crewAI/lib/crewai/src/crewai/translations/en.json

{
  "hierarchical_manager_agent": {
    "role": "Crew Manager",
    "goal": "Manage the team to complete the task in the best way possible.",
    "backstory": "You are a seasoned manager with a knack for getting the best out of your team.\nYou are also known for your ability to delegate work to the right people, and to ask the right questions to get the best out of your team.\nEven though you don't perform tasks by yourself, you have a lot of experience in the field, which allows you to properly evaluate the work of your team members."
  },
  "slices": {
    "observation": "\nObservation:",
    "task": "\nCurrent Task: {input}\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:",
    "memory": "\n\n# Memories from past conversations:\n{memory}\n\nIMPORTANT: The memories above are an automatic selection and may be INCOMPLETE. If the task involves counting, listing, or summing items (e.g. 'how many', 'total', 'list all'), you MUST use the Search memory tool with several different queries before answering — do NOT rely solely on the memories shown above. Enumerate each distinct item you find before giving a final count.",
    "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
    "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
    "no_tools": "",
    "task_no_tools": "\nCurrent Task: {input}\n\nProvide your complete response:",
    "native_tools": "",
    "native_task": "\nCurrent Task: {input}",
    "post_tool_reasoning": "Analyze the tool result. If requirements are met, provide the Final Answer. Otherwise, call the next tool. Deliver only the answer without meta-commentary.",
    "format": "Decide if you need a tool or can provide the final answer. Use one at a time.\nTo use a tool, use:\nThought: [reasoning]\nAction: [name from {tool_names}]\nAction Input: [JSON object]\n\nTo provide the final answer, use:\nThought: [reasoning]\nFinal Answer: [complete response]",
    "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",
    "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nHere is the expected format I must follow:\n\n```\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n This Thought/Action/Action Input/Result process can repeat N times. Once I know the final answer, I must return the following format:\n\n```\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n```",
    "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
    "expected_output": "\nThis is the expected criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
    "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
    "getting_input": "This is the agent's final answer: {final_answer}\n\n",
    "summarizer_system_message": "You are a precise assistant that creates structured summaries of agent conversations. You preserve critical context needed for seamless task continuation.",
    "summarize_instruction": "Analyze the following conversation and create a structured summary that preserves all information needed to continue the task seamlessly.\n\n<conversation>\n{conversation}\n</conversation>\n\nCreate a summary with these sections:\n1. **Task Overview**: What is the agent trying to accomplish?\n2. **Current State**: What has been completed so far? What step is the agent on?\n3. **Important Discoveries**: Key facts, data, tool results, or findings that must not be lost.\n4. **Next Steps**: What should the agent do next based on the conversation?\n5. **Context to Preserve**: Any specific values, names, URLs, code snippets, or details referenced in the conversation.\n\nWrap your entire summary in <summary> tags.\n\n<summary>\n[Your structured summary here]\n</summary>",
    "summary": "<summary>\n{merged_summary}\n</summary>\n\nContinue the task from where the conversation left off. The above is a structured summary of prior context.",
    "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
    "formatted_task_instructions": "Format your final answer according to the following OpenAPI schema: {output_format}\n\nIMPORTANT: Preserve the original content exactly as-is. Do NOT rewrite, paraphrase, or modify the meaning of the content. Only structure it to match the schema format.\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
    "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
    "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
    "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
    "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
    "lite_agent_response_format": "Format your final answer according to the following OpenAPI schema: {response_format}\n\nIMPORTANT: Preserve the original content exactly as-is. Do NOT rewrite, paraphrase, or modify the meaning of the content. Only structure it to match the schema format.\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
    "knowledge_search_query": "The original query is: {task_prompt}.",
    "knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions.",
    "human_feedback_collapse": "Based on the following human feedback, determine which outcome best matches their intent.\n\nFeedback: {feedback}\n\nPossible outcomes: {outcomes}\n\nRespond with ONLY one of the exact outcome values listed above, nothing else.",
    "hitl_pre_review_system": "You are reviewing content before a human sees it. Apply the lessons from past human feedback to improve the output. Preserve the original meaning and structure, but incorporate the corrections and preferences indicated by the lessons.",
    "hitl_pre_review_user": "Output to review:\n{output}\n\nLessons from past human feedback:\n{lessons}\n\nApply the lessons to improve the output.",
    "hitl_distill_system": "You extract generalizable lessons from human feedback on system outputs. A lesson should be a reusable rule or preference that applies to future similar outputs -- not a one-time correction specific to this exact content.\n\nExamples of good lessons:\n- Always include source citations when making factual claims\n- Use bullet points instead of long paragraphs for action items\n- Avoid technical jargon when the audience is non-technical\n\nIf the feedback is just approval (e.g. looks good, approved) or contains no generalizable guidance, return an empty list.",
    "hitl_distill_user": "Method: {method_name}\n\nSystem output:\n{output}\n\nHuman feedback:\n{feedback}\n\nExtract generalizable lessons. Return an empty list if none."
  },
  "errors": {
    "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",
    "force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.",
    "agent_tool_unexisting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
    "task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",
    "tool_usage_error": "I encountered an error: {error}",
    "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",
    "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.",
    "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}",
    "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}",
    "validation_error": "### Previous attempt failed validation: {guardrail_result_error}\n\n\n### Previous result:\n{task_output}\n\n\nTry again, making sure to address the validation error."
  },
  "tools": {
    "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely everything you know, don't reference things but instead explain them.",
    "ask_question": "Ask a specific question to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolutely everything you know, don't reference things but instead explain them.",
    "add_image": {
      "name": "Add image to content",
      "description": "See image to understand its content, you can optionally ask a question about the image",
      "default_action": "Please provide a detailed description of this image, including all visual elements, context, and any notable details you can observe."
