> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bitfab.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# TypeScript SDK
> Generate evaluations and iterate on your AI applications
The Bitfab TypeScript SDK captures your AI function calls to automatically generate evaluations. Re-run your prompts with different models, parameters, and inputs to iterate faster.
## Installation
```bash theme={null}
# npm
npm install @bitfab/sdk
# pnpm
pnpm add @bitfab/sdk
# yarn
yarn add @bitfab/sdk
```
## Quick Start
```typescript theme={null}
import { Bitfab } from "@bitfab/sdk"
const bitfab = new Bitfab({ apiKey: process.env.BITFAB_API_KEY })
```
Need an API key? Get one from the [Bitfab dashboard](https://bitfab.ai/setup) or see the [API Keys guide](/api-keys) for detailed setup instructions.
Copy this prompt into your coding agent (tested with Cursor and Claude Code using Sonnet 4.5):
```text theme={null}
Modify existing TypeScript code to add Bitfab tracing.
Do NOT browse or web search. Use ONLY the API described below.
Bitfab TypeScript SDK (authoritative excerpt):
- Install: `npm install @bitfab/sdk` or `pnpm add @bitfab/sdk`
- Init:
import { Bitfab } from "@bitfab/sdk"
const bitfab = new Bitfab({ apiKey: process.env.BITFAB_API_KEY })
- Framework integrations:
If the codebase uses LangGraph or LangChain (`@langchain/langgraph`, `@langchain/core`, or other `@langchain/*` packages), use the callback handler instead of manually wrapping graph nodes, tools, retrievers, or model calls:
const handler = bitfab.getLangGraphCallbackHandler("")
await graph.invoke(input, { callbacks: [handler] })
For plain LangChain chains, `getLangChainCallbackHandler("")` is an identical alias. The handler records a replayable root from the framework input, so no outer `withSpan` root is needed when the workflow is just the graph/chain invocation. Add a same-key outer root only for meaningful surrounding application work.
- Manual instrumentation (when no framework handler applies, or for meaningful work around a framework call):
// Declare trace function key once
const myService = bitfab.getFunction("")
// Wrap functions with withSpan
const tracedFn = myService.withSpan(originalFunction)
// Or with options:
const tracedFn = myService.withSpan({ name: "DisplayName", type: "function" }, originalFunction)
// Span types: "llm", "agent", "function", "guardrail", "handoff", "custom"
- DO NOT modify the original function.
- DO NOT extract helper methods.
Task:
1) Ensure bitfab is installed and initialization exists.
2) Read the codebase and identify ALL AI workflows (LLM calls, agent runs, AI-driven decisions). Check for LangGraph/LangChain before planning manual instrumentation.
3) Present me with a numbered list of workflows you found. For each, describe:
- What it does
- Why it's worth instrumenting -- what visibility tracing gives you into each step
4) After I choose which workflow(s) to instrument:
- If it uses LangGraph/LangChain, add the Bitfab callback handler to the framework invoke config instead of wrapping framework-managed internals. Use `getFunction("").getLangGraphCallbackHandler()` only when a same-key outer `withSpan` root is needed for surrounding application work.
- For non-framework workflows, create a function wrapper with `bitfab.getFunction("")`
- Wrap non-framework functions with `myService.withSpan(originalFunction)`
- Instrument intermediate steps (not just the final output) so each trace has enough context to diagnose issues
- Replace usages of manually wrapped non-framework functions with the traced versions
5) Do not change function signature, behavior, or return value. Minimal diff.
Output:
- First: your numbered list of workflows with why each is worth instrumenting
- After my selection: minimal diffs for dependencies, initialization, and the function wrapping
```
## Basic Configuration
```typescript theme={null}
new Bitfab({ apiKey: string })
// Omit apiKey entirely: the SDK reads BITFAB_API_KEY from the environment
new Bitfab({})
// Disable tracing (functions still execute, but no spans are sent)
new Bitfab({ apiKey: string, enabled: false })
```
**Missing API key doesn't crash.** If the API key is missing, empty, or whitespace-only, the SDK automatically disables tracing and logs a one-time warning at first use. All wrapped functions still execute normally -- no spans are sent, no errors are thrown. You don't need any conditional logic around the API key.
### API key resolution
The key is resolved **lazily, the first time a span runs**, not when the client is constructed. This matters in scripts: with ES modules, an `import` that constructs the client is evaluated *before* the importing file's body runs `dotenv.config()`, so a key read at construction would be empty even though it is set moments later. Resolving at first use reads the key after env loading has happened.
```typescript theme={null}
// Pass a function to defer resolution explicitly (resolved at first use):
new Bitfab({ apiKey: () => process.env.BITFAB_API_KEY })
```
When no key is passed (or it resolves empty), the SDK falls back to reading `BITFAB_API_KEY` from the environment, again at first use. For standalone scripts where a run that emits no traces should be treated as a failure rather than silently skipped, set `strict`:
```typescript theme={null}
// Throws on the first traced call if no key resolves, instead of disabling quietly
new Bitfab({ apiKey: process.env.BITFAB_API_KEY, strict: true })
```
If you load env with dotenv in a script, prefer loading it before the module graph is evaluated, for example `node --env-file=.env script.ts`, so every module-level read sees the key.
