* Implement DMN mode support in crew creation and execution
- Added `is_dmn_mode_enabled` utility to check for enterprise non-interactive mode based on the `CREWAI_DMN` environment variable.
- Updated `create` function in `cli.py` to enforce required parameters when DMN mode is active, raising appropriate usage errors.
- Enhanced `create_crew` and `create_json_crew` functions to skip provider prompts and handle folder existence checks in DMN mode.
- Introduced non-interactive defaults for agent and task creation in DMN mode, ensuring seamless project setup without user input.
- Modified `run_crew` to bypass TUI and handle runtime inputs directly when in DMN mode, improving execution flow for JSON-defined crews.
- Added tests to validate DMN mode behavior, ensuring correct handling of required inputs and non-interactive defaults.
* Implement DMN mode support in crew creation and execution
- Introduced `is_dmn_mode_enabled()` utility to check for non-interactive mode based on the `CREWAI_DMN` environment variable.
- Updated `create` function to enforce required parameters when DMN mode is active, raising appropriate usage errors.
- Modified `create_crew` and `create_json_crew` functions to skip provider prompts and utilize non-interactive defaults in DMN mode.
- Enhanced `run_crew` to bypass TUI and handle runtime inputs directly in DMN mode, ensuring smooth execution without user interaction.
- Added tests to validate DMN mode behavior, including requirements for type and name, and ensuring proper handling of existing folders and missing inputs.
* Enhance JSON crew project handling and validation
- Updated `create_json_crew.py` to specify input files with a brief path.
- Refactored `crew_loader.py` to improve agent and task loading logic, including the introduction of a `build_agent` function and better handling of task classes.
- Enhanced `json_loader.py` with additional validation for agent and task definitions, including support for Python references and conditional tasks.
- Added tests in `test_crew_loader.py` and `test_json_loader.py` to ensure proper loading of agents, tasks, and validation of project structures, including custom types and conditional tasks.
- Improved error handling and validation safety across the project loading process.
* Enhance JSON crew configuration options in create_json_crew.py
- Added optional fields for custom agent subclasses and advanced task options, including condition checks and output specifications.
- Improved documentation comments for better clarity on agent and task configurations.
- Updated JSON crew handling to support additional callbacks for pre- and post-execution processes.
* Enhance JSON crew template tests in test_create_crew.py
- Added assertions for new optional fields in crew and agent templates, including conditional tasks, custom converters, and input file specifications.
- Improved validation checks for manager agents and callback references to ensure proper configuration in JSON crew definitions.
- Expanded documentation references within the tests to provide clearer guidance on the expected structure and usage of crew templates.
* Fix JSON crew PR review issues
* Update crewAI CLI with various enhancements and fixes
- Updated `create_json_crew.py` to require `crewai[tools]>=1.14.7`.
- Enhanced `git.py` with improved repository initialization, including automatic initial commit creation and exclusion patterns for initial commits.
- Modified `install_crew.py` to allow error handling during installation with an optional `raise_on_error` parameter.
- Expanded `plus_api.py` to include methods for creating and updating crews from ZIP files.
- Introduced a new `archive.py` for creating deployable ZIP archives of CrewAI projects, ensuring local artifacts are excluded.
- Updated `run_crew.py` to manage JSON crew dependencies and run crews in the project's environment.
- Enhanced deployment logic in `main.py` to handle ZIP uploads and improve user feedback during deployment processes.
- Added tests for new functionalities and ensured existing tests reflect recent changes in behavior and requirements.
* fix(cli): address deploy zip review feedback
* fix(cli): sync missing lockfile before deploy
* fix(cli): preserve remote deploy on git setup warnings
* test(cli): use single deploy main import style
* fix(cli): skip project install for json crew sync
* fix(cli): load json runner from source checkout
* fix(cli): skip json crew sync when locked
* fix(cli): address deploy zip review feedback
* fix(cli): pass env on zip redeploy
* fix(cli): harden json run and zip fallback
* fix(cli): validate before deploy lock install
* fix(cli): respect poetry lock for json runs
* fix(cli): align json zip wrapper detection
* fix(deps): bump starlette audit floor
* fix(cli): avoid auth retry for deploy exits
* fix(cli): update json zip script entrypoints
* feat(cli): introduce JSON crew project support and TUI enhancements
- Added support for creating and running JSON-defined crew projects, allowing users to scaffold projects with a new `create_json_crew.py` file.
