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Maxim SDK

This is JS/TS SDK for enabling Maxim observability. Maxim is an enterprise grade evaluation and observability platform.

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How to integrate

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Install

npm install @maximai/maxim-js

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Initialize Maxim logger

const maxim = new Maxim({ apiKey: "maxim-api-key" });
const logger = await maxim.logger({ id: "log-repository-id" });

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Start sending traces

// Start a trace
logger.trace({ id: "trace-id" });
// Add a span
logger.traceSpan("trace-id", { id: "span-id", name: "Intent detection service" });
// Add llm call to this span
const generationId = uuid();
logger.spanGeneration("span-id", {
	id: generationId,
	name: "test-inference",
	model: "gpt-3.5-turbo-16k",
	messages: [
		{
			role: "user",
			content: "Hello, how are you?",
		},
	],
	modelParameters: {
		temperature: 3,
	},
	provider: "openai",
});
// Make the actual call to the LLM
const result = llm_call();
// Log back the result
logger.generationResult(generationId, result);
// Ending span
logger.spanEnd("span-id");
// Ending trace
logger.traceEnd("trace-id");

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Integrations with other frameworks

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LangChain

You can use the built-in MaximLangchainTracer to integrate Maxim observability with your LangChain and LangGraph applications.

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Installation

The LangChain integration is available as an optional dependency. Install the required LangChain package:

npm install @langchain/core

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⚡ 2-Line Integration

Add comprehensive observability to your existing LangChain code with just 2 lines:

const maximTracer = new MaximLangchainTracer(logger);
const result = await chain.invoke(input, { callbacks: [maximTracer] });

That’s it! No need to modify your existing chains, agents, or LLM calls.

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Complete Setup Example

import { MaximLangchainTracer } from "@maximai/maxim-js";
import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";

// Initialize Maxim (standard setup)
const maxim = new Maxim({ apiKey: "your-maxim-api-key" });
const logger = await maxim.logger({ id: "your-log-repository-id" });

// Step 1: Create the tracer
const maximTracer = new MaximLangchainTracer(logger);

// Your existing LangChain code remains unchanged
const prompt = ChatPromptTemplate.fromTemplate("What is {topic}?");
const model = new ChatOpenAI({ model: "gpt-3.5-turbo" });
const chain = prompt.pipe(model);

// Step 2: Add tracer to your invoke calls
const result = await chain.invoke({ topic: "AI" }, { callbacks: [maximTracer] });

// Alternative: Attach permanently to the chain
const chainWithTracer = chain.withConfig({ callbacks: [maximTracer] });
const result2 = await chainWithTracer.invoke({ topic: "machine learning" });

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LangGraph Integration

import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { MemorySaver } from "@langchain/langgraph";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

// Create a simple tool
const searchTool = tool(
	async ({ query }) => {
		// Your tool implementation
		return `Search results for: ${query}`;
	},
	{
		name: "search",
		schema: z.object({
			query: z.string(),
		}),
		description: "Search for information",
	},
);

// Create LangGraph agent
const checkpointer = new MemorySaver();
const agent = createReactAgent({
	llm: model,
	tools: [searchTool],
	checkpointSaver: checkpointer,
});

// Use the tracer with your graph
const result = await agent.invoke(
	{ messages: [{ role: "user", content: "Hello!" }] },
	{
		callbacks: [maximTracer],
		configurable: { thread_id: "conversation-1" },
	},
);

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What gets tracked

The MaximLangchainTracer automatically captures:

• Traces: Top-level executions with input/output
• Spans: Chain executions (sequences, parallel operations, etc.)
• Generations: LLM calls with messages, model parameters, and responses
• Retrievals: Vector store and retriever operations
• Tool Calls: Function/tool executions
• Errors: Failed operations with error details

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Supported Providers

The tracer automatically detects and supports:

• OpenAI (including Azure OpenAI)
• Anthropic
• Google (Vertex AI, Gemini)
• Amazon Bedrock
• Hugging Face
• Together AI
• Groq
• And more…

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Custom Metadata

You can pass custom metadata through LangChain’s metadata system to customize how your operations appear in Maxim. All Maxim-specific metadata should be nested under the maxim key:

const result = await chain.invoke(
	{ topic: "AI" },
	{
		callbacks: [maximTracer],
		metadata: {
			maxim: {
				// Your Maxim-specific metadata here
			},
		},
	},
);

Available Metadata Fields

Entity Naming:

• traceName - Override the default trace name
• chainName - Override the default chain/span name
• generationName - Override the default LLM generation name
• retrievalName - Override the default retrieval operation name
• toolCallName - Override the default tool call name

Entity Tagging:

