-adk integration provides automatic tracing and logging of Google ADK agent executions, capturing agent invocations, tool calls, parallel execution flows, and multi-step reasoning.

import asyncio
 
from braintrust_adk import setup_braintrust
from google.adk import Runner
from google.adk.agents import LlmAgent
from google.adk.sessions import InMemorySessionService
from google.genai import types
 
setup_braintrust(
    project_name="my-adk-project",
)
 
 
# Create your ADK agent as normal
def get_weather(city: str) -> dict:
    """Get weather for a city."""
    return {"temperature": 72, "condition": "sunny", "city": city}

Mastra integration

Mastra framework integration supports both AI SDK v4 and v5 approaches. For v5, use wrapMastraAgent and wrapLanguageModel, while v4 uses OpenTelemetry export configuration.

V5 implementation:

import { wrapMastraAgent, wrapLanguageModel } from "braintrust";
import { Agent } from "@mastra/core";
import { openai } from "@ai-sdk/openai";
 
// Example agent and model setup
const agent = new Agent({
  name: "my-agent",
  instructions: "You are a helpful assistant",
});
 
const model = openai("gpt-4");
 
const wrappedAgent = wrapMastraAgent(agent);
const wrappedModel = wrapLanguageModel(model);

Pydantic AI integration

Pydantic AI integration leverages OpenTelemetry support with automatic instrumentation for interactions, tool calls, and performance metrics.

from braintrust.otel import BraintrustSpanProcessor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from pydantic_ai import Agent
 
# Configure OpenTelemetry with Braintrust
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(BraintrustSpanProcessor())
 
# Enable instrumentation
agent = Agent("openai:gpt-4")

Key takeaways

Braintrust offers native integrations with leading LLM frameworks, enabling teams to collect traces and gain AI performance visibility with minimal setup. Most integrations require just a few lines of code to implement. The platform supports both modern frameworks like Vercel AI SDK and OpenAI Agents SDK, as well as established tools like LangChain and LlamaIndex. This comprehensive coverage allows teams to maintain their existing development workflows while adding robust evaluation capabilities.

2. Helicone

Integration Ecosystem: ⭐⭐⭐⭐

Helicone provides comprehensive observability through proxy-based and SDK approaches, supporting various LLM providers including OpenAI, Anthropic, Google Gemini, and framework integrations.

Key integrations:

• Multi-provider support: OpenAI, Anthropic, Google Gemini
• Framework support: LangChain, LlamaIndex, LiteLLM, Vercel AI SDK
• Advanced features: Session management, cost tracking, real-time monitoring

Strengths: Broad provider support, production-grade monitoring, comprehensive tracing capabilities. Limitations: Primarily observability-focused; limited built-in evaluation metrics.

3. Comet (Opik)

Integration Ecosystem: ⭐⭐⭐⭐

Comet's Opik platform provides comprehensive LLM evaluation with extensive framework support including OpenAI, LangChain, LlamaIndex, DSPy, and agent frameworks like Google ADK and AutoGen.

Key integrations:

• Framework support: OpenAI, LangChain, LlamaIndex, DSPy, AutoGen, AG2
• Agent platforms: Google ADK, Flowise AI
• Production features: Real-time monitoring, CI/CD integration, human annotation

Strengths: Full-featured evaluation platform, open-source foundation, agent optimization capabilities. Limitations: Newer platform with evolving enterprise features.

4. Arize (Phoenix)

Integration Ecosystem: ⭐⭐⭐⭐

Arize's Phoenix platform offers advanced AI observability with comprehensive framework support including LlamaIndex, LangChain, DSPy, and multiple LLM providers through OpenTelemetry-based instrumentation.

Key integrations:

• Frameworks: LlamaIndex, LangChain, DSPy, Haystack, AutoGen
• LLM providers: OpenAI, Bedrock, Mistral, Vertex AI, LiteLLM
• Enterprise features: Embedding analysis, production monitoring, human feedback

Strengths: Advanced observability, enterprise-grade capabilities, strong open-source foundation. Limitations: More observability-focused than evaluation-specific.

