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 Hermes Pipeline
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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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- 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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# 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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# 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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{
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"name": "agent-builder-workflow",
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"version": "1.0.0",
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"goal": "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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"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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],
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"steps": [
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"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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"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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"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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"Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations",
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"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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],
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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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],
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"failure_modes": [
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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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],
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"confidence": 0.95,
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"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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+1
-1
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# Tests: conditional-request-routing-workflow
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# Tests: agent-builder-workflow
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## Test Checklist
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@@ -1,81 +0,0 @@
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---
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name: conditional-request-routing-workflow
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version: 1.0.0
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description: Collect a request, classify it via branching logic, and route to an approval
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or rejection handler to produce a final result
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inputs:
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- name: request
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type: string
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description: The input text or request to be evaluated and routed
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steps:
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- node: Start
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agent_type: input
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description: Prompt user and capture the request into state field 'request'
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next: Classify
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- node: Classify
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agent_type: branching
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description: Evaluate the request and set 'decision' field, routing to Approve on
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success or Reject on failure
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input_fields:
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- request
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output_field: decision
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next_node: Approve
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on_failure: Reject
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- node: Approve
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agent_type: default
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description: Format and output an approval message containing the request
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input_fields:
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- request
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output_field: result
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prompt: 'Request approved: {request}'
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- node: Reject
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agent_type: default
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description: Format and output a rejection message containing the request
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input_fields:
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- request
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output_field: result
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prompt: 'Request rejected: {request}'
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outputs:
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- name: result
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type: string
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description: Final formatted message indicating the outcome (approved or rejected)
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- name: decision
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type: string
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description: Routing decision produced by the branching node
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tags: []
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metadata:
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source_repo: https://github.com/jwwelbor/AgentMap.git
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extracted_at: ''
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confidence: 0.92
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---
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# conditional-request-routing-workflow
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Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result
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## Steps
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1. {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'}
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2. {'node': 'Classify', 'agent_type': 'branching', 'description': "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure", 'input_fields': ['request'], 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'}
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3. {'node': 'Approve', 'agent_type': 'default', 'description': 'Format and output an approval message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request approved: {request}'}
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4. {'node': 'Reject', 'agent_type': 'default', 'description': 'Format and output a rejection message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request rejected: {request}'}
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## Inputs
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- {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
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## Outputs
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- {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}
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- {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
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## Failure Modes
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- Empty or missing request input prevents meaningful classification
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- Branching node fails to resolve a valid route and defaults to rejection path
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- Prompt template variable missing causes malformed output
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## Source
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Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
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Confidence: 0.92
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@@ -1,6 +0,0 @@
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# Commands: conditional-request-routing-workflow
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## Available Commands
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- `/skill conditional-request-routing-workflow` — Load this skill
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- `/run conditional-request-routing-workflow` — Execute workflow
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@@ -1,10 +0,0 @@
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# Examples: conditional-request-routing-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: {'name': 'request', 'type': 'string', 'description': 'The input text or request to be evaluated and routed'}
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# Process: {'node': 'Start', 'agent_type': 'input', 'description': "Prompt user and capture the request into state field 'request'", 'next': 'Classify'} → {'node': 'Classify', 'agent_type': 'branching', 'description': "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure", 'input_fields': ['request'], 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'} → {'node': 'Approve', 'agent_type': 'default', 'description': 'Format and output an approval message containing the request', 'input_fields': ['request'], 'output_field': 'result', 'prompt': 'Request approved: {request}'}
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# Outputs: {'name': 'result', 'type': 'string', 'description': 'Final formatted message indicating the outcome (approved or rejected)'}, {'name': 'decision', 'type': 'string', 'description': 'Routing decision produced by the branching node'}
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```
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@@ -1,72 +0,0 @@
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{
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"name": "conditional-request-routing-workflow",
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"version": "1.0.0",
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"goal": "Collect a request, classify it via branching logic, and route to an approval or rejection handler to produce a final result",
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"inputs": [
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{
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"name": "request",
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"type": "string",
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"description": "The input text or request to be evaluated and routed"
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}
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],
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"steps": [
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{
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"node": "Start",
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"agent_type": "input",
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"description": "Prompt user and capture the request into state field 'request'",
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"next": "Classify"
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},
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{
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"node": "Classify",
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"agent_type": "branching",
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"description": "Evaluate the request and set 'decision' field, routing to Approve on success or Reject on failure",
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"input_fields": [
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"request"
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],
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"output_field": "decision",
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"next_node": "Approve",
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"on_failure": "Reject"
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},
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{
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"node": "Approve",
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"agent_type": "default",
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"description": "Format and output an approval message containing the request",
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"input_fields": [
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"request"
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],
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"output_field": "result",
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"prompt": "Request approved: {request}"
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},
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{
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"node": "Reject",
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"agent_type": "default",
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"description": "Format and output a rejection message containing the request",
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"input_fields": [
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"request"
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],
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"output_field": "result",
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"prompt": "Request rejected: {request}"
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}
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],
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"outputs": [
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{
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"name": "result",
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"type": "string",
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"description": "Final formatted message indicating the outcome (approved or rejected)"
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},
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{
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"name": "decision",
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"type": "string",
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"description": "Routing decision produced by the branching node"
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}
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],
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"failure_modes": [
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"Empty or missing request input prevents meaningful classification",
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"Branching node fails to resolve a valid route and defaults to rejection path",
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"Prompt template variable missing causes malformed output"
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],
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"confidence": 0.92,
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"explanation": "Extracted from the ReviewFlow CSV example in the AgentMap README. This is a declarative, CSV-defined LangGraph workflow demonstrating the reusable pattern of input -> branching classification -> conditional handling paths. It can be generalized for ticket triage, content moderation, or any route-by-condition use case.",
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"source_repo": "https://github.com/jwwelbor/AgentMap.git",
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"score": 1.0
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}
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Reference in New Issue
Block a user