## Tracing
### Custom (Recommended)
#### Using `getFunction()` to Link Spans
Declare the trace function key once and wrap multiple functions:
```typescript theme={null}
const orderService = bitfab.getFunction("order-processing")
async function processOrder(orderId: string) {
return { orderId }
}
async function validateOrder(orderId: string) {
return { valid: true }
}
// Wrap functions - all share the same trace function key
const tracedProcessOrder = orderService.withSpan(processOrder)
const tracedValidateOrder = orderService.withSpan(validateOrder)
```
#### Multi-File Projects
For projects with instrumented functions spread across multiple files, create a dedicated file that initializes Bitfab and exports the function. Import it wherever you need to instrument.
```typescript theme={null}
// lib/bitfab.ts -- single source of truth
import { Bitfab } from "@bitfab/sdk"
const bitfab = new Bitfab({ apiKey: process.env.BITFAB_API_KEY })
export const orderService = bitfab.getFunction("order-processing")
```
```typescript theme={null}
// services/processOrder.ts
import { orderService } from "../lib/bitfab"
async function processOrder(orderId: string) {
return { orderId }
}
export const tracedProcessOrder = orderService.withSpan(processOrder)
```
```typescript theme={null}
// services/validateOrder.ts
import { orderService } from "../lib/bitfab"
async function validateOrder(orderId: string) {
return { valid: true }
}
export const tracedValidateOrder = orderService.withSpan(validateOrder)
```
Spans from different files are automatically linked as parent-child when one wrapped function calls another.
#### Wrapping Existing Functions Inline
When wrapping a function you didn't define (e.g. an SDK or library call), pass it directly to `withSpan` and call the result immediately. This ensures the arguments are captured as span input.
```typescript theme={null}
// ✅ GOOD -- pass function directly, arguments are captured as span input
const result = await orderService.withSpan(
{ name: "ProcessOrder", type: "function" },
processOrder,
)(orderId)
// ❌ BAD -- anonymous wrapper loses all input capture (span has no input)
const result = await orderService.withSpan(
{ name: "ProcessOrder", type: "function" },
async () => processOrder(orderId),
)()
```
Never wrap functions in an anonymous function like `async () => fn(args)`. The SDK captures the wrapper function's arguments as span input -- an anonymous wrapper has no arguments, so the span records nothing.
#### Using `withSpan()` Directly
For a single span without linking to a function group:
```typescript theme={null}
const standaloneTask = bitfab.withSpan("one-off-operation", () => {
return "done"
})
```
#### Automatic Nesting
Spans nest automatically based on call stack:
```typescript theme={null}
const outer = bitfab.withSpan("outer", { type: "agent" }, async () => {
await inner() // Becomes a child of "outer"
})
const inner = bitfab.withSpan("inner", { type: "function" }, async () => {
// ...
})
```
#### Span Options
**Parameters:**
* `traceFunctionKey` (required): String identifier for grouping spans
* `name` (optional): Display name. Defaults to function name, then trace function key
* `type` (optional): Span type. Defaults to `"custom"`. A label only, used to organize and filter spans in the dashboard; it does not change how the span is traced, replayed, or evaluated
* `finalize` (optional): `(result) => serializableView`. Record a serializable view of a non-serializable result (a live stream). See [Tracing streaming functions](#tracing-streaming-functions)
**Span Types:**
```typescript theme={null}
type SpanType =
| "llm" // LLM calls
| "agent" // Agent workflows
| "function" // Function calls
| "guardrail" // Safety checks
| "handoff" // Human handoffs
| "custom" // Default
```
**Examples:**
```typescript theme={null}
// Function name is automatically captured as span name
async function processOrder(orderId: string) {
return { orderId }
}
const traced = bitfab.withSpan("order-processing", processOrder)
// Span name: "processOrder"
// Override with name option
const traced = bitfab.withSpan(
"order-processing",
{ name: "OrderProcessor" },
processOrder
)
// Span name: "OrderProcessor"
// Set span type
const checkSafety = bitfab.withSpan(
"safety-check",
{ type: "guardrail" },
async (content: string) => ({ safe: true })
)
// With getFunction()
const service = bitfab.getFunction("order-processing")
const traced = service.withSpan({ name: "CustomName", type: "function" }, processOrder)
```
#### Tracing Streaming Functions
A streaming function returns a live stream object that the caller consumes directly (an SSE response, a UI message stream). That object isn't serializable as a trace output, and awaiting it to completion before returning would break streaming and first-byte latency. The `finalize` option solves this: `withSpan` hands the live stream back to the caller **unchanged**, but records `await finalize(result)` as the span output, a drained, serializable, replayable value such as `{ text, usage, toolCalls }`.
For the Vercel AI SDK, use the prebuilt `finalizers.aiSdk` helper. Reading the result's `text` / `totalUsage` promises does not consume the live stream (the AI SDK tees internally), so your own streaming is unaffected. (For automatic per-call `llm` spans with no `withSpan` at all, see the [Vercel AI SDK framework integration](/frameworks/vercel-ai-sdk); `finalizers.aiSdk` is for an explicit root span around the call.)
```typescript theme={null}
import { streamText } from "ai"
import { finalizers } from "@bitfab/sdk"
const runChatTurn = bitfab.withSpan(
"chat-turn",
{ type: "agent", finalize: finalizers.aiSdk },
() => streamText({ model, messages }),
)
// In your route handler:
const result = runChatTurn() // live StreamTextResult
return result.toUIMessageStreamResponse() // stream to the user as usual
// The span records { text, usage, finishReason, toolCalls } in the background.