- Implemented a full-screen Textual TUI for crew execution in `crew_run_tui.py`, enhancing user interaction with a two-column layout.
- Updated `run_crew.py` to prioritize JSON crew projects and added daemon mode for running without TUI.
- Introduced interactive pickers in `tui_picker.py` for improved CLI prompts.
- Enhanced validation for JSON crew files in `validate.py` to ensure proper structure and agent definitions.
- Updated `.gitignore` to exclude demo and crewai directories.
* feat: update LLM model references to gpt-5.4-mini
- Changed default LLM model from gpt-4o-mini to gpt-5.4-mini across various files, including CLI options, JSON crew configurations, and agent definitions.
- Enhanced benchmark and human feedback functionalities to utilize the new model.
- Improved user interface elements in the TUI for better interaction and feedback during execution.
- Added support for new skills directory in JSON crew project creation.
* feat(benchmark): add crew-level benchmarking functionality
- Introduced a new `benchmark` command in the CLI for crew-level benchmarking, allowing users to specify agents, models, and timeout settings.
- Implemented `CrewBenchmarkCase` to handle crew-level benchmark cases with inputs and criteria.
- Enhanced the benchmark runner to support progress tracking and detailed reporting of results for multiple models.
- Added tests for loading crew benchmark cases and validating their structure.
- Updated existing benchmark functions to accommodate the new crew-level execution model.
* feat(cli): enhance JSON crew project functionality and TUI improvements
- Added optional agent-level guardrails and advanced options in JSON crew configurations to improve output validation and flexibility.
- Updated the TUI to better handle plan step statuses, including visual indicators for task completion and failure.
- Introduced methods for parsing and managing step observation events, ensuring accurate updates to task statuses during execution.
- Enhanced validation for JSON crew projects, ensuring proper structure and error handling for agent and task definitions.
- Added comprehensive tests for new features and validation logic, ensuring robustness in JSON crew project handling.
* refactor(cli): streamline JSON crew project handling and improve validation
- Refactored JSON crew project loading and validation logic to enhance clarity and maintainability.
- Introduced utility functions for finding JSON crew files, improving code reuse across modules.
- Removed deprecated benchmark functionality and associated tests to simplify the codebase.
- Updated CLI commands to utilize the new JSON project structure, ensuring compatibility with recent changes.
- Enhanced test coverage for JSON crew project features, ensuring robust validation and error handling.
* feat(cli): enhance activity log navigation and focus management
- Added functionality to focus on the activity log when navigating through log entries.
- Implemented refresh logic for the log panel to ensure updates are displayed correctly during navigation.
- Improved keyboard navigation for log entries, allowing users to expand and scroll through logs seamlessly.
- Added tests to verify the correct behavior of log navigation and focus management in the TUI.
* feat(cli): enhance JSON crew project interaction and input handling
- Introduced a new function to enable prompt line editing for better user experience during input prompts.
- Updated the JSON crew project wizards to show interpolation hints for dynamic values, improving user guidance.
- Enhanced the handling of missing input placeholders by prompting users for required values during crew setup.
- Refactored the crew run logic to ensure proper loading and preparation of JSON-defined crews, including runtime input management.
- Added tests to verify the correct behavior of new input handling features and JSON crew project interactions.
* feat(cli): improve crew project input prompts and event handling
- Enhanced the `_prompt_text` function to allow for configurable spacing before prompts, improving user experience during input collection.
- Updated the wizards for agent and task creation to utilize the new prompt configuration, ensuring a more compact and streamlined interaction.
