yjjUdZddlmZddlZddlZddlmZmZddlm Z ddl m Z ddl m Z ddlmZmZdd lmZdd lmZmZdd lmZmZdd lmZdd lmZddlmZm Z ddl!m"Z"ddl#m$Z$ddl%m&Z&ddl'm(Z(m)Z)ddl*m+Z+m,Z,ddl-m.Z.ddl/m0Z0m1Z1m2Z2ej3e4Z5d)dZ6e GddZ7dhZ8Gd d!eZ9Gd"d#e9Z:Gd$d%e9Z;e:e;d&Zz:BaseSpecialist._build_peer_card_context..ys(<<$+t++<<<<<d?YSwxYw#tt$r9},tw|,j<} |dkrtj| z dz}d},~,wwxYw# | t7| jnd}(| | jnd})| | jnd}*| | j4nd}+tmtod@id/|d0|jd1|d2|d3|d4|+d5|(d|)d|*d6|d7| d8| d9|d:|d+|d;|dd?YwwxYwxYw)Aa Run the specialist agent. Uses short-lived DB sessions to avoid holding connections during LLM calls. Args: workspace_name: Workspace identifier observer: The observing peer observed: The peer being observed session_name: Session identifier hints: Optional hints to guide exploration (specialists explore freely if None) configuration: Resolved configuration for checking feature flags (optional) parent_run_id: Optional run_id from orchestrator for correlation Returns: SpecialistResult with metrics and content dreamer__FNrgzdream.specialist.preflightrA)rfrgrSsystemrC)roler6userTdream) rfrgrSrhinclude_observation_idshistory_token_limitrjr+ agent_typeparent_categoryzdream.r\zDreamer/)rf call_purposerxrwr+rgrS) r prompt max_tokenstools tool_choice tool_executormax_tool_iterationsmessages track_name telemetryitotal_durationms tool_callscountr0r1)rr" token_typez: Completed in z.0fzms, z tool calls, z in / z out> search_memorysearch_messagessearch_messages_temporal tool_namerAtool_result_metadata created_count deleted_countpeer_card_updatedlevelsc0g|]}|t|SN)r#)r`levels r& z&BaseSpecialist.run..\s*&&&+05CTCJJCTCTCTr() r+r,r.r/r0r1r3r5r6r+r,rfrgrSr.r/r3r5 error_classcreated_observation_countdeleted_observation_countsearch_tool_calls_countcreated_counts_by_leveldeleted_counts_by_levelz#Failed to emit DreamSpecialistEvent)exc_infor<)=generate_nanoidrAtime perf_counterrrr get_peerr PeerCreaterXcreate get_peer_cardrUrZrrDREAMHISTORY_TOKEN_LIMITrMmax_output_tokensrOrrIrRrrlentool_calls_mader0r1METRICSENABLEDrrecord_dreamer_tokensrINPUTvalueOUTPUTloggerinforget isinstancedictr r#r r-listupdater*r.r6rr Exceptiondebug BaseExceptiontyper7)-rHrfrgrSrhrVrjrlr+ task_name start_timespecialist_successspecialist_error_classresponserrrrr3rrdbrDcurrent_peer_cardrr~r configured_maxeffective_max_tokenscall_purpose_slug _search_toolstc tool_name_anymeta_any meta_dict created_val deleted_val levels_any levels_list level_strsr/r0r1r.es- r&runzBaseSpecialist.runsK 63/"3"33ty33633 &(( #-16:%&!$%!!"# 18 07 j S!">??       2m(:(I(I(Ix''-NG,>H,M,M,M "T)K]-D-K" 7;!$.2.@'5!)!) ///))))))%#                           8%#77 4E 8  ##55e=NOO .H +-!!)(,$,N$F+9 '           0022L *;N"+&4q&8&8((** !!5 4 4 -)/nn7HnII +$($;$;$=$=!1di11-#1!2$+#y!%%H, ,..;tCK i)9; M M M <X-E)F)F    i9NPW X X X ?H,BG    ' "8"/$(I)/5 #8"0$(I)06 KK9BB[BBBB122AAAB*NN(2HNNNO    $$:ty$:$:F C C CM . G G%'VVK%8%8%JBFF6NN "'= > > D" M11+q0+h--G !%T#s(^X > >I'0}}_'E'E'JK'0}}_'E'E'JK-[1A1AA--[1A1AA- }}%8991,0)'0mmH&=&=J!*d33 G&*49j&A&A &&4?&&& +i773:::FFFF, 993:::FFF!% # $ #.!$X%=!>!>%2&4' (   4" S5=5IC0111q!9A8Lx44RS :B:N 6 6TU 4<4HX00a (%v(, (6~"*  "*  $.: *:)9&2\'4m%0K!3 2%;$:3L2K3L2K +<*;!"1H0G#*155L0M0M0M+,155L0M0M0M-4 S S S BT RRRRR Sa    &*!WW%5 "c!!