70 Commits

Author	SHA1	Message	Date
Vinicius Brasil	340d23ae5d	Remove StateProxy from flow state access (#6327) ... `StateProxy` looked like a thread-safety boundary, but it only protected a small slice of state operations. Some examples of operations that were not covered: - `self.state.counter += 1`, `self.state["counter"] += 1` (increments) - `self.state.user.profile.score += 1` (nested object mutations) - `self.state.config["limits"]["max"] = 10` (mutation through model fields) - `self.state.items[0].status = "done"` (list/container mutations) This commit decided to remove it completely for simplicity and performance: - Simpler runtime code - attr read: 24x faster, attr write: 27x faster, list append: 19x faster (local benchmark) - Clearer concurrency contract (lifecycle locks remain, but arbitrary shared state mutation is not presented as thread-safe)	2026-06-24 16:37:51 -07:00
Vinicius Brasil	9db2d44766	Add typed output schemas for CrewAI tools (#6236) ... Currently, tools have a strong input contract through `args_schema`, but no output contract. This means that anything a tool outputs is converted to string. Not only the contract is weak, but the "invisible" conversion to string can have unexpected effects when the tool returns complex objects like dicts and arrays. With this PR, a tool can _optionally_ define an output contract with `output_schema`. CrewAI validates the raw result and sends the agent JSON. ```python class ProductResult(BaseModel): sku: str name: str in_stock: bool class ProductLookupTool(BaseTool): name: str = "Product Lookup" description: str = "Look up product availability by SKU." def _run(self, sku: str) -> ProductResult: return ProductResult(sku=sku, name="USB-C dock", in_stock=True) ``` If the result does not match the schema, CrewAI warns and falls back to `str(raw_result)` instead of failing the run: ```python @tool("Product Lookup", output_schema=ProductResult) def product_lookup(sku: str) -> dict[str, object]: return {"sku": sku, "name": "USB-C dock", "in_stock": True} #=> RuntimeWarning: Failed to validate or serialize output from tool 'Bad Product Lookup' using output_schema 'ProductResult'... Falling back to str(raw_result). ``` This is additive and non-breaking. Existing tools do not need to change. Tools without `output_schema` keep the old string behavior. Invalid typed outputs warn and fall back to the old formatting path.	2026-06-19 14:33:51 -07:00
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
Lorenze Jay	036b032ab6	handle supporting both custom prompts (#6108) ... * handle supporting both custom prompts * handle translations * handle deprecation warnings better	2026-06-10 17:52:53 -07:00
Vini Brasil	a9e7c3a44f	Simplify flow condition evaluation to be stateless per event (#6097) ... Re-evaluate the whole `@listen`/`@router` condition tree against the set of events seen so far, instead of tracking which AND sub-branches remain pending. Net effect: * Fixes a regression where `or_()` short-circuited at the first satisfied branch, leaving a sibling `and_()` half-complete so a later trigger could spuriously re-fire the listener * Removes the fragile per-branch pending state and `id()`-based keys * Shrinks the evaluator to one readable predicate	2026-06-10 10:35:25 -07:00
Lorenze Jay	770d1b284f	Lorenze/fix/file input not working reliably (#6020) ... * fix filesystem * Refine commit message formatting * fix for async kickoffs * added suggestion	2026-06-02 17:14:51 -07:00
Lorenze Jay	a9cb7867bb	Add crew trained agents file support (#6012) ... * Add crew trained agents file support * Add crew trained agents file support	2026-06-02 09:38:34 -07:00
Lorenze Jay	a1033e4bfe	Fix structured output leaks in tool-calling loops (#5897) ... * Fix structured output leaks in tool-calling loops * addressing comments * drop scripts * Update Gemini agent tests to include structured output with thoughts and bump model version to 2.5-flash * merge * Update Anthropic test cases to use new model and tool structure - Changed the model from "claude-3-5-haiku-20241022" to "claude-sonnet-4-6" in the test setup. - Updated the request and response formats in the YAML test cassette to reflect the new tool structure and improved content formatting. - Adjusted the expected response body to match the new output format from the assistant, including changes in tool usage and response details. - Increased rate limit values in the response headers for better testing scenarios. * adjusted bedrock cassettes * adjusting cassettes for bedrock * fix test * Update VCR configuration to use 'host' instead of 'bedrock_host' for request matching	2026-05-27 13:20:53 -07:00
Greyson LaLonde	fd10c64148	chore(crewai): drop self-explanatory comments	2026-05-26 10:23:33 -07:00
Lorenze Jay	77a61274dc	feat(planning): enhance planning configuration and observation handling (#5913) ... * feat(planning): enhance planning configuration and observation handling - Introduced attribute in to control LLM calls after each step. - Updated to set default to 1 when planning is enabled without explicit config. - Modified to support heuristic observations when LLM calls are disabled. - Adjusted to respect and settings for step observations. - Added tests to verify behavior of new configurations and ensure correct observation handling across different reasoning efforts. * fix(agent_executor): update handling of failed steps in low effort mode - Adjusted logic to ensure that failed steps are recorded without marking them as completed when using low reasoning effort. - Introduced feedback for failed steps, allowing the process to continue while tracking failures. - Added a test to verify that failed steps are correctly marked without triggering a replan. - And linted * linted	2026-05-26 09:10:43 -07:00
Vini Brasil	32f5e74449	Skip lock acquisition in CrewTrainingHandler.load when file is missing (#5935) ... Every agent kickoff calls _use_trained_data, which calls CrewTrainingHandler(...).load(). Since #4827 wrapped load() in store_lock, that means every kickoff acquires the cross-process (Redis-backed when REDIS_URL is set) lock even on deployments that never train and have no trained-agents file on disk. Move the missing/empty-file short-circuit above store_lock so the lock is only acquired when there is actually a file to read. save() and the real read remain locked.	2026-05-26 12:52:31 -03:00
iris-clawd	3322634625	feat: deprecate CrewAgentExecutor, default Crew agents to AgentExecutor (#5745) ... * feat: deprecate CrewAgentExecutor, default Crew agents to AgentExecutor * regen cassettes * fix tests * addressing pr comments --------- Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> Co-authored-by: lorenzejay <lorenzejaytech@gmail.com> Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>	2026-05-12 11:22:13 -07:00
Greyson LaLonde	93e786d263	refactor: extract CLI into standalone crewai-cli package	2026-05-06 20:46:46 +08:00
Greyson LaLonde	184c228ae9	fix: prevent shared LLM stop words mutation across agents	2026-05-04 14:23:17 +08:00
Greyson LaLonde	4c74dc0f86	fix(executor): reset messages and iterations between invocations ... CrewAgentExecutor is reused across sequential tasks but invoke/ainvoke only appended to self.messages and never reset self.iterations, so task 2 inherited task 1's history and iteration count.	2026-04-29 02:10:17 +08:00
Greyson LaLonde	4e9331a2c8	fix(agent): honor custom trained-agents file at inference	2026-04-28 22:09:34 +08:00
Greyson LaLonde	8e2a529d94	chore: add deprecation decorator to LiteAgent	2026-04-14 00:51:11 +08:00
Greyson LaLonde	0e590ff669	refactor: use shared I18N_DEFAULT singleton	2026-04-09 04:29:53 +08:00
Greyson LaLonde	0450d06a65	refactor: use shared PRINTER singleton	2026-04-08 07:17:22 +08:00
Greyson LaLonde	86ce54fc82	feat: runtime state checkpointing, event system, and executor refactor ... - Pass RuntimeState through the event bus and enable entity auto-registration - Introduce checkpointing API: - .checkpoint(), .from_checkpoint(), and async checkpoint support - Provider-based storage with BaseProvider and JsonProvider - Mid-task resume and kickoff() integration - Add EventRecord tracking and full event serialization with subtype preservation - Enable checkpoint fidelity via llm_type and executor_type discriminators - Refactor executor architecture: - Convert executors, tools, prompts, and TokenProcess to BaseModel - Introduce proper base classes with typed fields (CrewAgentExecutorMixin, BaseAgentExecutor) - Add generic from_checkpoint with full LLM serialization - Support executor back-references and resume-safe initialization - Refactor runtime state system: - Move RuntimeState into state/ module with async checkpoint support - Add entity serialization improvements and JSON-safe round-tripping - Implement event scope tracking and replay for accurate resume behavior - Improve tool and schema handling: - Make BaseTool fully serializable with JSON round-trip support - Serialize args_schema via JSON schema and dynamically reconstruct models - Add automatic subclass restoration via tool_type discriminator - Enhance Flow checkpointing: - Support restoring execution state and subclass-aware deserialization - Performance improvements: - Cache handler signature inspection - Optimize event emission and metadata preparation - General cleanup: - Remove dead checkpoint payload structures - Simplify entity registration and serialization logic	2026-04-07 03:22:30 +08:00
