langroid

langroid

https://github.com/langroid/langroid?tab=readme-ov-file

 

Documentation · Examples Repo · Discord · Contributing

Langroid is an intuitive, lightweight, extensible and principled Python framework to easily build LLM-powered applications, from CMU and UW-Madison researchers. You set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. This Multi-Agent paradigm is inspired by the Actor Framework (but you do not need to know anything about this!).

Langroid is a fresh take on LLM app-development, where considerable thought has gone into simplifying the developer experience; it does not use Langchain, or any other LLM framework, and works with practically any LLM.

🔥 Read the (WIP) overview of the langroid architecture, and a quick tour of Langroid.

🔥 MCP Support: Allow any LLM-Agent to leverage MCP Servers via Langroid's simple MCP tool adapter that converts the server's tools into Langroid's ToolMessage instances.

📢 Companies are using/adapting Langroid in production. Here is a quote:

Nullify uses AI Agents for secure software development. It finds, prioritizes and fixes vulnerabilities. We have internally adapted Langroid's multi-agent orchestration framework in production, after evaluating CrewAI, Autogen, LangChain, Langflow, etc. We found Langroid to be far superior to those frameworks in terms of ease of setup and flexibility. Langroid's Agent and Task abstractions are intuitive, well thought out, and provide a great developer experience. We wanted the quickest way to get something in production. With other frameworks it would have taken us weeks, but with Langroid we got to good results in minutes. Highly recommended!
-- Jacky Wong, Head of AI at Nullify.

🔥 See this Intro to Langroid blog post from the LanceDB team

 

https://github.com/langroid/langroid-examples/blob/main/examples/docqa/chat_multi_extract.py

"""
Two-agent chat with Retrieval-augmented LLM + function-call/tool.
ExtractorAgent (has no access to docs) is tasked with extracting structured
information from a commercial lease document, and must present the terms in
a specific nested JSON format.
This agent generates questions corresponding to each field in the JSON format,
and the RAG-enabled DocAgent (has access to the lease) answers the  questions.


Example:
python3 examples/docqa/chat_multi_extract.py

This uses a GPT4 model by default, but works very well with the `dolphin-mixtral`
local LLM, which you can specify via the -m arg:

```
ollama run dolphin-mixtral

python3 examples/docqa/chat_multi_extract.py -m ollama/dolphin-mixtral:latest
```

The challenging parts in this script are agent-to-agent delegation, and the extractor
agent planning out a sequence of questions to ask the doc agent, and finally presenting
the collected information in a structured format to the user using a Tool/Function-call.
The `dolphin-mixtral` model seems to handle this pretty well, however weaker models
may not be able to handle this.

For weaker LLMs, the script examples/docqa/chat-multi-extract-local.py performs a similar task
but uses a workflow where agents do not delegate to each other,
and uses more agents to break down tasks into smaller parts.

"""

import typer
from rich import print
from langroid.pydantic_v1 import BaseModel
from typing import List
import json
import os

import langroid.language_models as lm
from langroid.mytypes import Entity
from langroid.agent.special.doc_chat_agent import DocChatAgent, DocChatAgentConfig
from langroid.parsing.parser import ParsingConfig
from langroid.agent.chat_agent import ChatAgent, ChatAgentConfig
from langroid.agent.task import Task
from langroid.agent.tool_message import ToolMessage
from langroid.language_models.openai_gpt import OpenAIGPTConfig
from langroid.utils.configuration import set_global, Settings
from langroid.utils.constants import NO_ANSWER

app = typer.Typer()

os.environ["TOKENIZERS_PARALLELISM"] = "false"


class LeasePeriod(BaseModel):
    start_date: str
    end_date: str


class LeaseFinancials(BaseModel):
    monthly_rent: str
    deposit: str


class Lease(BaseModel):
    """
    Various lease terms.
    Nested fields to make this more interesting/realistic
    """

    period: LeasePeriod
    financials: LeaseFinancials
    address: str


class LeaseMessage(ToolMessage):
    """Tool/function to use to present details about a commercial lease"""

    request: str = "lease_info"
    purpose: str = """
        Collect information about a Commercial Lease.
        """
    terms: Lease
    result: str = ""