```
Provide your own `finalize` to record a specific shape:
```typescript theme={null}
const runChatTurn = bitfab.withSpan(
"chat-turn",
{
type: "agent",
finalize: async (r) => ({ answer: await r.text, tokens: await r.totalUsage }),
},
() => streamText({ model, messages }),
)
```
For a raw `ReadableStream`, use `finalizers.readableStream`, which `tee()`s the stream and collects its chunks; the caller must use the live branch it hands back:
```typescript theme={null}
import { finalizers } from "@bitfab/sdk"
let live: ReadableStream
const traced = bitfab.withSpan(
"render",
{ finalize: (r) => finalizers.readableStream(r, (s) => { live = s }) },
() => makeReadableStream(),
)
traced()
return new Response(live!)
```
`finalize` runs in the background and never affects the caller's value; a `finalize` that throws records an error on the span instead of crashing the host. It is ignored for async-generator results, which are captured automatically. Inputs to the wrapped function must still be serializable for the trace to replay.
An async generator has two async chains: the service produces values and the controller consumes them. To include spans from both chains in one trace, make the controller the outer root and trace the generator as its child:
```typescript theme={null}
const chatTurn = bitfab.getFunction("chat-turn")
const streamFromService = chatTurn.withSpan({ name: "stream-service" }, async function* () {
yield "first"
yield "second"
})
const handleChunk = chatTurn.withSpan({ name: "handle-chunk" }, async (chunk: string) => chunk)
const runController = chatTurn.withSpan({ name: "controller", type: "agent" }, async () => {
for await (const chunk of streamFromService()) {
await handleChunk(chunk)
}
})
await runController()
```
#### Span Context
Use `getCurrentSpan()` to get a handle to the active span, then call `.addContext()` to attach contextual key-value pairs from inside a traced function -- useful for runtime values like request IDs, computed scores, or dynamic context:
```typescript theme={null}
import { getCurrentSpan } from "@bitfab/sdk"
async function processOrder(orderId: string) {
const userId = await getCurrentUser()
getCurrentSpan()?.addContext({ user_id: userId, order_id: orderId })
return { orderId, status: "completed" }
}
const traced = bitfab.withSpan("order-processing", { type: "function" }, processOrder)
```
Each `addContext` call pushes the entire object as one entry. Multiple calls accumulate entries:
```typescript theme={null}
getCurrentSpan()?.addContext({ user_id: "u-123" })
getCurrentSpan()?.addContext({ request_id: "req-789" })
// Result: contexts: [{ user_id: "u-123" }, { request_id: "req-789" }]
```
#### Span IDs
Access the canonical Bitfab span and trace IDs from `getCurrentSpan().id` and `getCurrentSpan().traceId`. These are useful for persisted lookups, replay, or logging:
```typescript theme={null}
import { getCurrentSpan } from "@bitfab/sdk"
const traced = bitfab.withSpan("my-function", async () => {
const { id, traceId } = getCurrentSpan()
console.log("Current span:", id, "trace:", traceId)
return { id, traceId }
})
```
Outside a span context, both IDs are empty strings.
#### Span Prompt
Use `getCurrentSpan()` to set the prompt string on the current span. This is stored in `span_data.prompt` and is useful for capturing the exact prompt text sent to an LLM:
```typescript theme={null}
import { getCurrentSpan } from "@bitfab/sdk"
async function classifyText(text: string) {
const prompt = `Classify the following text: ${text}`
getCurrentSpan()?.setPrompt(prompt)
const result = await llm.complete(prompt)
return result
}
const traced = bitfab.withSpan("classification", { type: "llm" }, classifyText)
```
The prompt is metadata only. It records the prompt text for display and reference in the dashboard; it does not send the prompt to any model or change what the span executes. The last `setPrompt` call wins -- it overwrites any previously set prompt on the span. Calling `setPrompt` outside a span context is a no-op (it never crashes).
#### Framework Integrations
Bitfab provides automatic tracing for popular AI frameworks. See the dedicated guides for full API references:
Callback handler that records a replayable framework root plus graph nodes, LLM calls, tools, and retrievers
Trace processor for agent runs
Auto-capture prompts and LLM metadata
Capture LLM turns, tool calls, and subagents
Language model middleware for every generateText / streamText call
#### Trace Context
Use `getCurrentTrace()` to set context that applies to the entire trace (all spans within a single execution). This is useful for grouping traces by session or attaching trace-level metadata:
```typescript theme={null}
import { getCurrentTrace } from "@bitfab/sdk"
const traced = bitfab.withSpan("order-processing", { type: "function" }, async () => {
const trace = getCurrentTrace()
// Set session ID (stored as database column, filterable in dashboard)
trace?.setSessionId("session-123")
// Set trace metadata (stored in raw trace data)
trace?.setMetadata({ region: "us-west-2", environment: "production" })
// Add context entries (stored as key-value pairs, accumulates across calls)
trace?.addContext({ workflow: "checkout-flow", batch_id: "batch-2024-01" })
return { status: "completed" }
})
```
* `setSessionId(id)` -- Groups traces by user session. Stored as a database column for efficient filtering.
* `setMetadata(obj)` -- Arbitrary key-value metadata on the trace. Merges with existing metadata.
* `addContext(obj)` -- Key-value context entries. Accumulates across multiple calls.
#### Dropping a Trace
Call `.drop()` on the current-trace handle to discard the in-flight trace. Once flagged, spans that complete afterward are not uploaded at all, and the flag rides out on the completion payload, so when the trace completes the server scrubs any payloads that already raced out (the trace, its external trace, and sibling spans), deletes the archived S3 objects, and marks it `dropped` instead of `completed`, keeping only a skeleton audit row. Use it to discard runs you never want stored (health checks, test traffic) or a run you know carries sensitive data.
