FEBRUARY 2025
COELHO Agents
Multi-agent platform combining a memory-aware assistant, an autonomous Software Developer agent with self-fixing code loops, and a GraphRAG-based YouTube Content Search — all orchestrated with LangChain + LangGraph.
Executive summary
COELHO Agents packages three production-grade agentic tools behind a single Streamlit interface, all orchestrated with LangChain and LangGraph:
- Simple Assistant — chatbot with memory and real-time response
- Software Developer — autonomous coding agent that generates code, runs it, and fixes its own bugs in retry loops
- YouTube Content Search — GraphRAG-based video query system backed by a Neo4j knowledge graph (the research that became COELHO Nexus)
The project demonstrates three distinct agentic patterns — conversational memory, plan-execute-diagnose-patch loops, and graph-augmented retrieval — running on the same LangChain / LangGraph foundation.
See it in action
The agents demand a working Python environment, multiple LLM API keys, and a running Neo4j instance, so you can’t just spin them up. This walkthrough is the verifiable record of the three tools in operation — memory-aware chat, autonomous code-fix loops, and live GraphRAG video queries.
Three agentic tools, three distinct patterns
| Tool | Agent pattern | Why it’s non-trivial |
|---|---|---|
| Simple Assistant | Conversational memory over LangChain message history | Persistent context across turns — not stateless one-shot prompts |
| Software Developer | Plan → write → execute → diagnose → patch retry loop (LangGraph state machine) | Self-correcting code generation: the agent runs its own code, parses errors, rewrites, retries — autonomous bug fixing |
| YouTube Content Search | GraphRAG over Neo4j knowledge graph extracted from video transcripts | Multi-hop reasoning impossible with vector RAG alone |
Why Knowledge Graph + Agents > vanilla RAG
Vector RAG retrieves by semantic similarity — fine for paraphrase-level questions,
but it can’t answer multi-hop relational queries like “who has been interviewed
about LLM safety on this channel?” Knowledge Graphs encode entities and relationships
explicitly (X works_for Y → Y located_in Z), so a Cypher query walks the graph and
the LLM grounds its answer in real relationships rather than chunk-similarity lottery.
The YouTube Content Search tool here was the first prototype of this pattern. The operational primitives — agent loops, graph extraction, multi-hop traversal — became the architectural backbone of COELHO Nexus.
Stack
- Streamlit — interactive interface
- LangChain — agent and tool composition primitives
- LangGraph — multi-agent orchestration with explicit state graphs and retry edges (powers the Software Developer’s self-fix loop)
- Neo4j — Knowledge Graph storage + Cypher querying
What this project proves
- Three distinct agent patterns shipped in one codebase — memory-aware conversation, autonomous code-fix loops, and graph-augmented retrieval — not toy demos
- LangGraph state machines work for real production agent loops — including self-correcting code generation with retry semantics
- The GraphRAG pattern scales — what started here as YouTube transcript search became the production retrieval backbone of COELHO Nexus