Unblocked vs Onyx for AI Coding Agent Context

What Onyx and Unblocked actually do#

Onyx is open-source enterprise search and Q&A that indexes documents across your company's SaaS tools, so an employee can ask a question in one place and get an answer pulled from Confluence, Slack, Google Drive, and dozens of other connectors. It's built for general knowledge retrieval across an organization, and it does that job well. It reads and ranks text. It doesn't understand code as code.

Unblocked is a context engine that synthesizes source code, pull request history, conversations, and docs into one decision-grade answer and delivers it to any AI coding agent through a single MCP server. It's built for engineers and the agents they run in Cursor or Claude Code, not for company-wide document lookup.

The decision you're actually making comes down to scope. If you need a tool that searches across everything a company writes, Onyx fits. If you need context that a coding agent can act on, reconciled from code and the discussion around it, Unblocked is the tool built for that.

Unblocked vs Onyx at a glance#

The differences show up fastest side by side. Onyx indexes documents across your SaaS tools and answers questions from them. Unblocked pulls code, PR history, conversations, and docs into one answer and delivers it straight to your coding agent.

Onyx is best for company-wide document search, where marketing, support, and operations need answers across Confluence, Slack, Google Drive, and similar tools.

Unblocked is best for engineering teams running AI coding agents like Cursor or Claude Code that need code-aware context delivered without leaving the agent. Skip it if you have fewer than about 50 engineers or a greenfield codebase with little history to draw on.

How this comparison is scored#

This comparison scores both tools on eight axes: unified context, conflict resolution, targeted retrieval, data governance, token optimization, personalized relevance, delivery surface, and pricing model. These are the questions engineering teams actually ask when they vet a context engine or an AI-powered search tool. None of them measure interface polish or onboarding flow.

The weighting favors AI coding agent workflows over general knowledge search. A tool that retrieves documents well but can't feed reconciled context to Cursor or Claude Code loses points on the axes that decide whether your agents write correct code. If your priority is company-wide document search rather than engineering context, weigh the first three axes less heavily.

Unified context across code, PRs, conversations, and docs#

Onyx indexes documents. It connects to your SaaS tools, pulls the text from each source, and returns the passages that match your query. That works well when the answer lives in a single wiki page or ticket. It breaks down when the answer requires reading code and understanding how that code got there.

Unblocked synthesizes engineering artifacts into one answer. When you ask why a service handles retries the way it does, Unblocked pulls the current implementation, the pull request that introduced it, the Slack thread where two engineers argued about backoff strategy, and the design doc that predates all of it. Onyx can surface the doc and maybe the ticket. It can't read the diff that contradicts the doc, because it treats code as text rather than as a versioned history of decisions.

Code-aware means the tool understands pull request history and diffs, not just the words in a file. A text search finds the function named handleRetry. A code-aware engine knows that function was rewritten three months ago, links the change to the PR discussion that justified it, and tells your agent which version is current. That distinction decides whether an AI coding agent gets a stale snapshot or the reasoning behind the code it's about to edit.

For an engineering leader, the practical difference is how much reconciliation lands on the developer. With Onyx you get a stack of relevant documents and reconcile them yourself. With Unblocked you get one answer that already accounts for the code, the review history, and the conversations around it, delivered to the agent doing the work.

Conflict resolution across sources#

Unblocked resolves conflicts between sources. Onyx leaves that work to you. When a design doc says one thing and the current implementation says another, that gap is where coding agents produce confident, wrong answers.

Onyx indexes documents and returns the ones that match your query. If your Confluence page describes an authentication flow that your team rewrote six months ago, Onyx surfaces both the stale doc and the newer PR discussion as separate results. You read them, notice the mismatch, and decide which one reflects reality. That reconciliation happens in your head, not in the tool, and it happens every time you ask a question that touches code the docs haven't caught up with.

Unblocked applies cross-source conflict resolution before it hands anything to an agent. It reads the code, the PR history, the conversations where the change was discussed, and the docs, then weighs them against each other. When the code contradicts the doc, Unblocked treats the merged code as the source of truth and flags the doc as outdated rather than presenting both as equally valid.

That distinction matters most for an AI coding agent, because the agent can't tell a current fact from a stale one. It trusts whatever context you feed it. Feed it two contradictory sources and it picks one, often the wrong one. Feed it a reconciled answer and it writes against the code that actually ships.

