Add Skill: multi-agent-workflow-execution

Extracted from: https://github.com/redhat-community-ai-tools/UnifAI.git Score: 1.0
2026-08-05 17:03:56 +00:00
9 changed files with 165 additions and 142 deletions
@@ -1,96 +0,0 @@
---
-name: langgraph-multi-agent-router
-version: 1.0.0
-description: Orchestrate a multi-agent workflow where specialized agents collaborate
-  sequentially to gather information, structure it, and generate a final response
-inputs:
- User query string (e.g., destination location)
- BedrockModel configuration (model_id, temperature, top_p)
- Pre-configured agents with specific system prompts and tool sets
-steps:
- Researcher agent executes with system prompt to gather raw destination facts (places,
-  history, accommodations, food, web pages) using BedrockModel and available tools
-  (calculator, current_time)
- Travel guide agent receives raw research output and structures it into labeled sections
-  (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights,
-  Suggested Web Pages)
- Writer agent receives the structured guide and synthesizes it into a professional
-  client-facing response with clear formatting and emphasis on the suggested web pages
-outputs:
- Raw research data (JSON string containing gathered facts)
- Structured guide content (markdown-formatted travel guide with labeled sections)
- Final client response (professional formatted response ready for delivery)
-tags: []
-metadata:
-  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
-  extracted_at: ''
-  confidence: 0.95
---
-
-# langgraph-multi-agent-router
-
-Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install langchain langgraph bedrock-model pydantic
-```
-
-**Setup steps:**
-
-1. Install langchain and langgraph packages
-1. Configure BedrockModel with desired parameters (model_id, temperature, top_p)
-1. Create three Agent instances with specific system prompts and tool sets
-1. Initialize LangGraph with the agent chain and run the workflow
-
-## Key Files
-
- `agents/langchain_langgraph/00-basic-agent/agent.py`
- `agents/langchain_langgraph/02-agent-with-tools-structured-output/agent.py`
- `agents/langchain_langgraph/08-langgraph-multi-agents-sequential-pattern/agent.py`
-
-## Steps
-
-1. Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)
-2. Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)
-3. Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
-
-## Implementation Details
-
-```python
-Researcher agent uses BedrockModel with temperature=0.7, top_p=0.9 to gather destination facts
-```
-
-```python
-Travel guide agent receives raw output and formats into 5 labeled sections
-```
-
-```python
-Writer agent takes structured guide and writes professional client response
-```
-
-## Inputs
-
- User query string (e.g., destination location)
- BedrockModel configuration (model_id, temperature, top_p)
- Pre-configured agents with specific system prompts and tool sets
-
-## Outputs
-
- Raw research data (JSON string containing gathered facts)
- Structured guide content (markdown-formatted travel guide with labeled sections)
- Final client response (professional formatted response ready for delivery)
-
-## Failure Modes
-
- Researcher agent fails to gather sufficient data or returns incomplete results
- Travel guide agent fails to structure information correctly or produces unreadable output
- Writer agent fails to format the final response properly or loses key information from the guide
-
-## Source
-
-Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
-Confidence: 0.95
@@ -1,6 +0,0 @@
-# Commands: langgraph-multi-agent-router
-
-## Available Commands
-
- `/skill langgraph-multi-agent-router` — Load this skill
- `/run langgraph-multi-agent-router` — Execute workflow
@@ -1,10 +0,0 @@
-# Examples: langgraph-multi-agent-router
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: User query string (e.g., destination location), BedrockModel configuration (model_id, temperature, top_p), Pre-configured agents with specific system prompts and tool sets
-# Process: Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time) → Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages) → Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages
-# Outputs: Raw research data (JSON string containing gathered facts), Structured guide content (markdown-formatted travel guide with labeled sections), Final client response (professional formatted response ready for delivery)
-```
@@ -1,29 +0,0 @@
-{
-  "name": "langgraph-multi-agent-router",
-  "version": "1.0.0",
-  "goal": "Orchestrate a multi-agent workflow where specialized agents collaborate sequentially to gather information, structure it, and generate a final response",
-  "inputs": [
-    "User query string (e.g., destination location)",
-    "BedrockModel configuration (model_id, temperature, top_p)",
-    "Pre-configured agents with specific system prompts and tool sets"
-  ],
-  "steps": [
