AI Frameworks · strands-agents

harness-sdk

Strands Agents is an open-source Python and TypeScript SDK for building and running AI agents with any LLM provider (OpenAI, Anthropic, Bedrock, Gemini, etc.). It handles the agent loop, tool integration, context management, and observability so you can focus on agent logic without vendor lock-in.

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Source: GitHub — github.com/strands-agents/harness-sdk

6.5k

GitHub stars

932

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	strands-agents/harness-sdk
Owner	strands-agents
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	6.5k
Forks	932
Open issues	493
Latest release	python/v1.46.0 (2026-07-07)
Last updated	2026-07-07
Source	https://github.com/strands-agents/harness-sdk

What harness-sdk is

A model-agnostic agent framework with built-in support for multiple LLM providers, structured tool execution, MCP protocol, streaming, multi-agent patterns, and production observability hooks. Provides default Bedrock integration but allows swapping providers with unchanged application code.

Quickstart

Get the harness-sdk source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/strands-agents/harness-sdk.git2cd harness-sdk3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-provider LLM applications

Build agents that work with OpenAI, Anthropic, Bedrock, Gemini, or custom models. Switch providers at runtime or deployment without rewriting agent logic.

Production autonomous workflows

Deploy agents with built-in guardrails, execution limits, decision tracing, and steering handlers for self-correction. Suitable for customer-facing or backend automation tasks.

Tool-augmented conversational systems

Rapidly prototype and scale agents that call external APIs, calculators, databases, or MCP servers. Structured output and streaming support for real-time interactions.

Implementation considerations

Requires AWS credentials and Bedrock model access for default setup; alternative providers (Anthropic, OpenAI, Gemini) must be explicitly configured.
Agent loop and tool definitions are code-centric; no visual builder included. Team must be comfortable writing Python or TypeScript.
Guardrails, steering handlers, and execution limits are built-in but must be explicitly configured per agent. Review production deployment guide before shipping.
Streaming and multi-agent patterns are supported but add complexity to error handling and state management; prototype in single-agent mode first.
MCP server integration available but requires additional server setup and maintenance.

When to avoid it — and what to weigh

Minimal Python version support required — Requires Python 3.10+ and Node.js 20+. Unsuitable for legacy stacks or constrained environments on older runtimes.
Fully managed, no-code agent platform needed — This is a developer SDK requiring code to build agents. Not a visual/no-code platform. Requires engineering resources.
Strict vendor lock-in preferred — The SDK is explicitly multi-provider by design. If you need tight integration with a single vendor's proprietary features, this abstraction layer may feel restrictive.
Mature long-term stability critical — Repository created May 2025; still early-stage. 493 open issues and frequent releases suggest active development. Production use requires risk tolerance for API changes and frequent updates.

License & commercial use

Licensed under Apache License 2.0, a permissive OSI-approved open-source license allowing commercial use, modification, and distribution with attribution and liability disclaimers.

Apache 2.0 is a widely recognized permissive license supporting commercial deployment. However, you are using third-party LLM providers (OpenAI, Anthropic, Bedrock, Gemini) whose terms govern your use of their APIs and model outputs. Review those provider agreements separately. The SDK itself imposes no commercial restrictions.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

SDK provides tracing and interception hooks to log and validate agent decisions. LLM provider credentials must be managed securely (environment variables, secrets manager). Guardrails and steering handlers help catch mistakes before execution. No explicit security audit or penetration test results provided. Review CONTRIBUTING.md for security issue reporting process. Third-party model providers (Bedrock, OpenAI, Anthropic, Gemini) each have their own security and data handling policies that apply to your agent's LLM calls.

Alternatives to consider

LangChain / LangGraph

Mature, widely-adopted agent frameworks with extensive integrations and community tooling. LangGraph offers more explicit control flow. Langchain has broader ecosystem but heavier API surface.

Anthropic's Claude SDK / Model Context Protocol (MCP)

If you standardize on Anthropic Claude, their native SDK and MCP offer tight integration without multi-provider abstraction overhead. Simpler if vendor lock-in is acceptable.

AWS Bedrock Agents (managed service)

Fully managed alternative if you commit to AWS and Bedrock. Eliminates SDK and deployment complexity but reduces code-level control and observability hooks.

Software development agency

Build on harness-sdk with DEV.co software developers

Evaluate Strands Agents for your LLM application. Get started with the quickstart guide, explore the API reference, or review production deployment best practices.

Talk to DEV.co

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harness-sdk FAQ

Can I use Strands with my existing LLM provider (OpenAI, Anthropic, etc.)?

Yes. While Bedrock is the default, first-class support exists for Anthropic, OpenAI, Gemini, and many others. Configure your provider at agent init time; your code remains the same.

What Python versions are supported?

Python 3.10+. TypeScript requires Node.js 20+. Check PyPI and npm package pages for exact version matrices.

Is this suitable for production use?

The SDK has production features (guardrails, steering, observability hooks, execution limits). However, it is young (created May 2025) with 493 open issues. Suitable for production with engineering ownership and tolerance for updates. Review the production deployment guide and test thoroughly.

Do I need to run my own infrastructure?

You need to host your agent application code (Python/TypeScript). LLM inference is delegated to your provider (Bedrock, OpenAI, Anthropic, etc.). Tool integrations may require additional infrastructure depending on your tools.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If harness-sdk is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Build production AI agents with Strands

Evaluate Strands Agents for your LLM application. Get started with the quickstart guide, explore the API reference, or review production deployment best practices.

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