Skip to main content
Enable self_improve=True on an agent and after every task it asks itself “did I just learn something reusable?” — if yes, it writes or patches a skill for next time.

​
Quick Start

1

Simple Usage

from praisonaiagents import Agent

agent = Agent(
    name="Engineer",
    instructions="Help debug and fix issues.",
    self_improve=True,
)

agent.start("Debug this flaky deploy script and fix it.")
After the task ends, the agent runs a guarded review pass. If it found a reusable technique, it writes a SKILL.md for next time.
2

With Configuration

from praisonaiagents import Agent
from praisonaiagents.skills import DefaultSkillReviewPolicy

agent = Agent(
    name="Engineer",
    instructions="Help debug and fix issues.",
    self_improve=DefaultSkillReviewPolicy(min_tool_calls=2),
)

​
How It Works

After each task, if self_improve is enabled:
  1. The policy checks should_review(trajectory) — by default, only when ≥1 tool was called
  2. The agent runs one extra turn with only skill_manage available
  3. The agent calls skill_manage to create/patch a skill, or replies NO_SKILL
  4. The internal review exchange is trimmed back out of chat_history

​
Configuration Options

​
Agent(self_improve=...)

ValueBehavior
False (default)Off. No review pass runs.
TrueOn with DefaultSkillReviewPolicy() (reviews when ≥1 tool was used).
DefaultSkillReviewPolicy(min_tool_calls=N)On, requires at least N tool calls before a review fires.
Any object implementing SkillReviewProtocolOn, with your custom policy.

​
DefaultSkillReviewPolicy

OptionTypeDefaultDescription
min_tool_callsint1Minimum tools used during the task before a review fires. Clamped to >= 1.
MAX_PROMPT_CHARS (class constant)int500Hard cap on how much of the original prompt is echoed into the review directive.

​
SkillReviewProtocol (custom policy)

MethodSignaturePurpose
should_review(trajectory: dict) -> boolDecide whether to run the review pass at all.
review_prompt(trajectory: dict) -> strBuild the directive prompt for the guarded turn.
trajectory shape: {"prompt": str, "response": str, "tools_used": list[str]}.

​
Choosing What to Pass

​
Common Patterns

​
Agent that gets sharper over time

from praisonaiagents import Agent

engineer = Agent(
    name="Backend Engineer",
    instructions="Diagnose and fix production issues.",
    self_improve=True,
)

# Day 1: agent solves a flaky-deploy problem from scratch.
engineer.start("Why does deploy fail intermittently on the staging cluster?")
# After answering, writes skill: "diagnose-flaky-deploy"

# Day 2: the same skill is now available and the agent uses it directly.
engineer.start("Staging deploys are flaky again — same as last week?")

​
Conservative policy (only review heavy sessions)

from praisonaiagents import Agent
from praisonaiagents.skills import DefaultSkillReviewPolicy

agent = Agent(
    name="Researcher",
    instructions="Research and report.",
    self_improve=DefaultSkillReviewPolicy(min_tool_calls=3),
)

​
Fully custom policy

from praisonaiagents import Agent
from praisonaiagents.skills import SkillReviewProtocol

class TimedReviewPolicy:
    """Only review every 5th tool-using session."""
    def __init__(self):
        self.counter = 0

    def should_review(self, trajectory):
        if not trajectory.get("tools_used"):
            return False
        self.counter += 1
        return self.counter % 5 == 0

    def review_prompt(self, trajectory):
        return (
            "Reflect on this session. If a reusable technique emerged, "
            "call skill_manage to save it. Otherwise reply NO_SKILL."
        )

assert isinstance(TimedReviewPolicy(), SkillReviewProtocol)

agent = Agent(name="Worker", self_improve=TimedReviewPolicy())

​
Guarantees

  • Off by default — explicit opt-in via a single switch.
  • Never runs with the full toolset — the review turn sees only skill_manage.
  • Re-entrancy guarded — a review turn cannot trigger another review.
  • Chat-history isolated — the review exchange is trimmed back out after the pass; chatbots and REPLs are unaffected.
  • Best-effort — any failure is logged and swallowed; your main task response is never affected.
  • Distinct from reflection — reflection retries for answer quality within a task; self_improve captures durable skills for next time. They are independent flags and compose cleanly.

​
Best Practices

The default policy is conservative (≥1 tool call). Only switch to a custom min_tool_calls when you see review LLM cost you want to cut.
Skills written by the review go to the first existing skill directory (project .praisonai/skills/, then ancestors, then ~/.praisonai/skills/). Make sure the right one exists so captures land where you want them.
reflection improves this answer; self_improve captures a skill for next time. They compose cleanly — turn both on if you want both behaviors.
Any object with should_review and review_prompt satisfies SkillReviewProtocol. Use this to gate on cost, time-of-day, session length, or any other signal.

Skill Manage

The underlying tool the review uses to create and patch skills

Skills

What skills are and how agents use them

Hooks

Where the review trigger is wired — the after-agent funnel

Reflection

The other self-improvement loop — improves answer quality within a task