#022Z?4G  " S5=5IC0111q!9A8Lx44RS :B:N 6 6TU 4<4HX00a (%v(, (6~"*  "*  $.: *:)9&2\'4m%0K!3 2%;$:3L2K3L2K +<*;!"1H0G#*155L0M0M0M+,155L0M0M0M-4 S S S BT RRRRR Ss X75BD X7 D))X7,D)-P7X7%B%X  &X43X47 Y:4Y55Y::Y==]?B%\%$]%&] ] ]]rDr4r$rEr$rr$r-rSr#rDr4r$r#rrVrWrXrWr$r#)rXrWr$r#)NNN)rfr#rgr#rSr#rhrirVrWrjrkrlrir$r*)r7r8r9r:rAr;rBrrIrMrOrRrUrZrerr<r(r&r?r?Js^--D N!59     ^    ^ :>     ^ '+    ^     *#'6:$(aSaSaSaSaSaSaSr(r?cpeZdZUdZdZded<dZded<dddd ZddZddZ ddZ ddddZ dddZ dS) DeductionSpecialista Creates deductive observations from explicit observations. This specialist: 1. Explores recent observations and messages to understand what's there 2. Identifies logical implications, knowledge updates, and contradictions 3. Creates new deductive observations with premise linkage 4. Deletes outdated observations 5. Updates peer card with biographical facts deductionr#rAzOUpdate this with `update_peer_card` only for stable biographical/profile facts.rBTrCrDr4r$rEc6|rtSdtDS)Nc0g|]}|dtv|SrpPEER_CARD_TOOL_NAMESr`ts r&rz1DeductionSpecialist.get_tools..2   y 444 444r()rrGs r&rIzDeductionSpecialist.get_tools1  .- -  /    r(rcBttjjdS)NzDREAM DEDUCTIONr")r'rrDEDUCTION_MODEL_CONFIGrLs r&rMz$DeductionSpecialist.get_model_config$/ N 1-    r(r-cdSNi r<rLs r&rOz"DeductionSpecialist.get_max_tokenstr(cdS)N r<rLs r&rRz&DeductionSpecialist.get_max_iterationsrr(rSc d}|rd}d|d|dS)Nr\a ## PEER CARD (REQUIRED) The peer card is a summary of stable biographical facts. You MUST update it when you learn: - Name, age, location, occupation - Family members and relationships - Standing instructions ("call me X", "don't mention Y") - Core preferences and traits Never add temporary event summaries, one-off conclusions, reasoning traces, or contradiction notes. Format entries as: - Plain facts: "Name: Alice", "Works at Google", "Lives in NYC" - `INSTRUCTION: ...` for standing instructions - `PREFERENCE: ...` for preferences - `TRAIT: ...` for personality traits Call `update_peer_card` with the complete updated list when you have new biographical info. Keep it concise (max 40 entries), deduplicated, and current.zAYou are a deductive reasoning agent analyzing observations about u. ## YOUR JOB Create deductive observations by finding logical implications in what's already known. Think like a detective connecting evidence. ## PHASE 1: DISCOVERY Explore what's actually in memory. Use these tools freely: - `get_recent_observations` - See what's been learned recently - `search_memory` - Search for specific topics - `search_messages` - See actual conversation content Spend a few tool calls understanding the landscape before creating anything. ## PHASE 2: ACTION Once you understand what's there, create observations and clean up: ### Knowledge Updates (HIGH PRIORITY) When the same fact has different values at different times: - "meeting Tuesday" [old] → "meeting moved to Thursday" [new] - Create a deductive update observation - DELETE the outdated observation immediately ### Logical Implications Extract implicit information: - "works as SWE at Google" → "has software engineering skills", "employed