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
Greyson LaLonde	fd10c64148	chore(crewai): drop self-explanatory comments	2026-05-26 10:23:33 -07:00
Greyson LaLonde	93e786d263	refactor: extract CLI into standalone crewai-cli package	2026-05-06 20:46:46 +08:00
Greyson LaLonde	7e1672447b	fix: deflake MemoryRecord embedding serialization test ... Substring checks like `'0.1' not in json_str` collided with timestamps such as `2026-04-10T13:00:50.140557` on CI. Round-trip through `model_validate_json` to verify structurally that the embedding field is absent from the serialized output.	2026-04-11 02:01:23 +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
alex-clawd	bf2f4dbce6	fix: exclude embedding vectors from memory serialization (saves tokens) (#5298) ... * fix: exclude embedding vector from MemoryRecord serialization MemoryRecord.embedding (1536 floats for OpenAI embeddings) was included in model_dump()/JSON serialization and repr. When recall results flow to agents or get logged, these vectors burn tokens for zero value — agents never need the raw embedding. Added exclude=True and repr=False to the embedding field. The storage layer accesses record.embedding directly (not via model_dump), so persistence is unaffected. * test: validate embedding excluded from serialization Two tests: 1. MemoryRecord — model_dump, model_dump_json, and repr all exclude embedding. Direct attribute access still works for storage layer. 2. MemoryMatch — nested record serialization also excludes embedding.	2026-04-06 14:48:58 -03:00
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
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	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