Decision Log

Decisions with reasoning for auditing and learning.

The Decision Log Store records decisions made by agents with reasoning, context, and outcomes. Useful for auditing agent behavior, debugging unexpected outcomes, and building feedback loops.

Aspect	Value
Scope	Per agent
Persistence	Long-term
Default mode	Agentic
Supported modes	Always, Agentic

Basic Usage

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import LearningMachine, DecisionLogConfig
from agno.models.openai import OpenAIChat

agent = Agent(
    id="my-agent",
    model=OpenAIChat(id="gpt-4o"),
    db=PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"),
    learning=LearningMachine(
        decision_log=DecisionLogConfig(),
    ),
    instructions=[
        "When you make a significant choice, use log_decision to record it.",
        "Include your reasoning and alternatives you considered.",
    ],
)

agent.print_response(
    "I need help choosing between Python and JavaScript for web scraping.",
    session_id="session_1",
)

# View logged decisions
lm = agent.get_learning_machine()
lm.decision_log_store.print(agent_id="my-agent", limit=5)

Agentic Mode

The agent receives tools to explicitly log decisions.

from agno.learn import LearningMachine, LearningMode, DecisionLogConfig

agent = Agent(
    id="my-agent",
    model=OpenAIChat(id="gpt-4o"),
    db=db,
    learning=LearningMachine(
        decision_log=DecisionLogConfig(mode=LearningMode.AGENTIC),
    ),
)

Available tools: log_decision, record_outcome, search_decisions

The agent decides when a decision is significant enough to log.

Always Mode

Tool calls are automatically logged as decisions.

from agno.learn import LearningMachine, LearningMode, DecisionLogConfig

agent = Agent(
    id="my-agent",
    model=OpenAIChat(id="gpt-4o"),
    db=db,
    learning=LearningMachine(
        decision_log=DecisionLogConfig(mode=LearningMode.ALWAYS),
    ),
    tools=[DuckDuckGoTools()],
)

agent.print_response("What are the latest developments in AI agents?")
# Tool calls are automatically recorded as decisions

Tradeoff: logs every tool call, may generate noise.

Data Model

Field	Description
id	Unique identifier (e.g., “dec_abc123”)
decision	What was decided
reasoning	Why this decision was made
decision_type	Category: tool_selection, response_style, clarification
context	The situation that required a decision
alternatives	Other options considered
confidence	How confident (0.0 to 1.0)
outcome	What happened as a result
outcome_quality	Was it good, bad, or neutral
created_at	When the decision was made

Recording Outcomes

Update decisions with what actually happened to build feedback loops:

lm = agent.get_learning_machine()

# Via store directly
lm.decision_log_store.update_outcome(
    decision_id="dec_abc123",
    outcome="User was satisfied with Python recommendation",
    outcome_quality="good",
)

Or the agent can use the record_outcome tool during conversation.

Accessing Decisions

lm = agent.get_learning_machine()

# Search decisions
decisions = lm.decision_log_store.search(
    agent_id="my-agent",
    decision_type="tool_selection",
    days=7,
    limit=10,
)

for d in decisions:
    print(f"{d.decision}: {d.reasoning}")

# Debug output
lm.decision_log_store.print(agent_id="my-agent", limit=5)

Context Injection

Recent decisions are injected into the system prompt:

<decision_log>
Recent decisions:

- **Recommended Python over JavaScript**
  Reasoning: Web scraping libraries are more mature in Python
  Outcome: User was satisfied

- **Used web search for current info**
  Reasoning: Question about recent developments requires fresh data
</decision_log>

Decision Types

Common categories for organizing decisions:

Type	When to use
tool_selection	Choosing which tool to call
response_style	Deciding how to format or phrase response
clarification	Choosing to ask for more info
escalation	Deciding to defer to human
approach	Choosing between solution strategies

Use Cases

• Auditing: Review what decisions agents made and why
• Debugging: Understand unexpected behavior by examining reasoning
• Learning: Analyze outcome patterns to improve agent instructions
• Feedback loops: Record outcomes to identify successful patterns