llm-agent-base 0.1.2

Lightweight Python framework for building LLM agents with tool calling and RAG

llm-agent-base

A lightweight Python library for building LLM agents with tool calling and retrieval-augmented generation (RAG). Works with any OpenAI-compatible API (OpenRouter, OpenAI, Ollama, etc.).

Installation

pip install llm-agent-base

Features

• Tool calling — register plain Python functions as LLM-callable tools; schemas are built automatically from type hints and docstrings
• RAG — ingest a folder of documents (.txt, .md, .json, .pdf) into a FAISS vector index and inject relevant chunks into every prompt
• Pipelines — chain multiple agents so each agent's output becomes the next agent's input
• Debug mode — optional logging of tool calls and knowledge retrievals

Quick start

from llm_agent_base import AgentBase, LLMConnectionConfig

config = LLMConnectionConfig(model="openai/gpt-4o-mini", api_key="...")

agent = AgentBase(
    system_prompt="You are a helpful assistant.",
    llm_config=config,
)

print(agent.run("What is the capital of France?"))

The default base_url points to OpenRouter, which gives access to many models through a single API key. You can swap it for the OpenAI base URL or any other compatible endpoint.

Usage

Tool calling

Register any Python function as a tool. The function name becomes the tool name, the docstring becomes its description, and the type hints define the parameter schema.

from llm_agent_base import AgentBase, LLMConnectionConfig

config = LLMConnectionConfig(model="openai/gpt-4o-mini", api_key="...")

def get_weather(city: str) -> str:
    """Return the current weather for a given city."""
    return f"The weather in {city} is sunny and 22°C."

def add(a: int, b: int) -> int:
    """Add two integers and return the result."""
    return a + b

agent = AgentBase(
    system_prompt="You are a helpful assistant. Use the available tools when needed.",
    llm_config=config,
)
agent.register_tool(get_weather)
agent.register_tool(add)

print(agent.run("What is the weather in Tokyo and what is 10 + 20?"))

register_tool can also be used as a decorator:

@agent.register_tool
def search_orders(order_id: str) -> str:
    """Look up an order by ID."""
    ...

RAG (knowledge base)

Place your documents in a folder (organised into subdirectories by topic). Call ingest_knowledge once to embed and index them, then run as normal — relevant chunks are automatically injected into the system prompt.

knowledge/
├── products/
│   ├── faq.md
│   └── pricing.json
└── support/
    └── sla.md

from llm_agent_base import AgentBase, LLMConnectionConfig

config = LLMConnectionConfig(model="openai/gpt-4o-mini", api_key="...")

agent = AgentBase(
    system_prompt="You are a product assistant. Answer using only the provided context.",
    llm_config=config,
    knowledge_folder_path="knowledge",
    knowledge_index_dir=".kb_index",  # where the FAISS index is saved
    knowledge_top_k=3,                # number of chunks injected per prompt
)

# Build and persist the index (run once, or when documents change)
agent.ingest_knowledge(save=True)

# On subsequent runs, load from disk instead of re-embedding
# agent.load_knowledge()

print(agent.run("Who founded the company and when?"))

Agent pipelines

Chain agents so the output of one becomes the input of the next:

from llm_agent_base import AgentBase, AgentPipelineBase, LLMConnectionConfig

config = LLMConnectionConfig(model="openai/gpt-4o-mini", api_key="...")

researcher = AgentBase(
    system_prompt="Extract the key facts from the user's question.",
    llm_config=config,
)
writer = AgentBase(
    system_prompt="Turn the provided facts into a concise, friendly summary.",
    llm_config=config,
)

pipeline = AgentPipelineBase(agents=[researcher, writer])
print(pipeline.run("Tell me about the Acme Corp product lineup."))

Debug mode

Pass debug=True to any agent to print tool invocations and knowledge retrievals to stdout:

agent = AgentBase(..., debug=True)

[debug] Retrieving knowledge
[debug] tool 'get_weather' args={'city': 'Tokyo'} result=The weather in Tokyo is sunny and 22°C.

API reference

Class / function	Description
LLMConnectionConfig	Dataclass holding model name, base URL, API key, and embedding model
AgentBase	Single agent with optional tool calling and RAG
AgentPipelineBase	Chains multiple AgentBase instances in sequence
KnowledgeBase	Document ingestion, embedding, and FAISS retrieval
DocumentChunk	Dataclass representing a retrieved text chunk
build_tool_schema	Builds an OpenAI-compatible tool schema from a function
execute_tool_loop	Runs the agentic tool-calling loop against any OpenAI-compatible client