Fix from the call

module/move/assistant/design/agents_design.md

@@ -8,11 +8,45 @@ Main description of such system is writen using **YAML**.
 
 The system consists of **nodes** and **edges**.
 
-Currently, we assume that the only information type passed between nodes is **single text** and nodes are connected as a direct acyclic graph (DAC). Node can take input from several nodes, but it can generate only 1 output.
+Currently, we assume that the only information type passed between nodes is **single text** and nodes are connected as a direct acyclic graph (DAC). Node can take input from other nodes, but it can generate only 1 output.
 
 In future, we can support other types like *list of strings*. *booleans*; we can deliver input to several nodes *in parallel*, etc.
 
-## Nodes
+## YAML description structure
+
+Please refer to `examples/` directory.
+
+## Paths
+
+In several places in YAML file there are values of **paths**. Paths resemble paths in a real file system, parts are delimited with `::`. Absolute path starts from `::`.\
+
+Examples of paths:
+
+- `output`: relative path to single element `output`.
+- `event::stdout`: relative path to `stdout` through `event`.
+- `::trigger::stdin`: absolute path to `stdin` through `trigger`.
+
+Places where paths are used:
+
+- In nodes - `type`: type of the node. Different types of nodes live in different dirs.
+- In nodes - `next`: to which node pass the execution. Nodes live in `::nodes` dir.
+- In nodes - `agent_reuse`: reuse conversation history of previous agent.
+- In templates - `{{...}}`: take output from the node. Output of nodes live in `::output` dir.
+
+All paths (expect absolute) **are subject to absolutization**. This means that every relative path will be implicitly turned out to absolute path. In case of any ambiguities an error will be thrown. Absolutization also depends on the context: in `next` fields paths are absolutized to `::nodes` dir, in templates - to `::output` and so on.
+
+## Execution
+
+YAML file contains section about `nodes:`. You can think of them as statements in a programming language. Next statement is encoded in `next:` field. Output of the nodes are stored in `::output` dir.
+
+## Scenarios referencing
+
+There are two builtin scenarios:
+
+- `::scenario::entry`
+- `::scenario::termination`
+
+## Detailed description of nodes
 
 Each node has an `id` property (its name) and a `type` property.
 
@@ -109,7 +143,3 @@ Current **types**:
 **Description**: when the process of execution is passed to this node, the whole program of the multi-agent system terminates.
 
 This node is **implicitly present in every graph**, and to call it you just need to fill `next:` with the `scenario.terminate`.
-
-## YAML description structure
-
-Please refer to `examples/` directory.

module/move/assistant/design/agents_examples/language_learner.yaml

@@ -10,33 +10,32 @@ description: >
 
 nodes:
   - id: question_input
-    type: trigger.stdin
+    type: trigger::stdin
     prompt: 'Your question: '
     next: answer
 
   - id: answer
-    type: agent.completion
+    type: agent::completion
     system_message: 'You are a helpful Q&A assistant. Answer in English'
     user_message: 'Question: {{question_input}}'
-    next: 
-      - translator
-      - frases_extractor
-      - output # TODO: What will happen if output would be called first (or in paralel to other "next"s) but there is no output of `translator` or `frases_extractor`?
+    next: translator
 
   - id: translator
-    type: agent.completion
+    type: agent::completion
     system_message: 'Translate the input in Ukrainian language'
     user_message: '{{answer}}'
+    next: frases_extractor
 
   - id: frases_extractor
-    type: agent.completion
+    type: agent::completion
     system_message: >
       You have a text in English. Extract unique and interesting frases or words from the text
       that would be useful for English language learner. Present the output as a list of pairs:
       the first element is English phrase and the second is Ukrainian translation.
-    user_message: '{{answer}}'
+    user_message: '{{translator}}'
+    next: output
 
   - id: output
-    type: event.stdout
-    output: 'Answer: {{answer}}'
+    type: event::stdout
+    output: 'Answer: {{answer}}. In Ukrainian: {{translator}}. Words: {{frases_extractor}}'
     next: scenario.termination

module/move/assistant/design/agents_examples/qa_1.yaml

@@ -5,23 +5,23 @@ description: >
 
 nodes:
   - id: question_input
-    type: trigger.stdin
+    type: trigger::stdin
     prompt: 'Your question: '
     next: cleaner
 
   - id: cleaner
-    type: agent.completion
+    type: agent::completion
     system_message: 'Correct the orthography and other English mistakes. Make the input clean and easy to understand.'
     user_message: '{{question_input}}'
     next: answer
 
   - id: answer
-    type: agent.completion
+    type: agent::completion
     system_message: 'You are a helpful Q&A assistant.'
     user_message: 'Question: {{cleaner}}'
     next: output
 
   - id: output
     type: event.stdout
     output: 'Answer: {{answer}}'
-    next: scenario.termination
+    next: scenario::termination

module/move/assistant/design/agents_examples/sql.yaml

@@ -1,27 +1,23 @@
-title: DB information extractor
-
-nodes:
-  - id: question_input
-    type: trigger.stdin
+ nodes:
+  - id: input
+    type: trigger::stdin
     prompt: 'Your query: '
-    next: translator
+    next: node::translator
 
-  - id: thinker
-    type: agent.completion
-    # In real life scenario you should specify here:
-    # - Which DB is used?
-    # - What are the tables and columns?
-    # - Schema of the DB.
+  - id: sql_generator_stage1
+    type: agent::completion
     system_message: 'Your task is to think about how to translate the user query in natural langauge in SQL langauge. Think step by steps.'
-    user_message: '{{question_input}}'
-    next: query
+    user_message: '{{input}}'
+    next: node::sql_generator_stage2
 
-  - id: query
-    type: agent.completion
+  - id: sql_generator_stage2
+    type: agent::completion
     system_message: 'Your task to make an SQL code based on user query in natural language and the results of thinking on that query'.
-    user_message: 'Query: {{question_input}}. Thoughts: {{thinker}}'
+    user_message: '{{sql_generator_stage1}}'
+    agent_reuse: node::sql_generator_stage2
+    next: node::output
 
   - id: output
-    type: event.stdout
-    output: '{{query}}'
-    next: scenario.termination
+    type: event::stdout
+    output: '{{sql_generator_stage2}}'
+    next: scenario::termination

module/move/assistant/design/agents_examples/thinker.yaml

@@ -6,21 +6,21 @@ description: >
 
 nodes:
   - id: question_input
-    type: trigger.stdin
+    type: trigger::stdin
     prompt: 'Your question: '
     next: cleaner
 
   - id: thinker
-    type: agent.completion
+    type: agent::completion
     system_message: > # This system prompt could be improved more, but for the sake of the example, we will leave it simple.
       You are a methodical and knowledgeable assistant tasked with answering user questions thoroughly using a
       chain-of-thought approach. Break down complex queries into components, outline a logical plan, and explain
       each step in detail with examples or analogies as needed.
     user_message: '{{question_input}}'
-    next: answer
+    next: summarizer
 
   - id: summarizer
-    type: agent.completion
+    type: agent::completion
     system_message: >
       You are a summarizer tasked with distilling detailed responses into clear, concise summaries.
       Capture key points and main conclusions, removing unnecessary details while preserving essential meaning and logical flow.
@@ -31,4 +31,4 @@ nodes:
   - id: output
     type: event.stdout
     output: 'Answer: {{summarizer}}'
-    next: scenario.termination
+    next: scenario::termination