Overview
The Microchain Agent is an autonomous trading agent built on the Microchain framework, which enables function-calling capabilities for LLMs. This agent can perform complex multi-step operations including market research, trading, and learning from experience.
Base Class: DeployableMicrochainAgentAbstract
Abstract base class for all Microchain-based agents.
class DeployableMicrochainAgentAbstract(DeployableAgent, metaclass=abc.ABCMeta):
model = SupportedModel.gpt_4o
max_iterations: int | None = 50
import_actions_from_memory = 0
import_actions_from_memory_from: DatetimeUTC | None = None
sleep_between_iterations = 0
allow_stop: bool = True
identifier: AgentIdentifier
functions_config: FunctionsConfig
initial_system_prompt: str
Configuration Properties
model
SupportedModel
default:"SupportedModel.gpt_4o"
The LLM model to use for the agent
Maximum number of iterations per run. Set to None for unlimited iterations.
import_actions_from_memory
Number of past actions to import from memory on initialization
import_actions_from_memory_from
DatetimeUTC | None
default:"None"
Only import memories after this timestamp
Seconds to sleep between iterations
Whether the agent can use the Stop function to end execution
Unique identifier for the agent
Configuration specifying which function categories to enable
The system prompt that defines the agent’s behavior
Core Components
long_term_memory
LongTermMemoryTableHandler
Stores agent’s execution history and learnings
Manages system prompt versioning and updates
Handles dynamic prompt injection for special features
The Microchain Agent instance
Optional goal management system for autonomous behavior
Methods
load
Initializes the agent’s components including memory, prompt handlers, and the Microchain agent.
run
def run(self, market_type: MarketType) -> None
before_iteration_callback
def before_iteration_callback(self) -> CallbackReturn
CallbackReturn.STOP to halt execution.
after_iteration_callback
def after_iteration_callback(self) -> CallbackReturn
CallbackReturn.STOP to halt execution.
Production Agent: DeployableMicrochainAgent
Standard Microchain agent with full trading capabilities.
class DeployableMicrochainAgent(DeployableMicrochainAgentAbstract):
identifier = MICROCHAIN_AGENT_OMEN
functions_config = TRADING_AGENT_SYSTEM_PROMPT_CONFIG.functions_config
initial_system_prompt = TRADING_AGENT_SYSTEM_PROMPT_CONFIG.system_prompt
Usage
from prediction_market_agent.agents.microchain_agent.deploy import (
DeployableMicrochainAgent
)
from prediction_market_agent_tooling.markets.markets import MarketType
agent = DeployableMicrochainAgent(
enable_langfuse=True,
place_trades=True,
)
agent.deploy(
market_type=MarketType.OMEN,
)
Learning Variants
Agents with “just born” system prompts that can modify their own behavior.
DeployableMicrochainModifiableSystemPromptAgent0/1/2
Agents that start with minimal knowledge and learn through experience.
class DeployableMicrochainModifiableSystemPromptAgent0(
DeployableMicrochainModifiableSystemPromptAgentAbstract
):
identifier = MICROCHAIN_AGENT_OMEN_LEARNING_0
Uses JUST_BORN_SYSTEM_PROMPT_CONFIG with learning functions enabled
Minimal “just born” prompt allowing the agent to discover its capabilities
DeployableMicrochainModifiableSystemPromptAgent3
Llama 3.1 variant with reduced iterations.
class DeployableMicrochainModifiableSystemPromptAgent3(
DeployableMicrochainModifiableSystemPromptAgentAbstract
):
identifier = MICROCHAIN_AGENT_OMEN_LEARNING_3
model = SupportedModel.llama_31_instruct
max_iterations = 10 # Limited by API token constraints
Goal-Managed Agent
DeployableMicrochainWithGoalManagerAgent0
Agent with autonomous goal setting and evaluation.