• traceTags - Add custom tags to the trace (object: {key: value})
• chainTags - Add custom tags to chains/spans (object: {key: value})
• generationTags - Add custom tags to LLM generations (object: {key: value})
• retrievalTags - Add custom tags to retrieval operations (object: {key: value})
• toolCallTags - Add custom tags to tool calls (object: {key: value})

ID References (for linking to existing traces/sessions):

• sessionId - Link this trace to an existing session
• traceId - Use a specific trace ID
• spanId - Use a specific span ID

Complete Example

const result = await chain.invoke(
	{ query: "What is machine learning?" },
	{
		callbacks: [maximTracer],
		metadata: {
			maxim: {
				// Custom names for better organization
				traceName: "ML Question Answering",

				// Custom tags for filtering and analytics
				traceTags: {
					category: "educational",
					priority: "high",
					version: "v2.1",
				},

				// Link to existing session (optional)
				sessionId: "user_session_123",
			},
			// You can also include non-Maxim metadata
			user_id: "user_123",
			request_id: "req_456",
		},
	},
);

Per-Component Examples

For LLM calls:

const llmResult = await model.invoke("Explain quantum computing", {
	callbacks: [maximTracer],
	metadata: {
		maxim: {
			generationName: "Quantum Computing Explanation",
			generationTags: {
				topic: "quantum_computing",
				difficulty: "advanced",
				model: "gpt-4",
			},
		},
	},
});

For retrievers:

const docs = await retriever.invoke("machine learning algorithms", {
	callbacks: [maximTracer],
	metadata: {
		maxim: {
			retrievalName: "ML Algorithm Search",
			retrievalTags: {
				index_name: "ml_papers",
				search_type: "semantic",
				top_k: "5",
			},
		},
	},
});

For tool calls:

const toolResult = await tool.invoke(
	{ query: "weather in NYC" },
	{
		callbacks: [maximTracer],
		metadata: {
			maxim: {
				toolCallName: "Weather API Lookup",
				toolCallTags: {
					api: "openweather",
					location: "NYC",
					units: "metric",
				},
			},
		},
	},
);

Notes

• Automatic fallbacks: If you don’t provide custom names, the tracer uses sensible defaults based on the LangChain component names
• Session linking: Use sessionId to group multiple traces under the same user session for better analytics

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Legacy Langchain Integration

For projects still using our separate package Maxim Langchain Tracer (now deprecated in favor of the built-in tracer above), you can use our built-in tracer as is by just replacing the import and installing @langchain/core.

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Version changelog

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v6.5.0

• ⚠️ BREAKING CHANGES:

  • Prompt.messages type changed: The messages field type has been updated for better type safety

    • Before: { role: string; content: string | CompletionRequestContent[] }[]
    • After: (CompletionRequest | ChatCompletionMessage)[]
    • Migration: Update your code to use the new CompletionRequest interface which has more specific role types ("user" | "system" | "tool" | "function") instead of generic string
    Copy

    Ask AI

    // Before (v6.4.x and earlier)
    const messages: { role: string; content: string }[] = [{ role: "user", content: "Hello" }];
    
    // After (v6.5.0+)
    const messages: CompletionRequest[] = [
    	{ role: "user", content: "Hello" }, // role is now type-safe
    ];
    

  • GenerationConfig.messages type changed: For better type safety and tool call support

    • Before: messages: CompletionRequest[]
    • After: messages: (CompletionRequest | ChatCompletionMessage)[]
    • Migration: Your existing CompletionRequest[] arrays will still work, but you can now also pass ChatCompletionMessage[] for assistant responses with tool calls

  • Generation.addMessages() method signature changed:

    • Before: addMessages(messages: CompletionRequest[])
    • After: addMessages(messages: (CompletionRequest | ChatCompletionMessage)[])
    • Migration: Your existing calls will still work, but you can now also pass assistant messages with tool calls

  • MaximLogger.generationAddMessage() method signature changed:

    • Before: generationAddMessage(generationId: string, messages: CompletionRequest[])
    • After: generationAddMessage(generationId: string, messages: (CompletionRequest | ChatCompletionMessage)[])
    • Migration: Your existing calls will still work, but you can now also pass assistant messages with tool calls

• feat: Added LangChain integration with MaximLangchainTracer

  • Comprehensive tracing support for LangChain and LangGraph applications
  • Automatic detection of 8+ LLM providers (OpenAI, Anthropic, Google, Bedrock, etc.)
  • Support for chains, agents, retrievers, and tool calls
  • Custom metadata and tagging capabilities
  • Added @langchain/core as optional dependency