```typescript theme={null}
import { getCurrentTrace } from "@bitfab/sdk"
const traced = bitfab.withSpan("order-processing", { type: "function" }, async () => {
if (isHealthCheck) {
getCurrentTrace()?.drop()
}
return { status: "completed" }
})
```
* Safe to call outside a trace (a no-op), and never throws into your application.
#### Detached Trace
Use `client.getTrace(traceId)` to get a handle to a trace that has already closed. This lets you add context, merge metadata, or set the session ID from any process, thread, or agent that knows the trace ID, with no shared in-memory state.
```typescript theme={null}
const trace = client.getTrace(traceId)
await trace.addContext({ refund_status: "approved" })
await trace.setMetadata({ region: "us-west" })
await trace.setSessionId("session_xyz")
```
The `traceId` is Bitfab's canonical trace ID—the same UUID exposed by `getCurrentSpan().traceId` for native SDK traces and used in Bitfab trace URLs. All methods are fire-and-forget (return Promises you may await or ignore). Pending requests are tracked so `flushTraces()` waits for them.
* `addContext(context)` -- Appends a context entry. Existing entries are preserved.
* `setMetadata(metadata)` -- Shallow-merges new keys into existing metadata.
* `setSessionId(sessionId)` -- Replaces any existing session ID.
#### Read One Persisted Span
Use `getTraceSpan` to fetch one span without loading the full trace. Both the trace ID and exact span ID are canonical Bitfab IDs; ingestion source IDs are not accepted. Repeated name matches default to the last span.
```typescript theme={null}
const span = await client.getTraceSpan(traceId, { name: "GenerateAnswer" })
const first = await client.getTraceSpan(traceId, {
name: "GenerateAnswer",
occurrence: "first",
})
const exact = await client.getTraceSpan(traceId, { id: spanId })
```
`occurrence` also accepts a zero-based integer. A missing trace or span returns `null`.
#### Error Handling
Errors are captured in the span and re-raised:
```typescript theme={null}
const risky = bitfab.withSpan("risky-service", () => {
throw new Error("error")
})
try {
risky()
} catch (e) {
// Span records error and timing
}
```
Each error is classified by source. Errors thrown by your code are recorded with `error_source: "code"`. SDK-internal errors (e.g. serialization failures) are recorded with `source: "sdk"`. Both appear in the span's `errors` array in the Bitfab dashboard.
### Advanced Configuration
```typescript theme={null}
new Bitfab({
apiKey: string, // Required
serviceUrl?: string, // Default: https://bitfab.ai
timeout?: number, // Request timeout in ms (default: 120000)
envVars?: { OPENAI_API_KEY: string }, // For native function execution
enabled?: boolean, // Default: true
bamlClient?: unknown // Generated BAML client (for wrapBAML)
})
```
* `timeout`: Request timeout in milliseconds for API calls. Defaults to 120000 (2 minutes).
* `envVars`: Pass LLM provider API keys for native function execution via `call()`.
* `enabled`: When `false`, all tracing is disabled. Wrapped functions still execute normally but no spans are sent.
* `bamlClient`: The generated BAML client instance (e.g., `b` from `@baml`). See [BAML framework guide](/frameworks/baml) for full usage.
### Replay
A trace is replayable when its root span has serializable inputs, or when the workflow is instrumented through a [framework handler](#replaying-handler-instrumented-functions) (whose recorded root input is itself serializable). One of these must hold for replay to work.
Replay historical traces through an updated function version to compare outputs:
```typescript theme={null}
const pipeline = bitfab.getFunction("my-function")
const updatedFn = pipeline.withSpan(
{ name: "UpdatedPipeline", type: "function" },
async (input: string) => {
// New implementation to test
return { result: input.toUpperCase() }
},
)
// Replay all traces (up to limit)
const result = await bitfab.replay("my-function", updatedFn, {
name: "Uppercase candidate",
limit: 10,
maxConcurrency: 5, // Default: 10
})
// Replay specific traces by ID
const result2 = await bitfab.replay("my-function", updatedFn, {
traceIds: ["trace-id-1", "trace-id-2"],
})
// Result structure
console.log(result.testRunId) // Test run identifier
console.log(result.testRunUrl) // Dashboard URL
for (const item of result.items) {
console.log(item.input) // Original input
console.log(item.result) // New output
console.log(item.originalOutput) // Original output
console.log(item.error) // Error if any
console.log(item.durationMs) // Original trace duration in ms (or null)
console.log(item.tokens) // { input, output, cached, total } or null
console.log(item.model) // Original model name, or null
console.log(item.traceId) // Server trace ID for the replayed execution
}
```
Pass `replay()` **either** an already-`withSpan`-wrapped function (it carries its trace function key, so `replay()` runs it as-is) **or** a plain callable (which `replay()` wraps under the key for you). Do not wrap an already-instrumented function in a fresh closure: a plain arrow like `(input) => myWrappedFn(input)` carries no trace function key, so `replay()` adds its own root span around it while `myWrappedFn` records its own span underneath, nesting a duplicate. If your root is already wrapped, pass it directly: `bitfab.replay("my-function", myWrappedFn, ...)`.
