FlexiAgent is a simple and easy-to-use framework for creating LLM agents. The agent supports structured output and includes built-in practical agents such as a text2sql agent, allowing for quick deployment in applications.
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Efficient and Simple Agent Interface Design: Supports structured output, making it easier to utilize in practical applications.
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DAG-Based Task Implementation: The agent's tasks are built on DAGs, supporting distributed concurrent scheduling.
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Configuration-Driven Agent Creation: Offers high flexibility and extensibility through comprehensive configuration support.
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Built-In Practical Agents: Includes useful agents like
text2sql,llm_chat, andapi_callfor fast deployment and reduced development time.
To begin using FlexiAgent, you can install from pypi
pip install flexiagentOr clone the repository from GitHub:
git clone https://github.com/dzhsurf/flexiagent.git
cd flexiagent
pip install -e .
# or use poetry
# poetry installEnsure you have Python installed and set up a virtual environment(conda recommended):
# python3.11 recommended, support python3.8+
conda create -n proj python=3.11 If you encounter issues compiling llama-cpp-python during the dependency installation process, please visit https://github.com/abetlen/llama-cpp-python for documentation to help resolve the issue.
Before using FlexiAgent, you'll need to set up your OPENAI_API_KEY. You can set this environment variable in your system or include it in your code:
export OPENAI_API_KEY='your-api-key-here' FlexiAgent can be easily integrated into your existing projects. Below is a basic setup to get you started:
from flexiagent.llm.config import LLMConfig
from flexiagent.task.task_node import (
FxTaskAction,
FxTaskActionLLM,
FxTaskAgent,
FxTaskConfig,
FxTaskEntity,
)
llm_config = LLMConfig(engine="OpenAI", params={"openai_model": "gpt-4o-mini"})
class Step1Output(FxTaskEntity):
num1: float
num2: float
op: str
class Step2Output(FxTaskEntity):
result: float
def compute_nums(input: Dict[str, Any], addition: Dict[str, Any]) -> Step2Output:
nums: Step1Output = input["step_1"]
result = 0.0
if nums.op == "+":
result = nums.num1 + nums.num2
elif nums.op == "-":
result = nums.num1 - nums.num2
elif nums.op == "*":
result = nums.num1 * nums.num2
elif nums.op == "/":
result = nums.num1 / nums.num2
else:
result = 0
return Step2Output(
result=result,
)
agent = FxTaskAgent(
task_graph=[
# step 1: llm extract data
FxTaskConfig(
task_key="step_1",
input_schema={"input": str},
output_schema=Step1Output,
action=FxTaskAction(
type="llm",
act=FxTaskActionLLM(
llm_config=llm_config,
instruction="""
Extract the numbers and operators from mathematical expressions based on the user's questions.
Only support +, -, *, / operations with two numbers.
Question: {input}
""",
),
),
),
# step 2: compute
FxTaskConfig(
task_key="output",
input_schema={"step_1": Step1Output},
output_schema=Step2Output,
action=FxTaskAction(
type="function",
act=compute_nums,
),
),
],
)
output = agent.invoke("Compute: 3 + 5 =")
# output is Step2Output, result is 8Text2SqlQA Agent
For the complete code, please check it out here: https://github.com/dzhsurf/flexiagent/blob/master/examples/gradio_chatbot/text2sql_qa_chatbot.py
chatbot_agent = FxTaskAgent(
task_graph=[
# step 1: analyze user intent
FxTaskConfig(
task_key="user_intent",
input_schema={"input": ChatBotInput},
output_schema=UserIntent,
action=FxTaskAction(
type="llm",
act=FxTaskActionLLM(
llm_config=llm_config,
instruction="""Based on the user's question, analyze the user's intent. The classification is as follows:
- QA: If the question is about student information, grades, class information, etc.
- Other: Otherwise, it falls under this category.
{input.history_as_text}
Question: {input.input}
""",
),
),
),
# step 2.1: text2sql qa action
FxTaskConfig(
task_key="text2sql_qa",
input_schema={
"input": ChatBotInput,
"user_intent": UserIntent,
},
output_schema=str,
action=FxTaskAction(
type="agent",
act=create_text2sql_qa_agent(
llm_config,
_fetch_database_metainfo,
preprocess_hook=_convert_chatbot_input_to_text2sql_qa_input,
),
condition=_condition_text2sql_qa,
),
),
# step 2.2: fallback action
FxTaskConfig(
task_key="fallback_action",
input_schema={
"input": ChatBotInput,
"user_intent": UserIntent,
},
output_schema=str,
action=FxTaskAction(
type="llm",
act=FxTaskActionLLM(
llm_config=llm_config,
instruction="""You are a chatbot assistant. Assist user and response user's question.
{input.history_as_text}
Question: {input.input}
""",
),
condition=_condition_fallback_action,
),
),
# step 3:
FxTaskConfig(
task_key="output",
input_schema={
"user_intent": UserIntent,
"text2sql_qa": str,
"fallback_action": str,
},
output_schema=ChatBotResponse,
action=FxTaskAction(
type="function",
act=_generate_output,
),
),
]
)To get started quickly with FlexiAgent, please refer to the Quickstart Guide for example usage.
FlexiAgent utilizes llama-cpp-python, allowing the LLM to support not only OpenAI but also Llama.cpp. The above code example can load a local model using Llama.cpp by changing the configuration.
Parameter Explanation
repo_id_or_model_path:
If specified as a repo_id, the model will be downloaded to the cache directory (~/.cache/huggingface/...) using huggingface-cli. If it is a local model path (e.g., xxx_model.gguf), it will be loaded directly.
repo_filename:
This is only effective when repo_id_or_model_path is a repo_id. It selects the specified model based on the filename rules, such as Q4_K_M, Q8_0, etc., and should be set according to the file names in the huggingface repo.
n_ctx: Context window size; defaults to 512 if not set.
llm_config = LLMConfig(
engine="LlamaCpp",
params={
"repo_id_or_model_path": "QuantFactory/Llama-3.2-3B-Instruct-GGUF",
"repo_filename": "*Q4_K_M.gguf",
"n_ctx": 4096,
},
)Contributions are welcome! Please fork the repository and use a branch for your feature or bug fix. Submitting a pull request is the best way to see your feature merged.
This project is licensed under the MIT License - see the LICENSE file for details.