Defining Agents and Workflows
Basic Agents
Defining an agent is as simple as:
We can then send messages to the Agent:
Or start an interactive chat with the Agent:
Here is the complete sizer.py Agent application, with boilerplate code:
import asyncio
from mcp_agent.core.fastagent import FastAgent
# Create the application
fast = FastAgent("Agent Example")
@fast.agent(
instruction="Given an object, respond only with an estimate of its size."
)
async def main():
async with fast.run() as agent:
await agent()
if __name__ == "__main__":
asyncio.run(main())
The Agent can then be run with uv run sizer.py.
Specify a model with the --model switch - for example uv run sizer.py --model sonnet.
Workflows and MCP Servers
To generate examples use fast-agent quickstart workflow. This example can be run with uv run workflow/chaining.py. fast-agent looks for configuration files in the current directory before checking parent directories recursively.
Agents can be chained to build a workflow, using MCP Servers defined in the fastagent.config.yaml file:
# Example of a STDIO sever named "fetch"
mcp:
servers:
fetch:
command: "uvx"
args: ["mcp-server-fetch"]
@fast.agent(
"url_fetcher",
"Given a URL, provide a complete and comprehensive summary",
servers=["fetch"], # Name of an MCP Server defined in fastagent.config.yaml
)
@fast.agent(
"social_media",
"""
Write a 280 character social media post for any given text.
Respond only with the post, never use hashtags.
""",
)
@fast.chain(
name="post_writer",
sequence=["url_fetcher", "social_media"],
)
async def main():
async with fast.run() as agent:
# using chain workflow
await agent.post_writer("http://fast-agent.ai")
All Agents and Workflows respond to .send("message"). The agent app responds to .interactive() to start a chat session.
Saved as social.py we can now run this workflow from the command line with:
Add the --quiet switch to disable progress and message display and return only the final response - useful for simple automations.
Read more about running fast-agent agents here
Workflow Types
fast-agent has built-in support for the patterns referenced in Anthropic's Building Effective Agents paper.
Chain
The chain workflow offers a declarative approach to calling Agents in sequence:
@fast.chain(
"post_writer",
sequence=["url_fetcher","social_media"]
)
# we can them prompt it directly:
async with fast.run() as agent:
await agent.interactive(agent="post_writer")
This starts an interactive session, which produces a short social media post for a given URL. If a chain is prompted it returns to a chat with last Agent in the chain. You can switch agents by typing @agent-name.
Chains can be incorporated in other workflows, or contain other workflow elements (including other Chains). You can set an instruction to describe it's capabilities to other workflow steps if needed.
Chains are also helpful for capturing content before being dispatched by a router, or summarizing content before being used in the downstream workflow.
Human Input
Agents can request Human Input to assist with a task or get additional context:
@fast.agent(
instruction="An AI agent that assists with basic tasks. Request Human Input when needed.",
human_input=True,
)
await agent("print the next number in the sequence")
In the example human_input.py, the Agent will prompt the User for additional information to complete the task.
Parallel
The Parallel Workflow sends the same message to multiple Agents simultaneously (fan-out), then uses the fan-in Agent to process the combined content.
@fast.agent("translate_fr", "Translate the text to French")
@fast.agent("translate_de", "Translate the text to German")
@fast.agent("translate_es", "Translate the text to Spanish")
@fast.parallel(
name="translate",
fan_out=["translate_fr","translate_de","translate_es"]
)
@fast.chain(
"post_writer",
sequence=["url_fetcher","social_media","translate"]
)
If you don't specify a fan-in agent, the parallel returns the combined Agent results verbatim.
parallel is also useful to ensemble ideas from different LLMs.
When using parallel in other workflows, specify an instruction to describe its operation.
Evaluator-Optimizer
Evaluator-Optimizers combine 2 agents: one to generate content (the generator), and the other to judge that content and provide actionable feedback (the evaluator). Messages are sent to the generator first, then the pair run in a loop until either the evaluator is satisfied with the quality, or the maximum number of refinements is reached. The final result from the Generator is returned.
