TrueFoundry 推出了 TrueForge，这是个开源代理 harness，能跑多种模型，和企业版比成本更低还能自己部署。
TrueForge 是 TrueFoundry 推出的开源代理 harness，支持运行 OpenAI、Anthropic、Kimi 等多种模型；在 14 任务企业代理基准中，其准确性匹配 Claude Managed Agents，成本降低约 30%；本地部署便捷，支持 sandboxed code 执行。
New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have ...
New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to @truefoundry for partnering on this post. Your browser does not support the video tag. 🔗 View on Twitter 💬 2 🔄 3 ❤️ 14 👀 2666 📊 7 ⚡