effect

@coder/ai-sdk-effect

Warning

Experimental spike: not published to npm. Phase 1 of coder/ai-sdk#144. The package is private: true and excluded from release-please. Its API will change without notice until the spike review concludes.

An Effect bridge for @coder/ai-sdk-provider (Coder AI Gateway), @coder/ai-sdk-agent (Coder Agents), and @coder/ai-sdk-sandbox (workspace sandboxes):

Feature	API
@effect/ai LanguageModel over AI Gateway	CoderLanguageModel.layer, CoderLanguageModel.fromModel
@effect/ai LanguageModel over Coder Agents	CoderAgentModel.layer, CoderAgentModel.withAgentOptions
Typed AiError failures	classifyError, isTransient
Effect Schema → Vercel AI SDK schemas	toAiSdkSchema
Scoped workspace Layers	acquireWorkspace / layerWorkspace, acquireSession / layerSession

LanguageModel over Coder AI Gateway

CoderLanguageModel.layer(modelId, source, generation?) implements @effect/ai's LanguageModel service on top of the gateway.

• source: CoderProviderSettings, or { provider } for an existing CoderProvider. Both auth modes (centralized and BYOK) work unchanged.
• Supported calls: generateText, generateObject, and streamText. Structured outputs derive their JSON schema from the Effect Schema you pass.
• generation (GenerationOptions): maxOutputTokens, temperature, topP, topK, penalties, stopSequences, seed, reasoning, providerOptions. Set at construction time and forwarded on every call.
• Per-call overrides: CoderLanguageModel.withGenerationOptions(overrides) provides the GenerationConfig service to one effect. Its keys win over the construction options; nested overrides merge, innermost first.

import * as LanguageModel from "@effect/ai/LanguageModel";
import * as Effect from "effect/Effect";
import { CoderLanguageModel } from "@coder/ai-sdk-effect";

const program = Effect.gen(function* () {
  const response = yield* LanguageModel.generateText({
    prompt: "In one sentence, what is Coder AI Gateway?",
  });
  return response.text;
});

program.pipe(
  Effect.provide(
    CoderLanguageModel.layer("gpt-5.1", {
      baseURL: "https://coder.example.com",
      apiKey: process.env.CODER_SESSION_TOKEN,
    }),
  ),
  Effect.runPromise,
);

import * as LanguageModel from "@effect/ai/LanguageModel";
import { CoderLanguageModel } from "@coder/ai-sdk-effect";

const deterministic = LanguageModel.generateText({ prompt: "Name a color." }).pipe(
  CoderLanguageModel.withGenerationOptions({ temperature: 0, seed: 1 }),
);

CoderLanguageModel.fromModel (the bridge core) adapts any AI SDK LanguageModelV4; unit tests use it to run without HTTP.

LanguageModel over Coder Agents

CoderAgentModel.layer(settings) implements LanguageModel on top of a Coder Agents chat (chatd) through @coder/ai-sdk-agent's CoderLanguageModel. The agent loop runs server-side; the layer adapts it to Effect.

• settings: the agent's CoderLanguageModelConfig (organizationId, model, reasoningEffort, workspaceId, chatId, requestTimeoutMs, ...) plus client, or baseUrl + token (default: CODER_URL + CODER_SESSION_TOKEN).
• One chat per layer. chatd keeps the history. Each call sends only the prompt's newest user message, or the results of client tools. System messages apply when the chat is created. Calls are single-flight.
• Tools. Toolkit tools run client-side: @effect/ai resolves the tool calls, and the next call sends the results back to the same chat. chatd's server-side tools are reported in the finish part's metadata.coder.serverToolCalls. chatd registers client tools only when the chat is created, so the toolkit cannot change afterwards, and only the auto tool choice is supported. Other cases fail with MalformedInput. toolChoice governs toolkit tools only; chatd's server-side tools come from settings (workspaceId, mcpServerIds). A chat resumed with settings.chatId keeps the client tools it was created with, and the layer cannot verify them: pass the same toolkit, and set requestTimeoutMs.
• Structured output. generateObject fails with MalformedInput: chatd enforces no schema. Use the AI Gateway model for structured output.
• Interruption. Interrupting the fiber aborts the call. The agent then interrupts the chat's run server-side, exactly once.
• Scope. Closing the layer's scope disposes the model and closes its event stream. The chat is not archived. To archive chats, collect their ids from segment:* events in onTransportEvent.
• Per-call options. CoderAgentModel.withAgentOptions({ model, reasoningEffort }) changes these for one call on the same chat. A change is refused while client tool results are submitted, because that continues a turn in progress.
• Generation options. Sampling controls (temperature, maxOutputTokens, ...) and providerOptions other than coder fail with MalformedInput, because chatd chooses them.