- Introduced new plan step lifecycle events (`PlanStepStartedEvent`, `PlanStepCompletedEvent`) to better track the execution status of plan steps.
- Refactored the step executor to emit these events during the execution of tasks, improving observability and debugging capabilities.
- Added tests to verify the correct behavior of new prompt handling and event emissions during crew project execution.
* fix: refine json-first crew interactions
* fix: prioritize common json crew tools
* fix: make json crew more tools expandable
* fix: show json crew tools by category
* feat(memory): update default embedder to OpenAI text-embedding-3-large and enhance memory compatibility
- Changed the default embedding model for Memory to OpenAI text-embedding-3-large, which uses 3072-dimensional vectors.
- Added warnings regarding compatibility issues with existing local memory stores created with 1536-dimensional embeddings.
- Updated documentation to reflect the new default embedder and its configuration options.
- Enhanced the CLI and codebase to support the new embedding model across various components, ensuring a seamless transition for users.
* fix: address PR review feedback for JSON-first crews
Review blockers:
- Forward trained_agents_file to JSON crews: crewai run -f now exports
CREWAI_TRAINED_AGENTS_FILE for the in-process JSON crew path
- Wizard agent picker: Esc/cancel now reprompts instead of silently
assigning the first agent
- JSON tool resolution hard-fails: unknown tool names, missing custom
tool files, and invalid custom tool modules raise JSONProjectError
with actionable messages instead of warn-and-continue
- Embedding dimension mismatch: LanceDB and Qdrant Edge storages raise
EmbeddingDimensionMismatchError with reset/pin guidance instead of
silently zero-filling vectors or returning empty search results
- Custom tool code execution documented in loader docstring and the
scaffolded project README
CI fixes:
- ruff format across lib/
- All 133 PR-introduced mypy errors fixed (llm.py lazy-litellm and
cli.py lazy command shims now use TYPE_CHECKING imports; textual
is_mounted misuse fixed; pick_many overloads; misc annotations)
Bot review comments:
- Empty except blocks now have explanatory comments or debug logging
- Removed unused _C_BG/_C_PANEL/_C_BORDER globals and redundant
import re; tests use a single import style for create_json_crew
Tests: trained-agents propagation, wizard cancel, tool resolution
failures, and dimension mismatch guidance.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix: address second round of PR review comments
Cursor Bugbot:
- Wizard agent slugs: strip to [a-z0-9_] and fall back to agent_<n> so
symbol-only roles can't produce an empty agents/.jsonc filename
- Wizard task names: dedupe against prior task names and fall back to
task_<n> for symbol-only descriptions
CodeRabbit:
- Agent.message(): import Task explicitly at runtime instead of relying
on the namespace injection done by crewai/__init__
- Async executor: move the native-tools-unsupported fallback from
_ainvoke_loop_react (self-recursion) to _ainvoke_loop_native_tools,
mirroring the sync implementation
- StepExecutor downgrade: keep the in-step conversation and append the
text-tooling instructions instead of rebuilding messages, so completed
native tool calls are not re-executed
- crewai-files: extension-based MIME lookup now runs before byte
sniffing so csv/xml types are not degraded to text/plain
- Memory storages: validate every record in a save() batch against a
consistent embedding dimension (LanceDB previously checked only the
first record); added mixed-batch tests
- _print_post_tui_summary now typed against CrewRunApp
- Docs: Azure OpenAI default embedder change called out in the memory
migration warning and provider table
Code quality bots:
- Removed unused _C_YELLOW/_C_CYAN (crew_run_tui) and _GREEN (tui_picker)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat(cli): accordion tool picker in JSON crew wizard
The flat tool list had grown to ~90 rows. The picker now shows:
- Common tools always visible at the top
- Every other category as a single expandable row with tool and
selection counts (e.g. "Search & Research (27 tools, 2 selected)")
- Expanding a category collapses the previously expanded one
- Selections persist across expand/collapse via new preselected
support in pick_many; cursor follows the toggled category row
tui_picker gains preselected + initial_cursor options on pick_many,
and Esc in multi-select now confirms the current selection instead of