If the Generator has use_history off, the previous iteration is returned when asking for improvements - otherwise conversational context is used.
@fast.evaluator_optimizer(
name="researcher"
generator="web_searcher"
evaluator="quality_assurance"
min_rating="EXCELLENT"
max_refinements=3
)
async with fast.run() as agent:
await agent.researcher.send("produce a report on how to make the perfect espresso")
When used in a workflow, it returns the last generator message as the result.
See the evaluator.py workflow example, or fast-agent quickstart researcher for a more complete example.
Router
Routers use an LLM to assess a message, and route it to the most appropriate Agent. The routing prompt is automatically generated based on the Agent instructions and available Servers.
NB - If only one agent is supplied to the router, it forwards directly.
Look at the router.py workflow for an example.
Orchestrator
Given a complex task, the Orchestrator uses an LLM to generate a plan to divide the task amongst the available Agents. The planning and aggregation prompts are generated by the Orchestrator, which benefits from using more capable models. Plans can either be built once at the beginning (plantype="full") or iteratively (plantype="iterative").
See the orchestrator.py or agent_build.py workflow example.
Agent and Workflow Reference
Calling Agents
All definitions allow omitting the name and instructions arguments for brevity:
@fast.agent("You are a helpful agent") # Create an agent with a default name.
@fast.agent("greeter","Respond cheerfully!") # Create an agent with the name "greeter"
moon_size = await agent("the moon") # Call the default (first defined agent) with a message
result = await agent.greeter("Good morning!") # Send a message to an agent by name using dot notation
result = await agent.greeter.send("Hello!") # You can call 'send' explicitly
agent["greeter"].send("Good Evening!") # Dictionary access to agents is also supported
Read more about prompting agents here
Defining Agents
Basic Agent
@fast.agent(
name="agent", # name of the agent
instruction="You are a helpful Agent", # base instruction for the agent
servers=["filesystem"], # list of MCP Servers for the agent
model="o3-mini.high", # specify a model for the agent
use_history=True, # agent maintains chat history
request_params=RequestParams(temperature= 0.7), # additional parameters for the LLM (or RequestParams())
human_input=True, # agent can request human input
)
Chain
@fast.chain(
name="chain", # name of the chain
sequence=["agent1", "agent2", ...], # list of agents in execution order
instruction="instruction", # instruction to describe the chain for other workflows
cumulative=False # whether to accumulate messages through the chain
continue_with_final=True, # open chat with agent at end of chain after prompting
)
Parallel
@fast.parallel(
name="parallel", # name of the parallel workflow
fan_out=["agent1", "agent2"], # list of agents to run in parallel
fan_in="aggregator", # name of agent that combines results (optional)
instruction="instruction", # instruction to describe the parallel for other workflows
include_request=True, # include original request in fan-in message
)
Evaluator-Optimizer
@fast.evaluator_optimizer(
name="researcher", # name of the workflow
generator="web_searcher", # name of the content generator agent
evaluator="quality_assurance", # name of the evaluator agent
min_rating="GOOD", # minimum acceptable quality (EXCELLENT, GOOD, FAIR, POOR)
max_refinements=3, # maximum number of refinement iterations
)
Router
@fast.router(
name="route", # name of the router
agents=["agent1", "agent2", "agent3"], # list of agent names router can delegate to
instruction="routing instruction", # any extra routing instructions
servers=["filesystem"] # list of servers for the routing agent
model="o3-mini.high", # specify routing model
use_history=False, # router maintains conversation history
human_input=False, # whether router can request human input
)
Orchestrator
@fast.orchestrator(
name="orchestrator", # name of the orchestrator
instruction="instruction", # base instruction for the orchestrator
agents=["agent1", "agent2"], # list of agent names this orchestrator can use
model="o3-mini.high", # specify orchestrator planning model
use_history=False, # orchestrator doesn't maintain chat history (no effect).
human_input=False, # whether orchestrator can request human input
plan_type="full", # planning approach: "full" or "iterative"
max_iterations=5, # maximum number of full plan attempts, or iterations
)