Add Skill: langgraph-agent-workflow

Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git Score: 1.0
2026-08-05 17:04:25 +00:00
9 changed files with 168 additions and 165 deletions
@@ -0,0 +1,115 @@
+---
+name: langgraph-agent-workflow
+version: 1.0.0
+description: Orchestrate multi-step AI agents using LangGraph with SerperDevTool for
+  RAG, code execution, and citation generation
+inputs:
+- LangGraph chain configuration files defining agent workflows
+- SerperDevTool integration for LLM tool access
+- React agent creation scripts via create_react_agent
+- Knowledge graph retrieval and citation generation pipelines
+steps:
+- 'Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create
+  a LangGraph chain that combines retrieval, reasoning, and response generation using
+  SerperDevTool for tool access'
+- 'Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend
+  agent that can interact with the LangGraph chain'
+- 'Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge
+  graph retrieval (Neo4j/ArangoDB) with citation generation'
+- 'Step 4: Add code execution sandbox - Integrate artifact generation capabilities
+  for code-related tasks'
+- "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research\
+  \ \u2192 agent response in a single LangGraph workflow"
+outputs:
+- Reusable LangGraph chain definition (pyfile) with configurable steps
+- React agent frontend component that can be deployed independently
+- RAG pipeline that generates block citations and grounded answers
+- Code execution sandbox for artifact generation
+- Documentation for parameterizing workflows for different tasks
+tags: []
+metadata:
+  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-agent-workflow
+
+Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langgraph>=0.7.0 serper-dev-tool>=0.1.0 qdrant-client or opensearch-dsl neo4j-driver or arango-database-driver react, next.js
+```
+
+**Setup steps:**
+
+1. Install LangGraph and SerperDevTool dependencies
+1. Configure vector store (Qdrant/OpenSearch) and knowledge graph (Neo4j/ArangoDB)
+1. Define chain topology with retrieval, reasoning, and response steps
+1. Build React agent frontend using create_react_agent
+1. Test multi-step agent workflows end-to-end
+
+## Key Files
+
+- `pipeshub-ai/workflows/agent_chain.py - Main LangGraph chain definition`
+- `pipeshub-ai/workflows/agent_react.py - React agent wrapper`
+- `pipeshub-ai/workflows/rag_pipeline.py - RAG with citation generation`
+- `pipeshub-ai/workflows/code_sandbox.py - Code execution sandbox`
+
+## Steps
+
+1. Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access
+2. Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain
+3. Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
+4. Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks
+5. Step 5: Orchestrate multi-step research workflow - Chain search → deep research → agent response in a single LangGraph workflow
+
+## Implementation Details
+
+```python
+chain = LangGraph()
+```
+
+```python
+chain.add_step(SerperDevToolAgent())
+```
+
+```python
+agent = create_react_agent(chain, SerperDevToolAgent())
+```
+
+```python
+workflow = chain.start()
+```
+
+## Inputs
+
+- LangGraph chain configuration files defining agent workflows
+- SerperDevTool integration for LLM tool access
+- React agent creation scripts via create_react_agent
+- Knowledge graph retrieval and citation generation pipelines
+
+## Outputs
+
+- Reusable LangGraph chain definition (pyfile) with configurable steps
+- React agent frontend component that can be deployed independently
+- RAG pipeline that generates block citations and grounded answers
+- Code execution sandbox for artifact generation
+- Documentation for parameterizing workflows for different tasks
+
+## Failure Modes
+
+- GraphDB connection failures if Neo4j/ArangoDB is not properly configured
+- Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail
+- LLM tool access errors if SerperDevTool is not properly initialized
+- Agent timeout if complex multi-step reasoning exceeds time limits
+- Sandbox execution failures if code has security vulnerabilities or infinite loops
+
+## Source
+
+Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
+Confidence: 0.95
@@ -0,0 +1,6 @@
+# Commands: langgraph-agent-workflow
+
+## Available Commands
+
+- `/skill langgraph-agent-workflow` — Load this skill
+- `/run langgraph-agent-workflow` — Execute workflow
@@ -0,0 +1,10 @@
+# Examples: langgraph-agent-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: LangGraph chain configuration files defining agent workflows, SerperDevTool integration for LLM tool access, React agent creation scripts via create_react_agent, Knowledge graph retrieval and citation generation pipelines
+# Process: Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access → Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain → Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation
+# Outputs: Reusable LangGraph chain definition (pyfile) with configurable steps, React agent frontend component that can be deployed independently, RAG pipeline that generates block citations and grounded answers, Code execution sandbox for artifact generation, Documentation for parameterizing workflows for different tasks
+```
@@ -0,0 +1,36 @@
+{
+  "name": "langgraph-agent-workflow",
+  "version": "1.0.0",
+  "goal": "Orchestrate multi-step AI agents using LangGraph with SerperDevTool for RAG, code execution, and citation generation",
+  "inputs": [
+    "LangGraph chain configuration files defining agent workflows",
+    "SerperDevTool integration for LLM tool access",
+    "React agent creation scripts via create_react_agent",
+    "Knowledge graph retrieval and citation generation pipelines"
+  ],
+  "steps": [
+    "Step 1: Define LangGraph chain architecture with SerperDevTool integration - Create a LangGraph chain that combines retrieval, reasoning, and response generation using SerperDevTool for tool access",
+    "Step 2: Implement React agent wrapper - Use create_react_agent to build a frontend agent that can interact with the LangGraph chain",
+    "Step 3: Configure RAG pipeline - Set up vector search (Qdrant/OpenSearch) and knowledge graph retrieval (Neo4j/ArangoDB) with citation generation",
+    "Step 4: Add code execution sandbox - Integrate artifact generation capabilities for code-related tasks",
+    "Step 5: Orchestrate multi-step research workflow - Chain search \u2192 deep research \u2192 agent response in a single LangGraph workflow"
+  ],
+  "outputs": [
+    "Reusable LangGraph chain definition (pyfile) with configurable steps",
+    "React agent frontend component that can be deployed independently",
+    "RAG pipeline that generates block citations and grounded answers",
+    "Code execution sandbox for artifact generation",
+    "Documentation for parameterizing workflows for different tasks"
+  ],
+  "failure_modes": [
+    "GraphDB connection failures if Neo4j/ArangoDB is not properly configured",
+    "Vector store unavailability (Qdrant/OpenSearch) causing RAG pipeline to fail",
+    "LLM tool access errors if SerperDevTool is not properly initialized",
+    "Agent timeout if complex multi-step reasoning exceeds time limits",
+    "Sandbox execution failures if code has security vulnerabilities or infinite loops"
+  ],
+  "confidence": 0.95,
+  "explanation": "PipesHub provides a reusable LangGraph-based agent workflow framework that can be parameterized for different tasks. The core pattern involves defining a LangGraph chain with SerperDevTool integration for tool access, creating a React agent wrapper, and configuring RAG pipelines with citation generation. This framework can be reused across RAG, code execution, and research workflows by adjusting the chain definition and agent configuration.",
+  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
+  "score": 1.0
+}
@@ -1,4 +1,4 @@
-# Tests: multi-agent-workflow-execution
+# Tests: langgraph-agent-workflow

 ## Test Checklist

@@ -1,113 +0,0 @@
---
-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
@@ -1,6 +0,0 @@
-# Commands: multi-agent-workflow-execution
-
-## Available Commands
-
- `/skill multi-agent-workflow-execution` — Load this skill
- `/run multi-agent-workflow-execution` — Execute workflow
@@ -1,10 +0,0 @@
-# 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
-```
@@ -1,35 +0,0 @@
-{
-  "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
-}