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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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@@ -0,0 +1,35 @@
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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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@@ -1,4 +1,4 @@
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# Tests: conditional-input-routing
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# Tests: agent-builder-workflow
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## Test Checklist
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@@ -1,73 +0,0 @@
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---
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name: conditional-input-routing
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version: 1.0.0
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description: Collect user input, classify or branch on its content, and route to appropriate
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success or failure handling paths 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: User-provided input or request to be evaluated
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steps:
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- name: Start
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action: input agent captures initial request into state
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agent_type: input
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output_field: request
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- name: Classify
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action: branching agent evaluates request and routes to next_node or on_failure
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agent_type: branching
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input_fields: 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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- name: Approve
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action: default agent processes approved request and sets result
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agent_type: default
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input_fields: request
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output_field: result
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prompt: 'Request approved: {request}'
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- name: Reject
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action: default agent processes rejected request and sets result
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agent_type: default
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input_fields: 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 output from either the approve or reject branch
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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: 0.9
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---
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# conditional-input-routing
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Collect user input, classify or branch on its content, and route to appropriate success or failure handling paths to produce a final result
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## Steps
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1. {'name': 'Start', 'action': 'input agent captures initial request into state', 'agent_type': 'input', 'output_field': 'request'}
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2. {'name': 'Classify', 'action': 'branching agent evaluates request and routes to next_node or on_failure', 'agent_type': 'branching', 'input_fields': 'request', 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'}
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3. {'name': 'Approve', 'action': 'default agent processes approved request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request approved: {request}'}
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4. {'name': 'Reject', 'action': 'default agent processes rejected request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request rejected: {request}'}
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## Inputs
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- {'name': 'request', 'type': 'string', 'description': 'User-provided input or request to be evaluated'}
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## Outputs
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- {'name': 'result', 'type': 'string', 'description': 'Final output from either the approve or reject branch'}
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## Failure Modes
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- Input node fails to capture request (handled by on_failure if defined)
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- Branching condition not met and no on_failure path defined
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- Missing input_fields in state causing agent execution error
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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: 0.9
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@@ -1,6 +0,0 @@
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# Commands: conditional-input-routing
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## Available Commands
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- `/skill conditional-input-routing` — Load this skill
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- `/run conditional-input-routing` — Execute workflow
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@@ -1,10 +0,0 @@
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# Examples: conditional-input-routing
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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': 'User-provided input or request to be evaluated'}
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# Process: {'name': 'Start', 'action': 'input agent captures initial request into state', 'agent_type': 'input', 'output_field': 'request'} → {'name': 'Classify', 'action': 'branching agent evaluates request and routes to next_node or on_failure', 'agent_type': 'branching', 'input_fields': 'request', 'output_field': 'decision', 'next_node': 'Approve', 'on_failure': 'Reject'} → {'name': 'Approve', 'action': 'default agent processes approved request and sets result', 'agent_type': 'default', 'input_fields': 'request', 'output_field': 'result', 'prompt': 'Request approved: {request}'}
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# Outputs: {'name': 'result', 'type': 'string', 'description': 'Final output from either the approve or reject branch'}
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```
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@@ -1,61 +0,0 @@
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{
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"name": "conditional-input-routing",
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"version": "1.0.0",
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"goal": "Collect user input, classify or branch on its content, and route to appropriate success or failure handling paths 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": "User-provided input or request to be evaluated"
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}
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],
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"steps": [
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{
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"name": "Start",
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"action": "input agent captures initial request into state",
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"agent_type": "input",
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"output_field": "request"
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},
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{
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"name": "Classify",
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"action": "branching agent evaluates request and routes to next_node or on_failure",
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"agent_type": "branching",
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"input_fields": "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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},
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{
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"name": "Approve",
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"action": "default agent processes approved request and sets result",
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"agent_type": "default",
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"input_fields": "request",
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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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"name": "Reject",
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"action": "default agent processes rejected request and sets result",
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"agent_type": "default",
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"input_fields": "request",
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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 output from either the approve or reject branch"
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}
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],
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"failure_modes": [
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"Input node fails to capture request (handled by on_failure if defined)",
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"Branching condition not met and no on_failure path defined",
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"Missing input_fields in state causing agent execution error"
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],
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"confidence": 0.9,
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"explanation": "Extracted from AgentMap's documented conditional workflow example (ReviewFlow). This CSV-declared pattern of input to branching to dual-path handling is reusable for any approval, triage, or routing scenario without writing orchestration code.",
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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