5. MLflow

Integration Ecosystem: ⭐⭐⭐

MLflow provides enhanced LLM support beyond traditional ML workflows, with auto-tracing capabilities for OpenAI, LangChain, LlamaIndex, DSPy, and AutoGen, plus multi-provider evaluation support.

Key integrations:

• Auto-tracing: OpenAI, LangChain, LlamaIndex, DSPy, AutoGen
• LLM providers: OpenAI, Anthropic, AWS Bedrock, Google Vertex AI
• Evaluation: Built-in LLM-as-a-Judge capabilities

Strengths: Unified ML/AI platform, comprehensive model management. Limitations: More ML-platform focused than LLM-native; complex setup for advanced workflows.

6. Langfuse

Integration Ecosystem: ⭐⭐⭐

Langfuse offers solid open-source integration foundation with support for OpenAI, LangChain, LlamaIndex, and basic observability features, though with more limited framework coverage compared to comprehensive platforms.

Key integrations:

• Standard support: OpenAI, LangChain, LlamaIndex
• Basic features: Tracing, prompt management, cost tracking

Limitations: Self-hosting requirements; limited framework support for newer AI development tools.

7. Galileo AI

Integration Ecosystem: ⭐⭐⭐

Galileo provides enterprise-focused AI evaluation with support for major LLM providers (OpenAI, Anthropic, Google Vertex AI, AWS) and agent frameworks including LangGraph, CrewAI, and OpenAI Agent SDK.

Key integrations:

• LLM providers: OpenAI, Anthropic, Google Vertex AI, AWS Bedrock
• Agent frameworks: LangGraph, CrewAI, OpenAI Agent SDK, LlamaIndex
• Enterprise features: Luna-2 models, real-time guardrails, agentic evaluations

Strengths: Specialized agent evaluation, enterprise security, custom metrics. Limitations: Enterprise-focused pricing; complex setup; requires professional services for advanced features.

8. DeepEval

Integration Ecosystem: ⭐⭐

DeepEval focuses primarily on testing framework integration with pytest-like functionality for LLM applications, offering basic LlamaIndex support and development-focused evaluation capabilities.

Key integrations:

• Testing focus: Pytest integration, CI/CD pipelines
• Basic framework support: LlamaIndex evaluators, minimal LangChain compatibility

Strengths: Strong testing framework integration, synthetic dataset generation. Limitations: Development-focused; lacks production monitoring; missing modern framework support.

9. RAGAS

Integration Ecosystem: ⭐⭐

RAGAS provides specialized RAG-focused integration with deep LlamaIndex support and basic LangChain compatibility, maintaining its position as a dedicated RAG evaluation framework.

Key integrations:

• RAG-specific: LlamaIndex comprehensive integration, basic LangChain support
• Evaluation focus: Faithfulness, answer relevancy, context precision metrics

Strengths: Research-backed RAG metrics, testset generation capabilities. Limitations: Narrow RAG focus; limited applicability to broader AI applications.

10. OpenAI Evals

Integration Ecosystem: ⭐

OpenAI Evals offers basic evaluation capabilities limited exclusively to OpenAI models, with a simple CLI interface and template system for standard benchmarks.

Key features:

• OpenAI-only: Limited to GPT models
• Basic templates: Pre-built evaluation templates
• Registry system: Open-source benchmark registry

Limitations: OpenAI-only support; no platform integration; requires custom implementation for broader use.

Advanced integration implementation patterns

Pattern 1: Comprehensive native integration (Braintrust)

Braintrust provides the most sophisticated integration approach with framework-specific implementations that include automatic cost tracking, evaluation metrics, and production monitoring:

# Advanced LangChain implementation with custom filtering
from braintrust import BraintrustCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
 
# Configure with advanced span filtering
handler = BraintrustCallbackHandler(
    filter_ai_spans=True, custom_filter=lambda span: span.get_attribute("gen_ai.system") == "openai"
)
 
# Example chain setup
llm = OpenAI()
prompt = PromptTemplate(input_variables=["question"], template="Answer: {question}")
chain = LLMChain(llm=llm, prompt=prompt)
 
chain.run("What is AI?", callbacks=[handler])

