Compose agents out of LLM calls, tool calls, and plain functions. Everything is a Step<i, o>. Wire steps together with 22 primitives (sequence, parallel, chain, branch, retry, loop, ensemble_step, checkpoint, …) and run them as plain values. One generate surface fronts eight provider adapters: Anthropic, OpenAI, Google, OpenRouter, AWS Bedrock, Ollama, LM Studio, and a claude_cli subprocess that drives the Claude Code CLI.
No framework lifecycle. No ambient state. No decorators. Adapters are passed in per run.
pnpm add fascicleFascicle is ESM-only and requires Node >= 24. ai, zod, and every provider SDK are optional peers — install only the ones you use. pnpm add fascicle alone builds and runs a flow against transport: 'native' or claude_cli, with no AI SDK package and no schema library installed at all; schemas accept any Standard Schema — zod, ArkType, Valibot, whichever you already use. See docs/providers.md.
No API key on hand? Several runnable examples need zero keys: examples/hello.ts, examples/suspend_resume.ts, and examples/viewer_demo.ts use no engine at all, and examples/newsroom.ts runs the full vocabulary against the bundled stub engine from fascicle/testing.
import { run, sequence, step } from 'fascicle';
const flow = sequence([
step('add', (n: number) => n + 1),
step('double', (n: number) => n * 2),
]);
await run(flow, 1); // 4Add a model call:
import { create_engine, model_step, run, sequence, step } from 'fascicle';
const engine = create_engine({
providers: {
anthropic: { api_key: process.env.ANTHROPIC_API_KEY!, transport: 'native' },
},
});
const flow = sequence([
step('brief', (topic: string) => `Write a 2-sentence brief on: ${topic}`),
model_step({ engine, model: 'claude-sonnet-4-6', system: 'No preamble.' }),
]);
try {
console.log(await run(flow, 'Rust ownership'));
} finally {
await engine.dispose();
}transport: 'native' keeps the zero-peer promise above: raw HTTP against the Messages API, nothing to install beyond fascicle. Drop it to use the default ai_sdk transport, which needs pnpm add ai @ai-sdk/anthropic.
model_step returns the model's answer (a string, or the schema-validated value when schema is set), keeping flows at the step, step, model_step, step cadence. Underneath sits model_call, the only sanctioned bridge between composition and the engine: same config, but it returns the full GenerateResult envelope (usage, cost, tool calls, finish reason), with an optional project to map a slice of it at the source. Both thread ctx.abort, ctx.trajectory, and streaming chunks for you.
Composition primitives. Every composer takes Step<i, o> and returns Step<i, o>. Anything that fits a step fits any composition of steps. The everyday surface is small; most flows are built entirely from these nine:
| Primitive | Shape |
|---|---|
step |
lift a plain function into Step<i, o> |
sequence |
run A then B then C, threading the value |
parallel |
run a named map of steps concurrently |
branch |
route on a predicate of the input |
map |
run a step per array element, optional concurrency cap |
pipe |
post-process an inner step's output with a plain function |
retry |
re-run on failure with exponential backoff |
model_step |
the model's answer as a step (model_call projected to its content) |
chain |
named steps over a typed record: .step binds, .stage concludes a phase, .output projects |
The remaining primitives are specialized composers with the same contract, so they drop into any flow when the task calls for them: control flow (loop, fallback, timeout, compose), multi-model orchestration (adversarial, ensemble_step, consensus), and durability (checkpoint, suspend). Behind those sits an advanced tier — the pick-best variants ensemble and tournament, the raw named-state primitives scope / stash / use underlying chain, and the self-improvement pair improve / learn — covered in docs/advanced-composition.md, each entry paired with the primary primitive to try first. One-liners for all of them live in docs/composition.md, full shapes in docs/api-reference.md, and docs/leaf-arm-spine.md is the decision guide for which layer each belongs to.
Plus run, run.stream, and describe. And inside any step body, ctx.call(step, input) runs another Step with spans, abort, and error paths intact: the direct-style counterpart to composing, for control flow too dynamic to declare.
AI engine. create_engine(config) returns one generate surface across eight providers. Two axes: model is an opaque id sent to the provider verbatim (claude-opus-4-8, gpt-4o, us.anthropic.claude-sonnet-4-20250514-v1:0), and provider names the transport (anthropic, bedrock, openrouter, claude_cli, …) — swap provider to move a call between transports. Reasoning effort ('none' through 'max') is translated per provider. Cost estimation uses a pricing table with per-engine overrides.