    },
    "recall_memory": "Search through the team's shared memory for relevant information. Pass one or more queries to search for multiple things at once. Use this when you need to find facts, decisions, preferences, or past results that may have been stored previously. IMPORTANT: For questions that require counting, summing, or listing items across multiple conversations (e.g. 'how many X', 'total Y', 'list all Z'), you MUST search multiple times with different phrasings to ensure you find ALL relevant items before giving a final count or total. Do not rely on a single search — items may be described differently across conversations.",
    "save_to_memory": "Store one or more important facts, decisions, observations, or lessons in memory so they can be recalled later by you or other agents. Pass multiple items at once when you have several things worth remembering."
  },
  "memory": {
    "query_system": "You analyze a query for searching memory.\nGiven the query and available scopes, output:\n1. keywords: Key entities or keywords that can be used to filter by category.\n2. suggested_scopes: Which available scopes are most relevant (empty for all).\n3. complexity: 'simple' or 'complex'.\n4. recall_queries: 1-3 short, targeted search phrases distilled from the query. Each should be a concise phrase optimized for semantic vector search. If the query is already short and focused, return it as-is in a single-item list. For long task descriptions, extract the distinct things worth searching for.\n5. time_filter: If the query references a time period (like 'last week', 'yesterday', 'in January'), return an ISO 8601 date string for the earliest relevant date (e.g. '2026-02-01'). Return null if no time constraint is implied.",
    "extract_memories_system": "You extract discrete, reusable memory statements from raw content (e.g. a task description and its result, or a conversation between a user and an assistant).\n\nFor the given content, output a list of memory statements. Each memory must:\n- Be one clear sentence or short statement\n- Be understandable without the original context\n- Capture a decision, fact, outcome, preference, lesson, or observation worth remembering\n- NOT be a vague summary or a restatement of the task description\n- NOT duplicate the same idea in different words\n\nWhen the content is a conversation, pay special attention to facts stated by the user (first-person statements). These personal facts are HIGH PRIORITY and must always be extracted:\n- What the user did, bought, made, visited, attended, or completed\n- Names of people, pets, places, brands, and specific items the user mentions\n- Quantities, durations, dates, and measurements the user states\n- Subordinate clauses and casual asides often contain important personal details (e.g. \"by the way, it took me 4 hours\" or \"my Golden Retriever Max\")\n\nPreserve exact names and numbers — never generalize (e.g. keep \"lavender gin fizz\" not just \"cocktail\", keep \"12 largemouth bass\" not just \"fish caught\", keep \"Golden Retriever\" not just \"dog\").\n\nAdditional extraction rules:\n- Presupposed facts: When the user reveals a fact indirectly in a question (e.g. \"What collar suits a Golden Retriever like Max?\" presupposes Max is a Golden Retriever), extract that fact as a separate memory.\n- Date precision: Always preserve the full date including day-of-month when stated (e.g. \"February 14th\" not just \"February\", \"March 5\" not just \"March\").\n- Life events in passing: When the user mentions a life event (birth, wedding, graduation, move, adoption) while discussing something else, extract the life event as its own memory (e.g. \"my friend David had a baby boy named Jasper\" is a birth fact, even if mentioned while planning to send congratulations).\n\nIf there is nothing worth remembering (e.g. empty result, no decisions or facts), return an empty list.\nOutput a JSON object with a single key \"memories\" whose value is a list of strings.",
    "extract_memories_user": "Content:\n{content}\n\nExtract memory statements as described. Return structured output.",
    "query_user": "Query: {query}\n\nAvailable scopes: {available_scopes}\n{scope_desc}\n\nReturn the analysis as structured output.",
    "save_system": "You analyze content to be stored in a hierarchical memory system.\nGiven the content and the existing scopes and categories, output:\n1. suggested_scope: The best matching existing scope path, or a new path if none fit (use / for root).\n2. categories: A list of categories (reuse existing when relevant, add new ones if needed).\n3. importance: A number from 0.0 to 1.0 indicating how significant this memory is.\n4. extracted_metadata: A JSON object with any entities, dates, or topics you can extract.",
    "save_user": "Content to store:\n{content}\n\nExisting scopes: {existing_scopes}\nExisting categories: {existing_categories}\n\nReturn the analysis as structured output.",
    "consolidation_system": "You are comparing new content against existing memories to decide how to consolidate them.\n\nFor each existing memory, choose one action:\n- 'keep': The existing memory is still accurate and not redundant with the new content.\n- 'update': The existing memory should be updated with new information. Provide the updated content.\n- 'delete': The existing memory is outdated, superseded, or contradicted by the new content.\n\nAlso decide whether the new content should be inserted as a separate memory:\n- insert_new=true: The new content adds information not fully captured by existing memories (even after updates).\n- insert_new=false: The new content is fully captured by the existing memories (after any updates).\n\nBe conservative: prefer 'keep' when unsure. Only 'update' or 'delete' when there is a clear contradiction, supersession, or redundancy.",
    "consolidation_user": "New content to consider storing:\n{new_content}\n\nExisting similar memories:\n{records_summary}\n\nReturn the consolidation plan as structured output."
  },
  "reasoning": {
    "initial_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are creating a strategic plan for a task that requires your expertise and unique perspective.",
    "refine_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are refining a strategic plan for a task that requires your expertise and unique perspective.",
    "create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience, exactly what tools to use and how to use them\n5. The expected outcome and how it aligns with your goal\n\nAfter creating your plan, assess whether you feel ready to execute the task or if you could do better.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
    "refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties. As you refine your plan, be specific about which available tools you will use, how you will use them, and why they are the best choices for each step. Clearly outline your tool usage strategy as part of your improved plan.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\""
  }
}