discarding it (required so collapsing can't silently drop choices).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* refactor(cli): remove --daemon flag from crewai run
The flag only affected JSON crew projects — classic and flow projects
ignored it entirely, which made the behavior inconsistent. Removed the
option, the daemon code path (_run_json_crew_daemon), and its helper
(_load_json_crew_with_inputs).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test: update run command tests after --daemon removal
lib/crewai/tests/cli/test_run_crew.py still asserted the old
run_crew(trained_agents_file=..., daemon=False) call signature.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(cli): exit codes, mid-run quit, async statuses, hyphen placeholders
Addresses the latest Bugbot review round:
- Failed JSON crew runs now exit non-zero (SystemExit(1)) so scripts
and CI don't treat failures as success, mirroring the classic path
- Quitting the TUI mid-run now ends the process (os._exit(130));
kickoff runs in a thread worker that cannot be force-cancelled, so
letting the CLI return would leave LLM/tool work burning tokens in
the background
- Sidebar task statuses are now async-safe: completion/failure events
resolve the task's own row via identity instead of assuming the most
recently started task, and starting a task no longer blanket-marks
earlier active rows as done
- The runtime-input prompt regex now accepts hyphenated placeholder
names ({my-topic}), matching kickoff's interpolation pattern
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix: validation safety, custom tool sandboxing, TUI log integrity, memory error surfacing
- Deploy validation no longer executes project code: validation mode
checks tool declarations structurally (well-formed entries, custom
tool file exists) without importing or instantiating anything.
custom:<name> resolution only happens on the actual run path.
- custom:<name> is constrained to [A-Za-z_][A-Za-z0-9_]* and the
resolved path must stay inside the project's tools/ directory, so
custom:../foo or absolute-path names cannot execute code outside it.
Tool paths resolve relative to the crew project root, not cwd.
- TUI task logs are built from per-task state captured at task start
(idx, description, agent, start time); an out-of-order completion
takes its output from the event and no longer steals or resets the
current task's streamed steps/output.
- EmbeddingDimensionMismatchError now inherits ValueError instead of
RuntimeError so background saves surface it through
MemorySaveFailedEvent instead of silently dropping the save; the
shutdown catch in _background_encode_batch is narrowed to the
"cannot schedule new futures" case.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(cli): declared project type wins over crew.json presence
A flow project that also contains a crew.json(c) file now runs and
validates as the flow it declares in pyproject.toml instead of being
hijacked by the JSON crew path. Both crewai run (_has_json_crew) and
deploy validation (_is_json_crew) check tool.crewai.type; a missing or
unreadable pyproject still means a bare JSON crew project.
Also documents why StepObservationFailedEvent intentionally marks the
plan step "done": the event signals an observer failure, not a step
failure, and the executor continues past it.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(cli): type the declared_type locals so mypy stays clean
Comparing an Any-typed .get() chain returns Any, which tripped
no-any-return on the previous commit.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Let users run a Flow from a Flow Definition YAML file or inline string
without writing Python, passing kickoff inputs as `--inputs` JSON. The
flag is gated behind an experimental warning since the definition format
may still change.
Moves the registry/cache pieces of PR #5867 under crewai.experimental.skills
and the CLI commands under `crewai experimental skill`. The stable local-file
skills feature (loader, parser, validation, models) stays in crewai.skills.
Both entry points now require CREWAI_EXPERIMENTAL=1:
- resolve_registry_ref() calls require_experimental_skills() before resolving
- The `crewai experimental` CLI group raises UsageError when the flag is unset
SkillDownloadStarted/CompletedEvent move out of crewai.events.types.skill_events
into crewai.experimental.skills.events.
* refactor(skills): move 'version' off SkillFrontmatter into metadata
The skill version is now stored as `metadata.version` rather than a
top-level field on `SkillFrontmatter`. A `before` validator lifts any
top-level YAML `version:` into `metadata['version']` so existing SKILL.md
files keep parsing.