Lucas Gomide	1b7be63b60	Revert "refactor: remove unused and methods from (#5172)" (#5243) ... * Revert "refactor: remove unused and methods from (#5172)" This reverts commit bb9bcd6823. * test: fix tests	2026-04-02 18:02:59 -04:00
Greyson LaLonde	d6714a0e60	refactor: convert Flow to Pydantic BaseModel	2026-04-01 03:48:41 +08:00
Greyson LaLonde	dfc0f9a317	refactor: replace InstanceOf[T] with plain type annotations ... * refactor: replace InstanceOf[T] with plain type annotations InstanceOf[] is a Pydantic validation wrapper that adds runtime isinstance checks. Plain type annotations are sufficient here since the models already use arbitrary_types_allowed or the types are BaseModel subclasses. * refactor: convert BaseKnowledgeStorage to BaseModel * fix: update tests for BaseKnowledgeStorage BaseModel conversion * fix: correct embedder config structure in test	2026-03-31 08:11:21 +08:00
Lorenze Jay	bb9bcd6823	refactor: remove unused and methods from (#5172) ... This commit cleans up the class by removing the and methods, which are no longer needed. The changes help streamline the code and improve maintainability.	2026-03-30 15:01:58 -07:00
Greyson LaLonde	a49f9f982b	refactor: deduplicate sync/async task execution and kickoff in agent	2026-03-25 19:39:42 +08:00
Greyson LaLonde	ec8d444cfc	fix: resolve all mypy errors across crewai package	2026-03-25 06:03:43 +08:00
Greyson LaLonde	6495aff528	refactor: replace Any-typed callback and model fields with serializable types	2026-03-20 15:18:50 -04:00
Lorenze Jay	32d7b4a8d4	Lorenze/feat/plan execute pattern (#4817) ... * feat: introduce PlanningConfig for enhanced agent planning capabilities (#4344) * feat: introduce PlanningConfig for enhanced agent planning capabilities This update adds a new PlanningConfig class to manage agent planning configurations, allowing for customizable planning behavior before task execution. The existing reasoning parameter is deprecated in favor of this new configuration, ensuring backward compatibility while enhancing the planning process. Additionally, the Agent class has been updated to utilize this new configuration, and relevant utility functions have been adjusted accordingly. Tests have been added to validate the new planning functionality and ensure proper integration with existing agent workflows. * dropping redundancy * fix test * revert handle_reasoning here * refactor: update reasoning handling in Agent class This commit modifies the Agent class to conditionally call the handle_reasoning function based on the executor class being used. The legacy CrewAgentExecutor will continue to utilize handle_reasoning, while the new AgentExecutor will manage planning internally. Additionally, the PlanningConfig class has been referenced in the documentation to clarify its role in enabling or disabling planning. Tests have been updated to reflect these changes and ensure proper functionality. * improve planning prompts * matching * refactor: remove default enabled flag from PlanningConfig in Agent class * more cassettes * fix test * refactor: update planning prompt and remove deprecated methods in reasoning handler * improve planning prompt * Lorenze/feat planning pt 2 todo list gen (#4449) * feat: introduce PlanningConfig for enhanced agent planning capabilities This update adds a new PlanningConfig class to manage agent planning configurations, allowing for customizable planning behavior before task execution. The existing reasoning parameter is deprecated in favor of this new configuration, ensuring backward compatibility while enhancing the planning process. Additionally, the Agent class has been updated to utilize this new configuration, and relevant utility functions have been adjusted accordingly. Tests have been added to validate the new planning functionality and ensure proper integration with existing agent workflows. * dropping redundancy * fix test * revert handle_reasoning here * refactor: update reasoning handling in Agent class This commit modifies the Agent class to conditionally call the handle_reasoning function based on the executor class being used. The legacy CrewAgentExecutor will continue to utilize handle_reasoning, while the new AgentExecutor will manage planning internally. Additionally, the PlanningConfig class has been referenced in the documentation to clarify its role in enabling or disabling planning. Tests have been updated to reflect these changes and ensure proper functionality. * improve planning prompts * matching * refactor: remove default enabled flag from PlanningConfig in Agent class * more cassettes * fix test * feat: enhance agent planning with structured todo management This commit introduces a new planning system within the AgentExecutor class, allowing for the creation of structured todo items from planning steps. The TodoList and TodoItem models have been added to facilitate tracking of plan execution. The reasoning plan now includes a list of steps, improving the clarity and organization of agent tasks. Additionally, tests have been added to validate the new planning functionality and ensure proper integration with existing workflows. * refactor: update planning prompt and remove deprecated methods in reasoning handler * improve planning prompt * improve handler * linted * linted * Lorenze/feat/planning pt 3 todo list execution (#4450) * feat: introduce PlanningConfig for enhanced agent planning capabilities This update adds a new PlanningConfig class to manage agent planning configurations, allowing for customizable planning behavior before task execution. The existing reasoning parameter is deprecated in favor of this new configuration, ensuring backward compatibility while enhancing the planning process. Additionally, the Agent class has been updated to utilize this new configuration, and relevant utility functions have been adjusted accordingly. Tests have been added to validate the new planning functionality and ensure proper integration with existing agent workflows. * dropping redundancy * fix test * revert handle_reasoning here * refactor: update reasoning handling in Agent class This commit modifies the Agent class to conditionally call the handle_reasoning function based on the executor class being used. The legacy CrewAgentExecutor will continue to utilize handle_reasoning, while the new AgentExecutor will manage planning internally. Additionally, the PlanningConfig class has been referenced in the documentation to clarify its role in enabling or disabling planning. Tests have been updated to reflect these changes and ensure proper functionality. * improve planning prompts * matching * refactor: remove default enabled flag from PlanningConfig in Agent class * more cassettes * fix test * feat: enhance agent planning with structured todo management This commit introduces a new planning system within the AgentExecutor class, allowing for the creation of structured todo items from planning steps. The TodoList and TodoItem models have been added to facilitate tracking of plan execution. The reasoning plan now includes a list of steps, improving the clarity and organization of agent tasks. Additionally, tests have been added to validate the new planning functionality and ensure proper integration with existing workflows. * refactor: update planning prompt and remove deprecated methods in reasoning handler * improve planning prompt * improve handler * execute todos and be able to track them * feat: introduce PlannerObserver and StepExecutor for enhanced plan execution This commit adds the PlannerObserver and StepExecutor classes to the CrewAI framework, implementing the observation phase of the Plan-and-Execute architecture. The PlannerObserver analyzes step execution results, determines plan validity, and suggests refinements, while the StepExecutor executes individual todo items in isolation. These additions improve the overall planning and execution process, allowing for more dynamic and responsive agent behavior. Additionally, new observation events have been defined to facilitate monitoring and logging of the planning process. * refactor: enhance final answer synthesis in AgentExecutor This commit improves the synthesis of final answers in the AgentExecutor class by implementing a more coherent approach to combining results from multiple todo items. The method now utilizes a single LLM call to generate a polished response, falling back to concatenation if the synthesis fails. Additionally, the test cases have been updated to reflect the changes in planning and execution, ensuring that the results are properly validated and that the plan-and-execute architecture is functioning as intended. * refactor: enhance final answer synthesis in AgentExecutor This commit improves the synthesis of final answers in the AgentExecutor class by implementing a more coherent approach to combining results from multiple todo items. The method now utilizes a single LLM call to generate a polished response, falling back to concatenation if the synthesis fails. Additionally, the test cases have been updated to reflect the changes in planning and execution, ensuring that the results are properly validated and that the plan-and-execute architecture is functioning as intended. * refactor: implement structured output handling in final answer synthesis This commit enhances the final answer synthesis process in the AgentExecutor class by introducing support for structured outputs when