Document crewai create tool/skill/template, lifecycle commands, and
deprecated-but-supported scaffolding aliases in the project template
AGENTS.md. Remove the no-op --skip_provider flag from the flow example.
* fix(flow): clarify route/handler name collision validation errors
When @listen(...) includes the handler's own name, FlowDefinition validation
fails. This change improves the error text and how it surfaces for Python
Flow classes.
What changed
- _self_listen_error in flow_definition.py: two message variants (conversational
vs default), both include the listen condition
- build_flow_definition in dsl/_utils.py: wraps FlowDefinition ValidationError
with the Python Flow class name
- Tests for declarative and DSL-built flows; docs follow in a separate commit
When each error surfaces
1. Conversational message — FlowDefinition validation when
conversational.enabled is true and listen references the handler name.
Example: @listen("create_video") on def create_video in a conversational flow.
Surfaces via:
- FlowDefinition.from_declaration(dict/yaml) → pydantic ValidationError for
FlowDefinition (Value error, methods.create_video.listen listen condition...)
- MyFlow.flow_definition() / MyFlow() → ValueError Invalid flow definition
for MyFlow: ... (wrapped by pydantic as ValidationError for MyFlow on
instantiation)
2. Default (non-conversational) message — same trigger check when the flow is
not conversational. Example: @listen("publish") on def publish.
Surfaces via the same paths as (1).
3. Class-name wrapper — only on the Python DSL path when build_flow_definition
catches FlowDefinition ValidationError. Prepends Invalid flow definition for
{ClassName}: to the underlying message from (1) or (2). Does not apply to
from_declaration without a Flow class.
* docs(flow): explain conversational handler naming vs route labels
Document why @listen route labels must differ from handler method names and
recommend the handle_* naming pattern.
* docs(cli): warn against matching @listen labels to handler names
Add AGENTS.md guidance for crew and flow scaffolding so coding assistants
do not name handlers the same as their @listen route or event labels.
* docs(cli): clarify @listen self-reference fails at validation
Document that matching @listen labels to handler names raises a validation
error at flow instantiation, and that the runtime loop only occurs if
validation is bypassed.
* feat(cli): add canonical `crewai create tool` command
Unify tool scaffolding under the create verb and deprecate
`crewai tool create` with a yellow warning while keeping backward
compatibility.
* feat(cli): add canonical `crewai create skill` command
Unify skill scaffolding under the create verb and deprecate
`crewai skill create` with a yellow warning while keeping backward
compatibility.
* feat(cli): add canonical `crewai create template` command
Unify template scaffolding under the create verb and deprecate
`crewai template add` with a yellow warning while keeping backward
compatibility.
* docs: document unified `crewai create` scaffolding commands
Document canonical create forms for tool, skill, and template projects,
note deprecated aliases, and update skills and agents-md guides.
* feat(cli): extend create picker and DMN guidance for all types
Show tool, skill, and template in the interactive create picker and
list every supported type in the CREWAI_DMN usage error.
* fix(cli): allow create tool/skill/template in CREWAI_DMN mode
Only set skip_provider in DMN mode for crew creation, since tool,
skill, and template paths reject that flag as a crew-only option.
* test(cli): patch TemplateCommand at cli lookup site in DMN test
create() resolves TemplateCommand from crewai_cli.cli, not from
remote_template.main directly.
* feat: add project_id to link OSS usage to an enterprise account
Adds a stable per-project identifier so a project's OSS traces and runs can
be attributed to an account after signup. There was no such identifier
before: [tool.crewai] held only `type`, the deploy UUID was printed to the
console but never persisted, Settings.org_uuid is global rather than
per-project, and trace batches carried only crew_fingerprint/crew_name.
The id lives in the project's pyproject.toml, so it is committed with the
repository and stays stable across machines, teammates, CI, and containers -
unlike a machine- or user-derived identifier, which is unstable in exactly
the containerized production environments that matter most.
crewai-core:
- get_project_id(): read-only lookup of [tool.crewai].project_id. Safe for
library code; never creates or modifies anything.
- get_or_create_project_id(): mints a uuid4 and persists it, returning
(id, created) so callers can tell the user. Best-effort - returns
(None, False) for a missing, malformed, or read-only pyproject.toml rather
than raising.
- Insertion edits the raw TOML text instead of round-tripping through a
writer, so comments, key order, and formatting elsewhere survive. The key
is placed at the end of the [tool.crewai] table, before the next table
header, so it cannot land in a neighbouring section.
- LoginPayload and TraceExecutionContext gain optional project_id.
Sent on two paths:
- Traces: project_id is added to execution_context, which is sent on both
the ephemeral and authenticated paths, so a project's traces remain
attributable before and after the user creates an account.
- Login: `crewai login` already sends the pseudonymous user_identifier on an
authenticated request; adding project_id means one request carries account
+ user + project, which is the link itself.
Minting is restricted to CLI commands the user explicitly invoked - `crewai
create` for new projects and `crewai run` to backfill existing ones - and is
announced when it happens. Library code only ever reads. Silently rewriting
a user's pyproject.toml during Crew.kickoff() would be surprising.
Privacy: project_id is a random uuid4 in a file the user commits. It is
visible in a diff, contains nothing personal, and identifies a project
rather than a person - so this needs none of the notice changes that
attaching a user identifier to all telemetry would require.
Tests: 18 new tests covering minting, stability, table placement, comment
and formatting preservation, five pyproject layouts, the neighbouring-table
regression, and graceful handling of missing/malformed/read-only files.
