Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Hermes Pipeline
|
bd457c0b6d | ||
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Epictetus
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271f79610d |
@@ -16,10 +16,10 @@ llm:
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api_key: ""
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max_tokens: 8000
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# Secondary LLM for pipeline tasks — uses Ollama on 3060 (non-reasoning model)
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# Secondary LLM for pipeline tasks — uses LFM on 3060 (llama.cpp)
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llm_pipeline:
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base_url: http://100.64.0.4:11434
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model: qwen2.5:7b
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base_url: http://100.64.0.4:8080
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model: C:\models\LFM2.5-2.6B-Q4_K_M.gguf
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api_key: ""
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max_tokens: 6000
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@@ -0,0 +1,110 @@
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---
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name: agent-builder-workflow
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version: 1.0.0
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description: Build no-code AI agents that connect to enterprise knowledge sources,
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perform unified search and deep research, and generate explainable answers with
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citations
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inputs:
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- Task description and agent objectives (e.g., answer Q&A, research specific topics,
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generate reports)
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- Knowledge sources (documents, databases, enterprise systems) to connect to
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- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
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- Output requirements (citation format, response structure, code execution needs)
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steps:
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- 'Step 1: Define agent task and objectives - Specify what the agent should do (e.g.,
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answer a question, perform deep research on a topic, generate a report with citations)'
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- 'Step 2: Configure knowledge sources - Connect to enterprise documents, databases,
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or external systems that will serve as the agent''s context'
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- 'Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines
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retrieval (graph/vector) and LLM response generation with citation capabilities'
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- 'Step 4: Execute agent - Run the LangGraph chain to process the task and generate
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responses with grounded citations'
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- 'Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute
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code, deploy it to a safe sandbox environment for verification'
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outputs:
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- Agent execution logs showing retrieval steps and LLM responses
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- Grounded answers with block citations to source documents
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- Generated reports or artifacts (if code execution was performed)
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- Structured task completion status and results
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tags: []
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metadata:
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source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
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extracted_at: ''
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confidence: 0.95
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---
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# agent-builder-workflow
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Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations
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## Setup
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**Dependencies:**
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```text
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pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi pydantic
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```
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**Setup steps:**
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1. Install dependencies: pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi
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1. Configure knowledge sources in .env (graph DB connection, vector DB, document paths)
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1. Define agent task in agent_builder.py with objectives and retrieval strategy
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1. Run agent chain: python agent_chain.py --task "research_quantum_computing"
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1. For code execution: add sandbox step to agent_chain.py with code generation and safe execution
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## Key Files
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- `pipeshub-ai/backend/agent_chain.py - LangGraph chain definition for agent workflows`
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- `pipeshub-ai/backend/retrieval_pipeline.py - Knowledge graph and vector search implementation`
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- `pipeshub-ai/workflows/agent_builder.py - No-code agent creation interface`
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- `pipeshub-ai/workflows/citation_generator.py - Block citation generation from retrieved sources`
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## Steps
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1. Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)
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2. Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context
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3. Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
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4. Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations
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5. Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification
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## Implementation Details
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```python
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LangGraph chain with retrieval (graph/vector) and LLM response stages
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```
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```python
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Knowledge graph construction from enterprise documents
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```
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```python
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Citation formatting using block references to source documents
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```
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## Inputs
