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swarms/docs/swarms/structs/agent_docs_v1.md

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# `Agent` Documentation
Swarm Agent is a powerful autonomous agent framework designed to connect Language Models (LLMs) with various tools and long-term memory. This class provides the ability to ingest and process various types of documents such as PDFs, text files, Markdown files, JSON files, and more. The Agent structure offers a wide range of features to enhance the capabilities of LLMs and facilitate efficient task execution.
1. **Conversational Loop**: It establishes a conversational loop with a language model. This means it allows you to interact with the model in a back-and-forth manner, taking turns in the conversation.
2. **Feedback Collection**: The class allows users to provide feedback on the responses generated by the model. This feedback can be valuable for training and improving the model's responses over time.
3. **Stoppable Conversation**: You can define custom stopping conditions for the conversation, allowing you to stop the interaction based on specific criteria. For example, you can stop the conversation if a certain keyword is detected in the responses.
4. **Retry Mechanism**: The class includes a retry mechanism that can be helpful if there are issues generating responses from the model. It attempts to generate a response multiple times before raising an error.
## Architecture
```mermaid
graph TD
A[Task Initiation] -->|Receives Task| B[Initial LLM Processing]
B -->|Interprets Task| C[Tool Usage]
C -->|Calls Tools| D[Function 1]
C -->|Calls Tools| E[Function 2]
D -->|Returns Data| C
E -->|Returns Data| C
C -->|Provides Data| F[Memory Interaction]
F -->|Stores and Retrieves Data| G[RAG System]
G -->|ChromaDB/Pinecone| H[Enhanced Data]
F -->|Provides Enhanced Data| I[Final LLM Processing]
I -->|Generates Final Response| J[Output]
C -->|No Tools Available| K[Skip Tool Usage]
K -->|Proceeds to Memory Interaction| F
F -->|No Memory Available| L[Skip Memory Interaction]
L -->|Proceeds to Final LLM Processing| I
```
### `Agent` Attributes
| Attribute | Description |
|------------|-------------|
| `id` | A unique identifier for the agent instance. |
| `llm` | The language model instance used by the agent. |
| `template` | The template used for formatting responses. |
| `max_loops` | The maximum number of loops the agent can run. |
| `stopping_condition` | A callable function that determines when the agent should stop looping. |
| `loop_interval` | The interval (in seconds) between loops. |
| `retry_attempts` | The number of retry attempts for failed LLM calls. |
| `retry_interval` | The interval (in seconds) between retry attempts. |
| `return_history` | A boolean indicating whether the agent should return the conversation history. |
| `stopping_token` | A token that, when present in the response, stops the agent from looping. |
| `dynamic_loops` | A boolean indicating whether the agent should dynamically determine the number of loops. |
| `interactive` | A boolean indicating whether the agent should run in interactive mode. |
| `dashboard` | A boolean indicating whether the agent should display a dashboard. |
| `agent_name` | The name of the agent instance. |
| `agent_description` | A description of the agent instance. |
| `system_prompt` | The system prompt used to initialize the conversation. |
| `tools` | A list of callable functions representing tools the agent can use. |
| `dynamic_temperature_enabled` | A boolean indicating whether the agent should dynamically adjust the temperature of the LLM. |
| `sop` | The standard operating procedure for the agent. |
| `sop_list` | A list of strings representing the standard operating procedure. |
| `saved_state_path` | The file path for saving and loading the agent's state. |
| `autosave` | A boolean indicating whether the agent should automatically save its state. |
| `context_length` | The maximum length of the context window (in tokens) for the LLM. |
| `user_name` | The name used to represent the user in the conversation. |
| `self_healing_enabled` | A boolean indicating whether the agent should attempt to self-heal in case of errors. |
| `code_interpreter` | A boolean indicating whether the agent should interpret and execute code snippets. |
| `multi_modal` | A boolean indicating whether the agent should support multimodal inputs (e.g., text and images). |