a response model is specified. The synthesis method now utilizes the response model to produce outputs that conform to the expected schema, while still falling back to concatenation in case of synthesis failures. This change ensures that intermediate steps yield free-text results, but the final output can be structured, improving the overall coherence and usability of the synthesized answers. * regen tests * linted * fix * Enhance PlanningConfig and AgentExecutor with Reasoning Effort Levels This update introduces a new attribute in the class, allowing users to customize the observation and replanning behavior during task execution. The class has been modified to utilize this new attribute, routing step observations based on the specified reasoning effort level: low, medium, or high. Additionally, tests have been added to validate the functionality of the reasoning effort levels, ensuring that the agent behaves as expected under different configurations. This enhancement improves the adaptability and efficiency of the planning process in agent execution. * regen cassettes for test and fix test * cassette regen * fixing tests * dry * Refactor PlannerObserver and StepExecutor to Utilize I18N for Prompts This update enhances the PlannerObserver and StepExecutor classes by integrating the I18N utility for managing prompts and messages. The system and user prompts are now retrieved from the I18N module, allowing for better localization and maintainability. Additionally, the code has been cleaned up to remove hardcoded strings, improving readability and consistency across the planning and execution processes. * Refactor PlannerObserver and StepExecutor to Utilize I18N for Prompts This update enhances the PlannerObserver and StepExecutor classes by integrating the I18N utility for managing prompts and messages. The system and user prompts are now retrieved from the I18N module, allowing for better localization and maintainability. Additionally, the code has been cleaned up to remove hardcoded strings, improving readability and consistency across the planning and execution processes. * consolidate agent logic * fix datetime * improving step executor * refactor: streamline observation and refinement process in PlannerObserver - Updated the PlannerObserver to apply structured refinements directly from observations without requiring a second LLM call. - Renamed method to for clarity. - Enhanced documentation to reflect changes in how refinements are handled. - Removed unnecessary LLM message building and parsing logic, simplifying the refinement process. - Updated event emissions to include summaries of refinements instead of raw data. * enhance step executor with tool usage events and validation - Added event emissions for tool usage, including started and finished events, to track tool execution. - Implemented validation to ensure expected tools are called during step execution, raising errors when not. - Refactored the method to handle tool execution with event logging. - Introduced a new method for parsing tool input into a structured format. - Updated tests to cover new functionality and ensure correct behavior of tool usage events. * refactor: enhance final answer synthesis logic in AgentExecutor - Updated the finalization process to conditionally skip synthesis when the last todo result is sufficient as a complete answer. - Introduced a new method to determine if the last todo result can be used directly, improving efficiency. - Added tests to verify the new behavior, ensuring synthesis is skipped when appropriate and maintained when a response model is set. * fix: update observation handling in PlannerObserver for LLM errors - Modified the error handling in the PlannerObserver to default to a conservative replan when an LLM call fails. - Updated the return values to indicate that the step was not completed successfully and that a full replan is needed. - Added a new test to verify the behavior of the observer when an LLM error occurs, ensuring the correct replan logic is triggered. * refactor: enhance planning and execution flow in agents - Updated the PlannerObserver to accept a kickoff input for standalone task execution, improving flexibility in task handling. - Refined the step execution process in StepExecutor to support multi-turn action loops, allowing for iterative tool execution and observation. - Introduced a method to extract relevant task sections from descriptions, ensuring clarity in task requirements. - Enhanced the AgentExecutor to manage step failures more effectively, triggering replans only when necessary and preserving completed task history. - Updated translations to reflect changes in planning principles and execution prompts, emphasizing concrete and executable steps. * refactor: update setup_native_tools to include tool_name_mapping - Modified the setup_native_tools function to return an additional mapping of tool names. - Updated StepExecutor and AgentExecutor classes to accommodate the new return value from setup_native_tools. * fix tests * linted * linted * feat: enhance image block handling in Anthropic provider and update AgentExecutor logic - Added a method to convert OpenAI-style image_url blocks to Anthropic's required format. - Updated AgentExecutor to handle cases where no todos are ready, introducing a needs_replan return state. - Improved fallback answer generation in AgentExecutor to prevent RuntimeErrors when no final output is produced. * lint * lint * 1. Added failed to TodoStatus (planning_types.py) - TodoStatus now includes failed as a valid state: Literal[pending, running, completed, failed] - Added mark_failed(step_number, result) method to TodoList - Added get_failed_todos() method to TodoList - Updated is_complete to treat both completed and failed as terminal states - Updated replace_pending_todos docstring to mention failed items are preserved 2. Mark running todos as failed before replan (agent_executor.py) All three effort-level handlers now call mark_failed() on the current todo before routing to replan_now: - Low effort (handle_step_observed_low): hard-failure branch - Medium effort (handle_step_observed_medium): needs_full_replan branch - High effort (decide_next_action): both needs_full_replan and step_completed_successfully=False branches 3. Updated _should_replan to use get_failed_todos() Previously filtered on todo.status == failed which was dead code. Now uses the proper accessor method that will actually find failed items. What this fixes: Before these changes, a step that triggered a replan would stay in running status permanently, causing is_complete to never return True and next_pending to skip it — leading to stuck execution states. Now failed steps are properly tracked, replanning context correctly reports them, and LiteAgentOutput.failed_todos will actually return results. * fix test * imp on failed states * adjusted the var name from AgentReActState to AgentExecutorState * addressed p0 bugs * more improvements * linted * regen cassette * addressing crictical comments * ensure configurable timeouts, max_replans and max step iterations * adjusted tools * dropping debug statements * addressed comment * fix linter * lints and test fixes * fix: default observation parse fallback to failure and clean up plan-execute types When _parse_observation_response fails all parse attempts, default to step_completed_successfully=False instead of True to avoid silently masking failures. Extract duplicate _extract_task_section into a shared utility in agent_utils. Type PlanningConfig.llm as str | BaseLLM | None instead of str | Any | None. Make StepResult a frozen dataclass for immutability consistency with StepExecutionContext. * fix: remove Any from function_calling_llm union type in step_executor * fix: make BaseTool usage count thread-safe for parallel step execution Add _usage_lock and _claim_usage() to BaseTool for atomic check-and-increment of current_usage_count. This prevents race conditions when parallel plan steps invoke the same tool concurrently via execute_todos_parallel. Remove the racy pre-check from execute_single_native_tool_call since the limit is now enforced atomically inside tool.run(). --------- Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com> Co-authored-by: Greyson LaLonde <greyson@crewai.com>	2026-03-15 18:33:17 -07:00
Lucas Gomide	e72a80be6e	Addressing MCP tools resolutions & eliminates all shared mutable connection (#4792) ... * fix: allow hyphenated tool names in MCP references like notion#get-page The _SLUG_RE regex on BaseAgent rejected MCP tool references containing hyphens (e.g. "notion#get-page") because the fragment pattern only matched \w (word chars) * fix: create fresh MCP client per tool invocation to prevent parallel call races When the LLM dispatches parallel calls to MCP tools on the same server, the executor runs them concurrently via ThreadPoolExecutor. Previously, all tools from a server shared a single MCPClient instance, and even the same tool called twice would reuse one client. Since each thread creates its own asyncio event loop via asyncio.run(), concurrent connect/disconnect calls on the shared client caused anyio cancel-scope errors ("Attempted to exit cancel scope in a different task than it was entered in"). The fix introduces a client_factory pattern: MCPNativeTool now receives a zero-arg callable that produces a fresh MCPClient + transport on every _run_async() invocation. This eliminates all shared mutable connection state between concurrent calls, whether to the same tool or different tools from the same server. * test: ensure we can filter hyphenated MCP tool	2026-03-10 14:00:40 -04:00
