- Installation
- Flo: Composable AI Agents ?
- Getting Started
- Building your first agent
- Building a RAG with flo
- Understanding Flo Deeper
Using pip:
pip install flo-ai
Using poetry:
poetry add flo-ai
Importing:
from flo_ai import Flo, FloSession
Flo gives you a composable framework for creating agentic AI architectures. What we intend to do here is to create an easy framework for GenAI app developers to compose apps using pre-implemented architectural components while providing the flexibility to create their components.
Composability is the ability to use smaller components to build more composable applications. In a composible architecture, you will be given smaller building blocks which you can use to build you bigger component, or the final application. It is very similar to how legos work, you are given the smaller Lego blocks which when put together create a whole building.
In flo, we tried to put together a system where we have small micro components like vector store, or simple LLM prompts, and then higher components/architectures made of these micro components like RAGs, Agentic Teams etc.
Flo is built with langraph working under the hood. So everything that works in langraph still works here, including all tools and architectures. The following makes flo a better solution:
-
Langraph needs a good understanding of the underlying graphs and states, its a raw tool and asks developers to implement the required components. While flo is more use-case friendly and components can be easily created by using the flo classes, which have lot of internal abstraction for ease of use.
-
In every AI component that has gone into production there are a lot of nuances that need to be implemented to get production-quality output, flo inherently implements these architectures and gives them out of the box, you can enable and disable as you wish. This not only reduces the complexity but also improves the time to iterate solutions.
-
Every component in flo is combosable meaning you can easily put them together and flo takes care of routing between the components whereas in langraph the developer has to tie these up. Flo plans to support custom routers in the future
Flo supports two ways to set up and run the components, first is through code. This is much more flexible. This can help you write your own tools and add them to the flo.
Second way is to use yaml. You can write an yaml to define your agentic workflow and it compiles into an application. See examples below.
To create a small team of researcher + blogger for writing blogs
from flo_ai import Flo
from flo_ai import FloSession
from langchain_openai import ChatOpenAI
# This yaml defines a team of 2 agents + 1 supervisor
yaml_data = """
apiVersion: flo/alpha-v1
kind: FloRoutedTeam
name: blogging-team
team:
name: BloggingTeam
router:
name: TeamLead
kind: supervisor
agents:
- name: Researcher
role: Researcher
job: Do research on the internet and find articles of relevent to the topic asked by the user, always try to find the latest information on the same
tools:
- name: TavilySearchResults
- name: Blogger
role: Writer
job: From the documents provider by the researcher write a blog of 300 words with can be readily published, make in engaging and add reference links to original blogs
tools:
- name: TavilySearchResults
"""
input_prompt = """
Question: Write me an interesting blog about latest advancements in agentic AI
"""
llm = ChatOpenAI(temperature=0, model_name='gpt-4o')
# Register all the tools within the session and use them everywhere in the yaml
session = FloSession(llm).register_tool(
name="TavilySearchResults",
tool=TavilySearchResults()
)
# Build the final flow and run it
flo: Flo = Flo.build(session, yaml=yaml_data)
# call invoke or stream
flo.stream(input_prompt)
Create a simple agent
# define all you tools PurchaseTool, LoanRequestTool etc
agent_yaml = """
apiVersion: flo/alpha-v1
kind: FloAgent
name: banking-assistant
agent:
name: BankingCustomer
job: >
You have the capability to interact with the bank in different ways. Depending upon your need take the right actions
tools:
- name: PurchaseTool
- name: LoanRequestTool
- name: CustomerSupportTool
"""
# set you session, register tools and trigger agent
We are also made building RAG composable. This RAG system can then be plugged into agentic flows and create an agentic RAG, or be used independently.