The engine core is SDK-agnostic: providers plug in behind a neutral single-turn seam, as one of three kinds. Most built-ins wrap Vercel's AI SDK (ai_sdk). Five providers can instead run transport: 'native' (raw HTTP, no AI SDK in the path, no peer to install) — anthropic on the Messages API, openai/openrouter/lmstudio on a shared OpenAI Chat Completions core, and ollama on its own /api/chat endpoint. The third kind is claude_cli, which delegates to an external agent. All inherit the same tool loop, retry, cost, and trajectory, and custom_providers registers your own adapter of any kind without touching Fascicle. See docs/providers.md.
Adapters injected per run. Trajectory loggers and checkpoint stores ship under the fascicle/adapters subpath:
import { filesystem_logger, filesystem_store } from 'fascicle/adapters';
await run(flow, input, {
trajectory: filesystem_logger({ output_path: '.trajectory.jsonl' }),
checkpoint_store: filesystem_store({ root_dir: '.checkpoints' }),
});filesystem_logger writes synchronously (each event blocks the event loop briefly), which is fine for dev tools and short-lived runs; see docs/concepts.md before wiring it into a long-running server. Span parentage is threaded through the runner, so span trees stay correct under parallel/map concurrency. The TrajectoryLogger and CheckpointStore contracts (exported from fascicle) are tiny — roll your own to push events to Honeycomb, S3, and so on.
run.stream(flow, input) returns { events, result } for incremental observation, and run.until_suspended(flow, input) drives human-in-the-loop flows: a suspend gate resolves to a typed outcome with a resume closure instead of throwing.
Stub engines for tests. The fascicle/testing subpath ships the two doubles an app needs — make_stub_engine routes canned responses by the system prompt's stable first line and validates them through the call's own schema, make_capture_engine records every GenerateOptions for assertions — so tests run the real flow through the real run() with zero network and no mocking framework:
import { make_stub_engine } from 'fascicle/testing';
const engine = make_stub_engine([
{ prefix: 'myapp/reviewer', content: { findings: [], summary: 'clean' } },
]);Markdown-defined agents. When an agent is just a prompt plus an output schema, define_agent (the fascicle/agents subpath) folds a markdown file (frontmatter name / model / temperature, with the body as the prompt) and a zod schema into a Step<i, o>:
import { z } from 'zod';
import { create_engine } from 'fascicle';
import { define_agent } from 'fascicle/agents';
const engine = create_engine({ providers: { claude_cli: { auth_mode: 'oauth' } } });
const reviewer = define_agent({
md_path: new URL('./prompts/reviewer.md', import.meta.url),
schema: z.object({ findings: z.array(z.string()), summary: z.string() }),
engine,
});This is the blueprint's recommended shape for simple one-prompt agents; reference agents built on it (reviewer, documenter, researcher) live in examples/agents/.
MCP bridge. The fascicle/mcp subpath connects flows to the Model Context Protocol both ways. mcp_client turns an external MCP server's tools into plain Tool[]; serve_flow exposes a composed flow as an MCP tool to hosts like Claude Desktop or Cursor. It is pure adapter glue over the existing Tool and run contracts, and @modelcontextprotocol/sdk is an optional peer you only install when you use it.
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { z } from 'zod';
import { step } from 'fascicle';
import { mcp_client, serve_flow } from 'fascicle/mcp';
// Consume an external MCP server's tools inside a flow.
const remote = await mcp_client({ transport: 'stdio', command: 'my-mcp-server' });
console.log(remote.tools.map((t) => t.name));
await remote.close();
// Expose a composed flow as an MCP tool on your own server.
const server = new McpServer({ name: 'my-app', version: '1.0.0' });
serve_flow({
server,
flow: step('greet', (input: { name: string }) => `Hello, ${input.name}!`),
name: 'greet',
description: 'Greet a person by name.',
input_schema: z.object({ name: z.string() }),
});| Provider | Peer dep | Auth |
|---|---|---|
anthropic |
ai + @ai-sdk/anthropic, or none with transport: 'native' |
API key |
openai |
ai + @ai-sdk/openai, or none with transport: 'native' |
API key |
google |
ai + @ai-sdk/google |
API key |
openrouter |
ai + @openrouter/ai-sdk-provider, or none with transport: 'native' |
API key |
bedrock |
ai + @ai-sdk/amazon-bedrock |
region + AWS credentials |
ollama |
ai + ai-sdk-ollama, or none with transport: 'native' |
local base_url |
lmstudio |
ai + @ai-sdk/openai-compatible, or none with transport: 'native' |
local base_url |
claude_cli |
none (spawns claude) |
OAuth or API key |
Full details: docs/providers.md. The claude_cli adapter has its own guide: docs/cli.md.
