@contextcompany/custom

The @contextcompany/custom package provides full control over instrumentation for AI agents that don’t use standard frameworks. It supports two patterns: builder pattern (instrument as you go) and factory pattern (send pre-built data).

Installation

npm install @contextcompany/custom

Quick Start

Builder Pattern
Factory Pattern

import { run } from '@contextcompany/custom';

const r = run({ sessionId: 'session_123' });
r.prompt('What is the weather in SF?');

// Create a step for LLM call
const s = r.step();
s.prompt(JSON.stringify(messages));
s.response(assistantContent);
s.model('gpt-4o');
s.tokens({ uncached: 120, cached: 30, completion: 45 });
s.end();

r.response('72°F and sunny.');
await r.end();

import { sendRun } from '@contextcompany/custom';

await sendRun({
  prompt: { user_prompt: 'What is the weather in SF?' },
  response: '72°F and sunny.',
  startTime: new Date('2025-01-01T00:00:00Z'),
  endTime: new Date('2025-01-01T00:00:01Z'),
  steps: [{
    prompt: JSON.stringify(messages),
    response: assistantContent,
    model: 'gpt-4o',
    tokens: { uncached: 120, cached: 30, completion: 45 },
    startTime: new Date('2025-01-01T00:00:00Z'),
    endTime: new Date('2025-01-01T00:00:01Z'),
  }],
});

Configuration

configure

Set global SDK options that apply to all runs.

function configure(options: ClientConfig): void

options

ClientConfig

required

Show properties

apiKey

string

API key for authentication. Overrides TCC_API_KEY environment variable. Keys starting with dev_ automatically route to development endpoint.

debug

boolean

Enable debug logging to console. Overrides TCC_DEBUG environment variable.

url

string

Custom ingestion endpoint URL. Overrides TCC_URL environment variable.

runTimeout

number

Default auto-flush timeout (in milliseconds) for all new runs. Default: 1,200,000 (20 minutes). Can be overridden per-run.

Example:

import { configure } from '@contextcompany/custom';

configure({
  apiKey: 'tcc_abc123',
  debug: true,
  runTimeout: 600000, // 10 minutes
});

Builder Pattern API

run

Create a new Run builder. This is the main entry point for incremental instrumentation.

function run(options?: RunOptions): Run

options

RunOptions

Show properties

runId

string

Custom run identifier. Defaults to a random UUID.

sessionId

string

Group multiple runs into a logical session (e.g., a single user conversation). Runs sharing a sessionId appear together in the dashboard timeline.

conversational

boolean

Mark this run as part of a multi-turn conversation. When true, the dashboard links sequential runs in the same session.

startTime

Date

Override the run start time. Defaults to new Date().

timeout

number

Auto-flush timeout in milliseconds. If the run is not ended within this duration, it will be automatically flushed with an error status. Set to 0 to disable. Default: 1,200,000 (20 minutes).

Run

A Run builder instance with the following methods.

Run Methods

prompt

Set the user prompt/input that initiated the run.

run.prompt(input: string | { user_prompt: string; system_prompt?: string }): Run

input

string | object

required

User prompt as a string, or an object with user_prompt and optional system_prompt.

Example:

r.prompt('What is the weather?');
// or
r.prompt({ 
  user_prompt: 'Summarize this', 
  system_prompt: 'You are a helpful assistant.' 
});

response

Set the agent’s final response to the user.

run.response(text: string): Run

text

string

required

The final response text.

metadata

Attach arbitrary key-value metadata to the run. Multiple calls are merged together.

run.metadata(...entries: Record<string, string>[]): Run

entries

Record<string, string>[]

required

One or more metadata objects to merge. Values must be strings.

Example:

r.metadata({ agent: 'weather-bot', version: '1.2.0' });

status

Set the outcome status code and optional message.

run.status(code: number, message?: string): Run

code

number

required

Status code: 0 for success, 2 for error.

message

string

Human-readable status message (e.g., error description).

endTime

Override the run’s end time (defaults to automatic capture when .end() is called).

run.endTime(date: Date): Run

step

Create a new Step attached to this run.

run.step(stepIdOrOptions?: string | StepOptions): Step

stepIdOrOptions

string | StepOptions

A custom step ID string, or a StepOptions object. Omit to auto-generate an ID.

Step

A Step builder instance. See Step Methods.

toolCall

Create a new ToolCall attached to this run.

run.toolCall(nameOrOptions?: string | ToolCallOptions): ToolCall

nameOrOptions

string | ToolCallOptions

A tool name string, or a ToolCallOptions object.

