Nexevo AgentAI that gets things done — not just answers questions
Agent autonomously decomposes goals → plans steps → calls tools (web search / Python sandbox / browser automation / file ops) → self-verifies → delivers results. Change one model name, any OpenAI SDK upgrades instantly.
30
Max steps
10+
Built-in tools
Python
Sandbox env
Auto
Budget control
What Agent Can Do for You
Not just an answer — a complete deliverable.
Real-time Web
Live search + browser scraping + multi-source verification. Not stale training data — current truth.
Real Python Sandbox
Full Python environment (any import / pip install / file IO). Run scripts, process CSVs, plot charts. Sandboxed and resource-limited.
Multi-step Planning
Decomposes high-level goals into a DAG, parallelizes independent steps, retries on failure with self-reflection. No micro-management.
Downloadable Output
Persistent workspace, packaged as ZIP / Markdown report / PDF / charts at completion. Real deliverable, not a chat reply.
How Agent Works
Fully transparent — see every step.
Understand Goal
Convert your request into structured task spec, identify required capabilities (web? compute? browse?).
Build DAG
Planner breaks goal into atomic subtasks + dependencies. Independent ones run in parallel.
Execute + Tools
Run each step, call Python / browser / search / files as needed. Intermediate results feed forward.
Verify + Deliver
Verifier auto-judges quality, retries with self-reflection if not met. Final output is verified deliverable.
Use Cases
Anything single-shot chat can't fully deliver.
Competitive Research
Crawl competitor sites + pricing + announcements → synthesize comparison brief.
Data Analysis
Upload CSV/Excel, Agent runs pandas + matplotlib + writes interpretation.
Code Generation + Test
Spec → code → run in sandbox → fix on failure → deliver runnable scripts.
Image Series
Given a theme, Agent calls image API multiple times in different styles, picks the best.
Bulk Document Processing
Upload folder, Agent summarizes / translates / labels / extracts structured fields.
Automated ETL
API integration, async run, webhook callback. Production pipeline ready.
Upgrade chat to Agent in 1 line
Native OpenAI SDK compatible. `model: "nexevo/agent"` — no new endpoint, no new SDK, no migration.
from openai import OpenAI
client = OpenAI(
api_key="nexevo-sk-xxx",
base_url="https://api.nexevo.ai/v1",
)
# Chat 模式 — 普通快速问答(默认)
resp = client.chat.completions.create(
model="nexevo/balanced",
messages=[{"role": "user", "content": "今年 OpenRouter 涨价了吗?"}],
)
# Agent 模式 — 改 1 行 model 名,自动多步 + 联网 + 沙箱 + 验证
resp = client.chat.completions.create(
model="nexevo/agent", # ← 改这里就升级 Agent
messages=[{"role": "user", "content":
"调研 OpenRouter 2026 定价模式,跟主要竞品对比"}],
stream=True,
)
for chunk in resp:
print(chunk.choices[0].delta.content)Need task management (async / budget cap / file download / share link)? Use POST /v1/agents/run, see API Reference.
Chat vs Agent
When to use which — at a glance.
| Aspect | nexevo/balanced (chat) | nexevo/agent |
|---|---|---|
| LLM calls | 1 | 1-30 (autonomous loop) |
| Tools | None | Python sandbox / browser / files / web / custom |
| Latency | 1-3s | 5s - few minutes (depends on task) |
| Output | 1 chat reply | Full deliverable (report / data / file download) |
| Pricing | Flat $5/$20 per 1M | Pay-per-use: LLM tokens +15% / sandbox time +30% / tool calls +15% (rates may adjust) |
| Best for | Chat / Q&A / code complete / simple gen | Research / data analysis / multi-step / automated pipelines |
Safe & Controllable — No Surprise Burning
Agent is powerful, but you stay in control of every step.
Upgrade Your Chat to Agent
Change one model name, any OpenAI-compatible client gets Agent capability immediately.