智能AI工作流

Install

Sagent requires Python 3.12 or newer. ripgrep and fd-find are optional — sagent has Python fallbacks when absent — but recommended for faster Grep / Glob. PDF rendering uses the bundled pypdfium2 wheel and needs no system install.

Provider setup

Sagent ships API-key providers for Anthropic, OpenAI, OpenAISubscription, Google, Moonshot, DashScope, MiniMax, and generic OpenAI-compatible endpoints, plus a managed local LlamaCpp provider. Set the key for the provider you plan to use:

export ANTHROPIC_API_KEY=...
export OPENAI_API_KEY=...
export GOOGLE_API_KEY=...
export MOONSHOT_API_KEY=...
export DASHSCOPE_API_KEY=...
export MINIMAX_API_KEY=...

See Providers for the provider matrix, inference rules, and OpenAI-compatible provider setup.

Quickstart: CLI

Use Claude backend,

sagent/bin/cli.py --allow-providers AnthropicCLI --provider AnthropicCLI --auth credentials

export GOOGLE_API_KEY=...
sagent/bin/cli.py --provider Google --model gemini-3.1-pro-preview

For non-interactive use, pipe a prompt on stdin:

printf 'Say hi in one sentence.' | \
  sagent/bin/cli.py --provider Google --model gemini-3.1-pro-preview \
  --output-format json

Use --continue to resume the most recent session for this working directory, --session PATH for an explicit session directory, or --ephemeral when prompts and auto-memory should not be written to disk. Use --max-budget-usd N to cap API spend for the current run.

See CLI and Sessions for the full flag set.

Quickstart: Python

import asyncio

from sagent import tools
from sagent.agent import Agent
from sagent.lib.json import json_freeze
from sagent.providers import Anthropic


async def main() -> None:
    agent = Agent(
        model=Anthropic.from_env().model("claude-sonnet-4-6"),
        system="You are a concise coding assistant.",
        tools=[tools.Read(), tools.Grep(), tools.Glob()],
    )
    result = await agent.run(json_freeze({"prompt": "Summarize README.md"}))
    print(result.content)


asyncio.run(main())

Agent.run() accepts a JSON directive with a prompt key and returns a Message.

See API, Tutorial, and Concepts for more detail.

Examples

The examples/ directory contains small, runnable examples:

• offline_custom_tool.py: run an agent/tool/model loop without API keys.
• decorator_tool.py: wrap a function as a tool.
• custom_tool.py: implement the full Tool protocol.
• multi_agent_reviewer.py: spawn an isolated reviewer child.
• openai_compatible_provider.py: connect an OpenAI-compatible endpoint.

Start with the tutorial, then use the examples as copyable patterns. See Examples and Tools.

Uniquely also an API

- One runtime, every surface. The same Agent class powers the CLI, your application code, and recursive sub-agents. - Typed Python objects. Agent, Tool, Model, Provider, and Message are protocols and dataclasses you import, compose, and unit-test. - Peer-to-peer agent messaging. Any spawned agent can AgentSend to any other named peer — not just its parent. Like user input, peer messages preempt the receiving agent's tool calls, so no agent blocks waiting on a stuck child.

Use it as a library:

from sagent import tools
from sagent.agent import Agent
from sagent.lib.json import json_freeze
from sagent.providers import Google

agent = Agent(
    model=Google.from_env().model("gemini-3.1-pro-preview"),
    system="You are a scientist.",
    tools=[tools.Read(), tools.Glob(), tools.Grep()],
)
result = await agent.run(json_freeze({"prompt": "analyze the CSV in ./data/"}))
print(result.content)

Comparison

<details> <summary>How Sagent compares to aider, LangChain, Claude Code, Codex CLI, Gemini CLI, and other adjacent projects</summary>

Not yet in Sagent: MCP, LSP, native sandboxing, desktop UI, tree-sitter repo map, hosted service, browser automation.

This comparison focuses on the runtime shape rather than every feature of each project.

✅ = yes, 🟡 = partial, ❌ = no. Corrections welcome -- open a PR.