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- Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)
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- Knowledge sources (documents, databases, enterprise systems) to connect to
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- Retrieval strategy configuration (graph-based knowledge graph vs. vector search)
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- Output requirements (citation format, response structure, code execution needs)
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## Outputs
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- Agent execution logs showing retrieval steps and LLM responses
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- Grounded answers with block citations to source documents
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- Generated reports or artifacts (if code execution was performed)
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- Structured task completion status and results
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## Failure Modes
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|
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- Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality
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- Permission errors when accessing enterprise knowledge sources
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- LLM context window overflow when generating long explanations with citations
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- Sandbox execution failures for code generation or execution tasks
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- Timeout errors during multi-step agent chain execution
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## Source
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Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
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Confidence: 0.95
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@@ -0,0 +1,6 @@
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# Commands: agent-builder-workflow
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## Available Commands
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- `/skill agent-builder-workflow` — Load this skill
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- `/run agent-builder-workflow` — Execute workflow
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@@ -0,0 +1,10 @@
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# Examples: agent-builder-workflow
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## Usage Example
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```python
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# How to use this skill
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||||
# Inputs: Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports), Knowledge sources (documents, databases, enterprise systems) to connect to, Retrieval strategy configuration (graph-based knowledge graph vs. vector search), Output requirements (citation format, response structure, code execution needs)
|
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# Process: Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations) → Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context → Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
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# Outputs: Agent execution logs showing retrieval steps and LLM responses, Grounded answers with block citations to source documents, Generated reports or artifacts (if code execution was performed), Structured task completion status and results
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```
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@@ -0,0 +1,35 @@
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{
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||||
"name": "agent-builder-workflow",
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"version": "1.0.0",
|
||||
"goal": "Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations",
|
||||
"inputs": [
|
||||
"Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)",
|
||||
"Knowledge sources (documents, databases, enterprise systems) to connect to",
|
||||
"Retrieval strategy configuration (graph-based knowledge graph vs. vector search)",
|
||||
"Output requirements (citation format, response structure, code execution needs)"
|
||||
],
|
||||
"steps": [
|
||||
"Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)",
|
||||
"Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context",
|
||||
"Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities",
|
||||
"Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations",
|
||||
"Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification"
|
||||
],
|
||||
"outputs": [
|
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"Agent execution logs showing retrieval steps and LLM responses",
|
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"Grounded answers with block citations to source documents",
|
||||
"Generated reports or artifacts (if code execution was performed)",
|
||||
"Structured task completion status and results"
|
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],
|
||||
"failure_modes": [
|
||||
"Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality",
|
||||
"Permission errors when accessing enterprise knowledge sources",
|
||||
"LLM context window overflow when generating long explanations with citations",
|
||||
"Sandbox execution failures for code generation or execution tasks",
|
||||
"Timeout errors during multi-step agent chain execution"
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||||
],
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||||
"confidence": 0.95,
|
||||
"explanation": "PipesHub provides a reusable agent builder workflow that combines LangGraph orchestration with graph-based and vector-based retrieval. This pattern can be adapted to any enterprise context where AI agents need to search across multiple knowledge sources, generate explainable answers with citations, and optionally execute code in a safe sandbox. The workflow is defined by specific configuration files (LangGraph chain definitions) and follows a standard pattern: task definition \u2192 knowledge source connection \u2192 retrieval strategy \u2192 response generation \u2192 optional code sandbox.",
|
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"source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
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||||
"score": 1.0
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||||
}
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@@ -0,0 +1,9 @@
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# Tests: agent-builder-workflow
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## Test Checklist
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||||
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- [ ] Workflow has at least 3 steps
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- [ ] All inputs are defined
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||||
- [ ] All outputs are defined
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||||
- [ ] Failure modes are documented
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||||
- [ ] Skill can be loaded without errors
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||||
@@ -0,0 +1,96 @@
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||||
---
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||||
name: blacknode-graph-workflow
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version: 1.0.0
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||||
description: Build and execute node-based AI workflows with LLM agents and processing
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nodes
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||||
inputs:
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||||
- Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
|
||||
- Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite,
|
||||
etc.)