Greyson LaLonde	cd42bcf035	refactor(memory): convert memory classes to serializable ... * refactor(memory): convert Memory, MemoryScope, and MemorySlice to BaseModel * fix(test): update mock memory attribute from _read_only to read_only * fix: handle re-validation in wrap validators and patch BaseModel class in tests	2026-03-08 23:08:10 -04:00
Greyson LaLonde	ea70976a5d	fix: adjust executor listener value to avoid recursion (#4705) ... * fix: adjust executor listener value to avoid recursion * fix: clear call count to ensure zero state * feat: expose max method call kwarg	2026-03-04 10:47:22 -08:00
Greyson LaLonde	1ac5801578	fix: inject tool errors as observations and resolve name collisions	2026-03-01 00:46:04 -05:00
Lucas Gomide	d259150d8d	Enhance MCP tool resolution and related events (#4580) ... * feat: enhance MCP tool resolution * feat: emit event when MCP configuration fails * feat: emit event when MCP tool execution has failed * style: resolve linter issues * refactor: use clear and natural mcp tool name resolution * test: fix broken tests * fix: resolve MCP connection leaks, slug validation, duplicate connections, and httpx exception handling --------- Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com> Co-authored-by: Greyson LaLonde <greyson@crewai.com>	2026-02-26 13:59:30 -08:00
João Moura	86d3ee022d	feat: update lancedb version and add lance-namespace packages ... * chore(deps): update lancedb version and add lance-namespace packages - Updated lancedb dependency version from 0.4.0 to 0.29.2 in multiple files. - Added new packages: lance-namespace and lance-namespace-urllib3-client with version 0.5.2, including their dependencies and installation details. - Enhanced MemoryTUI to display a limit on entries and improved the LanceDBStorage class with automatic background compaction and index creation for better performance. * linter * refactor: update memory recall limit and formatting in Agent class - Reduced the memory recall limit from 10 to 5 in multiple locations within the Agent class. - Updated the memory formatting to use a new `format` method in the MemoryMatch class for improved readability and metadata inclusion. * refactor: enhance memory handling with read-only support - Updated memory-related classes and methods to support read-only functionality, allowing for silent no-ops when attempting to remember data in read-only mode. - Modified the LiteAgent and CrewAgentExecutorMixin classes to check for read-only status before saving memories. - Adjusted MemorySlice and Memory classes to reflect changes in behavior when read-only is enabled. - Updated tests to verify that memory operations behave correctly under read-only conditions. * test: set mock memory to read-write in unit tests - Updated unit tests in test_unified_memory.py to set mock_memory._read_only to False, ensuring that memory operations can be tested in a writable state. * fix test * fix: preserve falsy metadata values and fix remember() return type --------- Co-authored-by: lorenzejay <lorenzejaytech@gmail.com> Co-authored-by: Greyson LaLonde <greyson@crewai.com>	2026-02-26 15:05:10 -05:00
João Moura	8102d0a6ca	feat: enhance JSON argument parsing and validation in CrewAgentExecutor and BaseTool ... * feat: enhance JSON argument parsing and validation in CrewAgentExecutor and BaseTool - Added error handling for malformed JSON tool arguments in CrewAgentExecutor, providing descriptive error messages. - Implemented schema validation for tool arguments in BaseTool, ensuring that invalid arguments raise appropriate exceptions. - Introduced tests to verify correct behavior for both valid and invalid JSON inputs, enhancing robustness of tool execution. * refactor: improve argument validation in BaseTool - Introduced a new private method to handle argument validation for tools, enhancing code clarity and reusability. - Updated the method to utilize the new validation method, ensuring consistent error handling for invalid arguments. - Enhanced exception handling to specifically catch , providing clearer error messages for tool argument validation failures. * feat: introduce parse_tool_call_args for improved argument parsing - Added a new utility function, parse_tool_call_args, to handle parsing of tool call arguments from JSON strings or dictionaries, enhancing error handling for malformed JSON inputs. - Updated CrewAgentExecutor and AgentExecutor to utilize the new parsing function, streamlining argument validation and improving clarity in error reporting. - Introduced unit tests for parse_tool_call_args to ensure robust functionality and correct handling of various input scenarios. * feat: add keyword argument validation in BaseTool and Tool classes - Introduced a new method `_validate_kwargs` in BaseTool to validate keyword arguments against the defined schema, ensuring proper argument handling. - Updated the `run` and `arun` methods in both BaseTool and Tool classes to utilize the new validation method, improving error handling and robustness. - Added comprehensive tests for asynchronous execution in `TestBaseToolArunValidation` to verify correct behavior for valid and invalid keyword arguments. * Potential fix for pull request finding 'Syntax error' Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> --------- Co-authored-by: lorenzejay <lorenzejaytech@gmail.com> Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com> Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>	2026-02-25 13:13:31 -05:00
Greyson LaLonde	71b4f8402a	fix: ensure callbacks are ran/awaited if promise	2026-02-20 13:15:50 -05:00
Lorenze Jay	d09656664d	supporting parallel tool use (#4513) ... * supporting parallel tool use * ensure we respect max_usage_count * ensure result_as_answer, hooks, and cache parodity * improve crew agent executor * address test comments	2026-02-19 14:07:28 -08:00
João Moura	18d266c8e7	New Unified Memory System (#4420) ... * chore: update memory management and dependencies - Enhance the memory system by introducing a unified memory API that consolidates short-term, long-term, entity, and external memory functionalities. - Update the `.gitignore` to exclude new memory-related files and blog directories. - Modify `conftest.py` to handle missing imports for vcr stubs more gracefully. - Add new development dependencies in `pyproject.toml` for testing and memory management. - Refactor the `Crew` class to utilize the new unified memory system, replacing deprecated memory attributes. - Implement memory context injection in `LiteAgent` to improve memory recall during agent execution. - Update documentation to reflect changes in memory usage and configuration. * feat: introduce Memory TUI for enhanced memory management - Add a new command to the CLI for launching a Textual User Interface (TUI) to browse and recall memories. - Implement the MemoryTUI class to facilitate user interaction with memory scopes and records. - Enhance the unified memory API by adding a method to list records within a specified scope. - Update `pyproject.toml` to include the `textual` dependency for TUI functionality. - Ensure proper error handling for missing dependencies when accessing the TUI. * feat: implement consolidation flow for memory management - Introduce the ConsolidationFlow class to handle the decision-making process for inserting, updating, or deleting memory records based on new content. - Add new data models: ConsolidationAction and ConsolidationPlan to structure the actions taken during consolidation. - Enhance the memory types with new fields for consolidation thresholds and limits. - Update the unified memory API to utilize the new consolidation flow for managing memory records. - Implement embedding functionality for new content to facilitate similarity checks. - Refactor existing memory analysis methods to integrate with the consolidation process. - Update translations to include prompts for consolidation actions and user interactions. * feat: enhance Memory TUI with Rich markup and improved UI elements - Update the MemoryTUI class to utilize Rich markup for better visual representation of memory scope information. - Introduce a color palette for consistent branding across the TUI interface. - Refactor the CSS styles to improve the layout and aesthetics of the memory browsing experience. - Enhance the display of memory entries, including better formatting for records and importance ratings. - Implement loading indicators and error messages with Rich styling for improved user feedback during recall operations. - Update the action bindings and navigation prompts for a more intuitive user experience. * feat: enhance Crew class memory management and configuration - Update the Crew class to allow for more flexible memory configurations by accepting Memory, MemoryScope, or MemorySlice instances. - Refactor memory initialization logic to support custom memory configurations while maintaining backward compatibility. - Improve documentation for memory-related fields to clarify usage and expectations. - Introduce a recall oversample factor to optimize memory recall processes. - Update related memory types and configurations to ensure consistency across the memory management system. * chore: update dependency