Verified end-to-end that both create paths mint distinct ids, that the trace
payload carries project_id on both the ephemeral and authenticated paths,
and that the login payload carries user_identifier and project_id together.
Follow-ups, deliberately not included: adding project_id to telemetry spans,
and backend persistence of the (account, user_identifier, project_id) triple.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UNumDnNbiyw3pv1WakAe6t
* refactor: drop the console announcement when minting project_id
Minting now happens silently. With no message to print, the (id, created)
tuple had no consumer, so simplify the API rather than keep the flag around
for a hypothetical caller:
- get_or_create_project_id() returns `str | None` instead of
`tuple[str | None, bool]`.
- Remove crewai_cli.utils.ensure_project_id, which existed only to print the
message and discard the flag. The four call sites (crewai create crew,
crewai create flow, crewai run, and tool-repository login) now call
get_or_create_project_id directly.
- Update tests for the simplified signature; still 18 tests covering minting,
stability, table placement, formatting preservation, five pyproject
layouts, and missing/malformed/read-only handling.
Behaviour is otherwise unchanged: minting stays restricted to CLI commands
the user invoked, library code still only reads via get_project_id, and a
missing or read-only pyproject.toml still returns None rather than raising.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UNumDnNbiyw3pv1WakAe6t
* fix: harden project_id minting against TOML corruption; address review
Several reviewers found ways the raw-text edit could produce invalid TOML.
Each is now fixed and covered by a test that fails without the fix.
Duplicate project_id key (Cursor bugbot, Copilot x2):
- get_project_id() reports a blank or non-string value as "absent", so a file
containing `project_id = ""` took the insert path and gained a second
project_id line - a duplicate key, and therefore invalid TOML that no
tomli-based tool could read afterwards.
- _insert_project_id is now _set_project_id: it replaces an existing
assignment inside [tool.crewai] instead of appending unconditionally.
Table header with a trailing comment (CodeRabbit major, Cursor bugbot):
- `[tool.crewai] # config` is valid TOML but failed exact string equality,
so the fallback appended a second [tool.crewai] header - a redefined table,
also invalid TOML, and silent because get_project_id swallows the resulting
decode error.
- Added _is_table_header(), which tolerates a trailing comment and does not
match similar names such as [tool.crewai-extra].
Writing into malformed TOML (Cursor bugbot, Copilot):
- get_or_create_project_id relied on get_project_id, which cannot distinguish
"no id" from "unparsable file", so it appended to files it could not parse.
- The locked path now parses explicitly and bails on a decode error, and
re-parses the updated content before writing, so this feature can never be
the reason a project's pyproject.toml stops parsing.
Concurrency and atomicity (CodeRabbit major):
- Two CLI processes could both see no id, mint different uuids, and clobber
each other, leaving a caller holding an id that is not on disk. Minting now
takes the existing crewai_core cross-process lock, re-reads under it, and
returns the id that persists.
- Writes go through a temp file in the same directory plus os.replace, so an
interruption cannot truncate pyproject.toml. File mode is copied across, and
the temp file is removed on failure.
- os.replace only needs a writable directory, which would have let an atomic
write silently overwrite a file the user marked read-only; writability is
now checked explicitly so that case still returns None.
Line endings (CodeRabbit):
- Path.read_text/write_text normalized CRLF to LF, so minting would rewrite a
CRLF-committed file entirely. Read and write now use newline="" and the
inserted line ending is derived from the existing content.
Default create path skipped minting (Cursor bugbot):
- `crewai create crew` defaults to create_json_crew; only the --classic and
flow paths minted, so most new projects had no id until a later command.
Wired into create_json_crew as well. Verified all three paths now mint
distinct ids.
Do not mint during login (CodeRabbit major):
- ToolCommand.login ran get_or_create_project_id, which is outside the
sanctioned minting commands and is invoked by `crewai tools create` from a
freshly scaffolded directory before the project is persisted. It now uses
the read-only get_project_id. Verified login leaves pyproject.toml
untouched.
Not applied: Copilot asked for a console message when an id is written, in
create_crew and create_flow. Minting was made deliberately silent in the
previous commit, so the (id, created) tuple and the announcement are both
gone by design.
Tests: 32 in test_project_id.py, up from 18. New cases cover blank and
non-string existing ids, three commented-header forms, similar table names,
malformed input, CRLF and LF preservation, concurrent minting convergence,
file-mode preservation, and temp-file cleanup. Confirmed the header and
duplicate-key tests fail when the fixes are reverted.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UNumDnNbiyw3pv1WakAe6t
* fix: never create [tool.crewai], treat whitespace ids as absent, harden test
`crewai run` could rewrite unrelated projects (Cursor bugbot, high):
- get_or_create_project_id ran before the cwd was established as a CrewAI
project, and _set_project_id appended a [tool.crewai] table when none
existed. Any directory with a pyproject.toml could therefore gain one -
including on `crewai run --definition`, which may otherwise succeed.
- _set_project_id no longer creates the table; it returns None when
[tool.crewai] is absent, so a key is only ever added to a table the project
already declares. The templates all ship the table, so no create path needs
the old fallback.
- The minting call in run_crew moved after the --definition early return, so
an explicit-flow run does not touch the cwd at all.
- Presence is checked, not truthiness: an empty [tool.crewai] is still a
CrewAI marker, and get_crewai_project_config returns {} both for that and
for an absent table.
- Verified an unrelated project's pyproject.toml is byte-identical after a
mint attempt.
