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1<script type="application/ld+json">{"@context":"https://schema.org","@type":"BlogPosting","headline":"CodeAlive 3.0: From Context Engine to Code Research Agent","datePublished":"2026-07-17","dateModified":"2026-07-17","description":"A look at CodeAlive 3.0: our production context engine, code research agent, RepoContextBench, and the new Tool API and MCP surface.","keywords":["Product","Context Engineering","Agents"],"inLanguage":"en","image":"https://codealive.ai/og?title=CodeAlive%203.0%3A%20From%20Context%20Engine%20to%20Code%20Research%20Agent","url":"https://codealive.ai/en/blog/codealive-3-context-engine-agent-repocontextbench","author":{"@type":"Person","name":"Rodion Mostovoi","image":"https://codealive.ai/authors/rodion-mostovoi.jpg","sameAs":"https://www.linkedin.com/in/rodion-mostovoi/"}}</script>
1<div class="mx-auto mb-16 w-full max-w-[800px] space-y-4 px-4 pt-32 sm:px-6 lg:px-8"><a class="group mb-8 inline-flex items-center gap-2 font-medium text-muted-foreground text-sm transition-colors hover:text-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-4" href="/en/blog"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-arrow-left size-4 transition-transform duration-300 group-hover:-translate-x-1" aria-hidden="true"><path d="m12 19-7-7 7-7"></path><path d="M19 12H5"></path></svg>Back to blog</a><div class="flex flex-col"><h1 class="title font-medium text-3xl tracking-tighter">CodeAlive 3.0: From Context Engine to Code Research Agent</h1></div><div class="flex flex-col gap-3 rounded-xl border border-border bg-card px-4 py-3 text-card-foreground shadow-sm sm:flex-row sm:items-center sm:justify-between"><a class="group flex min-w-0 max-w-full items-center space-x-3 rounded-md outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 focus-visible:ring-offset-card" rel="noopener noreferrer" target="_blank" href="https://www.linkedin.com/in/rodion-mostovoi/"><img alt="Rodion Mostovoi" loading="lazy" width="40" height="40" decoding="async" data-nimg="1" class="rounded-full ring-1 ring-border transition-all group-hover:brightness-90" style="color:transparent" srcSet="/_next/image?url=%2Fauthors%2Frodion-mostovoi.jpg&w=48&q=75 1x, /_next/image?url=%2Fauthors%2Frodion-mostovoi.jpg&w=96&q=75 2x" src="/_next/image?url=%2Fauthors%2Frodion-mostovoi.jpg&w=96&q=75"/><div class="flex min-w-0 flex-col"><p class="font-semibold text-card-foreground">Rodion Mostovoi</p><p class="break-all text-muted-foreground text-sm">https://www.linkedin.com/in/rodion-mostovoi/</p></div></a><!--$--><time class="text-muted-foreground text-sm" dateTime="2026-07-17">July 17, 2026 (2 months ago)</time><!--/$--></div><article class="mx-auto max-w-full"><div class="space-y-4 whitespace-normal [&>*:first-child]:mt-0 [&>*:last-child]:mb-0 max-w-none"><p>CodeAlive has been running in production since April 2025. We spent most of that time building, so the blog stayed quiet. Time to fix that.</p> 2<p>Today we are introducing CodeAlive 3.0. The name covers the current generation of the product: a context engine hardened on real repositories, our own code research agent, a benchmark for measuring repository understanding, and a new Tool API and MCP surface for other agents.</p> 3<p>Here is what changed and why we built it.</p> 4<h2 class="mt-6 mb-2 font-semibold text-2xl" data-streamdown="heading-2">The context engine grew up</h2> 5<p>Early versions of CodeAlive focused on a familiar problem: finding useful context in a repository that is too large for an LLM prompt.</p> 6<p>Production added less glamorous requirements. Repositories mix languages, generated code can swamp search results, symbols move, providers fail, and long processing jobs occasionally need to recover halfway through. We spent the year working through those cases.