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Epictetus 271f79610d 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)
2026-08-05 17:05:21 +00:00

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name, version, description, inputs, steps, outputs, tags, metadata
name version description inputs steps outputs tags metadata
multi-agent-workflow-execution 1.0.0 Execute multi-agent AI workflows defined in YAML blueprints by creating sessions, submitting user prompts, and polling for completion until final answers are returned.
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)
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
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
source_repo extracted_at confidence
https://github.com/redhat-community-ai-tools/UnifAI.git 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:

pip install requests urllib3 python-langgraph temporalio

Setup steps:

  1. Install Python 3.11+ and required packages (requests, langgraph, temporalio)
  2. Configure API base URL and user credentials in environment variables or config
  3. Define or select a blueprint from the available workflows in the system
  4. 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

resolve_blueprint_id() - Resolves blueprint by ID or name lookup with error handling

create_session() - Creates a new session from a blueprint via POST /user.session.create

submit_session() - Submits user prompt to start workflow via POST /user.session.submit

poll_session_status() - Polls session.stream.status at configurable intervals

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 Confidence: 0.95

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