DeepSeek Harness

deepsec is an open-source, agent-powered vulnerability scanner and security harness from Vercel Labs for on-demand review of large codebases within the user's own infrastructure. It identifies candidate vulnerabilities, uses AI models to investigate them, supports triage and optional revalidation to reduce false positives, and can run work across parallel worker machines. The CLI initializes a repository with `npx deepsec init`, stores state and findings in a `.deepsec/` directory, and resumes interrupted runs while skipping files already analyzed. Subsequent commands separate fast pattern scanning from AI processing and revalidation, and findings can be exported as Markdown directories or JSON. Model access can use Vercel AI Gateway, direct OpenAI or Anthropic credentials, or a custom HTTPS provider; scan cost and duration can be capped with command-line options.

An experimental community preset for DeepSeek Harness, an LLM-agent harness. It starts a session with the real Minimal tool schema containing only a shell tool and a file-reading tool, using that condition to anchor the model's initial trajectory. After the first durable tool call or assistant reply, it promotes the session to the full Standard tool catalog, making heavier tools available on demand. The repository also includes related zero-anchored, self-introduction, prefab, and eternal-Minimal modes, and states that it is not an official DeepSeek preset or affiliated with DeepSeek.

DeepSeek (深度求索) is an AI research project and model provider that develops and open-sources large foundation models (notably named DeepSeek-V4 and DeepSeek-R1) and provides hosted inference via a chat interface (“DeepSeek AI”) and an API. It publishes open-source model releases and offers a hosted API for text-based prompt inference; video reports state DeepSeek models can be exposed as external backends and routed into the Codex model picker via Codex Router.

Strands Agents is an open-source AI agent SDK and harness for Python and TypeScript, developed by AWS. It runs the agent loop in the user’s process without a hosted control plane and uses a model-driven architecture based on system prompts, tools, and a selected model. It provides lifecycle controls, structured output, MCP support, multi-agent patterns, memory and sessions, model-provider portability, streaming, guardrails, tracing, evaluations, hooks, telemetry, context management, and deployment support. It supports providers including Amazon Bedrock, Anthropic, OpenAI, Gemini, and others. The repository includes assembled Python and TypeScript harnesses, lower-level SDKs for building custom agent loops, a command-line tool for prototyping and chatting with harness agents, supporting packages, and the documentation site. The harness packages provide batteries-included agents through create_harness() in Python or createHarness() in TypeScript, while the SDKs expose the loop, tools, model providers, memory, sessions, and hooks for deeper control.