    @classmethod
    def examples(cls) -> List["LeaseMessage"]:
        return [
            cls(
                terms=Lease(
                    period=LeasePeriod(start_date="2021-01-01", end_date="2021-12-31"),
                    financials=LeaseFinancials(monthly_rent="$1000", deposit="$1000"),
                    address="123 Main St, San Francisco, CA 94105",
                ),
                result="",
            ),
            cls(
                terms=Lease(
                    period=LeasePeriod(start_date="2021-04-01", end_date="2022-04-28"),
                    financials=LeaseFinancials(monthly_rent="$2000", deposit="$2000"),
                    address="456 Main St, San Francisco, CA 94111",
                ),
                result="",
            ),
        ]


class LeaseExtractorAgent(ChatAgent):
    def __init__(self, config: ChatAgentConfig):
        super().__init__(config)

    def lease_info(self, message: LeaseMessage) -> str:
        print(
            f"""
        DONE! Successfully extracted Lease Info:
        {message.terms}
        """
        )
        return "DONE " + json.dumps(message.terms.dict())


@app.command()
def main(
    debug: bool = typer.Option(False, "--debug", "-d", help="debug mode"),
    model: str = typer.Option("", "--model", "-m", help="model name"),
    nocache: bool = typer.Option(False, "--nocache", "-nc", help="don't use cache"),
) -> None:
    set_global(
        Settings(
            debug=debug,
            cache=not nocache,
        )
    )
    llm_cfg = OpenAIGPTConfig(
        chat_model=model or lm.OpenAIChatModel.GPT4o,
        chat_context_length=16_000,  # adjust based on model
        temperature=0,
        timeout=45,
    )
    doc_agent = DocChatAgent(
        DocChatAgentConfig(
            llm=llm_cfg,
            parsing=ParsingConfig(
                chunk_size=300,
                overlap=50,
                n_similar_docs=3,
            ),
            cross_encoder_reranking_model="",
        )
    )
    doc_agent.vecdb.set_collection("docqa-chat-multi-extract", replace=True)
    print("[blue]Welcome to the real-estate info-extractor!")
    doc_agent.config.doc_paths = [
        "examples/docqa/lease.txt",
    ]
    doc_agent.ingest()
    doc_task = Task(
        doc_agent,
        name="DocAgent",
        done_if_no_response=[Entity.LLM],  # done if null response from LLM
        done_if_response=[Entity.LLM],  # done if non-null response from LLM
        system_message="""You are an expert on Commercial Leases. 
        You will receive various questions about a Commercial 
        Lease contract, along with some excerpts from the Lease.
        Your job is to answer them concisely in at most 2 sentences.
        """,
    )

    lease_extractor_agent = LeaseExtractorAgent(
        ChatAgentConfig(
            llm=llm_cfg,
            vecdb=None,
        )
    )
    lease_extractor_agent.enable_message(LeaseMessage)

    lease_task = Task(
        lease_extractor_agent,
        name="LeaseExtractorAgent",
        interactive=False,  # set to True to slow it down (hit enter to progress)
        system_message=f"""
        You have to collect some SPECIFIC STRUCTURED information 
        about a Commercial Lease, as specified in the `lease_info` function/tool. 
        But you do not have access to the lease itself. 
        You can ask me questions about the lease, ONE AT A TIME, I will answer each 
        question. You only need to collect info to fill the fields in the 
        `lease_info` function/tool. 
        If I am unable to answer your question initially, try asking me 
        differently. If I am still unable to answer after 3 tries, fill in 
        {NO_ANSWER} for that field.
        When you have collected this info, present it to me using the 
        'lease_info' function/tool.
        DO NOT USE THIS Function/tool UNTIL YOU HAVE ASKED QUESTIONS 
        TO FILL IN ALL THE FIELDS.
        
        Start by asking me for the start date of the lease.
        """,
    )
    lease_task.add_sub_task(doc_task)
    lease_task.run()


if __name__ == "__main__":
    app()