</p> 7<p>CodeAlive now builds several views of a repository:</p> 8<ul class="list-inside list-disc whitespace-normal [li_&]:pl-6" data-streamdown="unordered-list"> 9<li class="py-1 [&>p]:inline" data-streamdown="list-item">symbols and their source content;</li> 10<li class="py-1 [&>p]:inline" data-streamdown="list-item">a Repository Ontology for orientation;</li> 11<li class="py-1 [&>p]:inline" data-streamdown="list-item">calls, references, and inheritance relationships;</li> 12<li class="py-1 [&>p]:inline" data-streamdown="list-item">semantic and exact-text indexes;</li> 13<li class="py-1 [&>p]:inline" data-streamdown="list-item">short descriptions of what individual artifacts do.</li> 14</ul> 15<p>Repository Processing is incremental and split into retryable stages. Retrieval combines semantic search with exact search. An agent can get a map of the repository, find a relevant symbol, inspect its relationships, and then read the source before answering.</p> 16<p>We have learned how this system fails as well as how it works. Production incidents led to better recovery paths, tests, telemetry, and operational tooling. That experience is now part of the product.</p> 17<h2 class="mt-6 mb-2 font-semibold text-2xl" data-streamdown="heading-2">Our own code research agent</h2> 18<p>This spring we added the ContextResearchAgent, a read-only agent for questions about code.</p> 19<p>It works against the processed repository rather than a shell checkout. Its research loop is straightforward:</p> 20<ol class="list-inside list-decimal whitespace-normal [li_&]:pl-6" data-streamdown="ordered-list"> 21<li class="py-1 [&>p]:inline" data-streamdown="list-item">Orient with the Repository Ontology or a bounded file tree.</li> 22<li class="py-1 [&>p]:inline" data-streamdown="list-item">Locate relevant code with semantic and exact search.</li> 23<li class="py-1 [&>p]:inline" data-streamdown="list-item">Follow calls, references, and inheritance.</li> 24<li class="py-1 [&>p]:inline" data-streamdown="list-item">Read the source used in the answer.</li> 25</ol> 26<p>The details took longer. The agent has limits for iterations, tool calls, and wall-clock time. It detects repeated calls, compacts long research traces, and receives practical hints with tool results. If a request is repairable, the tool explains what to change instead of returning an empty result.</p> 27<p>The agent now powers code research inside CodeAlive. It is also a useful test of our own interfaces. Awkward tool output becomes obvious when your agent has to use it all day.</p> 28<h2 class="mt-6 mb-2 font-semibold text-2xl" data-streamdown="heading-2">RepoContextBench</h2> 29<p>A good demo can make almost any agent look capable. We wanted repeatable tests that would tell us whether a change to retrieval, prompts, models, or tools actually helped.</p> 30<p>We built an evaluation system that we are introducing publicly as <span class="font-semibold" data-streamdown="strong">RepoContextBench</span>.</p> 31<p>Our internal evaluation asks practical questions about a real repository pinned to a fixed commit. Each question has weighted factual claims and source evidence. The agent writes a normal answer, and a fixed judge evaluates coverage, grounding, contradictions, and unsupported claims. We also record whether retrieval found the files containing the expected evidence.</p> 32<p>In one clean 20-question internal run, CodeAlive's deep research mode with Qwen 3.6-35B-A3B scored <span class="font-semibold" data-streamdown="strong">77.6 out of 100</span>. This was a single run on an internal dataset under fixed judging conditions, so it is a useful regression signal, not a public leaderboard result.</p> 33<p>RepoContextBench deserves its own post. We will publish the dataset design, judging method, evidence metrics, model and harness differences, and the limitations behind the scores there.</p> 34<h2 class="mt-6 mb-2 font-semibold text-2xl" data-streamdown="heading-2">Tool API v3 and MCP 3.0</h2> 35<p>On July 11, we shipped Tool API v3, MCP 3.0, and version 3 of the CodeAlive skill and Claude Code plugin. MCP production is now on the 3.0.2 maintenance release.