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As a reminder, this team member's description is the following: r;)rrrr_rrrrrrrrstatetyperhr%) r7rravailable_tools error_msgrrzrrlr;r;r<execute_tool_callsJ       zAgent.execute_tool_callr code_actionc Csn|jd|jd||jdtr(|jdt|tddtddn|jd||jddS) N=== Agent thoughts:r+z&>>> Agent is executing the code below:F)ensurenlnord)stylez====)rwarningrr r'r)r()r7rrr;r;r<log_rationale_code_action?s  zAgent.log_rationale_code_actioncKst)z$To be implemented in the child class)NotImplementedError)r7kwargsr;r;r<runKsz Agent.runr)r=r>r?rrr$rrrrrintrrrrrrrrrrr%r(r;r;r;r<rdsb      G$9+ rcseZdZdZ     ddeedeedeedeedee eefdeeef fd d Z d ed efd dZ ddede fddZ ZS) CodeAgentz A class for an agent that solves the given task using a single block of code. It plans all its actions, then executes all in one shot. Nrrrrradditional_authorized_importsc s|durt}|dur t}|durt}tjd|||||d|ts+ttdt |_ |r2|ng|_ t t tt |j B|_|jdt|j|_dS)Nrrrrrzepygments isn't installed. Installing pygments will enable color syntax highlighting in the CodeAgent.rr;)rrr#rrr transformers_logging warning_oncerr"python_evaluatorr+r_rr!rrrbr)r7rrrrrr+r'rr;r<rUs0  zCodeAgent.__init__resultrPcCst|S)zu Override this method if you want to change the way the code is cleaned in the `run` method. )rv)r7r0r;r;r<rvzszCodeAgent.parse_code_blobFrreturn_generated_codec Ks||_t|dkr|jdt|d7_||_|tj|jd}tj d|jd}||g|_ |j d|j |j |j durKd|j ini}|j|j fd d gi|}|r^|Sz |j|d d \}} Wn ty} z|j d | d|}} WYd} ~ nd} ~ wwz|| } Wn"ty} zd| d} |j j| dd| WYd} ~ Sd} ~ ww||| z"it|jj} |j| | i|j|jd} |j |jd| WSty} zd| d} |j j| dd| WYd} ~ Sd} ~ ww)aK Runs the agent for the given task. Args: task (`str`): The task to perform return_generated_code (`bool`, *optional*, defaults to `False`): Whether to return the generated code instead of running it kwargs (additional keyword arguments, *optional*): Any keyword argument to send to the agent when evaluating the code. Example: ```py from transformers.agents import CodeAgent agent = CodeAgent(tools=[]) agent.run("What is the result of 2 power 3.7384?") ``` r6 You have been provided with these initial arguments: rrrz"====Executing with this prompt====Nrstop_sequences Code:rrzSError in extracting action, trying to parse the whole output as code. Error trace: rwError in code parsing: z!. Be sure to provide correct coderr static_tools custom_toolsrr print_outputszError in execution: z". Be sure to provide correct code.)rlenrcopyrrrrrrrrinforrrrhrrvrr%rrrr/r)r7rr1r'rrrrrrrlrroutputr;r;r<r(sf       z CodeAgent.runNNNNNr)r=r>r?rrr$rrrrrrvrr(rr;r;rr<r*Ps, %r*cseZdZdZeddd      d!deedeedee d ee d ee e e fd ee d ee ffd d Z de fddZ edddd"de dedefddZde fddZde fddZd#dedee fdd ZZS)$ ReactAgentaC This agent that solves the given task step by step, using the ReAct framework: While the objective is not reached, the agent will perform a cycle of thinking and acting. The action will be parsed from the LLM output: it consists in calls to tools from the toolbox, with arguments chosen by the LLM engine. 