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* 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>
466 lines
16 KiB
Python
466 lines
16 KiB
Python
"""Tests for crewai.project.json_loader."""
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from __future__ import annotations
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import json
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from pathlib import Path
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import sys
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import pytest
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from crewai.llms.base_llm import BaseLLM
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from crewai.project.json_loader import (
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JSONProjectValidationError,
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find_json_project_file,
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load_agent,
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strip_jsonc_comments,
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)
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class TestStripJsoncComments:
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def test_strips_single_line_comments(self):
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text = '{\n "key": "value" // this is a comment\n}'
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result = strip_jsonc_comments(text)
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data = json.loads(result)
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assert data["key"] == "value"
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def test_strips_block_comments(self):
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text = '{\n /* block comment */\n "key": "value"\n}'
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result = strip_jsonc_comments(text)
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data = json.loads(result)
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assert data["key"] == "value"
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def test_preserves_urls_with_double_slash(self):
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text = '{\n "url": "https://example.com"\n}'
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result = strip_jsonc_comments(text)
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data = json.loads(result)
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assert data["url"] == "https://example.com"
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def test_preserves_comment_markers_inside_strings(self):
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text = """{
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"url": "https://example.com/a//b",
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"pattern": "keep /* this */ text",
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"text": "value // not a comment",
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}"""
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result = strip_jsonc_comments(text)
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data = json.loads(result)
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assert data["url"] == "https://example.com/a//b"
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assert data["pattern"] == "keep /* this */ text"
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assert data["text"] == "value // not a comment"
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def test_removes_trailing_commas(self):
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text = '{\n "a": 1,\n "b": 2,\n}'
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result = strip_jsonc_comments(text)
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data = json.loads(result)
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assert data == {"a": 1, "b": 2}
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def test_removes_trailing_commas_in_arrays(self):
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text = '{"arr": [1, 2, 3,]}'
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result = strip_jsonc_comments(text)
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data = json.loads(result)
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assert data["arr"] == [1, 2, 3]
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def test_plain_json_unchanged(self):
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text = '{"key": "value"}'
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result = strip_jsonc_comments(text)
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assert json.loads(result) == {"key": "value"}
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def test_find_json_project_file_prefers_jsonc(tmp_path: Path):
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(tmp_path / "agent.json").write_text("{}")
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jsonc_path = tmp_path / "agent.jsonc"
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jsonc_path.write_text("{}")
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assert find_json_project_file(tmp_path, "agent") == jsonc_path
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class TestLoadAgent:
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def test_load_minimal_agent(self, tmp_path: Path):
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agent_def = {
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"role": "Researcher",
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"goal": "Find information",
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"backstory": "Expert researcher.",
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert agent.role == "Researcher"
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assert agent.goal == "Find information"
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assert agent.backstory == "Expert researcher."
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def test_load_agent_with_llm(self, tmp_path: Path):
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agent_def = {
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"role": "Coder",
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"goal": "Write code",
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"backstory": "Expert coder.",
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"llm": "openai/gpt-4o",
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert agent.role == "Coder"
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def test_load_agent_with_llm_config_object(self, tmp_path: Path):
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agent_def = {
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"role": "Coder",
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"goal": "Write code",
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"backstory": "Expert coder.",
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"llm": {
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"model": "llama3",
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"provider": "ollama",
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"temperature": 0.2,
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"base_url": "http://localhost:11434",
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},
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert isinstance(agent.llm, BaseLLM)
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assert agent.llm.model == "llama3"
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assert agent.llm.provider == "ollama"
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assert agent.llm.temperature == 0.2
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assert agent.llm.base_url == "http://localhost:11434/v1"
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def test_load_agent_with_planning_config_llm_object(self, tmp_path: Path):
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agent_def = {
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"role": "Planner",
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"goal": "Plan work",
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"backstory": "Expert planner.",
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"llm": "ollama/llama3",
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"planning_config": {
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"reasoning_effort": "high",
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"llm": {
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"model": "deepseek-chat",
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"provider": "deepseek",
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"api_key": "test-key",
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},
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},
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert agent.planning_config is not None
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assert isinstance(agent.planning_config.llm, BaseLLM)
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assert agent.planning_config.llm.model == "deepseek-chat"
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assert agent.planning_config.llm.provider == "deepseek"
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assert agent.planning_config.llm.api_key == "test-key"
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def test_load_agent_with_settings_block(self, tmp_path: Path):
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agent_def = {
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"role": "Analyst",
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"goal": "Analyze data",
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"backstory": "Data expert.",
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"settings": {
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"verbose": True,
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"allow_delegation": True,
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"max_iter": 10,
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"cache": False,
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},
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert agent.role == "Analyst"
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assert agent.verbose is True
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assert agent.allow_delegation is True
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assert agent.max_iter == 10
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assert agent.cache is False
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def test_load_agent_with_top_level_settings(self, tmp_path: Path):
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agent_def = {
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"role": "Analyst",
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"goal": "Analyze data",
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"backstory": "Data expert.",
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"verbose": True,
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"max_iter": 15,
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert agent.verbose is True
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assert agent.max_iter == 15
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def test_load_agent_accepts_public_agent_config_fields(self, tmp_path: Path):
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agent_def = {
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"role": "Analyst",
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"goal": "Analyze data",
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"backstory": "Data expert.",
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"max_execution_time": 30,
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"use_system_prompt": False,
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"system_template": "system: {{ .System }}",
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"prompt_template": "prompt: {{ .Prompt }}",
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"response_template": "response: {{ .Response }}",
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"inject_date": True,
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"date_format": "%Y",
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"guardrail": "Only return concise answers.",
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"guardrail_max_retries": 1,
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"security_config": {"fingerprint": "agent-seed"},
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert agent.max_execution_time == 30
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assert agent.use_system_prompt is False
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assert agent.system_template == "system: {{ .System }}"
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assert agent.inject_date is True
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assert agent.guardrail == "Only return concise answers."