</p> 36<p>Tool API v3 has eleven read-only operations. They cover:</p> 37<ul class="list-inside list-disc whitespace-normal [li_&]:pl-6" data-streamdown="unordered-list"> 38<li class="py-1 [&>p]:inline" data-streamdown="list-item">discovering available repositories and workspaces;</li> 39<li class="py-1 [&>p]:inline" data-streamdown="list-item">orientation through ontologies and file trees;</li> 40<li class="py-1 [&>p]:inline" data-streamdown="list-item">semantic and exact-text search;</li> 41<li class="py-1 [&>p]:inline" data-streamdown="list-item">reading artifacts and files;</li> 42<li class="py-1 [&>p]:inline" data-streamdown="list-item">traversing artifact relationships;</li> 43<li class="py-1 [&>p]:inline" data-streamdown="list-item">querying repository metadata;</li> 44<li class="py-1 [&>p]:inline" data-streamdown="list-item">delegated research through stateless <code class="rounded bg-muted px-1.5 py-0.5 font-mono text-sm" data-streamdown="inline-code">chat</code> when needed.</li> 45</ul> 46<p>The API follows the way an agent researches code. Search returns useful leads, reading tools provide the evidence, and relationship tools help trace a flow without launching another broad search.</p> 47<p>Every operation has a generated OpenAPI contract. Responses can include structured JSON, compact agent-oriented text, or both. Repairable requests, such as an invali
47d path or ambiguous repository selector, return clear retry guidance. Authentication, quota, transport, and unexpected server failures remain HTTP errors.</p> 48<p>The public <code class="rounded bg-muted px-1.5 py-0.5 font-mono text-sm" data-streamdown="inline-code">chat</code> operation is stateless. A call must include the findings, artifact identifiers, scope, and constraints it needs. Agents with their own reasoning loop will usually get more control from the lower-level tools.</p> 49<p>You can explore the <a class="wrap-anywhere font-medium text-primary underline" data-incomplete="false" data-streamdown="link" href="https://docs.codealive.ai/api-reference/toolapi/list-visible-data-sources" rel="noopener noreferrer" target="_blank">Tool API reference</a>, connect an agent with the <a class="wrap-anywhere font-medium text-primary underline" data-incomplete="false" data-streamdown="link" href="https://docs.codealive.ai/integrations/mcp" rel="noopener noreferrer" target="_blank">MCP guide</a>, or use our <a class="wrap-anywhere font-medium text-primary underline" data-incomplete="false" data-streamdown="link" href="https://docs.codealive.ai/guides/build-code-research-agent" rel="noopener noreferrer" target="_blank">reference architecture for a code research agent</a>.</p> 50<h2 class="mt-6 mb-2 font-semibold text-2xl" data-streamdown="heading-2">More engineering notes to come</h2> 51<p>We have a backlog of things worth writing about: how semantic and exact search work together, what RepoContextBench can and cannot measure, how smaller models change tool design, and which production failures led to useful architectural changes.</p> 52<p>This time we plan to write the posts while the work is still fresh.</p></div></article><div class="flex flex-wrap gap-2 pt-2"><span class="rounded-full border border-border bg-muted px-3 py-1 font-medium text-muted-foreground text-xs">Product</span><span class="rounded-full border border-border bg-muted px-3 py-1 font-medium text-muted-foreground text-xs">Context Engineering</span><span class="rounded-full border border-border bg-muted px-3 py-1 font-medium text-muted-foreground text-xs">Agents</span></div><section aria-labelledby="blog-newsletter-article_end" class="ph-no-autocapture mt-16 border-border/70 border-y py-10 sm:mt-24 sm:py-12" data-ph-no-autocapture="true"><div class="grid gap-7 md:grid-cols-[minmax(0,1fr)_auto] md:items-center md:gap-12"><div class="max-w-2xl"><p class="font-medium text-amber-600 text-xs uppercase tracking-[0.18em] dark:text-amber-400">New articles</p><h2 class="mt-3 text-balance font-semibold text-2xl tracking-[-0.03em] sm:text-3xl" id="blog-newsletter-article_end">Follow the work, not a marketing funnel</h2><p class="mt-3 max-w-md text-muted-foreground text-sm leading-relaxed sm:text-base">One direct email when we publish. 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52<script>self.