4.51.0zSwitch to smolagents instead, with the same functionalities and similar API (https://huggingface.co/docs/smolagents/index)versionrNrrrrr plan_typeplanning_intervalc  s~|durt}|dur t}|durt}|durtd}n |tvs(Jd|dtjd|||||d|||_||_dS)Nrz plan type z is not supportedr,r;)rrr#rrrrGrF) r7rrrrrrFrGr'rr;r<rs(  zReactAgent.__init__rPc Cstjddg|_|j|dd7_|jtjd|dg7_z||jWStyC}z d|dWYd}~Sd}~ww)zq This method provides a final answer to the task, based on the logs of the agent's interactions. zAn agent tried to answer an user query but it got stuck and failed to do so. You are tasked with providing an answer instead. 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Nr iteration start_time final_answerrrr step_end_time step_durationReached max iterations.) rtimesteprrrrrrrrH r7rrOrMstep_start_timestep_log_entryrlrPcallback error_messagefinal_step_logr;r;r<rJ.s\                zReactAgent.stream_runc Csd}d}|dur||jkrt}||d}zhz%|jdur.||jdkr.|j||dk|d||d|vr;|d}WntyZ}z|jj|dd||d<WYd}~nd}~wwWt}||d <|||d <|j ||j D]}||qs|d7}n#t}||d <|||d <|j ||j D]}||q|d7}w|dur||jks |dur||jkrd } dt | i} |j | |jj| dd| |}|| d<d| d <|j D]}|| q|S) z Runs the agent in direct mode, returning outputs only at the end: should be launched only in the `run` method. NrrL) is_first_steprMrOrrrrPrQrR) rrSrG planning_steprTrrrrrrrrHrUr;r;r<rKVsX                 zReactAgent.direct_runr[rMcCs|rvtjtd}tjd|dd}|||g}tjt|jdd}tjt|jdj||j |j |j dur>t |j nd|dd}|j||gd gd } d | d } d |d  } |j| | d|jdd|jd| dS|jdd} tjtd} tjtd}|| g| |g}tjt|jdj|dd}tjt|jdj||j |j |j durt |j nd||j|dd}|j|g| |gd gd }tj||d} d|d } |j| | d|jdd|jd| dS)ax Used periodically by the agent to plan the next steps to reach the objective. 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The tool calls will be formulated by the LLM in JSON format, then parsed and executed. NrrrrrrGc sJ|durt}|dur t}|durt}tjd||||||d|dS)NrrrrrrGr;)rrr#rr)r7rrrrrrGr'rr;r<rs   zReactJsonAgent.__init__ log_entryc CsL|}||_|jd||d<|jd|j|jdz|jdur-d|jini}|j|jfddd gi|}WntyR}zt d |d d}~ww|jd |j|||d <|jd|j |dd\}}z | |\}} Wnty}zt d|d d}~ww||d<|| d|d<|j d|jd||j d|d| |dkrt| trd| vr| d} t| tr| |jvr|j| } n| } n| } | |d<| S| duri} ||| } t| } | ttfvr| tkrd} n| tkrd} | |j| <d| d}nt| }|j|||d<|S) Perform one step in the ReAct framework: the agent thinks, acts, and observes the result. The errors are raised here, they are caught and logged in the run() method. ===== New step =====rz/===== Calling LLM with this last message: =====rXNrr3r4r Error in generating llm output: rz&===== Output message of the LLM: =====rz===== Extracting action =====rr6z"Could not parse the given action: rrtool_argumentsr|r r+z>>> Calling tool: 'z' with arguments: rOanswerz image.pngz audio.mp3zStored 'z ' in memory.r)rrrrr=r>rrrhrrrrr$rrrrrrrrrrrs)r7rqrrrrlrrxrrrwrobservation_typeobservation_nameupdated_informationr;r;r<rTs~                zReactJsonAgent.stepr@r=r>r?rrr$rrrrr)rrrTrr;r;rr<rs* rcseZdZdZ      ddeedeedeedeedee eefdeeed ee ffd d Z d e ee ffd dZ ZS)ReactCodeAgenta This agent that solves the given task step by step, using the ReAct framework: While the objective is not reached, the agent will perform a cycle of thinking and acting. The tool calls will be formulated by the LLM in code format, then parsed and executed. Nrrrrrr+rGc s|durt}|dur t}|durt}tjd||||||d|ts,ttdt |_ |r3|ng|_ t t tt |j B|_|jdt|j|_i|_dS)Nrpzjpygments isn't installed. Installing pygments will enable color syntax highlighting in the ReactCodeAgent.rr;)rrr#rrr r-r.rr"r/r+r_rr!rrrbrr:) r7rrrrrr+rGr'rr;r<rXs4   zReactCodeAgent.