ToolCall

A ToolCall builder instance. See ToolCall Methods.

end

Finalize the run and send the payload (including all attached steps and tool calls) in a single batch request.

await run.end(): Promise<void>

Throws if:

The run has already been ended
.prompt() was not called
Any attached steps or tool calls have not been ended

error

End the run with error status (2) and send the payload. Any un-ended child steps and tool calls are automatically marked as errored.

await run.error(message?: string): Promise<void>

message

string

Optional error message.

Step Methods

A Step represents a single LLM invocation within a Run.

prompt

Set the prompt sent to the LLM.

step.prompt(text: string): Step

text

string

required

The prompt text (typically serialized messages).

response

Set the LLM’s response text.

step.response(text: string): Step

text

string

required

The response text from the LLM.

model

Set the model used for this step.

step.model(config: string | { requested?: string; used?: string }): Step

config

ModelConfig

required

Pass a string when requested and used model are the same, or an object to distinguish them.

Example:

s.model('gpt-4o');
// or
s.model({ requested: 'gpt-4o', used: 'gpt-4o-2024-08-06' });

tokens

Record token usage for this step. Multiple calls are merged.

step.tokens(usage: TokenUsage): Step

usage

TokenUsage

required

Show properties

uncached

number

Number of prompt tokens that were not served from cache.

cached

number

Number of prompt tokens served from cache (e.g., prompt caching).

completion

number

Number of tokens generated by the model in the completion.

Example:

s.tokens({ uncached: 120, cached: 30, completion: 45 });

cost

Set the actual cost of this step in USD.

step.cost(amount: number): Step

amount

number

required

Cost in USD.

finishReason

Set the model’s finish/stop reason.

step.finishReason(reason: string): Step

reason

string

required

Finish reason (e.g., "stop", "length", "tool_calls").

toolDefinitions

Set the tool definitions/function schemas available to the model during this step.

step.toolDefinitions(defs: string | unknown[]): Step

defs

string | unknown[]

required

A JSON string or an array of tool definition objects (auto-serialized).

status

Set the outcome status code and optional message.

step.status(code: number, message?: string): Step

code

number

required

Status code: 0 for success, 2 for error.

endTime

Override the step’s end time.

step.endTime(date: Date): Step

end

Finalize this step. Both .prompt() and .response() must have been called.

step.end(): void

Throws if the step has already been ended, or if .prompt() or .response() was not called.

error

Mark this step as errored (status code 2).

step.error(message?: string): void

ToolCall Methods

A ToolCall represents a single tool/function invocation within a Run.

name

Set the tool name.

toolCall.name(toolName: string): ToolCall

toolName

string

required

The tool name (e.g., "search", "get_weather").

args

Set the arguments passed to the tool.

toolCall.args(value: string | Record<string, unknown>): ToolCall

value

string | object

required

A JSON string or a plain object (auto-serialized).

Example:

tc.args({ city: 'San Francisco' });

result

Set the return value from the tool.

toolCall.result(value: string | Record<string, unknown>): ToolCall

value

string | object

required

A JSON string or a plain object (auto-serialized).

Example:

tc.result({ temp: 72, unit: 'F' });

status

Set the outcome status code and optional message.

toolCall.status(code: number, message?: string): ToolCall

endTime

Override the tool call’s end time.

toolCall.endTime(date: Date): ToolCall

end

Finalize this tool call. A tool name must have been set.

toolCall.end(): void

Throws if the tool call has already been ended, or if no tool name was set.

error

Mark this tool call as errored (status code 2).

toolCall.error(message?: string): void

Factory Pattern API

sendRun

Send a complete run (with optional nested steps and tool calls) in a single request. Use this when all data is already available.

async function sendRun(input: RunInput): Promise<void>

input

RunInput

required

Show properties

runId

string

Custom run identifier. Defaults to a random UUID.

sessionId

string

Session identifier to group related runs.

conversational

boolean

Whether this run is part of a multi-turn conversation.

prompt

object

required

The user prompt/input that initiated the run.

Show properties

user_prompt

string

required

The user’s input.

system_prompt

string

Optional system prompt.

response

string

The agent’s final response.

startTime

Date | string

required

When the run started (ISO-8601 string or Date).

endTime

Date | string

required

When the run finished (ISO-8601 string or Date).

statusCode

number

Outcome status code. 0 for success (default), 2 for error.

statusMessage

string

Human-readable status/error message.

metadata

Record<string, string>

Arbitrary key-value metadata.

steps

StepInput[]

LLM steps that occurred during the run. See StepInput.

toolCalls

ToolCallInput[]

Tool calls that occurred during the run. See ToolCallInput.