Replay waits for each item's trace (spans + completion) to be persisted server-side before completing the test run, so `item.traceId` is a real server trace ID for completed items. Plain callables are wrapped in `withSpan` internally, so every replayed invocation records a trace. If NO completed item's trace persisted (uploads wholesale failed), `replay()` throws a `BitfabError` instead of silently returning `null` trace IDs. If only SOME items' traces are missing (a transient per-item upload failure), those items get `null` trace IDs with a loud `console.error` and the rest of the run is returned intact. `item.traceId` is also `null` for errored (unreplayable) items, and for all items when the server predates the trace-ID mapping (a console warning explains which).
Per-item `durationMs` and `model` come from the historical trace that fed this replay item. `tokens` is the **replayed run's** token usage (the same numbers Studio's experiments view shows), so comparing each item's `tokens.total` against the original trace's recorded usage tells you how your change moved cost. Each field is `null` when it wasn't captured.
**Options:**
* `limit` -- Maximum number of recent traces to replay (default: 5; maximum: 5,000). Ignored when `traceIds` or `datasetId` is passed: an explicit ID list or dataset already determines how many traces replay.
* `traceIds` -- Specific trace IDs to replay (max 100). The ID count determines how many traces replay; `limit` is ignored when both are passed.
* `name` -- Optional display name for the resulting experiment/test run.
* `maxConcurrency` -- Number of traces to replay in parallel (default: 10)
* `codeChangeDescription` -- Optional rationale for the code change being tested in this replay (stored on the experiment)
* `codeChangeFiles` -- Optional list of edited files, each as `{ path, before, after }` (use `""` for newly created or deleted files)
* `mock` -- Mock strategy for child spans during replay: `"marked"` (default, only return historical output for child spans declared with `mockOnReplay: true`), `"none"` (run real code for every child), or `"all"` (return historical output for every child). See **Mocking child spans during replay** below.
* `mockOverride` -- One `{ match, value }` pair, or an array of them, that injects a custom value into matched spans (first matcher wins). Takes precedence over `registerMockOverride` and the base `mock` strategy. See **Injecting custom values with overrides** below.
* `experimentGroupId` -- Optional UUID string that groups multiple replay runs into a single experiment batch. Pass the same ID across successive `replay()` calls to link them together in the dashboard.
* `graderIds` -- Optional array of grader UUIDs (max 100) attached directly to this replay run, independent of the dataset's own graders. The resulting experiment is graded by the union of these and the dataset's runnable graders. Use it to grade a single run with a check you don't want to add to the dataset permanently. Each id must be an active grader in the same organization and trace function, or the replay is rejected with a 400. A replay with no dataset can still carry graders this way.
* `adaptInputs` -- Optional hook to reshape recorded inputs onto the function's current signature when its shape changed after the traces were captured. See **Adapting inputs after a signature change** below.
* `onProgress` -- Optional callback fired once per item as it settles, with running totals plus the settled item payload (source trace id, local replay trace id, input, result, original output, error, duration, tokens/model metadata). Use it to render live progress or start evaluating completed items while replay runs. A throwing callback never crashes the run. Bitfab plugin replay scripts can pass the SDK's ready-made `reportReplayProgress` callback straight in (`onProgress: reportReplayProgress`); it writes the event to stderr, which the Bitfab plugin polls to report live progress and write per-item result files while replay runs (stdout remains available for direct-run ReplayResult JSON).
* `environment` -- Optional `ReplayEnvironment`. When passed, the Bitfab server resolves a per-trace database branch from each source trace's captured snapshot reference, and the SDK exposes that branch's URL via `environment.databaseUrl` inside the replayed function (releasing the branch after each item). Read `environment.active` to fall back to your live database when no branch was resolved (e.g. the trace predates snapshot capture, or DB branching isn't configured). Construct one with `new ReplayEnvironment()` and read it only inside the replayed function.
#### Replaying handler-instrumented functions
Workflows instrumented through a framework handler (`getLangGraphCallbackHandler`, `getLangChainCallbackHandler`, `getClaudeAgentHandler`, `getOpenAiAgentHandler`) have no `withSpan`-wrapped root in the application code: the handler (or run wrapper) records the framework invocation itself as the root span, with the framework's own input (a LangGraph initial state, an agent prompt, the run input) as the recorded root input. LangGraph/LangChain roots are registered as pending traces when the root callback starts and completed when it ends, so long-running runs can appear before final output is available. **These traces are fully replayable.** Pass the handler's trace function key plus any plain callable that re-invokes the framework entrypoint:
The OpenAI Agents SDK uses `getOpenAiAgentHandler(key).wrapRun(agent, input)` (a drop-in for `run`) for the replayable root; the bare `getOpenAiTracingProcessor` captures internals only and records an empty-input root. The Claude Agent SDK handler needs a hint: the prompt is not present in the message stream, so pass it explicitly, `wrapQuery(stream, { input: prompt })` (or `wrapResponse(stream, { input })`), for the handler to record a replayable root.
```typescript theme={null}
// scripts/replay.ts
import { graph } from "../src/agent" // the compiled LangGraph graph
import { bitfab } from "../src/bitfabClient" // same client as instrumentation
const handler = bitfab.getLangGraphCallbackHandler("my-agent") // same key
const replayMyAgent = async (state: AgentState) =>
graph.invoke(state, {
callbacks: [handler],
configurable: buildReplayConfig(),
})
const result = await bitfab.replay("my-agent", replayMyAgent, { limit: 10 })
```
How it fits together:
* `replay()` fetches the handler-recorded production traces by the key string, and wraps a plain callable in `withSpan` under that key internally so each replayed invocation records a trace tied to the test run. The key is the only link; it does not matter that production traces were written by the handler and the callable was written today.
