---
name: agent-builder-workflow
version: 1.0.0
description: 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:
- 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
tags: []
metadata:
  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
  extracted_at: ''
  confidence: 0.95
---

# agent-builder-workflow

Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations

## Setup

**Dependencies:**

```text
pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi pydantic
```

**Setup steps:**

1. Install dependencies: pip install langchain langgraph serper-dev-tool qdrant-client redis fastapi
1. Configure knowledge sources in .env (graph DB connection, vector DB, document paths)
1. Define agent task in agent_builder.py with objectives and retrieval strategy
1. Run agent chain: python agent_chain.py --task "research_quantum_computing"
1. For code execution: add sandbox step to agent_chain.py with code generation and safe execution

## Key Files

- `pipeshub-ai/backend/agent_chain.py - LangGraph chain definition for agent workflows`
- `pipeshub-ai/backend/retrieval_pipeline.py - Knowledge graph and vector search implementation`
- `pipeshub-ai/workflows/agent_builder.py - No-code agent creation interface`
- `pipeshub-ai/workflows/citation_generator.py - Block citation generation from retrieved sources`

## Steps

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)
2. Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context
3. Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities
4. Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations
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

## Implementation Details

```python
LangGraph chain with retrieval (graph/vector) and LLM response stages
```

```python
Knowledge graph construction from enterprise documents
```

```python
Citation formatting using block references to source documents
```

## 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)

## 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

## 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

## Source

Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
Confidence: 0.95