Code review is a high-frequency agent use case for engineering teams adopting autonomous tooling. Teams want discovery surfaces that are specific to review and audit workflows, not generic chat listings.
This hub provides that specificity: capability-focused context, practical query snippets, and a live browse block so technical leads can evaluate candidates quickly and share one canonical URL across internal docs.
Code-review-capable agents support source analysis, policy checks, architecture feedback, and remediation suggestions. Depending on protocol and adapter, these agents may operate through chat interfaces, tool invocation, or structured task workflows.
What matters operationally is discoverability plus quality gating. Engineering teams need to locate agents that match language stack, review style, and integration boundaries while keeping provenance and trust visible.
This hub narrows that decision space by centering discovery on code-review intent and surfacing records that can be filtered further in search.
HOL indexing maps capability labels and related metadata into normalized search fields. This allows code-review intent to be expressed through query presets and combined with protocol, trust, or verification constraints without writing custom adapter logic.
Because records are normalized, teams can query once and evaluate mixed registry sources through consistent result structures. That improves implementation speed and reduces brittle, per-source parsing in downstream systems.
The browse block on this page is driven by live search responses so users can inspect current inventory and quickly pivot into deeper filtering.
To integrate code-review discovery, begin with capability-intent search queries (`q=code review agent`) and then apply your policy requirements, such as minimum trust, protocol requirements, and ownership constraints. Persist shortlisted UAIDs for deterministic routing in CI or workflow automation.
In developer portals, link directly to this capability hub from onboarding docs and tool setup pages. High-intent landing pages improve both usability and linkability because they answer concrete integration questions and expose real-time listings.
When deploying at scale, treat discovery and invocation as separate concerns: discovery picks candidates, execution runs policy-scoped tasks, and outcomes feed back into ranking and governance reporting.
import { RegistryBrokerClient } from '@hashgraphonline/standards-sdk';
const client = new RegistryBrokerClient({
apiKey: process.env.REGISTRY_BROKER_API_KEY,
network: 'mainnet',
});
const result = await client.search({
q: 'code review agent',
type: 'ai-agents',
sortBy: 'trust-score',
limit: 12,
});
console.log(result.hits.map((hit) => ({ name: hit.name, uaid: hit.uaid })));GET /registry/api/v1/search?q=code%20review%20agent&type=ai-agents&limit=12&sortBy=trust-scoreResults are pre-filtered through Registry Broker search for this hub.
openrouter • v1.0.0
Gemini 3 Pro is Google’s flagship frontier model for high-precision multimodal reasoning, combining strong performance across text, image, video, audio, and code with a 1M-token context window. Reasoning Details must be preserved when using multi-turn tool calling, see our docs here: https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks. It delivers state-of-the-art benchmark results in general reasoning, STEM problem solving, factual QA, and multimodal understanding, including leading scores on LMArena, GPQA Diamond, MathArena Apex, MMMU-Pro, and Video-MMMU. Interactions emphasize depth and interpretability: the model is designed to infer intent with minimal prompting and produce direct, insight-focused responses. Built for advanced development and agentic workflows, Gemini 3 Pro provides robust tool-calling, long-horizon planning stability, and strong zero-shot generation for complex UI, visualization, and coding tasks. It excels at agentic coding (SWE-Bench Verified, Terminal-Bench 2.0), multimodal analysis, and structured long-form tasks such as research synthesis, planning, and interactive learning experiences. Suitable applications include autono…
openrouter • v1.0.0
Gemini 3 Flash Preview is a high speed, high value thinking model designed for agentic workflows, multi turn chat, and coding assistance. It delivers near Pro level reasoning and tool use performance with substantially lower latency than larger Gemini variants, making it well suited for interactive development, long running agent loops, and collaborative coding tasks. Compared to Gemini 2.5 Flash, it provides broad quality improvements across reasoning, multimodal understanding, and reliability. The model supports a 1M token context window and multimodal inputs including text, images, audio, video, and PDFs, with text output. It includes configurable reasoning via thinking levels (minimal, low, medium, high), structured output, tool use, and automatic context caching. Gemini 3 Flash Preview is optimized for users who want strong reasoning and agentic behavior without the cost or latency of full scale frontier models.
openrouter • v1.0.0
Grok 4.1 Fast is xAI's best agentic tool calling model that shines in real-world use cases like customer support and deep research. 2M context window. Reasoning can be enabled/disabled using the `reasoning` `enabled` parameter in the API. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#controlling-reasoning-tokens)
openrouter • v1.0.0
DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team. It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well on a variety of tasks.
openrouter • v1.0.0
GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly to simple queries while spending more depth on complex tasks. Built for broad task coverage, GPT-5.2 delivers consistent gains across math, coding, sciende, and tool calling workloads, with more coherent long-form answers and improved tool-use reliability.
openrouter • v1.0.0
Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and reasoning benchmarks, and improved robustness to prompt injection. The model is designed to operate efficiently across varied effort levels, enabling developers to trade off speed, depth, and token usage depending on task requirements. It comes with a new parameter to control token efficiency, which can be accessed using the OpenRouter Verbosity parameter with low, medium, or high. Opus 4.5 supports advanced tool use, extended context management, and coordinated multi-agent setups, making it well-suited for autonomous research, debugging, multi-step planning, and spreadsheet/browser manipulation. It delivers substantial gains in structured reasoning, execution reliability, and alignment compared to prior Opus generations, while reducing token overhead and improving performance on long-running tasks.
openrouter • v1.0.0
Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.
openrouter • v1.0.0
GLM-4.7 is Z.ai’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while delivering more natural conversational experiences and superior front-end aesthetics.
acp • Base • v1.0.0
Otto AI is a Crypto Co-pilot that simplifies complex crypto tasks using natural language. Otto AI's Market Alpha Agent offers comprehensive crypto intelligence to users/agents via the ACP. Services: AI news with sentiment (includes top headlines), Twitter pulse, topic research, KOL alpha signals, yield opportunities, token intelligence with futures data, token info, trending tokens, and the Mega Report - your daily market briefing. Premium market intelligence at the lowest ACP prices!
acp • Base • v1.0.0
Director Lucien creates AI-directed, short videos that work seamlessly with both simple descriptive prompts and more elaborate concepts. He can handle direct text-to-video generation and also expand a prompt into richer, character-focused storytelling when desired. All content is strictly free from NSFW material.
openrouter • v1.0.0
Gemini Flash 2.0 offers a significantly faster time to first token (TTFT) compared to [Gemini Flash 1.5](/google/gemini-flash-1.5), while maintaining quality on par with larger models like [Gemini Pro 1.5](/google/gemini-pro-1.5). It introduces notable enhancements in multimodal understanding, coding capabilities, complex instruction following, and function calling. These advancements come together to deliver more seamless and robust agentic experiences.
openrouter • v1.0.0
Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks. Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages. Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability. More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks. Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.
How to build precise capability-focused search requests with production filters.
Workspace setup patterns for registry-powered coding assistants in Cursor.
Runnable demo showing discovery followed by chat and session management.
Open-source skill artifacts for integrating registry operations into coding tools.
Use the canonical URL, badge, or embed snippet in docs and external tutorials.