in tech" - "has kids ages 5 and 8" → "is a parent", "has school-age children" ### Contradictions When statements can't both be true (not just updates), flag them: - "I love coffee" vs "I hate coffee" → contradiction observation ao ## CREATING OBSERVATIONS Use `create_observations_deductive`. ```json { "observations": [{ "content": "The logical conclusion", "source_ids": ["id1", "id2"], "premises": ["premise 1 text", "premise 2 text"] }] } ``` ## RULES 1. Don't explain your reasoning - just call tools 2. Create observations based on what you ACTUALLY FIND, not what you expect 3. Always include source_ids linking to the observations you're synthesizing 4. Empty or missing source_ids will be rejected 5. Delete outdated observations - don't leave duplicates 6. Quality over quantity - fewer good deductions beat many weak onesr<rHrSrDpeer_card_sections r&rUz'DeductionSpecialist.build_system_promptsP  @!@ *8HU]8H8HBC8H8H8H8 Hr(NrVrWrXc||}|r/dd|ddD}|d|dS|dS)Nr]c3 K|] }d|V dSr_r<r`qs r&rbz8DeductionSpecialist.build_user_prompt..((!>!>q(q((!>!>!>!>!>!>r(z_Start by exploring recent observations and messages. These topics may be worth investigating: z But follow the evidence - if you find something more interesting, pursue that instead. Begin with `get_recent_observations` to see what's there.aeExplore the observation space and create deductive observations. Start with `get_recent_observations` to see what's been learned recently, then investigate whatever seems most promising. Look for: 1. Knowledge updates (same fact, different values over time) 2. Logical implications that haven't been made explicit 3. Contradictions that need flagging Go.rercrHrVrXpeer_card_context hints_strs r&rZz%DeductionSpecialist.build_user_prompt s !99)DD  = !>!>E"1"I!>!>!>>>I)== === =&    r(rrrrrr r7r8r9r:rAr;rBrIrMrOrRrUrZr<r(r&rrs  D(y yyyy59          ;?RHRHRHRHRHRHn'+r(rcpeZdZUdZdZded<dZded<dddd ZddZddZ ddZ ddddZ dddZ dS) InductionSpecialistan Creates inductive observations from explicit and deductive observations. This specialist: 1. Explores observations to understand what's there 2. Identifies patterns and generalizations across multiple observations 3. Creates new inductive observations with source linkage 4. Updates peer card with high-confidence traits and tendencies inductionr#rAzUOnly add highly stable profile traits/preferences; do not copy transient conclusions.rBTrCrDr4r$rEc6|rtSdtDS)Nc0g|]}|dtv|Srprrs r&rz1InductionSpecialist.get_tools..Nrr()rrGs r&rIzInductionSpecialist.get_toolsKrr(rcBttjjdS)NzDREAM INDUCTIONr)r'rrINDUCTION_MODEL_CONFIGrLs r&rMz$InductionSpecialist.get_model_configTrr(r-cdSrr<rLs r&rOz"InductionSpecialist.get_max_tokensZrr(cdS)N r<rLs r&rRz&InductionSpecialist.get_max_iterations]rr(rSc d}|rd}d|d|dS)Nr\a ## PEER CARD (REQUIRED) After identifying patterns, only update the peer card for durable profile-level traits/preferences: - `TRAIT: Analytical thinker` - `TRAIT: Tends to reschedule when stressed` - `PREFERENCE: Prefers detailed explanations` Do NOT add temporary patterns, episode-specific conclusions, or reasoning summaries. Call `update_peer_card` with the complete