* Passing an already-`withSpan`-wrapped function under the same key also works (older SDKs require this form); a wrapped function whose key contradicts the replay key throws.
* The recorded root input is whatever the handler captured at the framework boundary (a LangGraph state object arrives as a single argument).
* Attaching the handler inside the callable makes the replayed graph's node/LLM/tool spans nest under the replay span, so replay traces have the same tree as production ones.
* The callable rebuilds the runtime environment the trace never captured: framework `configurable`, dependency objects, API keys. Use safe no-op substitutes for side-effectful wiring (billing or credit callbacks, notification senders); replay should never charge or notify anyone.
#### Mocking child spans during replay
For the workflow-level guide, see [Replay Mocking](/replay-mocking).
When iterating on a root function, child spans sometimes fail in your local environment for reasons unrelated to the code under test: a paid API key is missing, an external service is flaky, or a production-only DB row isn't seeded locally. The `mock` option lets the child return its recorded output so the root function can still run.
Three strategies on `replay()`:
* **`"marked"`** (default): only descendants declared with `mockOnReplay: true` are short-circuited; everything else runs real. This is the iteration-friendly mode.
* **`"none"`**: every child span runs real code. Use when your local environment can faithfully reproduce the trace.
* **`"all"`**: every descendant `withSpan` returns its historical output. The root function still runs real, but every child is short-circuited. Useful for a quick sanity-check against recorded data; not the recommended iteration strategy because changes to descendants won't actually execute.
Per-span opt-in via `SpanOptions.mockOnReplay`:
```typescript theme={null}
const fetchArticle = bitfab.withSpan(
"fetch-article-from-db",
{ mockOnReplay: true },
async (id: string) => db.articles.findById(id),
)
const summarize = bitfab.withSpan(
"summarize-article",
async (article: Article) => /* real summarization, no flag */
)
const processArticle = bitfab.withSpan(
"process-article",
async (id: string) => summarize(await fetchArticle(id)),
)
// During replay, fetch-article-from-db returns its recorded output;
// summarize-article runs real so you can iterate on it.
const result = await bitfab.replay("process-article", processArticle, {
limit: 10,
})
```
`mockOnReplay` is a per-span tag at definition time -- it has no effect outside replay, and it's read by the default `mock: "marked"` strategy. The root function always runs real code; only descendants can be mocked.
When no historical span matches a child call (e.g. the recorded trace didn't reach that branch), execution falls through to the real function -- never silent omission.
#### Injecting custom values with overrides
Marking a span replays its *recorded* output. A **mock override** substitutes a value you supply for a matched span, so downstream real code runs against it -- for "what if this step returned X" experiments without editing the traced code. An override is a `{ match, value }` pair: `match` selects spans by structural metadata (`traceFunctionKey`, `spanName`, `type`, `originalSpanId`); `value` is the substitution (full replacement) -- a flat value, or a function of the context.
```typescript theme={null}
const result = await bitfab.replay("process-article", processArticle, {
mock: "none", // run everything real...
mockOverride: {
// ...except this span, which gets the value you supply
match: (node) => node.traceFunctionKey === "fetch-article-from-db",
value: { id: "fixed", title: "Fixed title" }, // a flat value
},
})
```
`value` can also be a function receiving the span's **live** replay `inputs` and a `getOriginalOutput()` that lazily fetches the recorded output (memoized per trace) when you want to tweak it rather than replace it:
```typescript theme={null}
value: async ({ inputs, getOriginalOutput }) => ({
...(await getOriginalOutput()),
score: 1,
})
```
A flat or synthetic `value` (one that never calls `getOriginalOutput`) fetches no recorded outputs at all. Register overrides on the client to apply them to every replay:
```typescript theme={null}
bitfab.registerMockOverride({
match: (node) => node.type === "llm",
value: { label: "refund" },
})
// or the ordered form: registerMockOverride(match, value)
bitfab.clearMockOverrides() // reset
```
Precedence per span: per-call `mockOverride`, then registered overrides, then the base `mock` strategy (a span no override matches falls back to it). Because `getOriginalOutput()` is async, a **synchronous** wrapped function cannot be mocked with a fetched value (recorded-output reuse, or a `value` function that awaits `getOriginalOutput`) -- make the span `async`, or use `mock: "all"`; a flat `value` (or a synchronous function) works on synchronous spans.
#### Adapting inputs after a signature change
Replay deserializes each trace's inputs exactly as they were captured against the function's signature **at trace time**, then spreads them into the current function. If the signature drifted since capture (a param renamed, reordered, collapsed into an options object, or a new required arg added), that spread no longer lines up and the call throws. The `adaptInputs` hook reshapes the recorded inputs onto the current signature so replay can still run:
```typescript theme={null}
const result = await bitfab.replay("my-function", updatedFn, {
// Recorded as (userId, limit); current signature is ({ userId, limit }).
adaptInputs: (inputs, ctx) => {
const [userId, limit] = inputs as [string, number]
return [{ userId, limit }]
},
})
```
The hook runs once per item, **inside the same error boundary as the function**: if it throws, that item's `error` is set and the run continues, so a single unmappable trace never crashes the batch. The array it returns is what gets spread into the function and what `item.input` reports.