overrides and enhance memory management - Added an override for the 'rich' dependency to allow compatibility with 'textual' requirements. - Updated the 'pyproject.toml' and 'uv.lock' files to reflect the new dependency specifications. - Refactored the Crew class to simplify memory configuration handling by allowing any type for the memory attribute. - Improved error messages in the CLI for missing 'textual' dependency to guide users on installation. - Introduced new packages and dependencies in the project to enhance functionality and maintain compatibility. * refactor: enhance thread safety in flow management - Updated LockedListProxy and LockedDictProxy to subclass list and dict respectively, ensuring compatibility with libraries requiring strict type checks. - Improved documentation to clarify the purpose of these proxies and their thread-safe operations. - Ensured that all mutations are protected by locks while reads delegate to the underlying data structures, enhancing concurrency safety. * chore: update dependency versions and improve Python compatibility - Downgraded 'vcrpy' dependency to version 7.0.0 for compatibility. - Enhanced 'uv.lock' to include more granular resolution markers for Python versions and implementations, ensuring better compatibility across different environments. - Updated 'urllib3' and 'selenium' dependencies to specify versions based on Python implementation, improving stability and performance. - Removed deprecated resolution markers for 'fastembed' and streamlined its dependencies for better clarity. * fix linter * chore: update uv.lock for improved dependency management and memory management enhancements - Incremented revision number in uv.lock to reflect changes. - Added a new development dependency group in uv.lock, specifying versions for tools like pytest, mypy, and pre-commit to streamline development workflows. - Enhanced error handling in CLI memory functions to provide clearer feedback on missing dependencies. - Refactored memory management classes to improve type hints and maintainability, ensuring better compatibility with future updates. * fix tests * refactor: remove obsolete RAGStorage tests and clean up error handling - Deleted outdated tests for RAGStorage that were no longer relevant, including tests for client failures, save operation failures, and reset failures. - Cleaned up the test suite to focus on current functionality and improve maintainability. - Ensured that remaining tests continue to validate the expected behavior of knowledge storage components. * fix test * fix texts * fix tests * forcing new commit * fix: add location parameter to Google Vertex embedder configuration for memory integration tests * debugging CI * adding debugging for CI * refactor: remove unnecessary logging for memory checks in agent execution - Eliminated redundant logging statements related to memory checks in the Agent and CrewAgentExecutor classes. - Simplified the memory retrieval logic by directly checking for available memory without logging intermediate states. - Improved code readability and maintainability by reducing clutter in the logging output. * udpating desp * feat: enhance thread safety in LockedListProxy and LockedDictProxy - Added equality comparison methods (__eq__ and __ne__) to LockedListProxy and LockedDictProxy to allow for safe comparison of their contents. - Implemented consistent locking mechanisms to prevent deadlocks during comparisons. - Improved the overall robustness of these proxy classes in multi-threaded environments. * feat: enhance memory functionality in Flows documentation and memory system - Added a new section on memory usage within Flows, detailing built-in methods for storing and recalling memories. - Included an example of a Research and Analyze Flow demonstrating the integration of memory for accumulating knowledge over time. - Updated the Memory documentation to clarify the unified memory system and its capabilities, including adaptive-depth recall and composite scoring. - Introduced a new configuration parameter, `recall_oversample_factor`, to improve the effectiveness of memory retrieval processes. * update docs * refactor: improve memory record handling and pagination in unified memory system - Simplified the `get_record` method in the Memory class by directly accessing the storage's `get_record` method. - Enhanced the `list_records` method to include an `offset` parameter for pagination, allowing users to skip a specified number of records. - Updated documentation for both methods to clarify their functionality and parameters, improving overall code clarity and usability. * test: update memory scope assertions in unified memory tests - Modified assertions in `test_lancedb_list_scopes_get_scope_info` and `test_memory_list_scopes_info_tree` to check for the presence of the "/team" scope instead of the root scope. - Clarified comments to indicate that `list_scopes` returns child scopes rather than the root itself, enhancing test clarity and accuracy. * feat: integrate memory tools for agents and crews - Added functionality to inject memory tools into agents during initialization, enhancing their ability to recall and remember information mid-task. - Implemented a new `_add_memory_tools` method in the Crew class to facilitate the addition of memory tools when memory is available. - Introduced `RecallMemoryTool` and `RememberTool` classes in a new `memory_tools.py` file, providing agents with active recall and memory storage capabilities. - Updated English translations to include descriptions for the new memory tools, improving user guidance on their usage. * refactor: streamline memory recall functionality across agents and tools - Removed the 'depth' parameter from memory recall calls in LiteAgent and Agent classes, simplifying the recall process. - Updated the MemoryTUI to use 'deep' depth by default for more comprehensive memory retrieval. - Enhanced the MemoryScope and MemorySlice classes to default to 'deep' depth, improving recall accuracy. - Introduced a new 'recall_queries' field in QueryAnalysis to optimize semantic vector searches with targeted phrases. - Updated documentation and comments to reflect changes in memory recall behavior and parameters. * refactor: optimize memory management in flow classes - Enhanced memory auto-creation logic in Flow class to prevent unnecessary Memory instance creation for internal flows (RecallFlow, ConsolidationFlow) by introducing a _skip_auto_memory flag. - Removed the deprecated time_hints field from QueryAnalysis and replaced it with a more flexible time_filter field to better handle time-based queries. - Updated documentation and comments to reflect changes in memory handling and query analysis structure, improving clarity and usability. * updates tests * feat: introduce EncodingFlow for enhanced memory encoding pipeline - Added a new EncodingFlow class to orchestrate the encoding process for memory, integrating LLM analysis and embedding. - Updated the Memory class to utilize EncodingFlow for saving content, improving the overall memory management and conflict resolution. - Enhanced the unified memory module to include the new EncodingFlow in its public API, facilitating better memory handling. - Updated tests to ensure proper functionality of the new encoding flow and its integration with existing memory features. * refactor: optimize memory tool integration and recall flow - Streamlined the addition of memory tools in the Agent class by using list comprehension for cleaner code. - Enhanced the RecallFlow class to build task lists more efficiently with list comprehensions, improving readability and performance. - Updated the RecallMemoryTool to utilize list comprehensions for formatting memory results, simplifying the code structure. - Adjusted test assertions in LiteAgent to reflect the default behavior of memory recall depth, ensuring clarity in expected outcomes. * Potential fix for pull request finding 'Empty except' Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> * chore: gen missing cassette * fix * test: enhance memory extraction test by mocking recall to prevent LLM calls Updated the test for memory extraction to include a mock for the recall method, ensuring that the test focuses on the save path without invoking external LLM calls. This improves test reliability and clarity. * refactor: enhance memory handling by adding agent role parameter Updated memory storage methods across multiple classes to include an optional `agent_role` parameter, improving the context of stored memories. Additionally, modified the initialization of several flow classes to suppress flow events, enhancing performance and reducing unnecessary event triggers. * feat: enhance agent memory functionality with recall and save mechanisms Implemented memory context injection during agent kickoff, allowing for memory recall before execution and passive saving of results afterward. Added new methods to handle memory saving and retrieval, including error handling for memory operations. Updated the BaseAgent class to support dynamic memory resolution and improved memory record structure with source and privacy attributes for better provenance tracking. * test * feat: add utility method to simplify tools field in console formatter Introduced a new static method `_simplify_tools_field` in the console formatter to transform the 'tools' field from full tool objects to a comma-separated