Whitespace-only project_id accepted as valid (CodeRabbit):
- `project_id = " "` is truthy, so it was returned as an identity and would
have propagated into login payloads and tracing context. It also meant the
'" "' parameter of the replacement test asserted nothing.
- Added _usable_project_id, which strips before deciding, used by both
get_project_id and the locked mint path.
Concurrency test could hang CI (CodeRabbit, major):
- Neither the barrier nor the joins had timeouts, so a thread dying early or
blocking on the lock would hang the job rather than fail it. The result
count was also unchecked, so a dead thread still passed.
- Added timeouts, an explicit liveness assertion, a result-count assertion, a
lock around the shared result list, and corrected the docstring: this covers
the read-modify-write race with threads, not the cross-process backend.
Tests: 35, up from 32. New coverage for the absent-table refusal and three
whitespace forms; the blank-id replacement case now asserts a real uuid
replaced the blank value.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UNumDnNbiyw3pv1WakAe6t
* chore(deps): force gitpython 3.1.57+ for GHSA-p538-c434-8v24 and GHSA-3f7w-8rr8-f37f
Unrelated to project_id; bundled here only because it blocks this PR's
vulnerability scan. Two advisories were published for gitpython 3.1.55 after
main last passed the scan:
- GHSA-p538-c434-8v24: arbitrary file truncation via `git rev-list --output`
argument injection. Fixed in 3.1.56.
- GHSA-3f7w-8rr8-f37f: unguarded git option forwarding in
IndexFile.checkout() and TagReference. Fixed in 3.1.57.
- Bump the override floor to gitpython>=3.1.57 and declare the same floor in
crewai-tools, so consumers installing the published package are covered and
not only this repo's lock.
- 3.1.57 was published 2026-07-26, past gitpython's exclude-newer-package
cutoff of 2026-07-24, so that cutoff moves to 2026-07-27. Without it the
floor is unresolvable.
pip-audit against the updated lock reports no known vulnerabilities.
Verified gitpython 3.1.57 resolves and that crewai_tools and crewai_cli.git
still import.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UNumDnNbiyw3pv1WakAe6t
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
* feat(skills)!: promote Skills Repository out of experimental
The registry-backed Skills Repository (crewai skill create/publish/
install/list, @org/name refs, global cache) is now mainline:
- CLI: `crewai skill ...` is a top-level group; the CREWAI_EXPERIMENTAL
gate and the now-empty `crewai experimental` group are removed.
- Runtime: registry.py, cache.py, and events.py move from
crewai.experimental.skills into crewai.skills next to the loader;
the require_experimental_skills() gate is gone.
crewai.experimental.skills remains as a deprecated re-export shim.
- Docs: concepts/skills now leads with the CLI workflow and documents
the create -> publish -> install lifecycle.
Linear: n/a (requested promotion)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat(skills): org-scoped publish only + docs in all languages
Skills are always scoped to the publishing organization, like tools:
drop the --public/--private flags from `crewai skill publish` and
always send is_public=False to the registry. CLI tests assert the flag
is rejected and the API never receives a public publish.
Translate the new CLI-first Quick Start and the create -> publish ->
install lifecycle section into ar, pt-BR, and ko concepts/skills docs.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(skills): address review comments on the promotion PR
- Back-compat shim now aliases the old submodules in sys.modules so
`crewai.experimental.skills.registry/cache/events` imports (and patch
targets) resolve to the real crewai.skills modules, not just the
package-root re-exports.
- `crewai skill publish` actually enforces the git-state check that
--force claims to skip: unsynced repos block publishing (mirroring
tool publish); standalone skill dirs outside any git repo publish
without a check.
- Explicit UTF-8 encoding on SKILL.md and cache-metadata reads/writes.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(skills): fail closed when git state cannot be validated on publish
Follow deploy's pattern: construct git.Repository(fetch=False) and only
treat "not a Git repository" as skippable — any other git error
(fetch/auth/misconfiguration) now blocks publish with a --force escape
hatch instead of silently bypassing the sync check.
Also single-style imports in the shim test (CodeQL) with the dotted
shim import covered via importlib.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(skills): fetch before sync check on publish; bump mcp past advisories
Publish now refreshes remote-tracking refs (repository.fetch()) before
is_synced(), so ahead/behind is judged against the actual remote rather
than stale local refs; a failing fetch blocks publish with the --force
escape hatch. Adds a fail-closed test for fetch errors.
Raise mcp to >=1.28.1,<2 (locks 1.28.1): the ~=1.26.0 pin blocked
GHSA-hvrp-rf83-w775 / GHSA-jpw9-pfvf-9f58 (fixed 1.27.2) and
GHSA-vj7q-gjh5-988w (fixed 1.28.1), which were failing pip-audit on
this PR.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Vinicius Brasil <vini@hey.com>
* feat(cli): run declarative flows on the TUI with a headless terminal fallback
Declarative flows now run on the CrewRunApp TUI when interactive, matching
declarative crews and conversational flows. Headless contexts — CREWAI_DMN
(deploy), piped output, CI, any non-TTY — fall back to the direct-terminal
kickoff, gated by is_interactive() (folds in the CREWAI_DMN check and requires
a real TTY).
The TUI shows per-method progress: a new STEPS panel driven by flow method
events (FlowStarted / MethodExecutionStarted/Finished/Failed), each labeled
with its declarative call type (crew/agent/expression/…) read from the flow
definition. Crews/agents inside a method keep streaming in the main panel via
the existing crew/task/LLM handlers.