// Vercel AI SDK with comprehensive tool tracing
import { wrapAISDK, wrapTraced } from "braintrust";
import * as ai from "ai";
import { openai } from "@ai-sdk/openai";
 
// Define tool functions
function getCurrentWeather(location: string) {
  return { temperature: 72, condition: "sunny", location };
}
 
function searchDatabase(query: string) {
  return { results: ["result1", "result2"], query };
}
 
const wrappedTools = {
  getCurrentWeather: wrapTraced(getCurrentWeather),
  searchDatabase: wrapTraced(searchDatabase),
};
 
const { generateText } = wrapAISDK(ai);
 
async function main() {
  const result = await generateText({
    model: openai("gpt-4"),
    tools: wrappedTools,
    experimental_telemetry: { isEnabled: true },
  });
 
  console.log(result.text);
}

Pattern 2: OpenTelemetry semantic conventions

Braintrust implements OpenTelemetry GenAI semantic conventions with automatic mapping:

Pattern 3: Manual OpenTelemetry tracing

For custom implementations, Braintrust supports manual trace creation:

import json
import os
 
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
 
# Configuration variables
api_key = os.environ.get("BRAINTRUST_API_KEY", "your-api-key")
project_name = "my-project"
 
# Configure OTLP exporter for Braintrust
exporter = OTLPSpanExporter(
    endpoint="https://api.braintrust.dev/otel/v1/traces",
    headers={"Authorization": f"Bearer {api_key}", "x-bt-parent": f"project_name:{project_name}"},
)
 
# Set up tracer
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(exporter))
 
tracer = trace.get_tracer(__name__)
 
# Example messages
messages = [{"role": "user", "content": "What is AI?"}]
 
# Create spans with GenAI semantic conventions
with tracer.start_as_current_span("llm_call") as span:
    span.set_attribute("gen_ai.request.model", "gpt-4")
    span.set_attribute("gen_ai.input.messages", json.dumps(messages))
    span.set_attribute("gen_ai.usage.prompt_tokens", 150)
    span.set_attribute("gen_ai.usage.completion_tokens", 75)

Framework-specific integration analysis

OpenTelemetry integration

Braintrust's OpenTelemetry implementation:

• Native exporter with automatic LLM span conversion using BraintrustSpanProcessor
• Support for both GenAI semantic conventions and custom Braintrust attributes
• Configurable span filtering with custom filter functions
• Direct OTLP endpoint integration at https://api.braintrust.dev/otel/v1/traces

Industry landscape: Most platforms require manual OpenTelemetry configuration and custom span processing. Braintrust's approach reduces setup complexity through automated LLM-specific attribute mapping, though teams using other platforms can achieve similar results with additional engineering effort.

Vercel AI SDK integration

Braintrust's Vercel AI SDK support:

• Native integration for both v4 and v5 implementations via wrapAISDK and wrapAISDKModel
• Automatic tool call tracing and execution monitoring
• Streaming function support for streamText and streamObject
• Zero-configuration setup for Next.js applications

Market context: While some platforms like Helicone offer Vercel AI SDK support, Braintrust provides the most comprehensive implementation with dedicated wrapper functions and automatic instrumentation. Most evaluation platforms lack native Vercel AI SDK integration, requiring custom implementation.

Agent framework integration

Braintrust's agent framework coverage:

• OpenAI Agents SDK: Specialized trace processors with @braintrust/openai-agents package
• LangGraph: Global callback handlers with automatic span creation
• Google ADK: Native integration through braintrust-adk with parallel execution tracking
• Mastra: Support for both v4 and v5 AI SDK implementations
• Pydantic AI: OpenTelemetry-based instrumentation with tool tracking

Competitive analysis: Leading platforms like Arize Phoenix and Comet Opik support established frameworks (LangChain, LlamaIndex, DSPy), while MLflow offers auto-tracing for several frameworks. Braintrust differentiates through support for newer agent architectures and specialized processors for emerging frameworks like Google ADK and Mastra.