`ctx` carries `{ originalTraceId, originalSpanId }` (with deprecated `sourceTraceId`/`sourceSpanId` aliases) so a table-driven adapter can look up a per-trace transform (`ctx.originalTraceId` is the original Bitfab trace ID). This is the escape hatch for reshapes that need judgement rather than mechanical rearrangement: compute the adapted inputs per trace up front, then have the hook look them up by `sourceTraceId`, keeping replay deterministic instead of calling a model mid-replay.
When the new signature has a genuinely new **required** input with no analog in the recorded trace, don't fabricate one -- there's nothing faithful to map it to. Leave those traces unmapped (let them error) rather than inventing test inputs.
For anything beyond a one-liner, keep the adapter in its own file next to the replay script and import it -- the `AdaptInputsFn` type is exported for this:
```typescript theme={null}
// scripts/replay-adapters/extraction.ts
import type { AdaptInputsFn } from "@bitfab/sdk"
export const adaptInputs: AdaptInputsFn = (inputs, ctx) => {
const [userId, limit] = inputs as [string, number]
return [{ userId, limit }]
}
```
```typescript theme={null}
// scripts/replay.ts
import { adaptInputs } from "./replay-adapters/extraction"
await bitfab.replay("my-function", updatedFn, { limit, adaptInputs })
```
That keeps the transform versioned and reviewable alongside the function it adapts, and you add the import only when a drift actually needs it.
#### Attaching a Code Change
Each replay creates an experiment (test run). When you're iterating on a function and replaying after every edit, attach the change so the dashboard can show *exactly what was edited* alongside the results. The agent reads each file before editing, edits, then reads it again -- the two strings go straight into `codeChangeFiles`. There's no diff format to construct.
```typescript theme={null}
import { readFileSync } from "node:fs"
const before = readFileSync("src/foo.ts", "utf8")
// ...edit src/foo.ts...
const after = readFileSync("src/foo.ts", "utf8")
const result = await bitfab.replay("my-function", updatedFn, {
name: "Retry fix candidate",
codeChangeDescription: "fix off-by-one in retry logic",
codeChangeFiles: [{ path: "src/foo.ts", before, after }],
})
```
Both options are optional and independent -- you can pass just `codeChangeDescription` for a quick rationale-only annotation, or just `codeChangeFiles` to record the literal edits.
If you pass neither, `replay()` falls back to capturing your working-tree diff against the trunk merge-base (best-effort, only inside a git repo), so an experiment still shows a diff. Passing `codeChangeFiles` explicitly always wins and is the way to record a precise per-edit before/after. Set `BITFAB_DISABLE_CODE_CHANGE_CAPTURE` to turn the fallback off.
**Notes:**
* **Use a single `Bitfab` client across instrumentation and replay.** If your instrumented module constructs `new Bitfab()` at import and your replay script constructs another, they do not share registered trace functions -- import the client from the instrumented module (or a shared singleton) rather than constructing a new one in the replay script.
#### Replay Output Contract
Replay results are typically consumed by automation (CI logs, code reviewers, and coding agents). When `BITFAB_REPLAY_RESULT_PATH` is set, `bitfab.replay()` automatically writes the full `ReplayResult` JSON to that file. For direct/manual runs, **emit the full `ReplayResult` as a single stdout JSON block** so a consumer can `JSON.parse` it and reason about every field, including the new per-item `durationMs`, `tokens`, and `model`. Never print only lengths, counts, hashes, or truncated previews, and never replace the JSON block with ad-hoc per-field log lines.
Recommended script tail (TypeScript):
```typescript theme={null}
const result = await bitfab.replay("my-function", updatedFn, { limit })
// Human-readable summary goes to stderr, so stdout stays pure JSON.
console.error(`Test run: ${result.testRunUrl}`)
console.error(`Items: ${result.items.length}`)
// Full structured dump to stdout, ready for JSON.parse.
console.log(JSON.stringify(result, null, 2))
```
The dumped object includes every item's `input`, `result`, `originalOutput`, `error`, `durationMs`, `tokens`, `model`, and `traceId`, plus `testRunId` and `testRunUrl`. When the Bitfab plugin runs this script, it sets `BITFAB_REPLAY_RESULT_PATH`; the SDK writes the final result there, and the plugin reads that file into the replay run's `.bitfab/replays//events.jsonl` while writing large per-item payloads under `.bitfab/replays//items/`.
**Per-item errors are part of the contract.** If the wrapped function throws on a given trace, `bitfab.replay` catches it, sets `item.error`, leaves `item.result` undefined, and continues. Treat items with `item.error` set as **unreplayable**, not as failing outputs -- compute pass/fail only over items where it's unset. This matters most for DB reads/writes: a stale FK, missing record, or rejected write is infra failure, not a regression.
**Don't swallow per-item errors in the script.** A custom try/catch that returns a placeholder turns infra failures into fake successes. Let the SDK record them. The only allowed top-level catch is a fatal handler around `main()` that exits non-zero, so callers can tell a whole-replay crash from a clean run with some unreplayable items.
**Environment.** Replay executes in the app's own process -- the instrumented function is imported as a library, and its DB clients, env vars, config loaders, and model IDs resolve from whatever environment the replay script is run under. The script must bootstrap the same environment the app uses (e.g. `import "dotenv/config"` at the top, or run via `pnpm with-env tsx scripts/replay.ts`). Do not mock these -- they're the same dependencies the app resolves in production. For replay to see the same DB rows the trace was captured against, point the script at the trace's source environment (the `environment` field on the trace -- production / staging / development).