|
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- Data sources (URLs, text content, or other inputs for the workflow)
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steps:
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- Initialize a blacknode.Graph instance to create the workflow structure
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- Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent,
|
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FileWrite)
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- Define edges connecting nodes to establish data flow between them
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- Execute the graph using cook() to run the workflow and generate outputs
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outputs:
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||||
- Processed results from the final node (e.g., printed text, written files, or generated
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||||
data)
|
||||
- Graph execution status and any errors encountered during execution
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||||
tags: []
|
||||
metadata:
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||||
source_repo: https://github.com/temiroff/Blacknode.git
|
||||
extracted_at: ''
|
||||
confidence: 0.95
|
||||
---
|
||||
|
||||
# blacknode-graph-workflow
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||||
|
||||
Build and execute node-based AI workflows with LLM agents and processing nodes
|
||||
|
||||
## Setup
|
||||
|
||||
**Dependencies:**
|
||||
|
||||
```text
|
||||
pip install blacknode (core package) anthropic>=0.25 openai>=1.0 petgraph (for graph operations)
|
||||
```
|
||||
|
||||
**Setup steps:**
|
||||
|
||||
1. Install blacknode package: pip install blacknode
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||||
1. Configure model API keys (NIM_API_KEY, OPENAI_API_KEY, etc.) in .env or editor
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||||
1. Create a Graph instance and add nodes with inputs/outputs
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||||
1. Define node connections in g._edges list
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||||
1. Execute with g.cook() to run the workflow and capture results
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||||
|
||||
## Key Files
|
||||
|
||||
- `blacknode/blacknode.py (Graph class implementation)`
|
||||
- `examples/hello_agent.py (simple LLM agent workflow)`
|
||||
- `examples/converted_nvidia_nim.py (NIM model workflow)`
|
||||
|
||||
## Steps
|
||||
|
||||
1. Initialize a blacknode.Graph instance to create the workflow structure
|
||||
2. Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)
|
||||
3. Define edges connecting nodes to establish data flow between them
|
||||
4. Execute the graph using cook() to run the workflow and generate outputs
|
||||
|
||||
## Implementation Details
|
||||
|
||||
```python
|
||||
g = bn.Graph()
|
||||
```
|
||||
|
||||
```python
|
||||
g._edges = [{'from': 'model', 'from_port': 'value', 'to': 'agent', 'to_port': 'model'}]
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||||
```
|
||||
|
||||
```python
|
||||
result = g.cook(output_node, 'value')
|
||||
```
|
||||
|
||||
## Inputs
|
||||
|
||||
- Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
|
||||
- Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)
|
||||
- Data sources (URLs, text content, or other inputs for the workflow)
|
||||
|
||||
## Outputs
|
||||
|
||||
- Processed results from the final node (e.g., printed text, written files, or generated data)
|
||||
- Graph execution status and any errors encountered during execution
|
||||
|
||||
## Failure Modes
|
||||
|
||||
- Missing or invalid model API key causing graph initialization failure
|
||||
- Incorrect node connections or missing edge definitions leading to runtime errors
|
||||
- Model not found or unavailable in the specified environment causing execution failure
|
||||
- Graph edges not properly defined or mismatched causing cook() to fail
|
||||
|
||||
## Source
|
||||
|
||||
Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
|
||||
Confidence: 0.95
|
||||
@@ -0,0 +1,6 @@
|
||||
# Commands: blacknode-graph-workflow
|
||||
|
||||
## Available Commands
|
||||
|
||||
- `/skill blacknode-graph-workflow` — Load this skill
|
||||
- `/run blacknode-graph-workflow` — Execute workflow
|
||||
@@ -0,0 +1,10 @@
|
||||
# Examples: blacknode-graph-workflow
|
||||
|
||||
## Usage Example
|
||||
|
||||
```python
|
||||
# How to use this skill
|
||||