-    "Researcher agent executes with system prompt to gather raw destination facts (places, history, accommodations, food, web pages) using BedrockModel and available tools (calculator, current_time)",
-    "Travel guide agent receives raw research output and structures it into labeled sections (Must-See Attractions, Historical Highlights, Accommodation Areas, Culinary Delights, Suggested Web Pages)",
-    "Writer agent receives the structured guide and synthesizes it into a professional client-facing response with clear formatting and emphasis on the suggested web pages"
-  ],
-  "outputs": [
-    "Raw research data (JSON string containing gathered facts)",
-    "Structured guide content (markdown-formatted travel guide with labeled sections)",
-    "Final client response (professional formatted response ready for delivery)"
-  ],
-  "failure_modes": [
-    "Researcher agent fails to gather sufficient data or returns incomplete results",
-    "Travel guide agent fails to structure information correctly or produces unreadable output",
-    "Writer agent fails to format the final response properly or loses key information from the guide"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow demonstrates a reusable multi-stage agent pattern where specialized agents collaborate in sequence. The Researcher agent gathers raw information using a domain-specific model, the Travel Guide agent structures that information into a consistent format, and the Writer agent synthesizes the final output. This pattern can be adapted to other domains (e.g., code generation, data analysis, research workflows) by swapping the agent types and system prompts while maintaining the same three-step structure.",
-  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
-  "score": 1.0
-}
@@ -0,0 +1,113 @@
+---
+name: multi-agent-workflow-execution
+version: 1.0.0
+description: Execute multi-agent AI workflows defined in YAML blueprints by creating
+  sessions, submitting user prompts, and polling for completion until final answers
+  are returned.
+inputs:
+- Blueprint ID or name (to identify the workflow to execute)
+- User shortcut (authentication identifier for the user)
+- User question or prompt (input to the workflow)
+- Base URL of the UnifAI API (endpoint for session management)
+- Polling interval (seconds between status checks during execution)
+steps:
+- Resolve the blueprint ID from either direct ID or name lookup via the API, handling
+  cases where the blueprint is not found or not unique
+- Create a new session from the resolved blueprint using the session creation endpoint
+- Submit the session with the user's prompt to start the multi-agent workflow execution
+- Poll the session status at regular intervals until the session completes, fails,
+  or is cancelled
+- Retrieve and return the final answer from the completed workflow
+outputs:
+- Final workflow result or answer (text or structured data)
+- Session status (completed, failed, or cancelled)
+- Error details if the workflow execution fails or times out
+tags: []
+metadata:
+  source_repo: https://github.com/redhat-community-ai-tools/UnifAI.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# multi-agent-workflow-execution
+
+Execute multi-agent AI workflows defined in YAML blueprints by creating sessions, submitting user prompts, and polling for completion until final answers are returned.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install requests urllib3 python-langgraph temporalio
+```
+
+**Setup steps:**
+
+1. Install Python 3.11+ and required packages (requests, langgraph, temporalio)
+1. Configure API base URL and user credentials in environment variables or config
+1. Define or select a blueprint from the available workflows in the system
+1. Run the execution_workflow.py script with blueprint ID/name and user prompt
+
+## Key Files
+
+- `scripts/execution_workflow.py - Main workflow execution script`
+- `multi-agent/lib/mas/engine/ - LangGraph-based orchestration modules`
+- `multi-agent/lib/mas/elements/ - Node definitions (custom_agent_node, merger_node, etc.)`
+- `multi-agent/lib/mas/blueprints/ - Blueprint resolution and validation logic`
+
+## Steps
+
+1. Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique
+2. Create a new session from the resolved blueprint using the session creation endpoint
+3. Submit the session with the user's prompt to start the multi-agent workflow execution
+4. Poll the session status at regular intervals until the session completes, fails, or is cancelled
+5. Retrieve and return the final answer from the completed workflow
+
+## Implementation Details
+
+```python
+resolve_blueprint_id() - Resolves blueprint by ID or name lookup with error handling
+```
+
+```python
+create_session() - Creates a new session from a blueprint via POST /user.session.create
+```
+
+```python
+submit_session() - Submits user prompt to start workflow via POST /user.session.submit
+```
+
+```python
+poll_session_status() - Polls session.stream.status at configurable intervals
+```
+