llm = ChatOpenAI(temperature=0, model_name='gpt-4o')
session = FloSession(llm)
# store in the vector store object you use, it can be any vector db like Chroma or Astra or Pinecorn etc
rag_builder = FloRagBuilder(session, store.as_retriever())
# this where you create a compression pipeline to easily add components like re-ranker
compression_pipeline = FloCompressionPipeline(OpenAIEmbeddings(model="<embeddings model>"))
compression_pipeline.add_embedding_reduntant_filter()
compression_pipeline.add_embedding_relevant_filter()
rag = rag_builder
# Use custom prompt for your augmented generation
.with_prompt(custom_prompt)
# Enable multi-query to create multiple queries from the user query and bring all these semantically similar documents
.with_multi_query()
# Use compression to perform duplicate removal and re-ranking etc
.with_compression(compression_pipeline)
# Build the runnable rag
.build_rag()
# Invoke the rag and get the output
print(rag.invoke({ "question": "What are the documents applying for housing loan" }))
# you can pass a chat history like this
print(rag.invoke({ "question": "What are the documents applying for housing loan", "chat_history": [] }))
Making agentic RAG tool is easy in flo
rag_tool = rag_builder
.with_multi_query()
.build_rag_tool(name="RAGTool", description="RAG to answer question by looking at db")
# Invoke as tool or make it part of structured agentic flo
print(rag_tool.invoke({"query": "What is the interest rate on housing loans"}))
Once you create the tool, register the tool and use the same to build your flo
# Register the tool to the existing session and add the tool to the previous yaml
session.register_tool(name="HousingLoanTool", tool=retriever_tool)
agent_yaml = """
apiVersion: flo/alpha-v1
kind: FloRoutedTeam
name: support-email-handler
team:
name: SupportTicketHandler
router:
name: SupportSupervisor
kind: supervisor
agents:
- name: EmailSender
role: Email Sender
job: You are capable of sending the reply email but constructing a apt response
tools:
- name: SendEmailTool
- name: TransactionFetcher
role: Transaction Fetcher
job: You are capable of fetching any kind of transactions from the database given transaction reference id
tools:
- name: FetchTransactionTool
- name: HousingLoanTeamLead
role: Housing Loan Specialist
job: Fetch the housing loan information from the db and answer the question
tools:
- name: HousingLoanTool
"""
flo: Flo = Flo.build(session, yaml=agent_yaml)
for s in flo.stream(input_prompt):
if "__end__" not in s:
print(s)
print("----")
Lets breakdown the structure of the yaml.
Name | Description |
---|---|
kind | The type of agentic flo. You have two options here, FloRoutedTeam or FloAgent |
name | This is the name of the agentic flo |
team/agent | The next key can be a team or an agent depending on whether you plan to create a team or an single agent |
team.router | Router this a component which manages the task in a team. The router takes care of properly routing the task, or sub-dividing the task depending on the current state. Currently we only support supervisor as router, more types are under construction |
(team/agent).name | This is the name of the team or agent |
agent.job | This is the job that is expected to be done by the agent. |
agent.role | This will assign a persona to the agent. This field is optional |
agent.tools | List of tools available to the agent. |
This is the current session in which flo is running. The session helps keep track of current execution and handles loops etc. We plan to add more control variables into sessions. Right now, the LLM to be used & tools available are configured within the session
# you can keep registering more and more tools
session = FloSession(llm).register_tool(
name="TavilySearchResults",
tool=TavilySearchResults()
)
The smallest component we have is an agent. It consist of the job to be done, a role, and its tools
name: HousingLoanTeamLead
role: Housing Loan Specialist
job: Fetch the housing loan information from the db and answer the question
tools:
- name: HousingLoanTool
Using just this agent you can create an agent flo, and it becomes ready for execution
apiVersion: flo/alpha-v1
kind: FloAgent
name: banking-assistant
agent:
name: HousingLoanTeamLead
role: Housing Loan Specialist
job: Fetch the housing loan information from the db and answer the question
tools:
- name: HousingLoanTool
A team is a group fo agents working towards a common goal. A team has to have a router to manage things, just like a manager your workplace. Right now we support supervisor
as your router, but more types are on the way.
apiVersion: flo/alpha-v1
kind: FloRoutedTeam
name: support-email-handler
team:
name: SupportTicketHandler
router:
name: SupportSupervisor
kind: supervisor
agents:
- name: HousingLoanTeamLead
role: Housing Loan Specialist
job: Fetch the housing loan information from the db and answer the question
tools:
- name: HousingLoanTool
Teams can also have sub-teams, for example:
apiVersion: flo/alpha-v1
kind: FloRoutedTeam
name: blogging-team
team:
name: BloggingTeam
supervisor:
name: supervisor
kind: supervisor
subteams:
- name: BloggingTeam
supervisor:
name: supervisor
kind: supervisor
agents:
- name: Reasercher
job: Do a research on the internet and find articles of relevent to the topic asked by the user, always try to find the latest information on the same
tools:
- name: TavilySearchResults
- name: Blogger
job: From the documents provider by the researcher write a blog of 300 words with can be readily published, make in engaging and add reference links to original blogs
tools:
- name: TavilySearchResults
- name: Writing Team
supervisor:
name: supervisor
kind: supervisor
agents:
- name: Figure
job: Do somethinh
tools:
- name: TavilySearchResults
This is the composability has flo unlocks, you can keep doing broader or deeper.
FloAI is open-source and we welcome contributions. If you're looking to contribute, please:
- Fork the repository.
- Create a new branch for your feature.
- Add your feature or improvement.
- Send a pull request.
We appreciate your input!
poetry lock
poetry install
FloAI is released under the MIT License.