| `pdf_path` | The file path of a PDF document to be ingested. |
| `list_of_pdf` | A list of file paths for PDF documents to be ingested. |
| `tokenizer` | An instance of a tokenizer used for token counting and management. |
| `long_term_memory` | An instance of a `BaseVectorDatabase` implementation for long-term memory management. |
| `preset_stopping_token` | A boolean indicating whether the agent should use a preset stopping token. |
| `traceback` | An object used for traceback handling. |
| `traceback_handlers` | A list of traceback handlers. |
| `streaming_on` | A boolean indicating whether the agent should stream its responses. |
| `docs` | A list of document paths or contents to be ingested. |
| `docs_folder` | The path to a folder containing documents to be ingested. |
| `verbose` | A boolean indicating whether the agent should print verbose output. |
| `parser` | A callable function used for parsing input data. |
| `best_of_n` | An integer indicating the number of best responses to generate (for sampling). |
| `callback` | A callable function to be called after each agent loop. |
| `metadata` | A dictionary containing metadata for the agent. |
| `callbacks` | A list of callable functions to be called during the agent's execution. |
| `logger_handler` | A handler for logging messages. |
| `search_algorithm` | A callable function representing the search algorithm for long-term memory retrieval. |
| `logs_to_filename` | The file path for logging agent activities. |
| `evaluator` | A callable function used for evaluating the agent's responses. |
| `output_json` | A boolean indicating whether the agent's output should be in JSON format. |
| `stopping_func` | A callable function used as a stopping condition for the agent. |
| `custom_loop_condition` | A callable function used as a custom loop condition for the agent. |
| `sentiment_threshold` | A float value representing the sentiment threshold for evaluating responses. |
| `custom_exit_command` | A string representing a custom command for exiting the agent's loop. |
| `sentiment_analyzer` | A callable function used for sentiment analysis on the agent's outputs. |
| `limit_tokens_from_string` | A callable function used for limiting the number of tokens in a string. |
| `custom_tools_prompt` | A callable function used for generating a custom prompt for tool usage. |
| `tool_schema` | A data structure representing the schema for the agent's tools. |
| `output_type` | A type representing the expected output type of the agent's responses. |
| `function_calling_type` | A string representing the type of function calling (e.g., "json"). |
| `output_cleaner` | A callable function used for cleaning the agent's output. |
| `function_calling_format_type` | A string representing the format type for function calling (e.g., "OpenAI"). |
| `list_base_models` | A list of base models used for generating tool schemas. |
| `metadata_output_type` | A string representing the output type for metadata. |
| `state_save_file_type` | A string representing the file type for saving the agent's state (e.g., "json", "yaml"). |
| `chain_of_thoughts` | A boolean indicating whether the agent should use the chain of thoughts technique. |
| `algorithm_of_thoughts` | A boolean indicating whether the agent should use the algorithm of thoughts technique. |
| `tree_of_thoughts` | A boolean indicating whether the agent should use the tree of thoughts technique. |
| `tool_choice` | A string representing the method for tool selection (e.g., "auto"). |
| `execute_tool` | A boolean indicating whether the agent should execute tools. |
| `rules` | A string representing the rules for the agent's behavior. |
| `planning` | A boolean indicating whether the agent should perform planning. |
| `planning_prompt` | A string representing the prompt for planning. |
| `device` | A string representing the device on which the agent should run. |
| `custom_planning_prompt` | A string representing a custom prompt for planning. |
| `memory_chunk_size` | An integer representing the maximum size of memory chunks for long-term memory retrieval. |
| `agent_ops_on` | A boolean indicating whether agent operations should be enabled. |
| `return_step_meta` | A boolean indicating whether or not to return JSON of all the steps and additional metadata |
| `output_type` | A Literal type indicating whether to output "string", "str", "list", "json", "dict", "yaml" |