import * as LanguageModel from "@effect/ai/LanguageModel";
import * as Effect from "effect/Effect";
import { CoderAgentModel } from "@coder/ai-sdk-effect";

const program = Effect.gen(function* () {
  const first = yield* LanguageModel.generateText({ prompt: "Summarize the repo README." });
  const second = yield* LanguageModel.generateText({ prompt: "Now in one line." }).pipe(
    CoderAgentModel.withAgentOptions({ reasoningEffort: "high" }),
  );
  return [first.text, second.text];
});

program.pipe(
  Effect.provide(
    CoderAgentModel.layer({ organizationId: "<org-uuid>", model: "claude-sonnet-4-5" }),
  ),
  Effect.runPromise,
);

Typed error taxonomy

classifyError maps an AiError, a raw AI SDK error, or a raw @coder/ai-sdk-agent error to one of: auth, rate-limit, provider-unavailable, malformed-response, transport, timeout, unknown. isTransient composes with Effect.retry:

import * as LanguageModel from "@effect/ai/LanguageModel";
import * as Effect from "effect/Effect";
import * as Schedule from "effect/Schedule";
import { isTransient } from "@coder/ai-sdk-effect";

const resilient = LanguageModel.generateText({ prompt: "hello" }).pipe(
  Effect.retry({
    while: (error) => isTransient(error),
    schedule: Schedule.exponential("250 millis"),
    times: 3,
  }),
);

Why there are no Coder-specific error classes

@effect/ai's AiError is a closed union, so the bridge cannot add its own error classes to the LanguageModel failure channel (a delta from the tracking issue's sketch). Failures are mapped losslessly into that union (HttpResponseError keeps status, headers, and body), and classifyError recovers the Coder-oriented taxonomy.

How @coder/ai-sdk-agent errors map

Agent error	AiError	Reason	isTransient
CoderApiError	HttpResponseError (status, path)	from the status, like gateway errors	from the reason
CoderStreamError	HttpRequestError (error as cause)	transport	isRetryable
CoderChatError with kind: "timeout"	UnknownError (error as cause)	timeout	retryable
CoderChatError with kind: "stream_closed"	UnknownError (error as cause)	transport	retryable
Any other CoderChatError	UnknownError (error as cause)	from statusCode if present, or unknown	retryable

An explicit verdict wins over the reason. The agent sets CoderStreamError.isRetryable to false when replaying the prompt would repeat server-side effects, so such a failure is not transient even though its reason is transport. Fiber interruption stays Effect interruption; it never becomes an AiError.

Effect Schema → Vercel AI SDK schemas

toAiSdkSchema makes an Effect Schema usable in AI SDK APIs such as tool(), with inputs fully decoded and typed:

import * as Schema from "effect/Schema";
import { tool } from "ai";
import { toAiSdkSchema } from "@coder/ai-sdk-effect";

const getWeather = tool({
  description: "Look up the current weather",
  inputSchema: toAiSdkSchema(Schema.Struct({ city: Schema.String })),
  execute: async ({ city }) => ({ city, temperature: 21 }),
});

Why a bare Effect standard schema fails

AI SDK v7 accepts Standard Schema V1 values, but deriving the wire JSON schema requires the optional ~standard.jsonSchema converter. Schema.standardSchemaV1 does not emit it, so passing a bare Effect standard schema throws Standard schema vendor 'effect' does not support JSON Schema conversion.

toAiSdkSchema derives the JSON schema from the Effect Schema (the same derivation @effect/ai uses), and validation decodes through it.

