ThinAgents

A lightweight, pluggable AI Agent framework for Python.
Build LLM-powered agents that can use tools, remember conversations, and connect to external resources with minimal code. ThinAgents leverages litellm under the hood for its language model interactions.

Docs

Installation

pip install thinagents

Basic Usage

Create an agent and interact with an LLM in just a few lines:

from thinagents import Agent

agent = Agent(
    name="Greeting Agent",
    model="openai/gpt-4o-mini",
)

response = await agent.arun("Hello, how are you?")
print(response.content)

Tools

Agents can use Python functions as tools to perform actions or fetch data.

from thinagents import Agent

def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny."

agent = Agent(
    name="Weather Agent",
    model="openai/gpt-4o-mini",
    tools=[get_weather],
)

response = await agent.arun("What is the weather in Tokyo?")
print(response.content)

Tools with Decorator

For richer metadata and parameter validation, use the @tool decorator:

from thinagents import Agent, tool

@tool(name="get_weather")
def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"The weather in {city} is sunny."

agent = Agent(
    name="Weather Pro",
    model="openai/gpt-4o-mini",
    tools=[get_weather],
)

You can also use Pydantic models for parameter schemas:

from pydantic import BaseModel, Field
from thinagents import tool

class MultiplyInputSchema(BaseModel):
    a: int = Field(description="First operand")
    b: int = Field(description="Second operand")

@tool(name="multiply_tool", pydantic_schema=MultiplyInputSchema)
def multiply(a: int, b: int) -> int:
    return a * b

Returning Content and Artifact

Sometimes, a tool should return both a summary (for the LLM) and a large artifact (for downstream use):

from thinagents import tool

@tool(return_type="content_and_artifact")
def summarize_and_return_data(query: str) -> tuple[str, dict]:
    data = {"rows": list(range(10000))}
    summary = f"Found {len(data['rows'])} rows for query: {query}"
    return summary, data

response = await agent.arun("Summarize the data for X")
print(response.content)      # Sent to LLM
print(response.artifact)     # Available for downstream use

Async Usage

ThinAgents is async by design. You can stream responses or await the full result:

# Streaming
async for chunk in agent.astream("List files and get weather", conversation_id="1"):
    print(chunk.content, end="", flush=True)

# Or get the full response at once (non-streaming)
response = await agent.arun("List files and get weather", conversation_id="1")
print(response.content)

Memory

Agents can remember previous messages and tool results by attaching a memory backend.

from thinagents.memory import InMemoryStore

agent = Agent(
    name="Memory Demo",
    model="openai/gpt-4o-mini",
    memory=InMemoryStore(),  # Fast, in-memory storage
)

conv_id = "demo-1"
print(await agent.arun("Hi, I'm Alice!", conversation_id=conv_id))
print(await agent.arun("What is my name?", conversation_id=conv_id))
# → "Your name is Alice."

Persistent Memory

from thinagents.memory import FileMemory, SQLiteMemory

file_agent = Agent(
    name="File Mem Agent",
    model="openai/gpt-4o-mini",
    memory=FileMemory(storage_dir="./agent_mem"),
)

db_agent = Agent(
    name="SQLite Mem Agent",
    model="openai/gpt-4o-mini",
    memory=SQLiteMemory(db_path="./agent_mem.db"),
)

Storing Tool Artifacts

Enable artifact storage in memory:

agent = Agent(
    ...,
    memory=InMemoryStore(store_tool_artifacts=True),
)

Model Context Protocol (MCP) Integration

Connect your agent to external resources (files, APIs, etc.) using MCP.

agent = Agent(
    name="MCP Agent",
    model="openai/gpt-4o-mini",
    mcp_servers=[
        {
            "transport": "sse",
            "url": "http://localhost:8100/sse"
        },
        {
            "transport": "stdio",
            "command": "npx",
            "args": [
                "-y",
                "@modelcontextprotocol/server-filesystem",
                "/path/to/dir"
            ]
        },
    ],
)

Web UI

Launch a beautiful web interface for your agents with a single line:

from thinagents import Agent
from thinagents.web import WebUI

agent = Agent(
    name="My Agent",
    model="openai/gpt-4o-mini",
)

WebUI(agent).run()  # Opens browser automatically

The WebUI provides:

• 🎨 Beautiful, modern chat interface
• 🚀 Real-time streaming responses
• 💬 Full conversation history
• 🛠️ Works with any agent configuration
• 📱 Mobile-friendly responsive design

See Web UI Documentation for more details.

License

MIT