# Inputs: Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic), Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.), Data sources (URLs, text content, or other inputs for the workflow)
|
||||
# Process: Initialize a blacknode.Graph instance to create the workflow structure → Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite) → Define edges connecting nodes to establish data flow between them
|
||||
# Outputs: Processed results from the final node (e.g., printed text, written files, or generated data), Graph execution status and any errors encountered during execution
|
||||
```
|
||||
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"name": "blacknode-graph-workflow",
|
||||
"version": "1.0.0",
|
||||
"goal": "Build and execute node-based AI workflows with LLM agents and processing nodes",
|
||||
"inputs": [
|
||||
"Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)",
|
||||
"Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)",
|
||||
"Data sources (URLs, text content, or other inputs for the workflow)"
|
||||
],
|
||||
"steps": [
|
||||
"Initialize a blacknode.Graph instance to create the workflow structure",
|
||||
"Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)",
|
||||
"Define edges connecting nodes to establish data flow between them",
|
||||
"Execute the graph using cook() to run the workflow and generate outputs"
|
||||
],
|
||||
"outputs": [
|
||||
"Processed results from the final node (e.g., printed text, written files, or generated data)",
|
||||
"Graph execution status and any errors encountered during execution"
|
||||
],
|
||||
"failure_modes": [
|
||||
"Missing or invalid model API key causing graph initialization failure",
|
||||
"Incorrect node connections or missing edge definitions leading to runtime errors",
|
||||
"Model not found or unavailable in the specified environment causing execution failure",
|
||||
"Graph edges not properly defined or mismatched causing cook() to fail"
|
||||
],
|
||||
"confidence": 0.95,
|
||||
"explanation": "Blacknode provides a standardized Graph-based workflow pattern where users create node graphs using the blacknode.Graph class. This pattern is reusable across projects as it follows a consistent structure: initialize a graph, add nodes with defined inputs/outputs, connect them with edges, and execute with cook(). The examples demonstrate this pattern with LLM agents and text processing pipelines, making it adaptable to various robotics and AI workflows.",
|
||||
"source_repo": "https://github.com/temiroff/Blacknode.git",
|
||||
"score": 1.0
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
# Tests: blacknode-graph-workflow
|
||||
|
||||
## Test Checklist
|
||||
|
||||
- [ ] Workflow has at least 3 steps
|
||||
- [ ] All inputs are defined
|
||||
- [ ] All outputs are defined
|
||||
- [ ] Failure modes are documented
|
||||
- [ ] Skill can be loaded without errors
|
||||
@@ -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
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
# Tests: langgraph-agent-workflow
|
||||
|
||||
## Test Checklist
|
||||
|
||||
- [ ] Workflow has at least 3 steps
|
||||
- [ ] All inputs are defined
|
||||
- [ ] All outputs are defined
|
||||
- [ ] Failure modes are documented
|
||||
- [ ] Skill can be loaded without errors
|
||||
@@ -0,0 +1,96 @@
|
||||
---
|
||||
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
|
||||
@@ -0,0 +1,6 @@
|
||||
# Commands: langgraph-multi-agent-router
|
||||
|
||||
## Available Commands
|
||||
|
||||
- `/skill langgraph-multi-agent-router` — Load this skill
|
||||
- `/run langgraph-multi-agent-router` — Execute workflow
|
||||
@@ -0,0 +1,10 @@
|
||||
# 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)
|
||||
```
|
||||
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"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 +1,9 @@
|
||||
# Tests: langgraph-multi-agent-router
|
||||
|
||||
## Test Checklist
|
||||
|
||||
- [ ] Workflow has at least 3 steps
|
||||
- [ ] All inputs are defined
|
||||
- [ ] All outputs are defined
|
||||
- [ ] Failure modes are documented
|
||||
- [ ] Skill can be loaded without errors
|
||||
@@ -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
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
# Tests: multi-agent-workflow-execution
|
||||
|
||||
## Test Checklist
|
||||
|
||||
- [ ] Workflow has at least 3 steps
|
||||
- [ ] All inputs are defined
|
||||
- [ ] All outputs are defined
|
||||
- [ ] Failure modes are documented
|
||||
- [ ] Skill can be loaded without errors
|
||||
Reference in New Issue
Block a user