Production deployment considerations

Braintrust's enterprise features

Performance and scalability:

• Asynchronous span processing with configurable batch sizes
• Intelligent span filtering to optimize overhead
• Global CDN distribution for reduced latency

Security and compliance:

• SOC2 Type II certified infrastructure
• GDPR compliant data handling with API-level and database-level control
• Configurable data retention policies with automated cleanup
• Hybrid deployment architecture with data plane isolation
• Self-hosting support via Terraform and Docker

LLM monitoring capabilities:

• Real-time performance dashboards for AI observability
• Integration with alerting systems (PagerDuty, Slack)
• Custom metric tracking for business KPIs

Implementation comparison:

// Braintrust - Minimal configuration
const { generateText } = wrapAISDK({ generateText });
 
// Alternative approaches typically require:
// - Custom instrumentation setup
// - Manual trace correlation
// - Framework-specific configuration

Integration best practices and implementation guidelines

1. Multi-framework architecture strategy

For teams using multiple AI frameworks, implement a unified observability approach:

// Unified configuration for mixed frameworks
import { initLogger, wrapAISDK, BraintrustCallbackHandler } from "braintrust";
import { setGlobalHandler } from "@braintrust/langchain-js";
 
const logger = initLogger({ projectName: "unified-ai-app" });
 
// Configure Vercel AI SDK
const { generateText } = wrapAISDK({ generateText });
 
// Configure LangChain/LangGraph
setGlobalHandler(new BraintrustCallbackHandler({ logger }));
 
// Configure OpenTelemetry for other frameworks
const spanProcessor = new BraintrustSpanProcessor({ logger });

2. Environment-specific configuration

Implement different monitoring strategies for development, staging, and production:

# Environment-aware configuration
import os
 
from braintrust.otel import BraintrustSpanProcessor
 
config = {
    "development": {
        "filter_ai_spans": False,  # Log everything for debugging
        "api_url": "https://api.braintrust.dev",
    },
    "production": {
        "filter_ai_spans": True,  # Only AI-related spans
        "custom_filter": lambda span: span.get_attribute("gen_ai.request.model") is not None,
    },
}
 
env = os.getenv("ENVIRONMENT", "development")
processor = BraintrustSpanProcessor(**config[env])

3. Cost optimization strategies

Implement intelligent span sampling for cost-effective monitoring:

// Intelligent sampling for cost optimization
const customFilter = (span: any) => {
  // Always log errors and high-value interactions
  if (span.status?.code === "ERROR") return true;
  if (span.attributes?.["user.tier"] === "premium") return true;
 
  // Sample non-critical spans at 10%
  return Math.random() < 0.1;
};
 
const processor = new BraintrustSpanProcessor({
  customFilter,
  filterAISpans: true,
});

Future-proofing your integration strategy

Emerging framework support

Braintrust's investment in emerging frameworks positions teams for future AI development:

• Mastra framework: Early adoption support for next-generation agent development
• Pydantic AI: Advanced structured output validation tracking
• OpenAI Agents SDK: Specialized evaluation for agentic workflows

API evolution compatibility

The platform's semantic convention approach ensures compatibility with evolving AI frameworks:

# Future-proof attribute mapping
span.set_attribute("braintrust.input_json", json.dumps(messages))
span.set_attribute("braintrust.metadata", json.dumps(metadata))
span.set_attribute("braintrust.metrics", json.dumps(usage_stats))

The Braintrust integration advantage: Technical summary

Comprehensive framework coverage

• 9+ native integrations vs. 2-3 for most competitors
• Production-grade implementations with enterprise reliability
• Zero-configuration philosophy reducing integration overhead
• Forward-looking framework support for emerging AI tools

Advanced technical capabilities

• OpenTelemetry semantic conventions with automatic LLM span mapping
• Intelligent span filtering for performance optimization
• Custom attribute support for business-specific metrics
• Comprehensive tool call tracing across all supported frameworks

Enterprise-grade features

• SOC2 Type II certification for security compliance
• Production reliability with hybrid deployment architecture
• Retention policies for data governance

The bottom line: In 2025, integration quality determines evaluation tool effectiveness. Braintrust's comprehensive integration ecosystem, technical depth, and production-grade reliability as an LLM evaluation platform enable teams to ship reliable AI applications faster, while alternatives often require significant engineering investment and ongoing maintenance. For teams serious about production AI, the integration advantage is clear and measurable.