### `Agent` Methods
| Method | Description | Inputs | Usage Example |
|--------|-------------|--------|----------------|
| `run(task, img=None, *args, **kwargs)` | Runs the autonomous agent loop to complete the given task. | `task` (str): The task to be performed.<br>`img` (str, optional): Path to an image file, if the task involves image processing.<br>`*args`, `**kwargs`: Additional arguments to pass to the language model. | `response = agent.run("Generate a report on financial performance.")` |
| `__call__(task, img=None, *args, **kwargs)` | An alternative way to call the `run` method. | Same as `run`. | `response = agent("Generate a report on financial performance.")` |
| `parse_and_execute_tools(response, *args, **kwargs)` | Parses the agent's response and executes any tools mentioned in it. | `response` (str): The agent's response to be parsed.<br>`*args`, `**kwargs`: Additional arguments to pass to the tool execution. | `agent.parse_and_execute_tools(response)` |
| `long_term_memory_prompt(query, *args, **kwargs)` | Generates a prompt for querying the agent's long-term memory. | `query` (str): The query to search for in long-term memory.<br>`*args`, `**kwargs`: Additional arguments to pass to the long-term memory retrieval. | `memory_retrieval = agent.long_term_memory_prompt("financial performance")` |
| `add_memory(message)` | Adds a message to the agent's memory. | `message` (str): The message
## Features
- **Language Model Integration**: The Swarm Agent allows seamless integration with different language models, enabling users to leverage the power of state-of-the-art models.
- **Tool Integration**: The framework supports the integration of various tools, enabling the agent to perform a wide range of tasks, from code execution to data analysis and beyond.
- **Long-term Memory Management**: The Swarm Agent incorporates long-term memory management capabilities, allowing it to store and retrieve relevant information for effective decision-making and task execution.
- **Document Ingestion**: The agent can ingest and process various types of documents, including PDFs, text files, Markdown files, JSON files, and more, enabling it to extract relevant information for task completion.
- **Interactive Mode**: Users can interact with the agent in an interactive mode, enabling real-time communication and task execution.
- **Dashboard**: The framework provides a visual dashboard for monitoring the agent's performance and activities.
- **Dynamic Temperature Control**: The Swarm Agent supports dynamic temperature control, allowing for adjustments to the model's output diversity during task execution.
- **Autosave and State Management**: The agent can save its state automatically, enabling seamless resumption of tasks after interruptions or system restarts.
- **Self-Healing and Error Handling**: The framework incorporates self-healing and error-handling mechanisms to ensure robust and reliable operation.
- **Code Interpretation**: The agent can interpret and execute code snippets, expanding its capabilities for tasks involving programming or scripting.
- **Multimodal Support**: The framework supports multimodal inputs, enabling the agent to process and reason about various data types, such as text, images, and audio.
- **Tokenization and Token Management**: The Swarm Agent provides tokenization capabilities, enabling efficient management of token usage and context window truncation.
- **Sentiment Analysis**: The agent can perform sentiment analysis on its generated outputs, allowing for evaluation and adjustment of responses based on sentiment thresholds.
- **Output Filtering and Cleaning**: The framework supports output filtering and cleaning, ensuring that generated responses adhere to specific criteria or guidelines.
- **Asynchronous and Concurrent Execution**: The Swarm Agent supports asynchronous and concurrent task execution, enabling efficient parallelization and scaling of operations.
- **Planning and Reasoning**: The agent can engage in planning and reasoning processes, leveraging techniques such as algorithm of thoughts and chain of thoughts to enhance decision-making and task execution.
- **Agent Operations and Monitoring**: The framework provides integration with agent operations and monitoring tools, enabling real-time monitoring and management of the agent's activities.