+```python
+get_final_answer() - Retrieves final output via GET /session.chat.get
+```
+
+## Inputs
+
+- Blueprint ID or name (to identify the workflow to execute)
+- User shortcut (authentication identifier for the user)
+- User question or prompt (input to the workflow)
+- Base URL of the UnifAI API (endpoint for session management)
+- Polling interval (seconds between status checks during execution)
+
+## Outputs
+
+- Final workflow result or answer (text or structured data)
+- Session status (completed, failed, or cancelled)
+- Error details if the workflow execution fails or times out
+
+## Failure Modes
+
+- Blueprint not found or not unique - script exits with an error listing available blueprints
+- Session creation fails - may be due to invalid blueprint ID, authentication issues, or rate limiting
+- Session submission fails - could be due to network issues, invalid parameters, or API rate limits
+- Polling loop times out - session may be stuck in a long-running state without progress
+- Final answer retrieval fails - could be due to session cleanup or network issues after completion
+
+## Source
+
+Extracted from: [https://github.com/redhat-community-ai-tools/UnifAI.git](https://github.com/redhat-community-ai-tools/UnifAI.git)
+Confidence: 0.95
@@ -0,0 +1,6 @@
+# Commands: multi-agent-workflow-execution
+
+## Available Commands
+
+- `/skill multi-agent-workflow-execution` — Load this skill
+- `/run multi-agent-workflow-execution` — Execute workflow
@@ -0,0 +1,10 @@
+# Examples: multi-agent-workflow-execution
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: Blueprint ID or name (to identify the workflow to execute), User shortcut (authentication identifier for the user), User question or prompt (input to the workflow), Base URL of the UnifAI API (endpoint for session management), Polling interval (seconds between status checks during execution)
+# Process: Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique → Create a new session from the resolved blueprint using the session creation endpoint → Submit the session with the user's prompt to start the multi-agent workflow execution
+# Outputs: Final workflow result or answer (text or structured data), Session status (completed, failed, or cancelled), Error details if the workflow execution fails or times out
+```
@@ -0,0 +1,35 @@
+{
+  "name": "multi-agent-workflow-execution",
+  "version": "1.0.0",
+  "goal": "Execute multi-agent AI workflows defined in YAML blueprints by creating sessions, submitting user prompts, and polling for completion until final answers are returned.",
+  "inputs": [
+    "Blueprint ID or name (to identify the workflow to execute)",
+    "User shortcut (authentication identifier for the user)",
+    "User question or prompt (input to the workflow)",
+    "Base URL of the UnifAI API (endpoint for session management)",
+    "Polling interval (seconds between status checks during execution)"
+  ],
+  "steps": [
+    "Resolve the blueprint ID from either direct ID or name lookup via the API, handling cases where the blueprint is not found or not unique",
+    "Create a new session from the resolved blueprint using the session creation endpoint",
+    "Submit the session with the user's prompt to start the multi-agent workflow execution",
+    "Poll the session status at regular intervals until the session completes, fails, or is cancelled",
+    "Retrieve and return the final answer from the completed workflow"
+  ],
+  "outputs": [
+    "Final workflow result or answer (text or structured data)",
+    "Session status (completed, failed, or cancelled)",
+    "Error details if the workflow execution fails or times out"
+  ],
+  "failure_modes": [
+    "Blueprint not found or not unique - script exits with an error listing available blueprints",
+    "Session creation fails - may be due to invalid blueprint ID, authentication issues, or rate limiting",
+    "Session submission fails - could be due to network issues, invalid parameters, or API rate limits",
+    "Polling loop times out - session may be stuck in a long-running state without progress",
+    "Final answer retrieval fails - could be due to session cleanup or network issues after completion"
+  ],
+  "confidence": 0.95,
+  "explanation": "The UnifAI repository contains a concrete, reusable workflow pattern for executing multi-agent AI workflows. The scripts/execution_workflow.py script demonstrates a complete pipeline: resolving blueprints by ID or name, creating sessions from blueprints, submitting user prompts to start workflows, polling session status until completion, and retrieving final answers. This pattern can be adapted to any multi-agent workflow defined in the YAML blueprint system, making it reusable across different use cases and teams.",
+  "source_repo": "https://github.com/redhat-community-ai-tools/UnifAI.git",
+  "score": 1.0
+}
@@ -1,4 +1,4 @@
-# Tests: langgraph-multi-agent-router
+# Tests: multi-agent-workflow-execution

 ## Test Checklist