FAQ

What makes integration capabilities so important for LLM evaluation tools?

Integration capabilities directly impact your team's velocity and adoption rates. Tools that integrate natively with your existing development stack—whether that's OpenTelemetry, Vercel AI SDK, or LangChain—eliminate the need to rewrite application code or maintain separate instrumentation. This reduces time-to-value from weeks to hours and ensures your evaluation infrastructure scales with your application development.

Which LLM evaluation platform has the most comprehensive integrations?

Braintrust provides the most extensive integration ecosystem, supporting 9+ major frameworks including OpenTelemetry, Vercel AI SDK, Agents SDK, Instructor, LangChain, LangGraph, Google ADK, Mastra, and Pydantic AI. This breadth of native integrations makes it the only platform capable of supporting diverse AI development stacks without requiring teams to compromise on their preferred frameworks.

How does Braintrust's Vercel AI SDK integration compare to alternatives?

Braintrust is the only platform offering native Vercel AI SDK integration through its dedicated wrapAISDK and wrapAISDKModel functions. This provides automatic logging, cost tracking, and evaluation for Next.js and React applications without code changes. The integration supports both v4 and v5 implementations, automatic tool call tracing, and streaming functions. Other platforms lack Vercel AI SDK support entirely.

What about OpenTelemetry integration—how do the platforms compare?

Braintrust offers the most sophisticated OpenTelemetry implementation with automatic LLM span conversion, zero-configuration setup, and support for both GenAI semantic conventions and custom Braintrust attributes. The platform provides configurable span processors with intelligent filtering and custom filter functions. Competitors typically require manual configuration and provide limited functionality compared to Braintrust's comprehensive observability capabilities.

How do the agent framework integrations differ between platforms?

Braintrust provides specialized integrations for multiple agent frameworks including OpenAI Agents SDK (with dedicated trace processors), LangGraph (global callback handlers), Google ADK (automatic tracing), Mastra (v4/v5 support), and Pydantic AI (OpenTelemetry instrumentation). Most competitors lack agent-specific support or provide only basic callback mechanisms without evaluation capabilities.

What's the difference between basic integration and Braintrust's native integration approach?

Basic integration typically means simple callback handlers or proxy-based logging that captures requests and responses. Braintrust's native integrations provide comprehensive observability including automatic cost tracking, latency monitoring, evaluation metrics, production alerting, tool call tracing, and framework-specific optimizations—all automatically configured for each framework's specific patterns and requirements.

How quickly can I get evaluation running with these integrations?

Integration speed varies significantly between platforms. Braintrust's native integrations typically enable full evaluation coverage in under an hour with automatic instrumentation and zero-configuration setup. For example, Vercel AI SDK integration requires just wrapping functions with wrapAISDK(). Platforms requiring manual setup or custom integration work can take days or weeks to achieve similar coverage.

Are there specific technical requirements for implementing these integrations?

Braintrust integrations have minimal technical requirements. Most frameworks require installing the appropriate SDK package (braintrust, @braintrust/openai-agents, etc.) and basic configuration. OpenTelemetry integration needs the braintrust[otel] package, while Vercel AI SDK just requires the core braintrust package. The platform handles complex technical details like span processing, attribute mapping, and trace correlation automatically.

How do these integrations handle complex multi-framework applications?

Braintrust's unified approach allows teams to use multiple frameworks with consistent observability. The platform provides a single logger that works across all integrations, automatic trace correlation between frameworks, and unified dashboards for mixed-framework applications. This eliminates the need for multiple monitoring tools as applications evolve.

What happens if I need to support multiple frameworks as my team grows?

Braintrust's comprehensive framework support means you can adopt new AI development tools without changing evaluation platforms. The unified configuration approach allows teams to gradually add new frameworks while maintaining consistent monitoring. Platforms with limited integrations may force you to either restrict framework choices or manage multiple evaluation tools as your stack evolves.