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Haystack Guidelines for AI Agents

Environment

Haystack uses Hatch for environment and dependency management.

Do not run python or pip directly.

Before running code on this project, you must be able to run hatch --version and get a correct output.

If not, ask the user where Hatch is or if they want to install it. For installation instructions, refer to https://hatch.pypa.io/latest/install/#installation.

Run scripts with test dependencies

hatch -e test run python SCRIPT.py

Open a shell with test dependencies

hatch -e test shell

Install temporary dependencies (for experiments only)

uv pip install PACKAGE

Delete the environment

hatch env prune

Tests

Tests run via Hatch and support pytest arguments.

Prefer running tests on a specific module or using -k, since the full suite is large.

Run unit tests

hatch run test:unit

Run integration tests

hatch run test:integration

Quality Checks

Type checking with mypy

hatch run test:types

Type checking targets Python 3.12, above the 3.11 floor in requires-python, because numpy's stubs use PEP 695 type statements that mypy rejects below 3.12. So mypy will not catch code that breaks on 3.11; the unit test matrix runs on 3.11 and covers that.

To fix type issues, avoid type: ignore, casts, or assertions when possible. If they are necessary, explain why.

Format and lint

hatch run fmt

Release Notes

Every user-facing PR (not docs, not CI) must include a release note:

hatch run release-note SHORT_DESCRIPTION

Edit the generated file in releasenotes/notes/. Release notes use reStructuredText formatting; see the release notes section in CONTRIBUTING.md for details.

Pull Requests

Community contributors can have only one open pull request at a time in this repository. Before opening a PR on the user's behalf, check whether they already have one open (gh pr list --author @me); if so, tell them instead of opening another.

In addition, there are rules inferred from previous code reviews. Follow them like a reviewer's note: they encode reasons, not ceremony, so when a rule genuinely does not fit the change at hand, say why rather than contorting the code to satisfy it.

Also see directory-specific guidelines:

API Design

  • Target the 3.0 API: ToolInvoker, AsyncPipeline, and non-chat generators were removed — let Agent own tool calls, use Pipeline.run()/Pipeline.run_async(), and use ChatGenerators like OpenAIChatGenerator
  • Serialize with the Haystack helpers (component_to_dict, default_from_dict): in from_dict() pass only the fields present to __init__() so defaults stay centralized, shallow-copy the payload instead of deepcopy(), and keep tools/toolsets nested under data — preserves wire compatibility and round-trip fidelity
  • Keep public signatures explicit and compatible: optional params keyword-only after * and appended rather than inserted, mirrored constructor params spelled out instead of *args/**kwargs, and chat generators keeping the run(messages, *, streaming_callback, generation_kwargs, tools) order — avoids breaking positional callers
  • Create API clients and load models in warm_up(), not __init__; implement warm_up() only for real setup and make it idempotent — keeps components constructible and serializable without credentials or network
  • Implement run_async only when there is a real async execution path — Pipeline.run_async() already falls back to asyncio.to_thread(component.run, ...) when it is missing; where it exists, keep run_async/warm_up_async/close_async genuinely async with separate async state and hooks, and share sync/async logic through private helpers — prevents event-loop blocking and sync/async drift
  • Pass explicit required_variables for optional prompt vars — avoids requiring optional inputs
  • Subclass Toolset only when inherited collection APIs match — prefer composition or raise NotImplementedError
  • Read live Haystack State resources via state.data.get(...) or state.data[...] — avoids deep-copy bugs
  • Prefer existing haystack/core/pipeline APIs or inline logic — avoid duplicate or one-off public APIs
  • Reuse splitter chunk metadata in haystack/components/preprocessors/ — keep traceability fields like page_number, source_id, header, parent_headers, and split positions; add new keys only for existing downstream contracts

Documentation

  • Keep inline comments and private-helper docs to what is non-obvious — remove restatements, keep durable caveats and rationale
  • Keep docstrings current with signatures and behavior, in the existing Haystack style: each public :param by meaning, default, and constraints; :returns: contracts; exceptions in the existing :raises ValueError: style; aliases like ToolsType reflected — stale docs mislead users and assistants
  • Keep doc examples minimal, runnable, and local: default constructors with required env vars like OPENAI_API_KEY noted nearby, only the imports the snippet uses, no restated defaults (name a model only for model-specific behavior), expected output as comments, generally # >> ...
  • When behavior, fields, or names change, update every relevant surface in the same PR: haystack/components/ docstrings and examples, documentation pages, and experimental wording, pydoc IDs, and generated markdown filenames when promoting features
  • Choose which documentation versions to update: update docs-website/docs/ by default; also update the latest stable versioned_docs/version-X.Y/ pages for corrections to stable docs or already-released core integration features.

Code Style

  • Use keyword args for multi-parameter calls — improves readability and prevents mix-ups
  • Keep lint suppressions exact and current — a # noqa: PLR0915 only while the code needs it
  • Scope diffs to the stated goal and leave them clean: no unrelated refactors or formatting, no stray print(), no filename-only header comments, no unused private helpers or redundant branches, no args that only restate callee defaults
  • Use {placeholder} logger templates with kwargs — preserves structured logs and avoids eager formatting
  • Prefix internal helpers and module-level constants with _ and keep private constants local; never name locals after imported functions, decorators, or utilities — clarifies API boundaries and avoids shadowing

Type System

  • Fix types at the source; where a suppression is unavoidable use a coded # type: ignore[...] with a nearby why-safe comment, and add @overload only for real API variants
  • Resolve callable annotations with typing.get_type_hints() — avoids bugs from raw or string inspect.signature() annotations
  • Keep union aliases non-redundant and concept-named — include base types only when subclasses inherit
  • Use T | None, not Optional[T], in annotations and casts — keeps typing concise
  • Annotate helper params broadly and accurately — type | types.UnionType for class-or-PEP-604 inputs, Any for arbitrary typing objects

Imports

  • Import via public APIs in the narrowest clear form (keep module imports like import httpx when namespaces matter); keep only used imports plus deliberate compatibility re-exports
  • Use top-level absolute haystack... imports in haystack/components/ and haystack/core/component/*.py — keeps component dependencies traceable
  • Use LazyImport only for optional third-party deps — otherwise import directly

Config

  • Pass allowed_modules= or set HAYSTACK_DESERIALIZATION_ALLOWLIST for YAML loads — never widen haystack/core/serialization_security.py allowlists in library code

Testing

  • Centralize DocumentStore tests in haystack/testing/document_store.py mixins — use DocumentStoreBaseTests as the minimum suite and compose capability mixins into DocumentStoreBaseExtendedTests; add explicit integration skips only for unsupported backend features.

General

  • Define package exports in __init__.py via __all__; avoid __all__ in implementation modules — keeps public APIs intentional
  • Update pyproject.toml for new package APIs — declare deps and minimum versions used