9 Commits

Author	SHA1	Message	Date
João Moura	bb477f8a91	JSON first crews (#6131) ... * 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>	2026-06-14 04:19:48 -03:00
Vini Brasil	bf291a7a55	Drive human feedback from the flow definition (#6133) ... * Drive human feedback from the flow definition @human_feedback previously wrapped methods with the full HITL runtime (feedback request, outcome collapse, learn loop), so flows built from a YAML definition — which carry no decorated callables — could not pause for or route on human feedback. # Conflicts: # lib/crewai/src/crewai/flow/persistence/decorators.py # lib/crewai/src/crewai/flow/runtime/__init__.py * Address code review comments	2026-06-12 14:48:43 -07:00
Vini Brasil	051fa0c1cb	Build FlowDefinition from Flow DSL metadata (#6017) ... * Build FlowDefinition from Flow DSL metadata Introduce `FlowDefinition`, a serializable model built from the Flow DSL's runtime metadata. It becomes the structural contract for Flow methods, triggers, routers, state, and configuration. The visualization layer is the first consumer: `flow_structure` and `build_flow_structure` now project from the definition instead of re-introspecting the class. The runner still executes from live registries, but the definition gives future runners a single static contract to read. This replaces AST source parsing for router return values, crew references, and state schema with runtime metadata plus explicit `@router(paths=...)` or `Literal`/`Enum` return hints. AST parsing was fragile and could silently fail for dynamic or non-inspectable methods. The refactor removes obsolete introspection and serializer code: * Delete `flow_serializer.py`, `flow/utils.py`, and `visualization/schema.py` * Move flow structure modeling into `flow_definition.py` * Simplify visualization building around the static definition contract * Format files	2026-06-03 18:02:56 -03:00
Greyson LaLonde	fd10c64148	chore(crewai): drop self-explanatory comments	2026-05-26 10:23:33 -07:00
alex-clawd	74976b157d	fix: preserve method return value as flow output for @human_feedback with emit (#5099) ... * fix: preserve method return value as flow output for @human_feedback with emit When a @human_feedback decorated method with emit= is the final method in a flow (no downstream listeners triggered), the flow's final output was incorrectly set to the collapsed outcome string (e.g., 'approved') instead of the method's actual return value (e.g., a state dict). Root cause: _process_feedback() returns the collapsed_outcome string when emit is set, and this string was being stored as the method's result in _method_outputs. The fix: 1. In human_feedback.py: After _process_feedback, stash the real method_output on the flow instance as _human_feedback_method_output when emit is set. 2. In flow.py: After appending a method result to _method_outputs, check if _human_feedback_method_output is set. If so, replace the last entry with the stashed real output and clear the stash. This ensures: - Routing still works correctly (collapsed outcome used for @listen matching) - The flow's final result is the actual method return value - If downstream listeners execute, their results become the final output Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * style: ruff format flow.py * fix: use per-method dict stash for concurrency safety and None returns Addresses review comments: - Replace single flow-level slot with dict keyed by method name, safe under concurrent @human_feedback+emit execution - Dict key presence (not value) indicates stashed output, correctly preserving None return values - Added test for None return value preservation --------- Co-authored-by: Joao Moura <joao@crewai.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>	2026-03-26 03:28:17 -03:00
Lorenze Jay	85199e9ffc	better serialization for human feedback in flow with models defined a… (#5029) ... * better serialization for human feedback in flow with models defined as dicts * linted * linted * fix and adjust tests	2026-03-23 14:43:43 -07:00
João Moura	4aedd58829	Enhance HITL self-loop functionality in human feedback integration tests (#4493) ... - Added tests to verify self-loop behavior in HITL routers, ensuring they can handle multiple rejections and immediate approvals. - Implemented `test_hitl_self_loop_routes_back_to_same_method`, `test_hitl_self_loop_multiple_rejections`, and `test_hitl_self_loop_immediate_approval` to validate the expected execution order and outcomes. - Updated the `or_()` listener to support looping back to the same method based on human feedback outcomes, improving flow control in complex scenarios.	2026-02-15 21:54:42 -05:00
Greyson LaLonde	f6fa04528a	fix: add async HITL support and chained-router tests ... asynchronous human-in-the-loop handling and related fixes. - Extend human_input provider with async support: AsyncExecutorContext, handle_feedback_async, async prompt helpers (_prompt_input_async, _async_readline), and async training/regular feedback loops in SyncHumanInputProvider. - Add async handler methods in CrewAgentExecutor and AgentExecutor (_ahandle_human_feedback, _ainvoke_loop) to integrate async provider flows. - Change PlusAPI.get_agent to an async httpx call and adapt caller in agent_utils to run it via asyncio.run. - Simplify listener execution in flow.Flow to correctly pass HumanFeedbackResult to listeners and unify execution path for router outcomes. - Remove deprecated types/hitl.py definitions. - Add tests covering chained router feedback, rejected paths, and mixed router/non-router listeners to prevent regressions.	2026-02-06 16:29:27 -05:00
João Moura	c73b36a4c5	Adding HITL for Flows (#4143) ... * feat: introduce human feedback events and decorator for flow methods - Added HumanFeedbackRequestedEvent and HumanFeedbackReceivedEvent classes to handle human feedback interactions within flows. - Implemented the @human_feedback decorator to facilitate human-in-the-loop workflows, allowing for feedback collection and routing based on responses. - Enhanced Flow class to store human feedback history and manage feedback outcomes. - Updated flow wrappers to preserve attributes from methods decorated with @human_feedback. - Added integration and unit tests for the new human feedback functionality, ensuring proper validation and routing behavior. * adding deployment docs * New docs * fix printer * wrong change * Adding Async Support feat: enhance human feedback support in flows - Updated the @human_feedback decorator to use 'message' parameter instead of 'request' for clarity. - Introduced new FlowPausedEvent and MethodExecutionPausedEvent to handle flow and method pauses during human feedback. - Added ConsoleProvider for synchronous feedback collection and integrated async feedback capabilities. - Implemented SQLite persistence for managing pending feedback context. - Expanded documentation to include examples of async human feedback usage and best practices. * linter * fix * migrating off printer * updating docs * new tests * doc update	2025-12-25 21:04:10 -03:00