Scoped sandbox Layers

acquireWorkspace / layerWorkspace wrap ensureCoderWorkspace in Effect.acquireRelease:

• Scope opens: the workspace is provisioned (get-or-create, start, agent-readiness wait).
• Scope closes: it is torn down, including when the fiber is interrupted after acquisition.
• Teardown policy: delete-if-created by default. It never touches a workspace the acquisition merely attached to.

acquireSession / layerSession do the same for createCoderWorkspace(...).createSession().

import * as Effect from "effect/Effect";
import { CoderWorkspace, layerWorkspace } from "@coder/ai-sdk-effect";

const program = Effect.gen(function* () {
  const workspace = yield* CoderWorkspace;
  yield* Effect.log(`workspace ${workspace.name} ready`);
});

program.pipe(
  Effect.provide(
    layerWorkspace({
      workspace: "agent-sandbox",
      create: { template: "docker" },
    }),
  ),
  Effect.runPromise,
);

Spike caveats

Unsupported inputs fail loudly with MalformedInput; response parts with no @effect/ai equivalent are dropped.

Limitation	Behavior
Provider-defined tools; the oneOf tool-choice mode (not expressible in LanguageModelV4 call options)	MalformedInput
Response parts of type custom, reasoning-file, tool-approval-request; file payloads that are not raw data (URL / provider-reference / inline-text)	Dropped
Provider-executed tool calls and results for tools outside the call's toolkit (@effect/ai types tool parts by toolkit)	Moved to the finish part's metadata.coder.serverToolCalls
Prompt provider options (per-part metadata)	Not forwarded
ProviderOptions.span (telemetry)	Not wired into request headers

Workspace acquisition is uninterruptible (standard acquireRelease semantics): a slow ensureCoderWorkspace cannot be cancelled mid-flight. If an acquisition creates a workspace and then fails (e.g. readiness timeout), the workspace is rolled back best-effort per the teardown policy.

Phase 2 (not in this package yet)

• Publishing decision: versioning, peerDependency policy on effect/@effect/ai, release-please wiring, workspace:* deps.

Pinned surface

Effect's AI packages move fast, so the spike pins exact versions and codes against their concrete API shapes.

Pinned versions and the API surface used

Dependency	Version	Surface used
effect	3.22.2	Effect, Layer, Stream, Schema, Context, Data, Option, Function.dual
@effect/ai	0.37.0	LanguageModel.make (ProviderOptions → encoded response parts), AiError, Prompt, Response, Tool
@ai-sdk/provider	4.0.21	LanguageModelV4 spec types (same pin as @coder/ai-sdk-provider)
@coder/ai-sdk-provider	0.4.29	createCoder, CoderProviderSettings (published release, not workspace:*)
@coder/ai-sdk-sandbox	0.4.32	ensureCoderWorkspace, createCoderWorkspace, CoderTransport
@coder/ai-sdk-agent	0.12.7	CoderLanguageModel (+ chatId, lastSeenMessageId, dispose), CoderChatClient, classifyTurnAction, error classes

The spike depends on the published @coder/ai-sdk-* releases rather than workspace:*, so repo-wide typecheck/test need no cross-package build ordering. Switching to workspace:* is part of the Phase 2 publishing decision.

Examples

Runnable against a real deployment (see each file's header):

• examples/01-generate.ts: text generation.
• examples/02-structured.ts: structured output via Effect Schema.
• examples/03-sandbox.ts: scoped workspace acquisition.

License

Apache-2.0