- crew_run_tui.py: _run_flow_worker (flow.kickoff in a thread worker; reuses
_on_crew_done/_on_crew_failed + _stringify_output), _is_flow_run gate so crew
rendering is byte-identical, flow-event subscriptions building _flow_steps,
and the STEPS sidebar + flow-aware header.
- run_declarative_flow.py: is_interactive() branch → _run_declarative_flow_tui
(EventListener, method-type map from flow._definition, crew-parity exit codes
and deploy chaining) or the existing terminal path.
Deviation from the approved plan: gate on is_interactive() rather than
is_dmn_mode_enabled() alone, so non-TTY runs (CI/pipes/CliRunner) never launch
a TUI — this also keeps existing headless flow tests green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): force flow events on for the TUI so STEPS renders under suppress_flow_events
Review follow-up: the STEPS panel and header are driven by flow method events
(FlowStarted / MethodExecution*), but the declarative runtime skips emitting
those when the flow declared config.suppress_flow_events. Interactive TUI runs
would then keep STEPS on "waiting…" and the header on "Starting flow…" while
nested crews still execute.
_run_declarative_flow_tui now forces flow.suppress_flow_events = False for the
interactive run (mirroring how the conversational path mutates the flow for the
TUI). The headless/terminal path never reaches this and keeps the flow's
declared setting. Regression test: test_run_declarative_flow_tui_enables_flow_events.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): clear flow header's current method when a method ends
Review follow-up: the flow header keys off _current_method, which was set on
MethodExecutionStarted but never cleared on Finished/Failed. Between steps (or
after a failed method before kickoff exits) the header kept spinning the old
method name while the STEPS sidebar already showed it done/failed.
_clear_current_method now drops the header's active method when it ends,
falling back to another still-active step (methods can overlap) or none. The
header's idle fallback shows "Working…" once a step has run and "Starting
flow…" only before the first method.
Tests: test_current_method_clears_and_falls_back_across_overlap, plus a
_current_method assertion in test_flow_method_events_build_steps.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix: suppress flow console panels in TUI mode; clear header agent on method change
Two review follow-ups:
1) Method panels break Textual TUI (Cursor): forcing suppress_flow_events off
so the STEPS panel receives events also un-gated the EventListener's Rich
flow/method panels (ConsoleFormatter.print_panel prints is_flow=True panels
regardless of verbose), which interleave with Textual and corrupt the TUI.
print_panel now skips is_flow panels when is_tui_mode() is set (the same
context the TUI worker already establishes and the tracing listeners already
honor). Non-TUI/headless flow runs are unaffected. Test:
test_console_formatter_tui_mode.
2) Flow header showed a stale agent (CodeRabbit): _current_agent persisted
across methods. It's now cleared when a method starts and when the active
method changes, so the header never shows the previous method's agent until
a new agent event arrives. Test: test_flow_method_transitions_clear_current_agent.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): keep flow name over nested crews; show paused flow methods
Two review follow-ups on the flow TUI:
1) Crew kickoff renamed the flow (Cursor): CrewKickoffStartedEvent overwrote
_crew_name / the app title with a nested `call: crew` step's crew name, so
the post-run summary could be labeled with a child crew. The rename is now
gated on `not _is_flow_run`, preserving the flow's name; crew runs still
adopt the crew name. Tests: test_crew_kickoff_does_not_rename_flow_run,
test_crew_kickoff_renames_in_crew_mode.
2) Paused methods showed active (Cursor): the TUI didn't handle
MethodExecutionPausedEvent, so a @human_feedback pause left the STEPS
spinner running (flow status panels are suppressed in TUI mode). It now
marks the step "paused" (⏸, teal) and the header shows "waiting for
feedback" instead of a spinner. Test: test_method_paused_marks_step_paused.
Note: interactively *providing* human feedback from the flow TUI is a separate
follow-up; this only makes the pause visible instead of a silent stuck spinner.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): run human-feedback declarative flows on the terminal, not the TUI
Two review follow-ups, both rooted in @human_feedback methods:
- Paused flow marked complete (Cursor): async human feedback makes kickoff
RETURN a HumanFeedbackPending marker (not raise), which _run_flow_worker
would stringify and report as a successful completion with exit 0.
- Sync feedback breaks TUI (Cursor): default (sync) @human_feedback collects
input via the flow runtime's Rich console.print + blocking input(), which
interleaves with Textual and leaves the user unable to review output or
submit feedback.
run_declarative_flow now routes any flow whose declarative definition declares
human feedback (_flow_uses_human_feedback) to the terminal path, where blocking
input and Rich prompts work natively — regardless of interactivity. Non-feedback
flows still get the TUI. Tests: test_flow_uses_human_feedback_detection,
test_human_feedback_flow_uses_terminal_even_when_interactive.
Fully interactive human feedback inside the TUI remains a separate follow-up.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* refactor(cli): address review — Flow typing, debug logging, flow-vs-crew naming
Review follow-ups from @lucasgomide:
- Type flow helpers as Flow[Any] (via TYPE_CHECKING import) instead of Any and
drop the defensive getattr chains — _definition is a typed PrivateAttr and
name/suppress_flow_events are typed fields, so attribute access is safe.
- Replace the silent `except Exception: pass` blocks with logger.debug(...,
exc_info=True) so unexpected failures are diagnosable in the field
(_flow_method_types, _flow_uses_human_feedback, suppress_flow_events toggle).
- Flow-vs-crew naming: the flow worker now uses group="flow" (was the
misleading "crew"), and the shared completion/failure handlers report the
run with an entity-aware noun ("flow" vs "crew") via _run_noun.
