arise-ai 0.1.6

Self-evolving agent framework — agents that create their own tools

ARISE — Adaptive Runtime Improvement through Self-Evolution

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.

Documentation | Quick Start | PyPI

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 tool → agent succeeds

What It Looks Like

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

Key Features

• Self-evolving tool library — fail → detect gap → synthesize → sandbox 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
• Dashboard — terminal TUI and web UI for monitoring

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. See the full benchmark results.

Documentation

Full documentation at arise-ai.dev:

• Installation — install and configure
• Quick Start — complete evolution loop walkthrough
• How It Works — the 5-step evolution pipeline
• Reward Functions — built-in and custom reward functions
• Safety & Validation — sandbox, adversarial testing, production recommendations
• Distributed Mode — S3 + SQS for stateless deployments
• Framework Adapters — Strands, LangGraph, CrewAI, raw OpenAI/Anthropic
• CLI Reference — all CLI commands
• API Reference — ARISE class, config, types

Examples

Example	Description
quickstart_evolution.py	Full evolution loop: agent fails → ARISE evolves tool → agent succeeds
quickstart.py	Math agent evolves statistics tools
api_agent.py	HTTP agent evolves auth + pagination (mock server)
devops_agent.py	DevOps agent evolves log analysis tools
strands_agent.py	Strands integration with Bedrock
demo/agentcore/	AgentCore deployment with A2A protocol

Install

pip install arise-ai              # core (just pydantic)
pip install arise-ai[aws]         # + boto3 for distributed mode
pip install arise-ai[litellm]     # + litellm for multi-provider LLM
pip install arise-ai[docker]      # + docker sandbox backend
pip install arise-ai[dashboard]   # + rich, fastapi for dashboard
pip install arise-ai[otel]        # + opentelemetry for tracing
pip install arise-ai[all]         # everything

Related Work

ARISE builds on ideas from LATM, VOYAGER, CREATOR, ADAS, and CRAFT. ARISE adds the production layer: framework-agnostic integration, sandboxed validation, adversarial testing, version control, distributed deployment, and A/B testing.

License

MIT