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def test_load_agent_accepts_serialized_tool_dict(
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self, tmp_path: Path, monkeypatch: pytest.MonkeyPatch
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):
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module = tmp_path / "test_tools.py"
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module.write_text(
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"from crewai.tools.base_tool import BaseTool\n"
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"class EchoTool(BaseTool):\n"
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" name: str = 'echo'\n"
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" description: str = 'Echo input'\n"
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" def _run(self, value: str = '') -> str:\n"
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" return value\n"
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)
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monkeypatch.syspath_prepend(str(tmp_path))
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sys.modules.pop("test_tools", None)
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agent_def = {
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"role": "Tool User",
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"goal": "Use tools",
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"backstory": "Tool expert.",
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"tools": [
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{
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"tool_type": "test_tools.EchoTool",
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"name": "echo",
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"description": "Echo input",
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}
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],
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert len(agent.tools or []) == 1
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assert agent.tools[0].name == "echo"
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def test_load_agent_rejects_runtime_fields(self, tmp_path: Path):
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agent_def = {
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"id": "00000000-0000-4000-8000-000000000000",
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"role": "Analyst",
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"goal": "Analyze data",
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"backstory": "Data expert.",
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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with pytest.raises(JSONProjectValidationError, match="runtime-only"):
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load_agent(agent_file)
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def test_settings_block_takes_precedence(self, tmp_path: Path):
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agent_def = {
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"role": "Analyst",
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"goal": "Analyze data",
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"backstory": "Data expert.",
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"verbose": False,
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"settings": {
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"verbose": True,
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},
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}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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agent = load_agent(agent_file)
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assert agent.verbose is True
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def test_load_agent_from_jsonc(self, tmp_path: Path):
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jsonc_content = """{
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// This is a JSONC file with comments
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"role": "Writer",
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"goal": "Write articles",
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"backstory": "Expert writer.",
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/* multi-line
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comment */
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}"""
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agent_file = tmp_path / "agent.jsonc"
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agent_file.write_text(jsonc_content)
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agent = load_agent(agent_file)
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assert agent.role == "Writer"
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def test_load_agent_missing_required_fields(self, tmp_path: Path):
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agent_def = {"role": "Incomplete"}
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agent_file = tmp_path / "agent.json"
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agent_file.write_text(json.dumps(agent_def))
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with pytest.raises(Exception):
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load_agent(agent_file)
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def test_load_agent_file_not_found(self):
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with pytest.raises(FileNotFoundError):
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load_agent(Path("/nonexistent/agent.json"))
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class TestResolveTools:
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def test_unknown_tool_raises_with_guidance(self):
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from crewai.project.json_loader import JSONProjectError, _resolve_tools
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with pytest.raises(JSONProjectError, match="Unknown tool 'NotARealToolXYZ'"):