Deferred (separate PR): the os._exit(130) hard-kill on user quit is kept as-is
to match the existing crew convention (run_crew._run_json_crew).
Tests: test_flow_done_uses_flow_wording_for_unfinished_tool; existing crew
wording tests unchanged.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Follow-ups to #6462's caching:
1. Key the catalog cache by the exact API key (via a short, non-reversible
sha256 digest — never the key itself), not just key-present vs absent.
Switching to a different key for the same provider now misses the previous
account's entry and refetches, instead of showing the old account's models.
2. Never cache local providers (Ollama). /api/tags is fast and installed
models change out-of-band, so caching could keep offering a model the user
just deleted until the entry expired. _is_cacheable() gates both cache read
and write; the picker now re-probes every call and reflects what's installed.
3. Shorten the dynamic catalog TTL from 6h to 5m — a stale list (new/removed
models, account changes) is worse than a ~1s refetch, and the cache only
needs to spare repeated fetches within a wizard session.
Tests: distinct-key cache entries, digest never stores the raw key, Ollama not
cached (reflects deletions / never written), and dynamic TTL expiry.
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(cli): unify `crewai run` flow input resolution; prompt from state schema
`crewai run` resolved the configured [tool.crewai] flow, but `--inputs` was
hard-gated behind `--definition` and routed through a separate branch — the two
ways of pointing at the same flow didn't share resolution, and required inputs
were never detected, prompted, or validated (a missing field only blew up at
runtime).
Now inputs and definition come from one place:
- Remove the "--inputs requires --definition" gate (cli.py, run_crew.py,
run_declarative_flow.py). `--inputs` alone resolves the configured flow,
exactly like a bare `crewai run`; `--definition` is purely an override. The
project-env re-exec forwards `--inputs` instead of rejecting it.
- Read the flow's state schema from the runtime Flow instance
(`type(flow.state).model_json_schema()`), which is reliable for both inline
`json_schema` and ref-imported `pydantic` state (the static definition's
json_schema is None for the common ref case).
- Plain `crewai run` detects required state fields (minus those satisfied by
state defaults) and prompts for them interactively, showing each field's
description; skipped in non-interactive / CREWAI_DMN mode.
- Validate against the schema before kickoff: pointed
"Missing required input 'x' — <description>" errors, and warn on unknown keys
with a did-you-mean suggestion (catches typos like `prospect_emai`).
`--inputs` on a non-flow project now errors clearly ("only supported for
declarative flows") instead of the old confusing gate.
Tests: schema-driven prompt/validate/override paths, unknown-key warning,
defaults-satisfy-required, type validation, and re-exec input forwarding.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): forward reserved `id` input to flow kickoff; ruff format
- Cursor: the schema filter treated an `id` key in --inputs as unknown and
dropped it, regressing kickoff's persistence-restore support (inputs["id"]).
Let `id` pass through untouched (test: reserved_id_input_is_forwarded).
- Apply ruff format to run_declarative_flow.py (fixes the lint-run
`ruff format --check` step).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): don't block persistence-restore resume on schema validation
Cursor (High): `crewai run --inputs '{"id":"…"}'` is a persistence resume —
kickoff hydrates full state from storage, so schema-required fields may come
from the restored state rather than --inputs. The new required-field
prompt/validation was erroring/prompting before kickoff, breaking resume. When
`id` is present in --inputs, forward the inputs unchanged and skip the
prompt/validation. Test: test_id_only_input_skips_required_validation.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): load project .env in the declarative-flow runner
The declarative-flow path never loaded .env — flow projects (type = "flow")
missed API keys/config that crew projects pick up. The JSON-crew path loads
Path.cwd()/.env with override=True (run_crew._run_json_crew); mirror that at
the top of run_declarative_flow() so flow projects behave the same regardless
of where crewai is installed. Test: run_declarative_flow_loads_project_env.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* feat(cli): unify runtime-input prompting across declarative flows and crews
Declarative (JSON) crews now resolve inputs the same way declarative flows
do, via a shared crewai_cli.input_prompt module (prompt_for_inputs,
parse_inputs_json, closest_name, is_interactive):
- accept --inputs (previously rejected for crews), forwarded to the crew
subprocess via CREWAI_JSON_CREW_INPUTS and validated before spinning up uv
- layer --inputs over the crew's declared `inputs` defaults
- prompt for missing {placeholder}s with the same UX as flows, and error
cleanly with a pointed per-name message when non-interactive
- warn on unknown keys with a "did you mean" suggestion
Unlike flows — whose state schema is authoritative, so unknown keys are
dropped — the crew placeholder scan is heuristic (agent/task text fields
only), so unrecognized keys are warned about but kept, to avoid discarding a
value a field the scan doesn't cover may rely on.
--inputs remains rejected for classic (Python/YAML) crews, which take their
inputs from main.py. run_declarative_flow's private input helpers move to the
shared module with no behavior change.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* test(crewai): update mirrored CLI test after run_crew input refactor
lib/crewai/tests/cli/test_run_crew.py imports crewai_cli internals and is
collected by the lib/crewai test job (Run Tests). It still imported
_prompt_for_missing_inputs, which was replaced by _resolve_crew_inputs, so
the module failed to import — erroring pytest at collection and cancelling
the rest of the matrix via fail-fast.