deduplicated list only when a durable profile update is warranted. Keep it concise (max 40 entries).z@You are an inductive reasoning agent identifying patterns about u. ## YOUR JOB Create inductive observations by finding patterns across multiple observations. Think like a psychologist identifying behavioral tendencies. ## PHASE 1: DISCOVERY Explore broadly to find patterns. Use these tools: - `get_recent_observations` - Recent learnings - `search_memory` - Topic-specific search - `search_messages` - Actual conversation content Look at BOTH explicit observations AND deductive ones. Patterns often emerge from synthesizing across both levels. ## PHASE 2: ACTION Create inductive observations when you see patterns: ### Behavioral Patterns - "Tends to reschedule meetings when stressed" - "Makes decisions after consulting with partner" - "Projects follow: enthusiasm → doubt → completion" ### Preferences - "Prefers morning meetings" - "Likes detailed technical explanations" ### Personality Traits - "Generally optimistic about outcomes" - "Detail-oriented in planning" ### Temporal Patterns - "Career goals have remained consistent" - "Living situation changes frequently" a ## CREATING OBSERVATIONS Use `create_observations_inductive`. ```json { "observations": [{ "content": "The pattern or generalization", "source_ids": ["id1", "id2", "id3"], "sources": ["evidence 1", "evidence 2"], "pattern_type": "tendency", // preference|behavior|personality|tendency|correlation "confidence": "medium" // low (2 sources), medium (3-4), high (5+) }] } ``` ## RULES 1. Minimum 2 source observations required - patterns need evidence 2. Don't just restate a single fact as a pattern 3. Confidence based on evidence count: 2=low, 3-4=medium, 5+=high 4. Look for HOW things change over time, not just static facts 5. Include source_ids - always link back to evidence 6. Empty or missing source_ids will be rejectedr<rs r&rUz'InductionSpecialist.build_system_prompt`sG  % !% <3T\<3<3FG<3<3<3< 3r(NrVrWrXc||}|r/dd|ddD}|d|dS|dS)Nr]c3 K|] }d|V dSr_r<rs r&rbz8InductionSpecialist.build_user_prompt..rr(rzDExplore and find patterns. These areas may be worth investigating: zp But follow the evidence - if you find patterns elsewhere, pursue those. Start with `get_recent_observations`.zExplore the observation space and identify patterns. Remember: patterns need 2+ sources. Look for tendencies, preferences, and behavioral regularities. Go.rrs r&rZz%InductionSpecialist.build_user_prompts !99)DD  ) !>!>E"1"I!>!>!>>>I))) ))) )& r(rrrrrrrr<r(r&rr=sD( 59          ;?N3N3N3N3N3N3f'+r(r)rrzdict[str, BaseSpecialist] SPECIALISTS)r r!r"r#r$r)>r: __future__rloggingrabcrr collectionsrcollections.abcr dataclassesrtypingr r nanoidr rsrcr r src.configrrsrc.dependenciesrsrc.exceptionsrsrc.llmrr src.llm.typesr src.schemasr src.telemetryrsrc.telemetry.eventsrrsrc.telemetry.loggingrr src.telemetry.prometheus.metricsrsrc.utils.agent_toolsrrr getLoggerr7rr'r*rr?rrrr;r<r(r&rs#""""" ########$$$$$$!!!!!!......88888888''''''......::::::::------------,,,,,,;;;;;;;;LLLLLLLL777777  8 $ $              ++[S[S[S[S[SS[S[S[S| RRRRR.RRRjHHHHH.HHHZ%$&&$$&&** r(