__init__rqc Cs|}||_|jd||d<|jd|j|jddz|jdur1d|jini}|j|jfddd gi|}WntyV}zt d |d d}~ww|jd |j|||d <|jdz |j |dd\}}Wn ty}z|jd|||}}WYd}~nd}~wwzt |}Wnty}z d|d} t | d}~ww||d<d|d|d<| ||z`it|jj} |jduri| |j} |j|| |j|j|jd} |jd|jd|jdd|jd} | dur"|jd|jdt| | dt| dd7} | |d <Wn#tyJ}zd!t|} d"t|vrB| d#7} t| d}~ww|d$D] } | dtd%d%kro|jd&d'|jd| | |d%<qP| S)(rrrsrz1===== Calling LLM with these last messages: =====rNrr3r4rrtrz=== Output message of the LLM:rz=== Extracting action ===r5r6zKError in extracting action, trying to parse the whole output. Error trace: r7z#. Make sure to provide correct coderzcode interpreterrur|r8zPrint outputs:r,r;zPrint outputs: zLast output from code snippet:zLast output from code snippet: irz2Code execution failed due to the following error: z%'dict' object has no attribute 'read'zd You get this error because you passed a dict as input for one of the arguments instead of a string.rrOr-z Final answer:)rr=rrrr>rrrhrrrvrr%rrrrr/r:rrr$rrrrr<)r7rqrrrrlrraw_code_actionrrr9r0rliner;r;r<rTs                zReactCodeAgent.stepror{r;r;rr<r|Qs0  (r|ic@s2eZdZedddd ddZdd Zd d ZdS) ManagedAgentrBrCrDNFcCs"||_||_||_||_||_dSr1)rrradditional_promptingprovide_run_summary)r7rrrrrr;r;r<rs  zManagedAgent.__init__cCsDd|jd|d}|jr|d|j}|S|dd}|S)NzYou're a helpful agent named 'z@'. You have been submitted this task by your manager. --- Task: a --- You're helping your manager solve a wider task: so make sure to not provide a one-line answer, but give as much information as possible so that they have a clear understanding of the answer. Your final_answer WILL HAVE to contain these parts: ### 1. Task outcome (short version): ### 2. Task outcome (extremely detailed version): ### 3. Additional context (if relevant): Put all these in your final_answer tool, everything that you do not pass as an argument to final_answer will be lost. And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback. <>z <>rw)rrrbrs)r7r full_taskr;r;r<write_full_tasks zManagedAgent.write_full_taskcKs||}|jj|fi|}|jrhd|jd}|t|7}|d|jd7}|jjddD]-}|d}tt|tksCdt|vrN|dt|d 7}q/|dt|dtd 7}q/|d |jd 7}|S|S) Nz2Here is the final answer from your managed agent 'z': zZ For more detail, find below a summary of this agent's work: SUMMARY OF WORK FROM AGENT 'Tr`rz [FACTS LIST]rz ---z0 (...Step was truncated because too long)... ---z$ END OF SUMMARY OF WORK FROM AGENT 'r]) rrr(rrrrr<LENGTH_TRUNCATE_REPORTS)r7requestr'rr?rwrrr;r;r<__call__s(  zManagedAgent.__call__)NF)r=r>r?r rrrr;r;r;r<rs  rr1)^rcr r`rStypingrrrrrrr"huggingface_hub.utils._deprecationr rwr utilsr-utils.import_utilsr agent_typesrr default_toolsrrrrrr monitoringrpromptsrrrrrrrrrr rr!r"rr#r$r%r&pygmentsr'pygments.formattersr(pygments.lexersr)r5r* get_loggerr=r propagate StreamHandlerch setFormatter addHandlerrrnrvr~rrrrrrhrrrrrrr_rrrrr*rArr|rrr;r;r;r<sl$     0     !  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