* feat: add Skills Repository — registry, cache, CLI, and SDK integration
Adds a Skills Repository feature allowing users to publish, install,
and use skills from the CrewAI registry with @org/skill-name refs.
## What's New
### SDK (lib/crewai/)
- SkillFrontmatter: added optional 'version' field (backward compatible)
- SkillCacheManager: manages ~/.crewai/skills/{org}/{name}/ with
.crewai_meta.json tracking, path-traversal-safe tar extraction
- SkillRegistry: parse @org/skill-name refs, local-first resolution
(./skills/ > cache > download), interactive prompt on first use,
CI-mode guard (CREWAI_NONINTERACTIVE/CI env vars)
- Agent.skills and Crew.skills widened to accept str refs (@org/name)
- set_skills() resolves registry refs with org-prefixed dedup keys
- New events: SkillDownloadStartedEvent, SkillDownloadCompletedEvent
### CLI (lib/cli/)
- crewai skill create <name> — context-aware (project vs standalone)
- crewai skill install @org/name — downloads to ./skills/ or cache
- crewai skill publish — ZIP + upload to org registry
- crewai skill list — show installed skills
### PlusAPI (lib/crewai-core/)
- Added SKILLS_RESOURCE, get_skill(), publish_skill(), list_skills()
### Scaffolding
- crew and flow templates now include skills/ directory
### Tests
- 91 SDK skill tests + 15 CLI skill tests, all passing
* fix: address all CI failures and CodeRabbit review comments
Lint:
- Remove unused imports (click, pytest, json)
- Replace try-except-pass with logging (S110)
- Fix unprotected zipfile.extractall (S202)
Security:
- Path traversal: startswith → is_relative_to for tar extraction
- Add path traversal protection to ZIP extraction via _safe_extract_zip
- Both cache.py and CLI main.py hardened
Type checker:
- Fix import path: crewai.events.event_bus (not crewai_event_bus)
- Remove unused type: ignore comments
- Fix type mismatches in set_skills() variable types
Code quality:
- Fix f-string interpolation in SkillNotCachedError
- Use ValidationError instead of Exception in test
* style: ruff format + autofix remaining lint errors
* refactor: reuse SDK parser and SkillCacheManager in CLI
- _parse_frontmatter() now delegates to crewai.skills.parser.parse_frontmatter
when available, with a minimal fallback for CLI-only installs
- install() global cache path now reuses SkillCacheManager.store() instead
of duplicating metadata writing logic
* refactor: add _print_current_organization to SkillCommand (matches ToolCommand pattern)
* fix: write .crewai_meta.json in fallback install path
CodeRabbit caught that the ImportError fallback in install() didn't write
cache metadata, making skills invisible to 'crewai skill list'.
* fix: tighten @org/name ref validation to prevent path traversal
Reject refs with multiple slashes (@org/a/b), dot segments (@../skill),
or leading dots in org/name. Applied to both CLI install() and SDK
parse_registry_ref() so the contract is enforced consistently.
* fix: update test assertions to match tightened error messages
* fix: align OSS client with AMP API contract
- download_skill(): fetch download_url (presigned URL) instead of
expecting inline base64. Falls back to 'file' field for compat.
- Read 'latest_version' field, fall back to 'version'
- Same fixes applied to CLI install() command
* fix: publish as tar.gz (matches AMP content_type validation) + add zip fallback to SDK cache
CLI publish:
- _build_skill_zip → _build_skill_tarball (tar.gz format)
- Content type: application/x-gzip (matches SkillVersion validation)
SDK cache:
- store() now tries tar.gz first, falls back to zip extraction
- Added _safe_extract_zip for path-traversal-safe zip handling
- Both formats work for download/install regardless of server format
---------
Co-authored-by: João Moura <joaomdmoura@gmail.com>
Adds typed containers for wire payloads, literal aliases for HTTP method
and log type, and Ffnal markers on resource constants. Updates
upstream returns in project_utils.py and deploy/main.py to match
the new contracts.