Point it at _resolve_crew_inputs and patch the prompt in the shared
crewai_cli.input_prompt module where prompting now lives.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): filter unknown --inputs keys even on flow persistence restore
Review follow-up: the `id` (persistence-restore) branch of
_resolve_flow_inputs returned the raw payload, so typo keys passed alongside
`id` skipped the unknown-key warning/drop and reached kickoff — which can
fail strict (extra="forbid") flow state models. The restore path now still
warns on and drops unknown keys (keeping `id` and known state fields); it
only skips the required-field prompt and pre-kickoff validation, which
persistence hydrates. Regression test: test_id_restore_still_drops_unknown_keys.
Also drop the duplicate module import in test_input_prompt.py (both `import`
and `from ... import` of crewai_cli.input_prompt) flagged by the code-quality
bot; monkeypatching now uses the string target form.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(cli): pull latest LLM models dynamically in the crew wizard
The JSON-crew creation wizard hardcoded a short model list per provider,
which goes stale as vendors ship new models every few weeks. Add a
three-tier resolver that prefers live data and falls back to a curated list.
- New `model_catalog.get_provider_models(provider, fallback)`:
1. Vendor API (openai/anthropic/gemini/groq/cerebras/ollama) when the
provider key is already in the environment — the only reliably-fresh
source (real release dates / display names).
2. Curated hardcoded fallback — hand-verified, used when no key is set.
3. LiteLLM feed — only for providers with no curated list; it lags real
releases, so it must never preempt the curated fallback.
- Rank by date/version parsed from model ids, humanize labels, 6h cache,
short timeouts, silent fallback on any error.
- Wire it into `create_json_crew._select_model()` (picker only).
- Refresh the curated fallback against each vendor's official model docs
(Anthropic Fable 5 / Opus 4.8 / Sonnet 5; OpenAI GPT-5.5(+pro); Gemini
3.5 Flash / 3.1 Pro preview / 3 Flash preview; Groq Llama 4 / GPT-OSS).
- Tests for ranking, chat filtering, caching, and the tier order (17 tests).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): address model_catalog review findings
- Key the Ollama catalog cache by its base URL so a changed OLLAMA_API_BASE /
API_BASE no longer serves the previous host's models for up to the TTL.
- Negatively cache the curated fallback after a failed/empty fetch (short
_NEGATIVE_TTL) so the picker doesn't repeat a timeout-prone vendor/LiteLLM
request on every call — most impactful for a down local Ollama server.
- Guard _read_catalog_cache / _write_catalog_cache against a non-dict cache
root (corrupt JSON array no longer raises AttributeError).
- Replace the two empty `except OSError: pass` blocks with
contextlib.suppress(OSError) plus an explanatory comment (CodeQL empty-except).
- Tests: negative cache, base-keyed Ollama cache, corrupt-cache no-crash (20 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): guard null litellm_provider and paginate Gemini models
- _from_litellm: coerce a present-but-null `litellm_provider` before string
ops so it's skipped instead of raising AttributeError (keeps the documented
"never raises" contract).
- _fetch_gemini: walk models.list pages via nextPageToken (bounded to 10) —
the API is paginated and not guaranteed newest-first, so a single page could
drop models the ranking should consider.
- Tests for both (22 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* ci: ignore nltk PYSEC-2026-597 in pip-audit (no fix, not reachable)
pip-audit newly flags nltk 3.9.4 for PYSEC-2026-597 (CVE-2026-12243), a path
traversal via percent-encoded `..%2f` in nltk.data.load()/find(). It affects
all nltk versions <=3.9.4 with no patched release, so it can't be resolved by a
version bump — same situation as the already-ignored PYSEC-2026-97.
nltk is a transitive dependency (unstructured[local-inference, all-docs] in
crewai-tools) used for text tokenization; we never pass untrusted resource
URLs/paths to nltk.data, so the traversal is not reachable. Add it to the
curated --ignore-vuln list with a justification, matching the existing pattern.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): address second Cursor review round on model_catalog
- Cache ignores new API keys: include API-key presence in the cache key
(`<provider>#key|#nokey`), so a key added after a no-key/negative-cached
lookup triggers a fresh live fetch instead of serving the stale fallback.
- Bad LiteLLM cache crashes picker: `_from_litellm` now requires a dict from
`_load_litellm_data` (a non-mapping JSON root is skipped, not `.items()`'d).
- Stale LiteLLM refetch loop: memoize the feed load once per process
(`_litellm_memo` + `_reset_litellm_memo` test hook) so repeated uncurated-
provider lookups don't each re-attempt a timed download when offline.
- Tests: new-key bypass, corrupt-litellm-cache no-crash, one-fetch-per-process
(25 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): keep partial Gemini results on later-page fetch error
_fetch_gemini paginates; a network/HTTP error on page 2+ previously raised out
through _from_vendor, discarding models already parsed from earlier pages and
forcing the curated fallback. Catch per-page fetch errors and return the
partial set instead (a first-page failure still yields an empty list -> fallback).
Test: test_vendor_gemini_keeps_partial_on_later_page_error (26 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): don't let an invalid fresh LiteLLM cache block download
_fetch_litellm_data treated any truthy JSON root in a fresh provider_cache.json
as the feed and returned it, so a non-mapping root (e.g. a JSON array) was
memoized and the tier never re-downloaded until the file aged out — leaving
uncurated providers with an empty picker despite a recoverable cache. Only
short-circuit on a usable dict; otherwise fall through to the download.