class DeployableMicrochainWithGoalManagerAgent0(DeployableMicrochainAgent):
identifier = MICROCHAIN_AGENT_OMEN_WITH_GOAL_MANAGER
model = SupportedModel.gpt_4o
functions_config = TRADING_AGENT_SYSTEM_PROMPT_MINIMAL_CONFIG.functions_config
initial_system_prompt = TRADING_AGENT_SYSTEM_PROMPT_MINIMAL_CONFIG.system_prompt
Goal Manager Configuration
def build_goal_manager(self, agent: Agent) -> GoalManager:
return GoalManager(
agent_id=self.identifier,
high_level_description="You are a trader agent in prediction markets, aiming to maximise your long-term profit.",
agent_capabilities=f"You have the following capabilities:\n{get_functions_summary_list(agent.engine)}",
retry_limit=1,
goal_history_limit=10,
)
Supported Models
class SupportedModel(str, Enum):
gpt_4o = "gpt-4o-2024-08-06"
gpt_4o_mini = "gpt-4o-mini-2024-07-18"
gpt_4_turbo = "gpt-4-turbo"
llama_31_instruct = "meta/meta-llama-3.1-405b-instruct"
deepseek_chat = "deepseek/deepseek-chat"
deepseek_r1 = "deepseek/deepseek-r1"
gemini_20_flash = "google/gemini-2.0-flash-001"
Function Categories
The agent can be configured with various function categories:
Agent self-modification capabilities
include_universal_functions
API calls, code execution, web search
Task scheduling and automation
include_learning_functions
Memory and learning capabilities
include_trading_functions
Market analysis and trading operations
include_sending_functions
Fund transfer capabilities
Balance checking and management
Building Custom Agents
Custom Function Configuration
from prediction_market_agent.agents.microchain_agent.deploy import (
DeployableMicrochainAgentAbstract
)
from prediction_market_agent.agents.microchain_agent.prompts import (
FunctionsConfig
)
from prediction_market_agent.agents.identifiers import AgentIdentifier
class MyCustomMicrochainAgent(DeployableMicrochainAgentAbstract):
identifier = "my-custom-agent"
model = SupportedModel.gpt_4o_mini
max_iterations = 30
functions_config = FunctionsConfig(
common_functions=True,
include_trading_functions=True,
include_universal_functions=True,
# Disable other function categories
include_agent_functions=False,
include_learning_functions=False,
)
initial_system_prompt = """
You are a focused trading agent.
Your goal is to find and trade on profitable prediction markets.
Use web search to research markets before trading.
"""
agent = MyCustomMicrochainAgent()
agent.deploy_local(market_type=MarketType.OMEN)
Importing Past Memory
from prediction_market_agent_tooling.tools.datetime_utc import DatetimeUTC
class MemoryImportingAgent(DeployableMicrochainAgent):
import_actions_from_memory = 20 # Import last 20 actions
import_actions_from_memory_from = DatetimeUTC(2024, 1, 1) # From 2024 onwards
agent = MemoryImportingAgent()
agent.deploy_local(market_type=MarketType.OMEN)
Custom Callbacks
class CallbackAgent(DeployableMicrochainAgent):
def before_iteration_callback(self) -> CallbackReturn:
# Check some condition
if should_stop():
logger.info("Stopping agent due to custom condition")
return CallbackReturn.STOP
return CallbackReturn.CONTINUE
def after_iteration_callback(self) -> CallbackReturn:
# Log metrics after each iteration
log_agent_metrics(self.agent)
return CallbackReturn.CONTINUE
Helper Functions
build_agent
def build_agent(
keys: APIKeys,
market_type: MarketType,
model: SupportedModel,
unformatted_system_prompt: str,
functions_config: FunctionsConfig,
enable_langfuse: bool,
long_term_memory: LongTermMemoryTableHandler | None = None,
max_tokens: int = 8196,
allow_stop: bool = True,
bootstrap: str | None = None,
raise_on_error: bool = True,
) -> Agent
save_agent_history
def save_agent_history(
long_term_memory: LongTermMemoryTableHandler,
agent: Agent,
initial_system_prompt: str,
save_last_n: int | None = None,
) -> None
get_functions_summary_list
def get_functions_summary_list(engine: Engine) -> str
Advanced Features
Smart Contract Integration
def build_functions_from_smart_contract(
keys: APIKeys,
contract_address: ChecksumAddress,
contract_name: str
) -> list[Function]
System Prompt Versioning
The agent automatically saves and restores system prompts:
def get_unformatted_system_prompt(
unformatted_prompt: str,
prompt_table_handler: PromptTableHandler | None
) -> str
Required API Keys
Required for Llama models
Required for DeepSeek and Gemini models
Required if search functions are enabled
Source Location
prediction_market_agent/agents/microchain_agent/deploy.py
prediction_market_agent/agents/microchain_agent/microchain_agent.py