__next_f.push([1,"CodeAlive has been running in production since April 2025. We spent most of that time building, so the blog stayed quiet. Time to fix that.\n\nToday we are introducing CodeAlive 3.0. The name covers the current generation of the product: a context engine hardened on real repositories, our own code research agent, a benchmark for measuring repository understanding, and a new Tool API and MCP surface for other agents.\n\nHere is what changed and why we built it.\n\n## The context engine grew up\n\nEarly versions of CodeAlive focused on a familiar problem: finding useful context in a repository that is too large for an LLM prompt.\n\nProduction added less glamorous requirements. Repositories mix languages, generated code can swamp search results, symbols move, providers fail, and long processing jobs occasionally need to recover halfway through. We spent the year working through those cases.\n\nCodeAlive now builds several views of a repository:\n\n- symbols and their source content;\n- a Repository Ontology for orientation;\n- calls, references, and inheritance relationships;\n- semantic and exact-text indexes;\n- short descriptions of what individual artifacts do.\n\nRepository Processing is incremental and split into retryable stages. Retrieval combines semantic search with exact search. An agent can get a map of the repository, find a relevant symbol, inspect its relationships, and then read the source before answering.\n\nWe have learned how this system fails as well as how it works. Production incidents led to better recovery paths, tests, telemetry, and operational tooling. That experience is now part of the product.\n\n## Our own code research agent\n\nThis spring we added the ContextResearchAgent, a read-only agent for questions about code.\n\nIt works against the processed repository rather than a shell checkout. Its research loop is straightforward:\n\n1. Orient with the Repository Ontology or a bounded file tree.\n2. Locate relevant code with semantic and exact search.\n3. Follow calls, references, and inheritance.\n4. Read the source used in the answer.\n\nThe details took longer. The agent has limits for iterations, tool calls, and wall-clock time. It detects repeated calls, compacts long research traces, and receives practical hints with tool results. If a request is repairable, the tool explains what to change instead of returning an empty result.\n\nThe agent now powers code research inside CodeAlive. It is also a useful test of our own interfaces. Awkward tool output becomes obvious when your agent has to use it all day.\n\n## RepoContextBench\n\nA good demo can make almost any agent look capable. We wanted repeatable tests that would tell us whether a change to retrieval, prompts, models, or tools actually helped.\n\nWe built an evaluation system that we are introducing publicly as **RepoContextBench**.\n\nOur internal evaluation asks practical questions about a real repository pinned to a fixed commit. Each question has weighted factual claims and source evidence. The agent writes a normal answer, and a fixed judge evaluates coverage, grounding, contradictions, and unsupported claims. We also record whether retrieval found the files containing the expected evidence.\n\nIn one clean 20-question internal run, CodeAlive's deep research mode with Qwen 3.6-35B-A3B scored **77.6 out of 100**. This was a single run on an internal dataset under fixed judging conditions, so it is a useful regression signal, not a public leaderboard result.\n\nRepoContextBench deserves its own post. We will publish the dataset design, judging method, evidence metrics, model and harness differences, and the limitations behind the scores there.\n\n## Tool API v3 and MCP 3.0\n\nOn July 11, we shipped Tool API v3, MCP 3.0, and version 3 of the CodeAlive skill and Claude Code plugin. MCP production is now on the 3.0.2 maintenance release.\n\nTool API v3 has eleven read-only operations. They cover:\n\n- discovering available repositories and workspaces;\n- orientation through ontologies and file trees;\n- semantic and exact-text search;\n- reading artifacts and files;\n- traversing artifact relationships;\n- querying repository metadata;\n- delegated research through stateless `chat` when needed.\n\nThe API follows the way an agent researches code. Search returns useful leads, reading tools provide the evidence, and relationship tools help trace a flow without launching another broad search.\n\nEvery operation has a generated OpenAPI contract. Responses can include structured JSON, compact agent-oriented text, or both. Repairable requests, such as an invali