Test renamed to test_invalid_litellm_cache_falls_through_to_download (asserts
recovery via refetch).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): honor OLLAMA_HOST and treat empty vendor list as authoritative
- Ollama host mismatch: _ollama_base now also reads OLLAMA_HOST (the Ollama
runtime convention) after OLLAMA_API_BASE/API_BASE, normalizing a scheme-less
value (e.g. "127.0.0.1:11434" -> "http://127.0.0.1:11434"), so users who set
only OLLAMA_HOST see models from the server the crew will actually use.
- Empty vendor list: a successful vendor fetch returning no models is now
authoritative instead of collapsing to the curated fallback. A reachable
Ollama with nothing installed yields an empty list (the picker prompts for
manual entry) rather than offering hardcoded models that aren't installed; a
failed fetch still falls back. _from_vendor now returns [] on success-empty
and None only when the tier is unavailable.
- Tests: ollama empty->manual, ollama down->fallback, OLLAMA_HOST resolution
(29 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): Gemini first-page failure falls back instead of showing empty
Interaction from the prior two fixes: _fetch_gemini swallowed a first-page
error and returned [], which _from_vendor reported as a successful-empty result
and get_provider_models treated as authoritative — skipping the curated Gemini
fallback and jumping to manual entry. Now a first-page failure (nothing gathered
yet) re-raises so _from_vendor returns None and the curated list is used; a
later-page failure still keeps the partial results.
Test: test_vendor_gemini_first_page_error_uses_fallback (30 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): Gemini GOOGLE_API_KEY + Ollama recovery not blocked by cache
- Gemini ignores GOOGLE_API_KEY: _PROVIDER_KEY_ENV now maps each provider to a
tuple of accepted env vars; Gemini accepts GEMINI_API_KEY or GOOGLE_API_KEY
(matching crewai's own Gemini provider). A new _provider_api_key() resolver
is used by both _from_vendor and the cache key, so a GOOGLE_API_KEY user gets
the live models API instead of the stale curated fallback.
- Ollama recovery blocked by cache: skip the negative (fallback) cache for
Ollama. It's a local, fast-failing server, so re-probing each call is cheap
and lets the picker pick up real installed models as soon as the server comes
up, instead of serving suggestions for the negative-cache TTL.
- Tests: GOOGLE_API_KEY live fetch, Ollama down->recover (32 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* style(cli): ruff format model_catalog.py
Add the blank line ruff format expects after _provider_api_key; no behavior
change. Fixes the lint-run `ruff format --check lib/` step.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): don't treat 'search' substring as a non-chat model marker
The 'search' entry in _NON_CHAT_MARKERS matched anywhere in a model id, dropping
legitimate completion models like gpt-4o-search-preview and anything containing
'research' (e.g. o3-deep-research, since 'search' is a substring). Remove it;
the remaining markers (embedding/audio/image/moderation/etc.) still filter
genuine non-chat models. Test: test_search_substring_not_treated_as_non_chat (33).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* Revert "ci: ignore nltk PYSEC-2026-597 in pip-audit"
Do not suppress an unpatched security advisory to make CI green. Remove
PYSEC-2026-597 from the pip-audit ignore list; leave the scan failing so it
keeps surfacing the nltk path traversal (CVE-2026-12243). This PR should not be
merged until nltk ships a fix (or the vulnerable transitive dep is otherwise
resolved).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): exclude fine-tuned models and checkpoints from the picker
With a live OPENAI_API_KEY, /v1/models returns the user's fine-tunes and
training checkpoints (ft:..., ...:ckpt-step-N). Their recent `created`
timestamps ranked them above the base models and filled every slot, so the
picker showed a wall of `ft:gpt-4o-mini-...:crewai::...` with mangled labels and
no foundation models at all. Skip fine-tunes/checkpoints in the OpenAI-shaped
fetcher so clean base models surface; a user who wants a fine-tune can still
enter it via the picker's "Other" option. Test:
test_openai_excludes_fine_tunes_and_checkpoints (34 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
* fix(cli): cleaner model labels + filter Ollama non-chat models
Anthropic/Gemini already use vendor display names; OpenAI/Groq/Cerebras/Ollama
fall to _humanize for anything outside the curated map, which produced mediocre
labels ("GPT Oss 120b", "qwen3 32b", "Deepseek r1", "llama3.3:70b").
Improve _humanize:
- split on ':' too (Ollama tags: llama3.3:70b -> "Llama3.3 70B")
- uppercase size suffixes (70b -> 70B), acronyms OSS/IT, brand casing
(DeepSeek, ChatGPT, QwQ)
- capitalize the leading letter of fused family+version tokens (qwen3 -> Qwen3)
while preserving OpenAI o-series lowercase (o3, o1-mini)
Also fix _fetch_ollama: /api/tags lists everything installed, so filter
non-chat (embedding) and fine-tune entries the same way the other tiers do.
Tests: expanded test_humanize + test_ollama_excludes_embedding_models (35 total).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RBYGqJHC2TMC6fonFziuuh
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
Flow action inputs now support `${...}` inside strings, not only
strings that are fully wrapped in one expression. This lets authored
flows use simple prompt-like values such as:
* `query: "News about ${state.topic}"`
* `input: "Ticket ${text(state, "ticket.id", "unknown")}"`
* `sources: ["${state.primary_source}", "archive-${state.topic}"]`
Whole-expression values still preserve their runtime type, so
`${state.limit}` remains a number and `${state.domains}` remains a list.
Mixed literal and expression strings render as text.
This removes the need to build labeled strings with CEL concatenation,
which was hard to read, easy to quote incorrectly in YAML, and a poor
fit for the Flow authoring skill examples.