The fascicle-viewer bin ships with the umbrella package (there is no separate fascicle-viewer package). Point it at a trajectory file and it opens a browser tree of spans, errors, and emits as the run executes:
# installed locally:
pnpm exec fascicle-viewer .trajectory.jsonl
# or one-off via the umbrella package:
pnpm dlx --package=fascicle fascicle-viewer .trajectory.jsonlOr embed it programmatically:
import { start_viewer } from 'fascicle/viewer';
const handle = await start_viewer({ port: 4242 });
// later
await handle.close();For zero-latency streaming from inside a long-running flow, pair it with http_logger from fascicle/adapters. See docs/viewer.md for the full transport story.
docs/blueprint.md is the recommended architecture for apps built on Fascicle — distilled from the reference apps and production consumers. One composition layer (flow.ts) that holds the whole topology, create_engine confined to one file, prompts as markdown with frontmatter, zod schemas as the stage contracts, stub-engine testing, and ast-grep rules that turn each boundary into a build failure. If you are a coding agent scaffolding a new Fascicle app, follow the blueprint and its checklist.
The canonical worked example is examples/pr-improve/, with its design rationale in examples/pr-improve/docs/architecture.md.
- docs/blueprint.md — the agent blueprint: the standard app architecture (start here when building an app)
- docs/getting-started.md — install and run your first flow
- docs/concepts.md — step-as-value, trajectories, cancellation
- docs/composition.md — full composition surface: the 22 primitives, run/stream, checkpointing
- docs/leaf-arm-spine.md — the three-layer shape of a Fascicle flow and how to choose the right primitive at each layer
- docs/advanced-composition.md — the primitives to reach for last (
scope/stash/use,ensemble,tournament,improve/learn) and when they earn their keep - docs/api-reference.md — the public surface at a glance
- docs/configuration.md — engine config, defaults, pricing, retries
- docs/providers.md — per-provider adapter notes
- docs/cli.md — the
claude_clisubprocess adapter - docs/cookbook.md — retries, fan-out, judges, HITL, tool loops
- docs/deliberation-as-composition.md — how ensembles, tournaments, consensus, and adversarial loops decompose into the primitives
- docs/testing.md — testing flows with stub, script, and capture engines
- docs/regression-testing-model-behavior.md — regression-testing model behavior with mutation-tested judges and a committed score baseline
- docs/human-in-the-loop.md — suspend/resume approval over HTTP and streaming to a
useChatUI viafascicle/ui - docs/writing-a-harness.md — building a runner around Fascicle
- docs/embedding-under-a-harness.md — running a Fascicle agent as somebody's child process
- docs/troubleshooting.md — first-run errors and what they mean
- docs/comparison.md — how Fascicle compares to LangChain, Mastra, and others
- docs/adoption-decision.md weighs whether to adopt Fascicle: the honest case, the risks, and when to reach for something else
- docs/roadmap.md — what has shipped, what is being considered, and what Fascicle deliberately will not do
- examples/ — runnable reference flows; start with examples/newsroom.ts, the vocabulary tour
- docs/viewer.md — viewer details and transport options
Fascicle is early and not accepting outside pull requests yet. Bug reports and feature ideas via GitHub Issues are welcome. See CONTRIBUTING.md.
This repo is a single package. The code is organized as deep modules under src/ (core, engine, composites, agents, adapters, mcp, stdio, ui, otel, policy, schema, testing, viewer), each reachable only through its barrel via the #<module> import alias. The umbrella surface at the src/ root is what publishes to npm as fascicle. Architectural boundaries (for example, core cannot import adapters, engine imports core type-only, and no process.env outside the audited exceptions) are enforced by the ast-grep rules in rules/ and a directory-level boundary DAG in fallow.toml. The 7 apps under examples/*/ are separate workspace members that consume the library via fascicle: workspace:*.
pnpm install
pnpm check # types, lint, structural rules, dead-code, tests, docs, spell
pnpm check:all # adds Stryker mutation testing + the packaging gate (final gate)pnpm check is the single source of truth for "is this done?". Output lands in .check/ (one JSON per check). See AGENTS.md for the full contract and CLAUDE.md for Claude-specific notes.