52d path or ambiguous repository selector, return clear retry guidance. Authentication, quota, transport, and unexpected server failures remain HTTP errors.\n\nThe public `chat` operation is stateless. A call must include the findings, artifact identifiers, scope, and constraints it needs. Agents with their own reasoning loop will usually get more control from the lower-level tools.\n\nYou can explore the [Tool API reference](https://docs.codealive.ai/api-reference/toolapi/list-visible-data-sources), connect an agent with the [MCP guide](https://docs.codealive.ai/integrations/mcp), or use our [reference architecture for a code research agent](https://docs.codealive.ai/guides/build-code-research-agent).\n\n## More engineering notes to come\n\nWe have a backlog of things worth writing about: how semantic and exact search work together, what RepoContextBench can and cannot measure, how smaller models change tool design, and which production failures led to useful architectural changes.\n\nThis time we plan to write the posts while the work is still fresh."])</script>
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52s\":{\"question\":\"Does CodeAlive handle very large codebases (1M+ LOC)?\",\"answer\":\"Yes. CodeAlive is built for monorepos and multi-repo systems at enterprise scale. The Context Engine indexes millions of lines across dozens of repositories and stays up to date as you push. One of our self-hosted enterprise deployments runs across ~800 repositories (~1 GB of code, ~20 M lines) with no degradation in retrieval quality.\"},\"ideIntegration\":{\"question\":\"Which IDEs and AI agents integrate with CodeAlive?\",\"answer\":\"CodeAlive exposes an MCP server so any MCP-aware client works: Cursor, Claude Code, Codex, Windsurf, Zed, OpenCode, Antigravity, VS Code with Continue, and others. The same MCP also powers our REST API for custom agent integrations.\"},\"codePrivacy\":{\"question\":\"What happens to my code? Do you train AI models on it?\",\"answer\":\"No. We never use your code to train AI models â that commitment is a material term of our Terms of Service. 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No open tracking, click tracking, sales copy, or more than one message a day.\",\"openForm\":\"Subscribe by email\",\"closeForm\":\"Close subscription form\",\"languageLabel\":\"Email language\",\"formTitle\":\"CodeAlive blog subscription form in {language}\",\"privacyNote\":\"Newsletter consent is separate from analytics. See our \u003cprivacy\u003ePrivacy Notice\u003c/privacy\u003e.\",\"requestedTitle\":\"Check your inbox\",\"requestedMessage\":\"If the address is eligible for subscription, we sent a confirmation email.\",\"backToBlog\":\"Back to the blog\"},\"footer\":{\"copyright\":\"© {year} CodeAlive. All rights reserved.\",\"privacyPolicy\":\"Privacy Policy\",\"termsOfService\":\"Terms of Service\",\"cookieSettings\":\"Cookie settings\",\"products\":{\"title\":\"Products\"},\"solutions\":{\"title\":\"Solutions\"},\"company\":{\"title\":\"Company\"},\"resources\":{\"title\":\"Resources\",\"benchmark\":\"Benchmark\"},\"legal\":{\"title\":\"Legal\"},\"social\":{\"title\":\"Social\"},\"contact\":{\"title\":\"Contact\"}},\"legal\":{\"privacyPolicy\":\"Privacy Policy\",\"termsOfService\":\"Terms of Service\",\"comingSoon\":\"Content coming soon.\"},\"drawer\":{\"navigation\":\"Navigation\",\"login\":\"Sign In\",\"getStarted\":\"Get Started\",\"openMenu\":\"Open navigation menu\",\"brandHome\":\"CodeAlive home\",\"github\":\"GitHub\"},\"date\":{\"today\":\"Today\",\"daysAgo\":\"d ago\",\"weeksAgo\":\"w ago\",\"monthsAgo\":\"mo ago\",\"yearsAgo\":\"y ago\"},\"common\":{\"loading\":\"Loading...