ARISE — Self-Evolving Agent Framework

Your agent works great on the tasks you planned for. ARISE handles the ones you didn’t.

ARISE is a framework-agnostic middleware that gives LLM agents the ability to create their own tools at runtime. When your agent fails at a task, ARISE detects the capability gap, synthesizes a Python tool, validates it in a sandbox, and promotes it to the active library — no human intervention required.

pip install arise-ai

from arise import ARISE
from arise.rewards import task_success

arise = ARISE(
    agent_fn=my_agent,       # any (task, tools) -> str function
    reward_fn=task_success,
    model="gpt-4o-mini",     # cheap model for tool synthesis
)

result = arise.run("Fetch all users from the paginated API")
# Agent fails → ARISE synthesizes fetch_all_paginated → agent succeeds

What it looks like in your terminal:

Episode 1  | FAIL  | reward=0.00 | skills=2   Task: "Fetch paginated users with auth"
Episode 2  | FAIL  | reward=0.00 | skills=2
Episode 3  | FAIL  | reward=0.00 | skills=2

[Evolution triggered — 3 failures on API tasks]
  → Synthesizing 'parse_json_response'... 3/3 tests passed ✓
  → Synthesizing 'fetch_all_paginated'... sandbox fail → refine → 1/1 passed ✓

Episode 4  | OK    | reward=1.00 | skills=4   Agent now has the tools it needs

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Key Features

• Self-evolving tool library — fail → detect gap → synthesize → test → promote
• Framework-agnostic — any (task, tools) -> str function, Strands, LangGraph, CrewAI
• Sandboxed validation — subprocess or Docker, adversarial testing, import restrictions
• Distributed mode — S3 + SQS for stateless deployments (Lambda, ECS, AgentCore)
• Skill registry — share evolved tools across projects
• Version control + rollback — SQLite checkpoints, arise rollback <version>
• A/B testing — refined skills tested against originals before promotion
• Reward learning — learn reward functions from human feedback

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Get Started

• Installation
• Quick Start
• How It Works

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Benchmark Results

Model	Condition	AcmeCorp (SRE)	DataCorp (Data Eng)
Claude Sonnet	ARISE	78%	—
Claude Sonnet	No tools	63%	—
GPT-4o-mini	ARISE	57%	92%
GPT-4o-mini	No tools	48%	50%

ARISE improves task success by +9–42 percentage points across models and domains. Self-evolved tools consistently outperform hand-written baselines because they’re shaped by the agent’s actual failure patterns.

Full benchmark details →