Example:

await sendRun({
  prompt: { user_prompt: 'What\'s the weather?' },
  response: '72°F in SF',
  startTime: new Date('2025-01-01T00:00:00Z'),
  endTime: new Date('2025-01-01T00:00:01Z'),
  steps: [{
    prompt: '...',
    response: '...',
    model: 'gpt-4o',
    startTime: new Date('2025-01-01T00:00:00Z'),
    endTime: new Date('2025-01-01T00:00:01Z'),
  }],
});

sendStep

Send a single step independently. Requires runId.

async function sendStep(input: StepInput & { runId: string }): Promise<void>

input

StepInput & { runId: string }

required

Show properties

runId

string

required

The parent run identifier.

stepId

string

Custom step identifier. Defaults to a random UUID.

prompt

string

required

The prompt sent to the LLM.

response

string

required

The LLM’s response.

startTime

Date | string

required

When the step started.

endTime

Date | string

required

When the step finished.

statusCode

number

Status code: 0 for success (default), 2 for error.

model

ModelConfig

Which model was used. String or object with requested and used.

finishReason

string

The model’s finish/stop reason.

tokens

TokenUsage

Token usage breakdown.

cost

number

Actual cost of this step in USD.

toolDefinitions

string | unknown[]

Tool definitions available to the model.

Example:

await sendStep({
  runId: 'run_abc',
  prompt: '...',
  response: '...',
  model: 'gpt-4o',
  startTime: new Date(),
  endTime: new Date(),
});

sendToolCall

Send a single tool call independently. Requires runId.

async function sendToolCall(input: ToolCallInput & { runId: string }): Promise<void>

input

ToolCallInput & { runId: string }

required

Show properties

runId

string

required

The parent run identifier.

toolCallId

string

Custom tool call identifier. Defaults to a random UUID.

name

string

required

The name of the tool that was invoked.

startTime

Date | string

required

When the tool call started.

endTime

Date | string

required

When the tool call finished.

statusCode

number

Status code: 0 for success (default), 2 for error.

args

string | Record<string, unknown>

Arguments passed to the tool.

result

string | Record<string, unknown>

Return value from the tool.

Example:

await sendToolCall({
  runId: 'run_abc',
  name: 'search',
  args: { query: 'weather' },
  result: { temp: 72 },
  startTime: new Date(),
  endTime: new Date(),
});

submitFeedback

Submit user feedback for a specific run.

function submitFeedback(params: {
  runId: string;
  score?: "thumbs_up" | "thumbs_down";
  text?: string;
}): Promise<Response | undefined>

params

object

required

Show properties

runId

string

required

The unique identifier of the run to attach feedback to.

score

"thumbs_up" | "thumbs_down"

Feedback score as thumbs up or thumbs down.

text

string

Text feedback from the user. Maximum 2000 characters.

Environment Variables

TCC_API_KEY

string

required

Your Context Company API key. Get one from the dashboard.

TCC_URL

string

Custom ingestion endpoint URL. Overrides the default endpoint.

TCC_DEBUG

boolean

Enable debug logging. Set to true or 1.

Next Steps

View Dashboard

View your instrumented runs in the dashboard

Metadata

Learn about adding metadata to traces

TypeScript

Python

​Installation

​Quick Start

​Configuration

​configure

​Builder Pattern API

​run

​Run Methods

​prompt

​response

​metadata

​status

​endTime

​step

​toolCall

​end

​error

​Step Methods

​prompt

​response

​model

​tokens

​cost

​finishReason

​toolDefinitions

​status

​endTime

​end

​error

​ToolCall Methods

​name

​args

​result

​status

​endTime

​end

​error

​Factory Pattern API

​sendRun

​sendStep

​sendToolCall

​submitFeedback

​Environment Variables

​Next Steps

View Dashboard

Metadata

Build docs developers (and LLMs) love

Installation

Quick Start

Configuration

configure

Builder Pattern API

run

Run Methods

prompt

response

metadata

status

endTime

step

toolCall

end

error

Step Methods

prompt

response

model

tokens

cost

finishReason

toolDefinitions

status

endTime

end

error

ToolCall Methods

name

args

result

status

endTime

end

error

Factory Pattern API

sendRun

sendStep

sendToolCall

submitFeedback

Environment Variables

Next Steps