string of tool names. This enhancement improves the readability of tool information in the output. * refactor: improve lazy initialization of LLM and embedder in Memory class Refactored the Memory class to implement lazy initialization for the LLM and embedder, ensuring they are only created when first accessed. This change enhances the robustness of the Memory class by preventing initialization failures when constructed without an API key. Additionally, updated error handling to provide clearer guidance for users on resolving initialization issues. * refactor: consolidate memory saving methods for improved efficiency Refactored memory handling across multiple classes to replace individual memory saving calls with a batch method, `remember_many`, enhancing performance and reducing redundancy. Updated related tools and schemas to support single and multiple item memory operations, ensuring a more streamlined interface for memory interactions. Additionally, improved documentation and test coverage for the new functionality. * feat: enhance MemoryTUI with improved layout and entry handling Updated the MemoryTUI class to incorporate a new vertical layout, adding an OptionList for displaying entries and enhancing the detail view for selected records. Introduced methods for populating entry and recall lists, improving user interaction and data presentation. Additionally, refined CSS styles for better visual organization and focus handling. * fix test * feat: inject memory tools into LiteAgent for enhanced functionality Added logic to the LiteAgent class to inject memory tools if memory is configured, ensuring that memory tools are only added if they are not already present. This change improves the agent's capability to utilize memory effectively during execution. * feat: add synchronous execution method to ConsolidationFlow for improved integration Introduced a new `run_sync()` method in the ConsolidationFlow class to facilitate procedural execution of the consolidation pipeline without relying on asynchronous event loops. Updated the EncodingFlow class to utilize this method for conflict resolution, ensuring compatibility within its async context. This change enhances the flow's ability to manage memory records effectively during nested executions. * refactor: update ConsolidationFlow and EncodingFlow for improved async handling Removed the synchronous `run_sync()` method from ConsolidationFlow and refactored the consolidate method in EncodingFlow to be asynchronous. This change allows for direct awaiting of the ConsolidationFlow's kickoff method, enhancing compatibility within the async event loop and preventing nested asyncio.run() issues. Additionally, updated the execution plan to listen for multiple paths, streamlining the consolidation process. * fix: update flow documentation and remove unused ConsolidationFlow Corrected the comment in Flow class regarding internal flows, replacing "ConsolidationFlow" with "EncodingFlow". Removed the ConsolidationFlow class as it is no longer needed, streamlining the memory handling process. Updated related imports and ensured that the memory module reflects these changes, enhancing clarity and maintainability. * feat: enhance memory handling with background saving and query analysis optimization Implemented a background saving mechanism in the Memory class to allow non-blocking memory operations, improving performance during high-load scenarios. Added a query analysis threshold to skip LLM calls for short queries, optimizing recall efficiency. Updated related methods and documentation to reflect these changes, ensuring a more responsive and efficient memory management system. * fix test * fix test * fix: handle synchronous fallback for save operations in Memory class Updated the Memory class to implement a synchronous fallback mechanism for save operations when the background thread pool is shut down. This change ensures that late save requests still succeed, improving reliability in memory management during shutdown scenarios. * feat: implement HITL learning features in human feedback decorator Added support for learning from human feedback in the human feedback decorator. Introduced parameters to enable lesson distillation and pre-review of outputs based on past feedback. Updated related tests to ensure proper functionality of the learning mechanism, including memory interactions and default LLM usage. --------- Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>	2026-02-13 21:34:37 -03:00
Greyson LaLonde	7d498b29be	fix: event ordering; flow state locks, routing ... * fix: add current task id context and flow updates introduce a context var for the current task id in `crewai.context` to track task scope. update `Flow._execute_single_listener` to return `(result, event_id)` and adjust callers to unpack it and append `FlowMethodName(str(result))` to `router_results`. set/reset the current task id at the start/end of task execution (async + sync) with minor import and call-site tweaks. * fix: await event futures and flush event bus call `crewai_event_bus.flush()` after crew kickoff. in `Flow`, await event handler futures instead of just collecting them: await pending `_event_futures` before finishing, await emitted futures immediately with try/except to log failures, then clear `_event_futures`. ensures handlers complete and errors surface. * fix: continue iteration on tool completion events expand the loop bridge listener to also trigger on tool completion events (`tool_completed` and `native_tool_completed`) so agent iteration resumes after tools finish. add a `requests.post` mock and response fixture in the liteagent test to simulate platform tool execution. refresh and sanitize vcr cassettes (updated model responses, timestamps, and header placeholders) to reflect tool-call flows and new recordings. * fix: thread-safe state proxies & native routing add thread-safe state proxies and refactor native tool routing. * introduce `LockedListProxy` and `LockedDictProxy` in `flow.py` and update `StateProxy` to return them for list/dict attrs so mutations are protected by the flow lock. * update `AgentExecutor` to use `StateProxy` on flow init, guard the messages setter with the state lock, and return a `StateProxy` from the temp state accessor. * convert `call_llm_native_tools` into a listener (no direct routing return) and add `route_native_tool_result` to route based on state (pending tool calls, final answer, or context error). * minor cleanup in `continue_iteration` to drop orphan listeners on init. * update test cassettes for new native tool call responses, timestamps, and ids. improves concurrency safety for shared state and makes native tool routing explicit. * chore: regen cassettes * chore: regen cassettes, remove duplicate listener call path	2026-02-06 14:02:43 -05:00
Greyson LaLonde	6bb1b178a1	chore: extension points ... Introduce ContextVar-backed hooks and small API/behavior changes to improve extensibility and testability. Changes include: - agents: mark configure_structured_output as abstract and change its parameter to task to reflect use of task metadata. - tracing: convert _first_time_trace_hook to a ContextVar and call .get() to safely retrieve the hook. - console formatter: add _disable_version_check ContextVar and skip version checks when set (avoids noisy checks in certain contexts). - flow: use current_triggering_event_id variable when scheduling listener tasks to keep naming consistent. - hallucination guardrail: make context optional, add _validate_output_hook to allow custom validation hooks, update examples and return contract to allow hooks to override behavior. - agent utilities: add _create_plus_client_hook for injecting a Plus client (used in tests/alternate flows), ensure structured tools have current_usage_count initialized and propagate to original tool, and fall back to creating PlusAPI client when no hook is provided.	2026-02-05 12:49:54 -05:00
Greyson LaLonde	6bfc98e960	refactor: extract hitl to provider pattern ... * refactor: extract hitl to provider pattern - add humaninputprovider protocol with setup_messages and handle_feedback - move sync hitl logic from executor to synchuman inputprovider - add _passthrough_exceptions extension point in agent/core.py - create crewai.core.providers module for extensible components - remove _ask_human_input from base_agent_executor_mixin	2026-02-04 15:40:22 -05:00
Greyson LaLonde	5fb602dff2	fix: replace timing-based concurrency test with state tracking	2026-02-03 08:58:51 -05:00
Greyson LaLonde	9d7f45376a	fix: use contextvars for flow execution context	2026-02-02 11:24:02 -05:00
Greyson LaLonde	102b6ae855	feat: add a2a liteagent, auth, transport negotiation, and file support ... * feat: add server-side auth schemes and protocol extensions - add server auth scheme base class and implementations (api key, bearer token, basic/digest auth, mtls) - add server-side extension system for a2a protocol extensions - add extensions middleware for x-a2a-extensions header management - add extension validation and registry utilities - enhance auth utilities with server-side support - add async intercept method to match client call interceptor protocol - fix type_checking import to resolve mypy errors with a2aconfig * feat: add transport negotiation and content type handling - add transport negotiation logic with fallback support - add content type parser and encoder utilities - add transport configuration models (client and server) - add transport types and enums - enhance config with transport settings - add negotiation events for transport and content type * feat: add a2a delegation support to LiteAgent * feat: add file input support to a2a delegation and tasks Introduces handling of file inputs in A2A delegation flows by converting file dictionaries to protocol-compatible parts and propagating them through delegation and task execution functions. Updates include utility functions for file conversion, changes to message construction, and passing input_files through relevant APIs. * feat: liteagent a2a delegation support to kickoff methods	2026-01-30 17:10:00 -05:00