**Input serialization caveat.** Replay deserializes historical span inputs and passes them back to your function. This works for strings, numbers, and plain objects. If your span wraps a function that takes hydrated domain objects (ORM models, class instances, DB records), they won't round-trip through serialization -- move the span to where inputs are IDs or plain data and let the function fetch objects internally, or reshape arguments in the wrapper.
#### Replay Script
Create a standalone script to regression-test your trace functions against production data with one command. The script maps pipeline names to their replay functions, accepts CLI flags, and prints a side-by-side comparison with delta summaries.
```typescript theme={null}
/**
* Replay production traces through instrumented functions.
*
* Uses bitfab.replay() to fetch real traces and re-run them through
* the current code, creating a test run for side-by-side comparison.
*
* Usage:
* npx tsx scripts/replay.ts
* npx tsx scripts/replay.ts --limit 20
* npx tsx scripts/replay.ts --trace-ids id1,id2
*/
import "dotenv/config"
import { reportReplayProgress } from "@bitfab/sdk"
import { bitfab } from "../lib/bitfab"
import { extractMemories } from "../services/extraction"
import { searchDocuments } from "../services/search"
const FUNCTIONS = {
extraction: "my-extraction-pipeline",
search: "my-search-pipeline",
} as const
type Pipeline = keyof typeof FUNCTIONS
const pipeline = process.argv[2] as Pipeline | undefined
const args = process.argv.slice(3)
if (!pipeline || !FUNCTIONS[pipeline]) {
console.error(
`Usage: npx tsx scripts/replay.ts <${Object.keys(FUNCTIONS).join("|")}> [--limit N] [--trace-ids id1,id2]`,
)
process.exit(1)
}
let limit = 10
let traceIds: string[] | undefined
for (let i = 0; i < args.length; i++) {
if (args[i] === "--limit" && args[i + 1]) {
limit = Number.parseInt(args[i + 1], 10)
i++
} else if (args[i] === "--trace-ids" && args[i + 1]) {
traceIds = args[i + 1].split(",").map((id) => id.trim())
i++
}
}
// Each pipeline gets its own replay function -- replay deserializes
// historical inputs and spreads them into the function. If a signature drifts
// after traces were captured, add an input adapter (see the "Adapting inputs"
// section): write scripts/replay-adapters/.ts, import its `adaptInputs`,
// and pass it here, e.g.
// import { adaptInputs } from "./replay-adapters/extraction"
// return bitfab.replay(FUNCTIONS.extraction, fn, { limit, adaptInputs })
async function replayExtraction() {
const fn = async (conversation: string, existingItems: unknown[]) => {
return extractMemories(conversation, existingItems)
}
// limit is ignored when traceIds is passed -- an explicit ID list
// already determines how many traces replay.
return bitfab.replay(FUNCTIONS.extraction, fn, {
...(traceIds ? { traceIds } : { limit }),
// Live progress the Bitfab plugin polls while replay runs in the background.
onProgress: reportReplayProgress,
})
}
async function replaySearch() {
const fn = async (query: string, opts: Record) => {
return searchDocuments(query, {
userId: opts.userId as string,
limit: (opts.limit as number) ?? 10,
})
}
return bitfab.replay(FUNCTIONS.search, fn, {
...(traceIds ? { traceIds } : { limit }),
// Live progress the Bitfab plugin polls while replay runs in the background.
onProgress: reportReplayProgress,
})
}
const REPLAY_FNS: Record ReturnType> = {
extraction: replayExtraction,
search: replaySearch,
}
async function main() {
const functionKey = FUNCTIONS[pipeline!]
// Human-readable output goes to stderr; stdout carries only the ReplayResult JSON.
console.error(`[replay] Replaying ${traceIds?.length ?? limit} traces from "${functionKey}"...\n`)
const result = await REPLAY_FNS[pipeline!]()
console.error(`Test run: ${result.testRunUrl}\n`)
let changed = 0, same = 0, errors = 0
for (const item of result.items) {
const input = item.input as unknown[] | undefined
const label = typeof input?.[0] === "string"
? input[0].slice(0, 80)
: JSON.stringify(input?.[0] ?? "unknown").slice(0, 80)
if (item.error) {
console.error(` ✗ "${label}"`)
console.error(` Error: ${item.error}`)
errors++
} else {
const origStr =
typeof item.originalOutput === "string"
? item.originalOutput
: JSON.stringify(item.originalOutput)
const newStr =
typeof item.result === "string"
? item.result
: JSON.stringify(item.result)
const isSame = origStr === newStr
const marker = isSame ? "=" : "Δ"
console.error(` ${marker} "${label}"`)
console.error(` Original: ${origStr}`)
console.error(` New: ${newStr}`)
isSame ? same++ : changed++
}
}
console.error(`\n─── Summary ───`)
console.error(` Pipeline: ${pipeline}`)
console.error(` Replayed: ${result.items.length}`)
console.error(` Same: ${same}`)
console.error(` Changed: ${changed}`)
if (errors > 0) console.error(` Errors: ${errors}`)
console.error(`\n ${result.testRunUrl}`)
// Full ReplayResult as JSON to stdout (the Replay Output Contract).
console.log(JSON.stringify(result, null, 2))
}
main().catch((err) => {
console.error("[replay] Fatal error:", err)
process.exit(1)
})
```
Adapt the imports, pipeline names, and per-pipeline replay functions to match your project's instrumented workflows.