\",\"error\":\"An error occurred\",\"close\":\"Close\",\"skipToContent\":\"Skip to main content\",\"toggleTheme\":\"Toggle theme\",\"previousSlide\":\"Previous slide\",\"nextSlide\":\"Next slide\",\"carouselRoleDescription\":\"carousel\",\"slideRoleDescription\":\"slide\",\"videoPlayer\":\"Video player\",\"brandLogo\":\"brand-logo\",\"dashboardPreview\":\"Dashboard preview\",\"breadcrumbHome\":\"Home\"},\"hero\":{\"badge\":\"RepoQA benchmark: -45% tokens at a higher score\",\"headline\":\"The context engine for humans and AI agents for \u003chighlight\u003e1M+ LoC codebase
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For an OpenAI-compatible LLM running elsewhere (your own vLLM cluster, an LLM gateway, or a cloud API), CodeAlive itself runs CPU-only. Real footprint depends on repo size, indexing cadence, and concurrent users. Happy to size with you on a deployment call.\"},\"integrations\":{\"eyebrow\":\"INTEGRATIONS\",\"headline\":\"Talks to your existing stack\",\"subheadline\":\"The Git servers, identity providers, and observability stack you already run.\",\"categories\":{\"git\":{\"label\":\"Git Providers (Internal)\",\"items\":[\"GitLab Self-Managed\",\"Bitbucket Data Center\",\"Gitea\",\"GitHub Enterprise Server\",\"Azure DevOps Server\"]},\"identity\":{\"label\":\"Identity Providers\",\"note\":\"Roadmap; available on Enterprise via custom deployment.\",\"items\":[\"SAML 2.0 / OIDC (planned for general availability)\",\"Okta\",\"Azure AD / Entra ID\",\"Ping Identity\",\"OneLogin\",\"Keycloak\"],\"todayLabel\":\"Today:\",\"today\":\"Email/password + API keys with org-scoped RBAC\"},\"agents\":{\"label\":\"AI Agents \u0026 IDEs (via MCP)\",\"items\":[\"Cursor\",\"Claude Code\",\"Continue\",\"Cline\",\"VS Code (via extension)\",\"JetBrains IDEs\"]},\"observability\":{\"label\":\"Observability\",\"items\":[\"Prometheus metrics endpoint\",\"OpenTelemetry traces\",\"Structured logging (JSON)\",\"Integration with Grafana, Datadog, Splunk\"]},\"cicd\":{\"label\":\"CI/CD Integration\",\"items\":[\"API for pipeline integration\",\"Webhook supp
52ort\",\"GitHub Actions (self-hosted runners)\",\"GitLab CI\",\"Jenkins\"]}}},\"customerStories\":{\"headline\":\"Customer stories\",\"body\":\"Self-hosted customers land here once they go live and approve a quote. No invented personas in the meantime. Want to be the first reference? Talk to us.\",\"homepageRef\":\"For now, see the homepage testimonials: Hauke Feddersen (grasbyte GmbH), Zhaksylyk Ualiyev (Esqadra Technologies), Alexander Kolotov (Blockscout), and Sergey Loginov. Those are real, published with permission.\",\"ctaLabel\":\"Talk to Sales\",\"homepageCtaLabel\":\"Read Homepage Testimonials\"},\"gettingStarted\":{\"headline\":\"First answer in a day, production in a week\",\"subheadline\":\"You're not figuring out the Helm chart alone. We deploy with you.\",\"journeyTitle\":\"Deployment timeline\",\"stages\":[{\"key\":\"discovery\",\"title\":\"Discovery Call\",\"timing\":\"Day 0\",\"bullets\":[\"Understand your requirements\",\"Assess infrastructure compatibility\",\"Define success criteria\"]},{\"key\":\"architecture\",\"title\":\"Architecture Review\",\"timing\":\"Day 1-2\",\"bullets\":[\"Finalize deployment topology\",\"Plan integration points\",\"Security review\"]},{\"key\":\"deployment\",\"title\":\"Deployment\",\"timing\":\"Day 3-5\",\"bullets\":[\"Guided installation\",\"Configuration assistance\",\"Initial repository indexing\"]},{\"key\":\"validation\",\"title\":\"Validation\",\"timing\":\"Day 5-7\",\"bullets\":[\"Functional testing\",\"Performance tuning\",\"User acceptance\"]},{\"key\":\"golive\",\"title\":\"Go Live\",\"timing\":\"Day 7+\",\"bullets\":[\"Team onboarding\",\"Ongoing support\",\"Regular check-ins\"]}],\"supportTiersTitle\":\"Support Tiers\",\"supportTiersHeaders\":{\"level\":\"Support Level\",\"response\":\"Response Time\",\"included\":\"Included With\"},\"supportTiers\":[{\"level\":\"Standard\",\"response\":\"24 hours\",\"included\":\"All self-hosted\"},{\"level\":\"Priority\",\"response\":\"4 hours\",\"included\":\"Enterprise tier\"},{\"level\":\"Dedicated\",\"response\":\"1 hour + Slack\",\"included\":\"Enterprise+\"}],\"ctaPrimary\":\"Schedule Deployment Call\",\"ctaSecondary\":\"Download Technical Requirements\"},\"faq\":{\"headline\":\"Common Questions About Self-Hosted Deployment\",\"items\":[{\"question\":\"What's the minimum hardware requirement?