Awesome LLM Apps: Agent Templates Guide (2026)

The awesome-llm-apps repository by Shubhamsaboo (107K+ stars) is the largest collection of runnable LLM agent and RAG templates on GitHub. Over 100 production-ready templates span chatbots, coding agents, data pipelines, multi-agent systems, and tool-use patterns. All Apache-2.0 licensed.

What It Is

A curated library of complete, runnable applications that demonstrate LLM agent patterns. Unlike tutorial code that stops at “hello world,” these templates handle authentication, error recovery, streaming, and persistence. Each template is a standalone project you can clone, configure, and deploy.

Categories include:

  • Coding Agents — autonomous code generation, review, and refactoring
  • RAG Pipelines — retrieval-augmented generation with vector stores
  • Multi-Agent Systems — orchestrated agent teams with task delegation
  • Tool-Use Patterns — agents that call APIs, databases, and external services
  • Chat Applications — conversational interfaces with memory and context management
  • Data Processing — agents for ETL, analysis, and reporting

Why It Matters

Building LLM agents from scratch means solving the same problems every team has already solved: context management, tool calling, error handling, and response streaming. This repo provides battle-tested reference implementations.

For Claude Code users specifically, the coding agent templates demonstrate patterns you can adapt for your CLAUDE.md configurations and MCP workflows. The multi-agent orchestration patterns map directly to Claude Code’s sub-agent capabilities.

Installation

Each template is self-contained. Clone the repo and navigate to the template you want:

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps
# Browse available templates
ls -la
# Pick one and follow its README
cd coding-agent-template
pip install -r requirements.txt

Or clone just one template using sparse checkout:

git clone --no-checkout https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps
git sparse-checkout set coding-agent-template
git checkout

Key Features

  1. 100+ Runnable Templates — every template includes a complete requirements file, environment config, and run instructions. No assembly required.

  2. Apache-2.0 License — use in commercial projects without restriction. Copy, modify, distribute freely.

  3. Multi-Framework Coverage — templates use LangChain, LlamaIndex, CrewAI, AutoGen, raw API calls, and Claude-native patterns. Compare approaches side by side.

  4. Production Patterns — templates include error handling, retry logic, rate limiting, and graceful degradation. Not just demos.

  5. Vector Store Integration — RAG templates work with Pinecone, Weaviate, ChromaDB, Qdrant, and pgvector. Swap the vector store without rewriting the retrieval logic.

  6. Streaming Support — chat and coding templates include streaming response handlers for real-time output.

  7. Environment Templates — .env.example files with every required variable documented. Copy, fill in your keys, run.

For more on this topic, see CLAUDE.md Templates Library.

  1. Complexity Gradient — templates range from simple (single-agent chatbot) to advanced (multi-agent orchestration with human-in-the-loop). Pick your starting point.

Real Usage Example

Adapting a Coding Agent Pattern for Claude Code

The repo’s coding-agent-template demonstrates:

# Simplified pattern from the template
class CodingAgent:
    def __init__(self):
        self.tools = [ReadFile, WriteFile, RunTests, SearchCode]
        self.memory = ConversationMemory(max_tokens=100000)
    def execute_task(self, task: str):
        # 1. Understand the codebase
        context = self.tools.SearchCode(task)
        # 2. Plan the implementation
        plan = self.plan(task, context)
        # 3. Execute step by step
        for step in plan:
            result = self.execute_step(step)
            self.memory.add(step, result)
            # 4. Verify after each step
            if step.requires_tests:
                test_result = self.tools.RunTests()
                if not test_result.passed:
                    self.fix_failures(test_result)

This pattern translates into a CLAUDE.md rule:

## Implementation Workflow
1. Search the codebase for relevant files before making changes
2. Create a step-by-step plan and present it before coding
3. Implement one step at a time
4. Run tests after each step that modifies functionality
5. Fix any test failures before proceeding to the next step

Using RAG Templates for Documentation

The RAG templates show how to build documentation-aware agents:

# Pattern: index project docs, then reference during coding
indexer = DocumentIndexer(
    source_dir="./docs",
    chunk_size=1000,
    vector_store=ChromaDB("./vectors")
)
indexer.index()
agent = RAGCodingAgent(
    vector_store=indexer.vector_store,
    retrieval_k=5  # Top 5 relevant doc chunks per query
)

For Claude Code, achieve the same effect with local docs and CLAUDE.md references.

When To Use

  • Starting a new agent project — browse templates to find a close match, then customize
  • Learning agent patterns — the code demonstrates proven approaches to common problems
  • Comparing frameworks — templates implement the same task in multiple frameworks
  • Building MCP servers — tool-use templates show how to structure tool interfaces that map to MCP
  • Prototyping — clone, configure, run in under 10 minutes

When NOT To Use

  • Production deployment without review — templates are starting points, not production-ready applications
  • Claude Code-specific features — templates are framework-agnostic; for Claude Code-specific tools, see claude-code-templates
  • Enterprise compliance — review each template’s dependencies for license compatibility before production use

FAQ

Are these templates Claude-specific?

No. Most templates work with OpenAI, Claude, Gemini, and other providers. Configuration typically involves setting an environment variable for the API key and model name.

How do I contribute a template?

Open a PR with a new directory containing your template, requirements file, README, and .env.example. The maintainers review for quality and completeness.

How current are the templates?

The repo is actively maintained with multiple updates per week. Individual templates may use older library versions — check the requirements file and update as needed.

Can I use these templates commercially?

Yes. Apache-2.0 allows commercial use, modification, and distribution with attribution.

Our Take

8/10. The gold standard for LLM application reference code. The sheer volume (100+ templates) and quality (production patterns, not toy demos) make this worth bookmarking for any developer working with AI agents. For Claude Code users specifically, the coding agent and multi-agent templates provide patterns that translate directly into CLAUDE.md rules and workflow designs. Loses points because the templates span many frameworks, so finding Claude-native patterns requires filtering.

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