## Getting Started
First run the following:
```bash
pip3 install -U swarms
```
And, then now you can get started with the following:
```python
import os
from swarms import Agent
from swarm_models import OpenAIChat
from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
)
# Get the OpenAI API key from the environment variable
api_key = os.getenv("OPENAI_API_KEY")
# Create an instance of the OpenAIChat class
model = OpenAIChat(
api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)
# Initialize the agent
agent = Agent(
agent_name="Financial-Analysis-Agent_sas_chicken_eej",
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
llm=model,
max_loops=1,
autosave=True,
dashboard=False,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path="finance_agent.json",
user_name="swarms_corp",
retry_attempts=1,
context_length=200000,
return_step_meta=False,
output_type="str",
)
agent.run(
"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
)
print(out)
```
This example initializes an instance of the `Agent` class with an OpenAI language model and a maximum of 3 loops. The `run()` method is then called with a task to generate a report on financial performance, and the agent's response is printed.
## Advanced Usage
The Swarm Agent provides numerous advanced features and customization options. Here are a few examples of how to leverage these features:
### Tool Integration
To integrate tools with the Swarm Agent, you can pass a list of callable functions with types and doc strings to the `tools` parameter when initializing the `Agent` instance. The agent will automatically convert these functions into an OpenAI function calling schema and make them available for use during task execution.
## Requirements for a tool
- Function
- With types
- with doc strings
```python
from swarms import Agent
from swarm_models import OpenAIChat
from swarms_memory import ChromaDB
import subprocess
import os
# Making an instance of the ChromaDB class
memory = ChromaDB(
metric="cosine",
n_results=3,
output_dir="results",
docs_folder="docs",
)
# Model
model = OpenAIChat(
api_key=os.getenv("OPENAI_API_KEY"),
model_name="gpt-4o-mini",
temperature=0.1,
)
# Tools in swarms are simple python functions and docstrings
def terminal(
code: str,
):
"""
Run code in the terminal.
Args:
code (str): The code to run in the terminal.
Returns:
str: The output of the code.
"""
out = subprocess.run(
code, shell=True, capture_output=True, text=True
).stdout
return str(out)
def browser(query: str):
"""
Search the query in the browser with the `browser` tool.
Args:
query (str): The query to search in the browser.
Returns:
str: The search results.
"""
import webbrowser
url = f"https://www.google.com/search?q={query}"
webbrowser.open(url)
return f"Searching for {query} in the browser."
def create_file(file_path: str, content: str):
"""
Create a file using the file editor tool.
Args:
file_path (str): The path to the file.
content (str): The content to write to the file.
Returns:
str: The result of the file creation operation.
"""
with open(file_path, "w") as file:
file.write(content)
return f"File {file_path} created successfully."
def file_editor(file_path: str, mode: str, content: str):
"""
Edit a file using the file editor tool.
Args:
file_path (str): The path to the file.
mode (str): The mode to open the file in.
content (str): The content to write to the file.
Returns:
str: The result of the file editing operation.
"""
with open(file_path, mode) as file:
file.write(content)
return f"File {file_path} edited successfully."
# Agent
agent = Agent(
agent_name="Devin",
system_prompt=(
"Autonomous agent that can interact with humans and other"
" agents. Be Helpful and Kind. Use the tools provided to"
" assist the user. Return all code in markdown format."
),
llm=model,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
streaming=True,
long_term_memory=memory,
)
# Run the agent
out = agent(
"Create a CSV file with the latest tax rates for C corporations in the following ten states and the District of Columbia: Alabama, California, Florida, Georgia, Illinois, New York, North Carolina, Ohio, Texas, and Washington."
)
print(out)
```
### Long-term Memory Management
The Swarm Agent supports integration with various vector databases for long-term memory management. You can pass an instance of a `BaseVectorDatabase` implementation to the `long_term_memory` parameter when initializing the `Agent`.