\",\"answer\":\"For a small team, the documented minimum is 4 CPU cores, 8GB RAM, and 50GB SSD. For production we recommend 8+ cores, 16GB+ RAM, 200GB+ SSD. GPU is optional and only needed if you also run a local LLM on the same host.\"},{\"question\":\"Can I run CodeAlive without any internet connection?\",\"answer\":\"Not out-of-the-box today. The current self-hosted setup expects access to our private container registry (you can mirror it internally) and, by default, Let's Encrypt for TLS (you can swap in your own certs). A fully air-gapped bundle is on the roadmap; if you need it now, talk to us and we'll discuss what your environment needs.\"},{\"question\":\"Which LLM should I use?\",\"answer\":\"Anything exposing an OpenAI-compatible API. Today our default in self-hosted is Gemini, with OpenAI / DeepInfra / Anthropic supported via API keys. For local inference, current open-weights flagships like gpt-oss-120b, GLM 5.1, Kimi 2.6, DeepSeek V4, or Qwen 3 Coder all work. Pick whichever fits the GPU budget you have.\"},{\"question\":\"How long does deployment take?\",\"answer\":\"Typical deployments complete within 5-7 business days, including configuration and initial indexing. Docker Compose deployments can be running within hours.\"},{\"question\":\"Can I migrate from cloud to self-hosted later?\",\"answer\":\"Yes. We provide migration tools to transfer your configuration, workspaces, and indexed data from cloud to self-hosted deployment.\"},{\"question\":\"What Git providers are supported?\",\"answer\":\"GitLab Self-Managed, Bitbucket Data Center, GitHub Enterprise Server, Gitea, Azure DevOps Server, and any Git server accessible via SSH or HTTPS.\"},{\"question\":\"How are updates delivered?\",\"answer\":\"Updates are published as container images to our private registry. Use ./manage.sh update (Docker Compose) or helm upgrade (Kubernetes) to roll forward; pre/post-deployment migrations run automatically.\"},{\"question\":\"Is training included?\",\"answer\":\"Yes. All self
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52ample\",\"setupCaption\":\"Wire both MCP servers into one agent and let it investigate.\",\"promptLabel\":\"Agent prompt\",\"scenarios\":[{\"title\":\"Step 1 â Gather Observability Context (Grafana MCP)\",\"prompt\":\"Query payment service metrics for the last 2 hours. Show error rates, latency P99, and any anomalies.\",\"response\":\"Error rate spike at 08:45 UTC (2.3% to 8.7%). Latency P99 normal. Affected endpoint /api/v2/payments/process. 87% of errors are CONVERSION_FAILED. 94% from EU region. Correlated log: 'Currency conversion timeout after 5000ms'.\"},{\"title\":\"Step 2 â Understand the Code Path (CodeAlive MCP)\",\"prompt\":\"Explain the code path for /api/v2/payments/process when handling currency conversion. Include timeout handling.\",\"response\":\"Entry PaymentController.processPayment(). Flow: PaymentService to CurrencyConverter to ExternalRateAPI. Timeout 5000ms hardcoded in CurrencyConverter.java:142. No fallback for timeouts. 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Answers come back from the current code, with the files they were built from attached. Engineers stop being the help desk.\",\"ctaPrimary\":\"Get Started Free\",\"ctaSecondary\":\"See the workflow\"},\"problem\":{\"eyebrow\":\"The Problem\",\"headline\":\"Every question about the system waits in an engineer's queue\",\"bullets\":[\"\\\"Can the system already do this?\\\" turns into a thread and a two-day wait.\",\"Support answers from a wiki page that was accurate six releases ago.\",\"Analysts reconstruct flows from tickets instead of the code that runs them.\",\"PMs scope features without knowing what the system already supports.\",\"The people with the answers are the engineers you least want to interrupt.\"]}