• feat: Enhanced prompt and prompt chain execution capabilities

  • NEW METHOD: Prompt.run(input, options?) - Execute prompts directly from Prompt objects
  • NEW METHOD: PromptChain.run(input, options?) - Execute prompt chains directly from PromptChain objects
  • Support for image URLs when running prompts via ImageUrl type
  • Support for variables in prompt execution

• feat: New types and interfaces for improved type safety

  • NEW TYPE: PromptResponse - Standardized response format for prompt executions
  • NEW TYPE: AgentResponse - Standardized response format for prompt chain executions
  • ENHANCED TYPE: ChatCompletionMessage - More specific interface for assistant messages with tool call support
  • ENHANCED TYPE: CompletionRequest - More specific interface with type-safe roles
  • NEW TYPE: Choice, Usage - Supporting types for response data with token usage
  • NEW TYPE: ImageUrl - Type for image URL content in prompts (extracted from CompletionRequestImageUrlContent)
  • NEW TYPE: AgentCost, AgentUsage, AgentResponseMeta - Supporting types for agent responses

• feat: Test run improvements with prompt chain support

  • Enhanced test run execution with cost and usage tracking for prompt chains
  • Support for prompt chains alongside existing prompt and workflow support
  • NEW METHOD: TestRunBuilder.withPromptChainVersionId(id, contextToEvaluate?) - Add prompt chain to test runs

• feat: Enhanced exports for better developer experience

  • NEW EXPORT: MaximLangchainTracer - Main LangChain integration class
  • NEW EXPORTS: ChatCompletionMessage, Choice, CompletionRequest, PromptResponse - Core types now available for external use
  • Enhanced type safety and IntelliSense support for prompt handling

• feat: Standalone package configuration

  • MIGRATION: Moved from NX monorepo to standalone package (internal change, no user action needed)
  • Added comprehensive build, test, and lint scripts
  • Updated TypeScript configuration for ES2022 target
  • Added Prettier and ESLint configuration files
  • NEW EXPORT: VariableType from dataset models

• deps: LangChain ecosystem support (all optional)

  • NEW OPTIONAL: @langchain/core as optional dependency (^0.3.0) - only needed if using MaximLangchainTracer

Migration Guide for v6.5.0:

1. If you access Prompt.messages directly: Update your type annotations to use CompletionRequest | ChatCompletionMessage types
2. If you create custom prompt objects: Ensure your messages array uses the new interface structure
3. If you use Generation.addMessages(): The method now accepts (CompletionRequest | ChatCompletionMessage)[] - your existing code will work unchanged
4. If you use MaximLogger.generationAddMessage(): The method now accepts (CompletionRequest | ChatCompletionMessage)[] - your existing code will work unchanged
5. If you create GenerationConfig objects: The messages field now accepts (CompletionRequest | ChatCompletionMessage)[] - your existing code will work unchanged
6. To use LangChain integration: Install @langchain/core and import MaximLangchainTracer
7. No action needed for: Regular SDK usage through maxim.logger(), test runs, or prompt management APIs

⚠️ Note: While these are technically breaking changes at the type level, most existing code will continue to work because CompletionRequest[] is compatible with (CompletionRequest | ChatCompletionMessage)[]. You may only see TypeScript compilation errors if you have strict type checking enabled.

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v6.4.0

• feat: adds provider field to the Prompt type. This field specifies the LLM provider (e.g., ‘openai’, ‘anthropic’, etc.) for the prompt.
• feat: include Langchain integration in the main repository

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v6.3.0

• feat: adds attachments support to Trace, Span, and Generation for file uploads.
  • 3 attachment types are supported: file path, buffer data, and URL
  • has auto-detection of MIME types, file sizes, and names for attachments wherever possible
• fix: refactored message handling for Generations to prevent keeping messages reference but rather duplicates the object to ensure point in time capture.
• fix: ensures proper cleanup of resources

Adding attachments

// Add file as attachment
entity.addAttachment({
	id: uuid(),
	type: "file",
	path: "/path/to/file.pdf",
});

// Add buffer data as attachment
const buffer = fs.readFileSync("image.png");
entity.addAttachment({
	id: uuid(),
	type: "fileData",
	data: buffer,
});

// Add URL as attachment
entity.addAttachment({
	id: uuid(),
	type: "url",
	url: "https://example.com/image.jpg",
});

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v6.2.2

• fix: Added support for OpenAI’s logprobs output in generation results (ChatCompletionResult and TextCompletionResult).

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v6.2.1

• fix: Refactored message handling in Generation class to prevent duplicate messages

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v6.2.0

• chore: Adds maximum payload limit to push to the server
• chore: Adds max in-memory size of the queue for pending commit logs. Beyond that limit, writer automatically flushes logs to the server

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v6.1.8

• Feat: Adds new error component
• Chore: Adds ID validator for each entity. It will spit out error log or exception based on raiseExceptions flag.