Lorenze Jay	19ce56032c	fix: improve output handling and response model integration in agents (#4307) ... * fix: improve output handling and response model integration in agents - Refactored output handling in the Agent class to ensure proper conversion and formatting of outputs, including support for BaseModel instances. - Enhanced the AgentExecutor class to correctly utilize response models during execution, improving the handling of structured outputs. - Updated the Gemini and Anthropic completion providers to ensure compatibility with new response model handling, including the addition of strict mode for function definitions. - Improved the OpenAI completion provider to enforce strict adherence to function schemas. - Adjusted translations to clarify instructions regarding output formatting and schema adherence. * drop what was a print that didnt get deleted properly * fixes gemini * azure working * bedrock works * added tests * adjust test * fix tests and regen * fix tests and regen * refactor: ensure stop words are applied correctly in Azure, Gemini, and OpenAI completions; add tests to validate behavior with structured outputs * linting	2026-01-30 12:27:46 -08:00
Greyson LaLonde	1e27cf3f0f	fix: ensure verbosity flag is applied	2026-01-28 11:52:47 -05:00
Greyson LaLonde	c4c9208229	feat: native multimodal file handling; openai responses api ... - add input_files parameter to Crew.kickoff(), Flow.kickoff(), Task, and Agent.kickoff() - add provider-specific file uploaders for OpenAI, Anthropic, Gemini, and Bedrock - add file type detection, constraint validation, and automatic format conversion - add URL file source support for multimodal content - add streaming uploads for large files - add prompt caching support for Anthropic - add OpenAI Responses API support	2026-01-23 15:13:25 -05:00
Lorenze Jay	bd4d039f63	Lorenze/imp/native tool calling (#4258) ... * wip restrcuturing agent executor and liteagent * fix: handle None task in AgentExecutor to prevent errors Added a check to ensure that if the task is None, the method returns early without attempting to access task properties. This change improves the robustness of the AgentExecutor by preventing potential errors when the task is not set. * refactor: streamline AgentExecutor initialization by removing redundant parameters Updated the Agent class to simplify the initialization of the AgentExecutor by removing unnecessary task and crew parameters in standalone mode. This change enhances code clarity and maintains backward compatibility by ensuring that the executor is correctly configured without redundant assignments. * wip: clean * ensure executors work inside a flow due to flow in flow async structure * refactor: enhance agent kickoff preparation by separating common logic Updated the Agent class to introduce a new private method that consolidates the common setup logic for both synchronous and asynchronous kickoff executions. This change improves code clarity and maintainability by reducing redundancy in the kickoff process, while ensuring that the agent can still execute effectively within both standalone and flow contexts. * linting and tests * fix test * refactor: improve test for Agent kickoff parameters Updated the test for the Agent class to ensure that the kickoff method correctly preserves parameters. The test now verifies the configuration of the agent after kickoff, enhancing clarity and maintainability. Additionally, the test for asynchronous kickoff within a flow context has been updated to reflect the Agent class instead of LiteAgent. * refactor: update test task guardrail process output for improved validation Refactored the test for task guardrail process output to enhance the validation of the output against the OpenAPI schema. The changes include a more structured request body and updated response handling to ensure compliance with the guardrail requirements. This update aims to improve the clarity and reliability of the test cases, ensuring that task outputs are correctly validated and feedback is appropriately provided. * test fix cassette * test fix cassette * working * working cassette * refactor: streamline agent execution and enhance flow compatibility Refactored the Agent class to simplify the execution method by removing the event loop check and clarifying the behavior when called from synchronous and asynchronous contexts. The changes ensure that the method operates seamlessly within flow methods, improving clarity in the documentation. Additionally, updated the AgentExecutor to set the response model to None, enhancing flexibility. New test cassettes were added to validate the functionality of agents within flow contexts, ensuring robust testing for both synchronous and asynchronous operations. * fixed cassette * Enhance Flow Execution Logic - Introduced conditional execution for start methods in the Flow class. - Unconditional start methods are prioritized during kickoff, while conditional starts are executed only if no unconditional starts are present. - Improved handling of cyclic flows by allowing re-execution of conditional start methods triggered by routers. - Added checks to continue execution chains for completed conditional starts. These changes improve the flexibility and control of flow execution, ensuring that the correct methods are triggered based on the defined conditions. * Enhance Agent and Flow Execution Logic - Updated the Agent class to automatically detect the event loop and return a coroutine when called within a Flow, simplifying async handling for users. - Modified Flow class to execute listeners sequentially, preventing race conditions on shared state during listener execution. - Improved handling of coroutine results from synchronous methods, ensuring proper execution flow and state management. These changes enhance the overall execution logic and user experience when working with agents and flows in CrewAI. * Enhance Flow Listener Logic and Agent Imports - Updated the Flow class to track fired OR listeners, ensuring that multi-source OR listeners only trigger once during execution. This prevents redundant executions and improves flow efficiency. - Cleared fired OR listeners during cyclic flow resets to allow re-execution in new cycles. - Modified the Agent class imports to include Coroutine from collections.abc, enhancing type handling for asynchronous operations. These changes improve the control and performance of flow execution in CrewAI, ensuring more predictable behavior in complex scenarios. * adjusted test due to new cassette * ensure native tool calling works with liteagent * ensure response model is respected * Enhance Tool Name Handling for LLM Compatibility - Added a new function to replace invalid characters in function names with underscores, ensuring compatibility with LLM providers. - Updated the function to sanitize tool names before validation. - Modified the function to use sanitized names for tool registration. These changes improve the robustness of tool name handling, preventing potential issues with invalid characters in function names. * ensure we dont finalize batch on just a liteagent finishing * max tools per turn wip and ensure we drop print times * fix sync main issues * fix llm_call_completed event serialization issue * drop max_tools_iterations * for fixing model dump with state * Add extract_tool_call_info function to handle various tool call formats - Introduced a new utility function to extract tool call ID, name, and arguments from different provider formats (OpenAI, Gemini, Anthropic, and dictionary). - This enhancement improves the flexibility and compatibility of tool calls across multiple LLM providers, ensuring consistent handling of tool call information. - The function returns a tuple containing the call ID, function name, and function arguments, or None if the format is unrecognized. * Refactor AgentExecutor to support batch execution of native tool calls - Updated the method to process all tools from in a single batch, enhancing efficiency and reducing the number of interactions with the LLM. - Introduced a new utility function to streamline the extraction of tool call details, improving compatibility with various tool formats. - Removed the parameter, simplifying the initialization of the . - Enhanced logging and message handling to provide clearer insights during tool execution. - This refactor improves the overall performance and usability of the agent execution flow. * Update English translations for tool usage and reasoning instructions - Revised the `post_tool_reasoning` message to clarify the analysis process after tool usage, emphasizing the need to provide only the final answer if requirements are met. - Updated the `format` message to simplify