```python
import os
from swarms_memory import ChromaDB
from swarms import Agent
from swarm_models import Anthropic
from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
)
# Initilaize the chromadb client
chromadb = ChromaDB(
metric="cosine",
output_dir="fiance_agent_rag",
# docs_folder="artifacts", # Folder of your documents
)
# Model
model = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
# Initialize the agent
agent = Agent(
agent_name="Financial-Analysis-Agent",
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
agent_description="Agent creates ",
llm=model,
max_loops="auto",
autosave=True,
dashboard=False,
verbose=True,
streaming_on=True,
dynamic_temperature_enabled=True,
saved_state_path="finance_agent.json",
user_name="swarms_corp",
retry_attempts=3,
context_length=200000,
long_term_memory=chromadb,
)
agent.run(
"What are the components of a startups stock incentive equity plan"
)
```
### Document Ingestion
The Swarm Agent can ingest various types of documents, such as PDFs, text files, Markdown files, and JSON files. You can pass a list of document paths or contents to the `docs` parameter when initializing the `Agent`.
```python
from swarms.structs import Agent
# Initialize the agent with documents
agent = Agent(llm=llm, max_loops=3, docs=["path/to/doc1.pdf", "path/to/doc2.txt"])
```
### Interactive Mode
The Swarm Agent supports an interactive mode, where users can engage in real-time communication with the agent. To enable interactive mode, set the `interactive` parameter to `True` when initializing the `Agent`.
```python
from swarms.structs import Agent
# Initialize the agent in interactive mode
agent = Agent(llm=llm, max_loops=3, interactive=True)
# Run the agent in interactive mode
agent.interactive_run()
```
### Sentiment Analysis
The Swarm Agent can perform sentiment analysis on its generated outputs using a sentiment analyzer function. You can pass a callable function to the `sentiment_analyzer` parameter when initializing the `Agent`.
```python
from swarms.structs import Agent
from my_sentiment_analyzer import sentiment_analyzer_function
# Initialize the agent with a sentiment analyzer
agent = Agent(
agent_name = "sentiment-analyzer-agent-01", system_prompt="..."
llm=llm, max_loops=3, sentiment_analyzer=sentiment_analyzer_function)
```
### Undo Functionality
```python
# Feature 2: Undo functionality
response = agent.run("Another task")
print(f"Response: {response}")
previous_state, message = agent.undo_last()
print(message)
```
### Response Filtering
```python
# Feature 3: Response filtering
agent.add_response_filter("report")
response = agent.filtered_run("Generate a report on finance")
print(response)
```
### Saving and Loading State
```python
# Save the agent state
agent.save_state('saved_flow.json')
# Load the agent state
agent = Agent(llm=llm_instance, max_loops=5)
agent.load('saved_flow.json')
agent.run("Continue with the task")
```
### Async and Concurrent Execution
```python
# Run a task concurrently
response = await agent.run_concurrent("Concurrent task")
print(response)
# Run multiple tasks concurrently
tasks = [
{"task": "Task 1"},
{"task": "Task 2", "img": "path/to/image.jpg"},
{"task": "Task 3", "custom_param": 42}
]
responses = agent.bulk_run(tasks)
print(responses)
```
### Various other settings
```python
# # Convert the agent object to a dictionary
print(agent.to_dict())
print(agent.to_toml())
print(agent.model_dump_json())
print(agent.model_dump_yaml())
# Ingest documents into the agent's knowledge base
agent.ingest_docs("your_pdf_path.pdf")
# Receive a message from a user and process it
agent.receive_message(name="agent_name", message="message")
# Send a message from the agent to a user
agent.send_agent_message(agent_name="agent_name", message="message")
# Ingest multiple documents into the agent's knowledge base
agent.ingest_docs("your_pdf_path.pdf", "your_csv_path.csv")
# Run the agent with a filtered system prompt
agent.filtered_run(
"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?"
)
# Run the agent with multiple system prompts
agent.bulk_run(
[
"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?",
"Another system prompt",
]
)
# Add a memory to the agent
agent.add_memory("Add a memory to the agent")
# Check the number of available tokens for the agent
agent.check_available_tokens()
# Perform token checks for the agent
agent.tokens_checks()
# Print the dashboard of the agent
agent.print_dashboard()
# Fetch all the documents from the doc folders
agent.get_docs_from_doc_folders()
# Activate agent ops
# Dump the model to a JSON file
agent.model_dump_json()
print(agent.to_toml())
```
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