* Document flow agent options
Document and type inline Flow agent options so authored flows can set:
* `llm.model`, `llm.max_tokens`, and `llm.max_completion_tokens`
* `planning_config.max_attempts`
* `allow_delegation`
* `max_iter`
* `max_rpm`
* `max_execution_time` in seconds
Also tell the flow skill to omit optional fields unless needed.
* Potential fix for pull request finding 'Unused import'
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
---------
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
CEL string concatenation currently fails when prompt builders read
missing or null fields. This commit adds `text(root, "path", "default")`
custom CEL helper so prompt text can safely read nested state/output
values.
Point `crewai template list`/`template add` at the crewAIInc-fde GitHub
org so the FDE template_* repos are listed and installed instead of the
crewAIInc ones.
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
* Add generated Flow Definition authoring skill
Generate a portable skill from the Flow Definition schema so agents can
author valid declarative flows with the same reference CrewAI uses to
validate them. New declarative flow projects now write this skill.
```python
from crewai.flow.flow_definition import FlowDefinition
skill = FlowDefinition.skill(skips=(), examples_format="yaml")
```
* `examples_format` accepts `"yaml"` or `"json"`.
* Supported skips: `conversational`, `non_linear_flows`, `each`, `hitl`, `persistence`, `config`, `expression_action`, `script_action`, `tool_action`
The generated skill includes authoring rules, a routed crew example, and
an API reference extracted from the Flow, action, state, agent, crew,
and task Pydantic schemas.
* Fix declarative flow scaffold without framework import
* Fix skipped expression action guidance
* Fix markdown links in skill
* Require explicit CrewAI project definitions
JSON crews and declarative flows now resolve from `[tool.crewai]`
metadata instead of implicit filename discovery. This makes project type
selection deterministic, prevents stray `crew.json(c)` files from changing
CLI behavior, and centralizes definition path validation for run, install,
deploy validation, plotting, and memory reset paths.
`[tool.crewai].definition` must be a project-local file path. Absolute
paths, `~`, missing files, directories, and paths escaping the project root
are rejected so deploy and runtime commands use the same contract.
Breaking changes and migration paths:
* JSON crew projects are no longer discovered from `crew.json` or
`crew.jsonc` alone. Add explicit metadata:
```toml
[tool.crewai]
type = "crew"
definition = "crew.jsonc"
```
* Declarative flow projects must use a valid project-local definition path:
```toml
[tool.crewai]
type = "flow"
definition = "flows/research.yaml"
```
* `Flow.from_definition(definition)` is removed. Use:
```python
Flow.from_declaration(contents=definition)
```
* `FlowDefinition.to_json()` and `FlowDefinition.to_yaml()` are removed.
Use `FlowDefinition.to_dict()` and serialize with the caller's JSON or
YAML library.
* `FlowDefinition.from_dict()` is removed. Use:
```python
FlowDefinition.from_declaration(contents=data)
```
* `FlowDefinition.json_schema()` is removed. Use Pydantic's schema API only
where schema generation is intentionally needed:
```python
FlowDefinition.model_json_schema(by_alias=True)
```
* `crewai_cli.run_crew.find_crew_json_file()` and `_has_json_crew()` are
removed. Use `configured_project_json_crew()` or the shared
`crewai_core.project.configured_project_definition("crew")` helper.
* `crewai reset-memories` now only loads JSON crews declared through
`[tool.crewai].definition`, and invalid declared JSON crew definitions
fail instead of silently falling back to classic crew discovery.
* Address code review comments
* Track conversational flow turn usage in telemetry
* adjusted name to flow:conversation_turn
* only mark on turn completed event
* ensure tui also emits these events
* Fix symlink path traversal in skill archive extraction
`_safe_extractall` (the Python < 3.12 fallback used by `crewai skills`
archive unpacking) validated each member's *name* against the destination
but never validated symlink/hardlink *targets*. A malicious skill tarball
could plant a symlink escaping the destination (e.g. `link -> /home/user/.ssh`)
followed by a regular member written through it (`link/authorized_keys`),
escaping `dest` even though every member name resolves inside it — the
classic symlink-extraction traversal.
The 3.12+ path (`extractall(..., filter="data")`) already blocks this; the
fallback now mirrors it by rejecting absolute link targets and any link
target that resolves outside the destination directory.
Adds regression tests covering absolute and relative escaping symlinks plus
benign in-tree symlinks and ordinary archives.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Harden skill cache archive extraction
* Reject special skill archive members
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Add a single declaration loader shared by API and CLI callers.
- Add FlowDefinition.from_declaration for FlowDefinition instances, dictionaries, YAML/JSON strings, and file paths
- Add Flow.from_declaration to build runnable flows directly from the same inputs
- Route declarative flow CLI loading through Flow.from_declaration so path handling and validation stay centralized
```
# Load just the serializable definition when you do not need to run it yet.
definition = FlowDefinition.from_declaration(path="flows/research.crewai")
definition = FlowDefinition.from_declaration(contents=flow_yaml)
definition = FlowDefinition.from_declaration(contents=flow_dict)
# Build a runnable flow directly from the same declaration inputs.
flow = Flow.from_declaration(path="flows/research.crewai")
flow = Flow.from_declaration(contents=flow_yaml)
flow = Flow.from_declaration(contents=flow_dict)
flow = Flow.from_declaration(contents=definition)
# Run it like any other flow.
result = flow.kickoff(inputs={"topic": "AI agents"})
# The CLI now goes through the same path-based loader.
# crewai run --definition flows/research.crewai
```