the instructions for deciding between using a tool or providing a final answer, enhancing clarity for users. - These changes improve the overall user experience by providing clearer guidance on task execution and response formatting. * fix * fixing azure tests * organizae imports * dropped unused * Remove debug print statements from AgentExecutor to clean up the code and improve readability. This change enhances the overall performance of the agent execution flow by eliminating unnecessary console output during LLM calls and iterations. * linted * updated cassette * regen cassette * revert crew agent executor * adjust cassettes and dropped tests due to native tool implementation * adjust * ensure we properly fail tools and emit their events * Enhance tool handling and delegation tracking in agent executors - Implemented immediate return for tools with result_as_answer=True in crew_agent_executor.py. - Added delegation tracking functionality in agent_utils.py to increment delegations when specific tools are used. - Updated tool usage logic to handle caching more effectively in tool_usage.py. - Enhanced test cases to validate new delegation features and tool caching behavior. This update improves the efficiency of tool execution and enhances the delegation capabilities of agents. * Enhance tool handling and delegation tracking in agent executors - Implemented immediate return for tools with result_as_answer=True in crew_agent_executor.py. - Added delegation tracking functionality in agent_utils.py to increment delegations when specific tools are used. - Updated tool usage logic to handle caching more effectively in tool_usage.py. - Enhanced test cases to validate new delegation features and tool caching behavior. This update improves the efficiency of tool execution and enhances the delegation capabilities of agents. * fix cassettes * fix * regen cassettes * regen gemini * ensure we support bedrock * supporting bedrock * regen azure cassettes * Implement max usage count tracking for tools in agent executors - Added functionality to check if a tool has reached its maximum usage count before execution in both crew_agent_executor.py and agent_executor.py. - Enhanced error handling to return a message when a tool's usage limit is reached. - Updated tool usage logic in tool_usage.py to increment usage counts and print current usage status. - Introduced tests to validate max usage count behavior for native tool calling, ensuring proper enforcement and tracking. This update improves tool management by preventing overuse and providing clear feedback when limits are reached. * fix other test * fix test * drop logs * better tests * regen * regen all azure cassettes * regen again placeholder for cassette matching * fix: unify tool name sanitization across codebase * fix: include tool role messages in save_last_messages * fix: update sanitize_tool_name test expectations Align test expectations with unified sanitize_tool_name behavior that lowercases and splits camelCase for LLM provider compatibility. * fix: apply sanitize_tool_name consistently across codebase Unify tool name sanitization to ensure consistency between tool names shown to LLMs and tool name matching/lookup logic. * regen * fix: sanitize tool names in native tool call processing - Update extract_tool_call_info to return sanitized tool names - Fix delegation tool name matching to use sanitized names - Add sanitization in crew_agent_executor tool call extraction - Add sanitization in experimental agent_executor - Add sanitization in LLM.call function lookup - Update streaming utility to use sanitized names - Update base_agent_executor_mixin delegation check * Extract text content from parts directly to avoid warning about non-text parts * Add test case for Gemini token usage tracking - Introduced a new YAML cassette for tracking token usage in Gemini API responses. - Updated the test for Gemini to validate token usage metrics and response content. - Ensured proper integration with the Gemini model and API key handling. --------- Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>	2026-01-22 17:44:03 -08:00
Lorenze Jay	741bf12bf4	Lorenze/enh decouple executor from crew (#4209) ... * wip restrcuturing agent executor and liteagent * fix: handle None task in AgentExecutor to prevent errors Added a check to ensure that if the task is None, the method returns early without attempting to access task properties. This change improves the robustness of the AgentExecutor by preventing potential errors when the task is not set. * refactor: streamline AgentExecutor initialization by removing redundant parameters Updated the Agent class to simplify the initialization of the AgentExecutor by removing unnecessary task and crew parameters in standalone mode. This change enhances code clarity and maintains backward compatibility by ensuring that the executor is correctly configured without redundant assignments. * ensure executors work inside a flow due to flow in flow async structure * refactor: enhance agent kickoff preparation by separating common logic Updated the Agent class to introduce a new private method that consolidates the common setup logic for both synchronous and asynchronous kickoff executions. This change improves code clarity and maintainability by reducing redundancy in the kickoff process, while ensuring that the agent can still execute effectively within both standalone and flow contexts. * linting and tests * fix test * refactor: improve test for Agent kickoff parameters Updated the test for the Agent class to ensure that the kickoff method correctly preserves parameters. The test now verifies the configuration of the agent after kickoff, enhancing clarity and maintainability. Additionally, the test for asynchronous kickoff within a flow context has been updated to reflect the Agent class instead of LiteAgent. * refactor: update test task guardrail process output for improved validation Refactored the test for task guardrail process output to enhance the validation of the output against the OpenAPI schema. The changes include a more structured request body and updated response handling to ensure compliance with the guardrail requirements. This update aims to improve the clarity and reliability of the test cases, ensuring that task outputs are correctly validated and feedback is appropriately provided. * test fix cassette * test fix cassette * working * working cassette * refactor: streamline agent execution and enhance flow compatibility Refactored the Agent class to simplify the execution method by removing the event loop check and clarifying the behavior when called from synchronous and asynchronous contexts. The changes ensure that the method operates seamlessly within flow methods, improving clarity in the documentation. Additionally, updated the AgentExecutor to set the response model to None, enhancing flexibility. New test cassettes were added to validate the functionality of agents within flow contexts, ensuring robust testing for both synchronous and asynchronous operations. * fixed cassette * Enhance Flow Execution Logic - Introduced conditional execution for start methods in the Flow class. - Unconditional start methods are prioritized during kickoff, while conditional starts are executed only if no unconditional starts are present. - Improved handling of cyclic flows by allowing re-execution of conditional start methods triggered by routers. - Added checks to continue execution chains for completed conditional starts. These changes improve the flexibility and control of flow execution, ensuring that the correct methods are triggered based on the defined conditions. * Enhance Agent and Flow Execution Logic - Updated the Agent class to automatically detect the event loop and return a coroutine when called within a Flow, simplifying async handling for users. - Modified Flow class to execute listeners sequentially, preventing race conditions on shared state during listener execution. - Improved handling of coroutine results from synchronous methods, ensuring proper execution flow and state management. These changes enhance the overall execution logic and user experience when working with agents and flows in CrewAI. * Enhance Flow Listener Logic and Agent Imports - Updated the Flow class to track fired OR listeners, ensuring that multi-source OR listeners only trigger once during execution. This prevents redundant executions and improves flow efficiency. - Cleared fired OR listeners during cyclic flow resets to allow re-execution in new cycles. - Modified the Agent class imports to include Coroutine from collections.abc, enhancing type handling for asynchronous operations. These changes improve the control and performance of flow execution in CrewAI, ensuring more predictable behavior in complex scenarios. * adjusted test due to new cassette * ensure we dont finalize batch on just a liteagent finishing * feat: cancellable parallelized flow methods * feat: allow methods to be cancelled & run parallelized * feat: ensure state is thread safe through proxy * fix: check for proxy state * fix: mimic BaseModel method * chore: update final attr checks; test * better description * fix test * chore: update test assumptions * extra --------- Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>	2026-01-20 21:44:45 -08:00
Greyson LaLonde	ceef062426	feat: add additional a2a events and enrich event metadata	2026-01-16 16:57:31 -05:00