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v6.1.7

• Feat: Adds trace.addToSession method for attaching trace to a new session

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v6.1.6

• Fix: minor bug fixes around queuing of logs.

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v6.1.5

• Fix: updates create test run api to use v2 api

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v6.1.4

• Fix: Handles marking test run as failed if the test run throws at any point after creating it on the platform.
• Feat: Adds support for contextToEvaluate in withPromptVersionId and withWorkflowId (by passing it as the second parameter) to be able to choose whichever variable or dataset column to use as context to evaluate, as opposed to only having the dataset column as context through the CONTEXT_TO_EVALUATE datastructure mapping.

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v6.1.3

• Feat: Adds createCustomEvaluator and createCustomCombinedEvaluatorsFor for adding custom evaluators to add them to the test runs.
• Feat: Adds withCustomLogger to the test run builder chain to have a custom logger that follows the TestRunLogger interface.
• Feat: Adds createDataStructure function to create a data struture outside the test run builder. This is done to help use the data structure to infer types outside the test run builder.
• Feat: Adds withWorkflowId and withPromptVersionId to the test run builder chain.

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v6.1.2

• Fix: makes eventId mandatory while logging an event.
• Feat: adds addMetadata method to all log components for tracking any additional metadata.
• Feat: adds evaluate method to Trace, Span, Generation and Retrieval classes for agentic (or node level) evaluation.

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v6.1.1

• Feat: Adds support for tool_calls as a separate entity.

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v6.1.0

• Change: Adds a new config parameter raiseExceptions to control exceptions thrown by the SDK. Default value is false.
• getPrompt(s), getPromptChain(s) and getFolder(s) could return undefined if raiseExceptions is false.

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v6.0.4

• Change: Prompt management needs to be enabled via config.
• Chore: On multiple initializations of the SDK, SDK will warn the user. This start throwing exceptions in future releases.

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v6.0.3

• Chore: removed optional dependencies

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v6.0.2

• Feat: Adds a new logger.flush method to explicitly flushing logs

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v6.0.1

• Fix: fixes logger cleanup

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v6.0.0

• Feat: Jinja 2.0 variables support

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v5.2.6

• Fix: fixes incorrect message format for openai structured output params

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v5.2.5

• Fix: fixes incorrect mapping of messages for old langchain sdk

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v5.2.4

• Fix: config fixes for static classes

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v5.2.3

• Improvement: Adds AWS lambda support for Maxim SDK.

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v5.2.2

• Fix: There was a critical bug in the implementation of HTTP POST calls where some of the payloads were getting truncated.

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v5.2.1

• Fix: For ending any entity, we make sure endTimestamp is captured from client side. This was not the case earlier in some scenarios.
• Fix: Data payload will always be a valid JSON

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v5.2.0

• Improvement: Adds exponential retries to the API calls to Maxim server.

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v5.1.2

• Improvement: Readme updates.

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v5.1.1

• Improvement: Detailed logs in debug mode

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v5.1.0

• Adds scaffold to support LangchainTracer for Maxim SDK.

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v5.0.3

• Exposes MaximLogger for writing wrappers for different developer SDKs.

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v5.0.2

• Adds more type-safety for generation messages

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v5.0.1

• Adds support input/output for traces

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v5.0.0

• Adds support for node 12+

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V4.0.2

• Fixed a critical bug related to pushing generation results to the Maxim platform
• Improved error handling for network connectivity issues
• Enhanced performance when logging large volumes of data

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V4.0.1

• Adds retrieval updates
• Adds ChatMessage support

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v4.0.0 (Breaking changes)

• Adds prompt chain support
• Adds vision model support for prompts

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v3.0.7

• Adds separate error reporting method for generations

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v3.0.6

• Adds top level methods for easier SDK integration

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v3.0.5

• Fixes logs push error

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v3.0.4

• Minor bug fixes

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v3.0.3

• Updates default base url

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v3.0.2

• Prompt selection algorithm v2

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v3.0.1

• Minor bug fixes

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v3.0.0

• Moves to new base URL
• Adds all new logging support

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v2.1.0

• Adds support for adding dataset entries via SDK.

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v2.0.0

• Folders, Tags and advanced filtering support.
• Add support for customizing default matching algorithm.

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v1.1.0

• Adds realtim sync for prompt deployment.

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v1.0.0

• Adds support for deployment variables and custom fields. [Breaking change from earlier versions.]

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v0.5.0

• Adds support for new SDK apis.

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v0.4.0

• Adds support for custom fields for Prompts.