Add Skill: agent-builder-workflow
Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git Score: 1.0
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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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