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_resolve_tools(["NotARealToolXYZ"])
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|
|
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def test_missing_custom_tool_raises(self, tmp_path, monkeypatch):
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from crewai.project.json_loader import JSONProjectError, _resolve_tools
|
|
|
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monkeypatch.chdir(tmp_path)
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with pytest.raises(JSONProjectError, match="custom:missing"):
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_resolve_tools(["custom:missing"])
|
|
|
|
def test_custom_tool_without_basetool_subclass_raises(self, tmp_path, monkeypatch):
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from crewai.project.json_loader import JSONProjectError, _resolve_tools
|
|
|
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monkeypatch.chdir(tmp_path)
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tools_dir = tmp_path / "tools"
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tools_dir.mkdir()
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(tools_dir / "empty.py").write_text("x = 1\n")
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|
|
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with pytest.raises(JSONProjectError, match="No BaseTool subclass"):
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_resolve_tools(["custom:empty"])
|
|
|
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def test_custom_tool_resolves(self, tmp_path, monkeypatch):
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|
from crewai.project.json_loader import _resolve_tools
|
|
|
|
monkeypatch.chdir(tmp_path)
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tools_dir = tmp_path / "tools"
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|
tools_dir.mkdir()
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|
(tools_dir / "echo.py").write_text(
|
|
"from crewai.tools.base_tool import BaseTool\n"
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|
"\n"
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|
"class EchoTool(BaseTool):\n"
|
|
" name: str = 'echo'\n"
|
|
" description: str = 'echo input'\n"
|
|
"\n"
|
|
" def _run(self, text: str) -> str:\n"
|
|
" return text\n"
|
|
)
|
|
|
|
tools = _resolve_tools(["custom:echo"])
|
|
|
|
assert len(tools) == 1
|
|
assert tools[0].name == "echo"
|
|
|
|
def test_serialized_tool_dicts_pass_through(self):
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|
from crewai.project.json_loader import _resolve_tools
|
|
|
|
spec = {"tool_type": "some.module.Tool"}
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assert _resolve_tools([spec]) == [spec]
|
|
|
|
|
|
class TestValidationDoesNotExecuteTools:
|
|
def _write_project(self, root, tool_line='"custom:landmine"'):
|
|
agents_dir = root / "agents"
|
|
agents_dir.mkdir()
|
|
(agents_dir / "worker.jsonc").write_text(
|
|
"{\n"
|
|
' "role": "Worker",\n'
|
|
' "goal": "Work",\n'
|
|
' "backstory": "Works hard",\n'
|
|
f' "tools": [{tool_line}]\n'
|
|
"}\n"
|
|
)
|
|
crew_path = root / "crew.jsonc"
|
|
crew_path.write_text(
|
|
"{\n"
|
|
' "agents": ["worker"],\n'
|
|
' "tasks": [\n'
|
|
' {"name": "t1", "description": "Do work", '
|
|
'"expected_output": "Done", "agent": "worker"}\n'
|
|
" ]\n"
|
|
"}\n"
|
|
)
|
|
return crew_path
|
|
|
|
def test_validate_does_not_execute_custom_tool_code(self, tmp_path):
|
|
from crewai.project.json_loader import validate_crew_project
|
|
|
|
sentinel = tmp_path / "executed.txt"
|
|
tools_dir = tmp_path / "tools"
|
|
tools_dir.mkdir()
|
|
(tools_dir / "landmine.py").write_text(
|
|
f"open({str(sentinel)!r}, 'w').write('boom')\n"
|
|
)
|
|
crew_path = self._write_project(tmp_path)
|
|
|
|
project = validate_crew_project(crew_path, tmp_path / "agents")
|
|
|
|
assert not sentinel.exists(), "validation must not execute tools/<name>.py"
|
|
assert project.agent_names == ["worker"]
|
|
|
|
def test_validate_reports_missing_custom_tool_file(self, tmp_path):
|
|
from crewai.project.json_loader import (
|
|
JSONProjectValidationError,
|
|
validate_crew_project,
|
|
)
|
|
|
|
crew_path = self._write_project(tmp_path)
|
|
|
|
with pytest.raises(JSONProjectValidationError) as exc_info:
|
|
validate_crew_project(crew_path, tmp_path / "agents")
|
|
|
|
assert "custom:landmine" in str(exc_info.value)
|
|
assert "not found" in str(exc_info.value)
|
|
|
|
def test_validate_reports_path_escaping_custom_tool(self, tmp_path):
|
|
from crewai.project.json_loader import (
|
|
JSONProjectValidationError,
|
|
validate_crew_project,
|
|
)
|
|
|
|
crew_path = self._write_project(tmp_path, tool_line='"custom:../evil"')
|
|
|
|
with pytest.raises(JSONProjectValidationError) as exc_info:
|
|
validate_crew_project(crew_path, tmp_path / "agents")
|
|
|
|
assert "Invalid custom tool name" in str(exc_info.value)
|
|
|
|
|
|
class TestCustomToolPathSafety:
|
|
@pytest.mark.parametrize(
|
|
"bad_name",
|
|
["../evil", "..", "sub/inner", "/etc/passwd", "a-b", "", "name.py"],
|
|
)
|
|
def test_unsafe_names_rejected_at_runtime(self, bad_name, tmp_path, monkeypatch):
|
|
from crewai.project.json_loader import JSONProjectError, _resolve_tools
|
|
|
|
monkeypatch.chdir(tmp_path)
|
|
with pytest.raises(JSONProjectError, match="Invalid custom tool name"):
|
|
_resolve_tools([f"custom:{bad_name}"])
|
|
|
|
def test_resolves_relative_to_project_root_not_cwd(self, tmp_path, monkeypatch):
|
|
from crewai.project.json_loader import _resolve_tools
|
|
|
|
project_root = tmp_path / "project"
|
|
tools_dir = project_root / "tools"
|
|
tools_dir.mkdir(parents=True)
|
|
(tools_dir / "echo.py").write_text(
|
|
"from crewai.tools.base_tool import BaseTool\n"
|
|
"\n"
|
|
"class EchoTool(BaseTool):\n"
|
|
" name: str = 'echo'\n"
|
|
" description: str = 'echo input'\n"
|
|
"\n"
|
|
" def _run(self, text: str) -> str:\n"
|
|
" return text\n"
|
|
)
|
|
elsewhere = tmp_path / "elsewhere"
|
|
elsewhere.mkdir()
|
|
monkeypatch.chdir(elsewhere)
|
|
|
|
tools = _resolve_tools(["custom:echo"], project_root=project_root)
|
|
|
|
assert len(tools) == 1
|
|
assert tools[0].name == "echo"
|