Add 5 skills from LFM + 12 skills total
New skills: - blacknode-graph-workflow - multi-agent-workflow-execution - langgraph-agent-workflow - langgraph-multi-agent-router - three-tier-evaluation-pipeline Config: LLM pipeline uses LFM on llama.cpp (8080)
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Epictetus
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name: multi-agent-workflow-execution
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version: 1.0.0
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description: Execute multi-agent AI workflows defined in YAML blueprints by creating
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sessions, submitting user prompts, and polling for completion until final answers
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are returned.
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inputs:
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- Blueprint ID or name (to identify the workflow to execute)
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- User shortcut (authentication identifier for the user)
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- User question or prompt (input to the workflow)
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- Base URL of the UnifAI API (endpoint for session management)
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- Polling interval (seconds between status checks during execution)
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steps:
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- Resolve the blueprint ID from either direct ID or name lookup via the API, handling
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cases where the blueprint is not found or not unique
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- Create a new session from the resolved blueprint using the session creation endpoint
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- Submit the session with the user's prompt to start the multi-agent workflow execution
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- Poll the session status at regular intervals until the session completes, fails,
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or is cancelled
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- Retrieve and return the final answer from the completed workflow
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outputs:
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- Final workflow result or answer (text or structured data)
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- Session status (completed, failed, or cancelled)
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- Error details if the workflow execution fails or times out
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tags: []
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metadata:
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source_repo: https://github.com/redhat-community-ai-tools/UnifAI.git
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extracted_at: ''
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confidence: 0.95
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---
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# multi-agent-workflow-execution
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Execute multi-agent AI workflows defined in YAML blueprints by creating sessions, submitting user prompts, and polling for completion until final answers are returned.
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## Setup
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**Dependencies:**
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```text
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pip install requests urllib3 python-langgraph temporalio
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```
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**Setup steps:**
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1. Install Python 3.11+ and required packages (requests, langgraph, temporalio)
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1. Configure API base URL and user credentials in environment variables or config
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1. Define or select a blueprint from the available workflows in the system
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1. Run the execution_workflow.py script with blueprint ID/name and user prompt
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## Key Files
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- `scripts/execution_workflow.py - Main workflow execution script`
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- `multi-agent/lib/mas/engine/ - LangGraph-based orchestration modules`
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- `multi-agent/lib/mas/elements/ - Node definitions (custom_agent_node, merger_node, etc.)`
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- `multi-agent/lib/mas/blueprints/ - Blueprint resolution and validation logic`
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## Steps
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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
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2. Create a new session from the resolved blueprint using the session creation endpoint
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3. Submit the session with the user's prompt to start the multi-agent workflow execution
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4. Poll the session status at regular intervals until the session completes, fails, or is cancelled
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5. Retrieve and return the final answer from the completed workflow
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## Implementation Details
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```python
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resolve_blueprint_id() - Resolves blueprint by ID or name lookup with error handling
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```
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```python
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create_session() - Creates a new session from a blueprint via POST /user.session.create
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```
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```python
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submit_session() - Submits user prompt to start workflow via POST /user.session.submit
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```
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```python
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poll_session_status() - Polls session.stream.status at configurable intervals
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```
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```python
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get_final_answer() - Retrieves final output via GET /session.chat.get
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```
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## Inputs
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- Blueprint ID or name (to identify the workflow to execute)
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- User shortcut (authentication identifier for the user)
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- User question or prompt (input to the workflow)
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- Base URL of the UnifAI API (endpoint for session management)
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- Polling interval (seconds between status checks during execution)
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## Outputs
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- Final workflow result or answer (text or structured data)
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- Session status (completed, failed, or cancelled)
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- Error details if the workflow execution fails or times out
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## Failure Modes
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- Blueprint not found or not unique - script exits with an error listing available blueprints
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- Session creation fails - may be due to invalid blueprint ID, authentication issues, or rate limiting
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- Session submission fails - could be due to network issues, invalid parameters, or API rate limits
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- Polling loop times out - session may be stuck in a long-running state without progress
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- Final answer retrieval fails - could be due to session cleanup or network issues after completion
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## Source
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Extracted from: [https://github.com/redhat-community-ai-tools/UnifAI.git](https://github.com/redhat-community-ai-tools/UnifAI.git)
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Confidence: 0.95
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