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CodeAlive grounds every review in your full architecture, patterns, and business logic across repos.\"},\"hero\":{\"eyebrow\":\"Code Review\",\"headline\":\"Code review that reads the whole system, not just the diff\",\"subheadline\":\"Stop reviewing diffs in isolation. CodeAlive gives reviewers full context of your architecture, patterns, and business logic.\",\"ctaPrimary\":\"Get Started Free\",\"ctaSecondary\":\"Watch Demo\"},\"problem\":{\"eyebrow\":\"The Problem\",\"headline\":\"Diff-only review misses what matters\",\"bullets\":[\"Reviewers see diffs, not system impact.\",\"\\\"Looks good to me\\\" hides missed logic errors.\",\"Duplicated code slips through because reviewers don't know it exists elsewhere.\",\"Junior reviewers can't catch architectural violations.\",\"AI-generated code gets merged without understanding consequences.\"]},\"solution\":{\"eyebrow\":\"The Solution\",\"headline\":\"Reviews grounded in your entire codebase\",\"body\":\"CodeAlive reviews code with full knowledge of your codebase (architecture, patterns, dependencies, and business rules), so reviewers see the change against the system, not just the diff.\"},\"capabilities\":{\"eyebrow\":\"Key Capabilities\",\"headline\":\"What reviewers get with every PR\",\"items\":{\"architecture\":{\"title\":\"Architecture-Aware Reviews\",\"body\":\"Automatically flags violations of your established patterns and conventions.\"},\"duplication\":{\"title\":\"Duplication Detection\",\"body\":\"\\\"This logic already exists in PaymentService.processRefund()\\\", surfaced before merge.\"},\"impact\":{\"title\":\"Impact Analysis\",\"body\":\"\\\"This change affects 3 downstream services.\\\" See the blast radius before approving.\"},\"security\":{\"title\":\"Security Scanning\",\"body\":\"Identifies changes to critical paths (auth, payments, data access) that need extra eyes.\"},\"context\":{\"title\":\"Context for Reviewers\",\"body\":\"One-click explanation of any code area, so reviewers don't start from zero.\"}}},\"workflow\":{\"eyebrow\":\"How It Works\",\"headline\":\"From PR open to merge\",\"steps\":{\"open\":{\"title\":\"PR opened\",\"body\":\"Author pushes a branch and opens a PR in GitHub or GitLab.\"},\"analyze\":{\"title\":\"CodeAlive analyzes\",\"body\":\"Changes are checked against the knowledge graph of your entire codebase.\"},\"ask\":{\"title\":\"Reviewers ask\",\"body\":\"\\\"What does the existing implementation do?\\\" Answered inline with code links.\"},\"merge\":{\"title\":\"Merge with confidence\",\"body\":\"Reviewers approve with shared context. 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Every review starts with the system context already pulled.\"}},\"useCaseOnboarding\":{\"meta\":{\"title\":\"Developer Onboarding with Codebase AI\",\"description\":\"Ramp new engineers in days, not months. CodeAlive lets them ask the codebase directly and get answers backed by real files and call chains.\"},\"hero\":{\"eyebrow\":\"Onboarding\",\"headline\":\"New engineers productive in days, not months\",\"subheadline\":\"New hires query the codebase directly instead of waiting on whoever's on Slack.\",\"ctaPrimary\":\"Get Started Free\",\"ctaSecondary\":\"Watch Demo\"},\"problem\":{\"eyebrow\":\"The Problem\",\"headline\":\"Onboarding still costs your senior team weeks\",\"bullets\":[\"New hires spend weeks asking \\\"Where is X?\\\" and \\\"How does Y work?\\\"\",\"Senior engineers lose hours explaining the same things repeatedly.\",\"Documentation is outdated or doesn't exist.\",\"Tribal knowledge leaves when people leave.\",\"Onboarding quality varies by who's available to help.\"]},\"solution\":{\"eyebrow\":\"The Solution\",\"headline\":\"New hires get answers from the codebase, not the senior on Slack\",\"body\":\"New hires query the codebase directly and get answers with file links and history. 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