Comparisons / Flue vs Haystack

Flue vs Haystack: Which Agent Framework to Use?

Flue vs Haystack, head to head

Flue and Haystack both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

Flue is a declarative TypeScript agent framework from Fred K.

Haystack by deepset is a framework for building NLP and LLM pipelines.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

Pick Flue if

Pick Flue if flue is the natural choice when the deploy target is Cloudflare and you want a TypeScript-first, declarative agent framework tuned for Durable Objects. Its cross-runtime story (Cloudflare + Node + CI) is genuinely useful if agents run in more than one place. For a single-agent loop that doesn't need persistence, plain TypeScript is simpler. The tradeoffs in its intro should match how your team already thinks about agents; Haystack will feel like translation if they don't.

Full Fluecomparison →

Pick Haystack if

Pick Haystack if haystack earns its complexity when you're building RAG pipelines with multiple retrieval stages, document processing, and production deployment needs. But for straightforward agents with a few tools, the plain Python version is simpler to write and debug. The tradeoffs in its intro should match how your team already thinks about agents; Flue will feel like translation if they don't.

Full Haystackcomparison →

What both add

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. Flue has its own class hierarchy and tool registration conventions; Haystack has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.

By the numbers

By the numbers

Flue

GitHub Stars

2.4k

Forks

140

Language

TypeScript

License

MIT

Created

2026-05-01

Created by

Fred K. Schott + Astro team (at Cloudflare)

Backed by

Cloudflare

Cloud/SaaS

Cloudflare Durable Objects; also deploys to Node, GitHub Actions, GitLab CI

Production ready

Yes

github.com/withastro/flue

Haystack

GitHub Stars

24.7k

Forks

2.7k

Language

Python

License

Apache-2.0

Created

2019-11-14

Created by

deepset

github.com/deepset-ai/haystack

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptFlueHaystack
Agent`createAgent({ model, instructions, tools })` — declarative config, framework runs the loop`Agent` component with `ChatGenerator`, tool definitions, and message routing
ToolsRegistered with valibot schemas: `{ name, description, schema, execute }``Tool` dataclass with function reference, name, description, parameters schema
StateDurable Streams — replayable, checkpointed event log stored in Cloudflare Durable Objects
DeploymentOne config controls deploys to Cloudflare, Node, GitHub Actions, or GitLab CIPipeline YAML serialization, `Hayhooks` REST server
RuntimeThe Pi harness — same runtime as OpenClaw, so agents share tooling with that ecosystem
Cloudflare-nativeDurable Objects give per-agent persistence and locking without an external DB
Pipeline Architecture`Pipeline()` with `add_component()` and `connect()` — a directed graph of typed components
RAG / Retrieval`DocumentStore` + `Retriever` + `PromptBuilder` + `Generator` wired in a `Pipeline`
Memory`ChatMessageStore` with `ConversationMemory` component in pipeline

Or build your own in 60 lines

Both Flue and Haystack implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.

No framework. No dependencies. No opinions. Just the code.

Build it from scratch →