Agenter

A backend-agnostic Python SDK for orchestrating autonomous coding agents. Decode your intent into working code and maintain freedom from vendor lock-in.

TL;DR

  • The What: Single unified integration for AI coding agents,

  • The Why: Avoid duplicating work, consistent safeguards, customer flexibility, open source potential,

  • Cost: One integration step instead of three separate ones.

  • Timeline: Phase 1 delivers Claude backend with LangGraph adapter.

  • If we don’t have this: Each project builds its own integration → 3x maintenance, inconsistent quality, and lack of uniformity.

What Are We Building?

A unified SDK that wraps multiple AI coding agents (such as Claude Code, OpenAI Codex, OpenHands, or OpenCode) with:

  1. One universal interface — Switch backends via configuration, no code changes.

  2. Validation loop — Code is checked (syntax validation) and retried automatically.

  3. Budget controls — Hard stops on tokens, cost, and time limits.

  4. Framework adapters — Drop-in for LangGraph and pydantic-ai.

Why Does This Matter?

For LangGraph users: Autonomous coding step completes without human interrupts.

For pydantic-ai users: Same coding capability with backend flexibility.

For Engineering: Build once, avoid vendor lock-in, and evaluate alternatives as the AI landscape evolves.

For Open Source: Community visibility; external contributions; recruitment support; thought leadership and evangelisation; clean API; Apache-2.0 license; useful beyond Moonsong Labs.

Team: learning by doing, and skills development.

Risks If We Don’t Do This

  1. Duplicated effort — Each project builds its own coding agent integration.

  2. Runaway costs — No consistent budget controls across projects.

  3. Vendor lock-in — Can’t easily switch backends when customer requirements change.

  4. Quality issues — Lack of a built-in validation loop means that bad code can ship to customers.

  5. Maintenance burden — Three separate integrations to maintain and debug.

Implementation Phases

Phase 1 (v0): Claude backend with validation loop; pydantic-ai and LangGraph adapters.

Phase 2: Add Codex and OpenHands backends as customer requirements emerge.

Phase 3: Community extensions (additional validators, adapters, backends).

Project Metadata

Field

Value

Package

agenter

Python

3.12+

Open Source

Yes (Apache 2.0)

For Engineers

Skip this section if you’re not technical — see ARCHITECTURE.md for system design.

The Core Insight

OpenHands SDK provides LLM abstraction: swap Claude for GPT-4 or DeepSeek but always use OpenHands’ agent logic, tools, and runtime.

This SDK provides agent system abstraction: use Anthropic’s Claude Code agent, OpenAI’s Codex agent, or OpenHands’ agent, each with their own optimized logic, tools, and runtime.

The agent loop, prompts, and tool implementations are where the critical engineering effort lives. Anthropic has optimized Claude Code for enterprise coding while OpenHands has fine-tuned their model for SWE-bench. These are different systems, not just different LLMs.

When to Use This SDK

  • You need to switch between Claude Code, Codex, and OpenHands based on customer requirements.

  • You want explicit validation loops with hard budget stops.

  • You’re integrating into LangGraph or PydantiAI workflows.

When to Use the Backend SDKs Directly

  • Claude Agent SDK: If you only need Claude Code and want Anthropic-specific features (hooks, subagents, MCP servers).

  • OpenHands SDK: If OpenHands’ agent logic is sufficient and you only need LLM-level flexibility.

Competitive Positioning

Project

Abstraction Level

Gap

OpenHands SDK

LLM providers (100+ via LiteLLM)

Single agent system

Claude Agent SDK

Claude Code only

Single agent system

Codex SDK

Codex only

Single agent system

OpenCode

LLM providers (Claude, OpenAI, Google, local)

Single agent system, TUI-focused

This SDK

Agent systems + validation

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Document Index

Document

Audience

Purpose

OVERVIEW.md

Executives

Business case, value proposition (this document)

ARCHITECTURE.md

Tech leads

System design, components, data flow