Targeted retrieval and token optimization#

Onyx retrieves documents. It indexes SaaS sources, ranks matching pages by relevance, and returns the ones a query hits. That model works when a person reads the results and decides what to keep. It breaks down when the consumer is a coding agent with a fixed context window.

Unblocked runs targeted retrieval tuned for engineering questions. When an agent asks why a function behaves a certain way, Unblocked pulls the specific code, the PR that changed it, and the conversation that explains the decision, then returns those pieces rather than a stack of documents to sift through. The difference is precision. General-purpose search surfaces candidates, and engineering-specific retrieval returns the answer.

Token optimization is where the two diverge in practice. A coding agent working in Cursor or Claude Code has a hard limit on how much context it can hold. Every token you spend on a stale doc or a near-miss search result is a token the agent can't spend on the actual code it's editing. Onyx doesn't budget for that constraint because it was built for human readers who can skim and discard.

Unblocked packs decision-grade context into the smallest token footprint that answers the question. It drops the noise before delivery, so the agent receives what it needs and nothing it has to reason past. For an agent, a tighter payload means fewer wrong turns and fewer correction cycles from the developer.

Data governance and personalized relevance#

Both tools respect the permissions attached to your connected sources, but they govern different data. Onyx inherits access control from each SaaS connector, so a document a user can't see in Confluence or Google Drive stays hidden in Onyx search. That model works for company-wide knowledge because most of the sensitive material already lives behind those SaaS permissions.

Unblocked governs code and engineering artifacts, which behave differently from documents. Access to a repository, its pull request history, and the conversations tied to it depends on who touched the code and who owns the service, not just a folder permission. Unblocked maps context to the repositories and teams an engineer already has access to, so a synthesized answer never surfaces code or discussion the engineer couldn't reach directly.

Personalized relevance is where the two diverge most. Onyx tailors results by role and permission, which answers "what can this person see." Unblocked goes further and tunes retrieval to the task in front of the engineer right now. If you're debugging a payment service, Unblocked weights the recent PRs, the incident threads, and the code paths tied to that service, not every document you technically have rights to read.

That distinction matters for coding agents. A role-based filter still hands the agent a broad pile of accessible material. Task-based relevance narrows it to the code, history, and conversations that bear on the change you're making, which is what an agent needs to produce a correct answer.

Delivery surface and integration with coding agents#

The delivery surface decides whether context reaches your coding agent at all, and this is where the two tools split hardest. Onyx delivers answers through its own chat UI and search interface. An engineer opens Onyx, asks a question, reads the result, and then copies whatever's useful back into Cursor or Claude Code by hand. That round trip works for a human looking something up, but it breaks the moment an autonomous agent needs context mid-task.

Unblocked delivers context through a single Model Context Protocol server that any agent connects to directly. You point Cursor, Claude Code, or another MCP-compatible agent at one endpoint, and the agent queries Unblocked as it works. The agent asks for context on the file it's editing, Unblocked synthesizes the relevant code, PR history, and conversations, and returns it straight into the agent's context window. No human sits in the loop copying text between tabs.

That mechanism matters because coding agents run in tight loops. An agent editing a function needs the reasoning behind that function now, not after a developer pauses to search a separate tool. One MCP server also means you configure the connection once and every agent on the team draws from the same synthesized context, rather than each engineer maintaining their own search habits.

Onyx doesn't offer agent-native MCP delivery to coding tools today. If your goal is feeding decision-grade context to an AI coding agent without manual handoff, Onyx's chat-first design isn't built for it, and Unblocked's server model is.

Does Onyx work with AI coding agents like Cursor or Claude Code?#

No. Onyx delivers answers through its own chat UI and web search interface, not through the Model Context Protocol that coding agents read from. If you ask a question in Onyx, you get a document-search result you copy back into your editor by hand. Cursor and Claude Code can't call Onyx for context mid-task, because Onyx exposes no MCP server for them to query.

Unblocked closes that gap with a single MCP server your agent connects to directly. Cursor, Claude Code, and other MCP-aware agents query Unblocked while they work and pull synthesized context from code, PR history, conversations, and docs into the prompt without a human in the loop. The agent asks, Unblocked answers, and the reply lands in the context window ready to use.

If your team wants context that reaches the agent automatically rather than through a browser tab, Unblocked's MCP delivery is the path Onyx doesn't offer today.

Which tool resolves conflicts between sources?#

Unblocked resolves conflicts between sources. Onyx returns matching documents and leaves you to figure out which one is right.

Consider a common case. Your architecture doc says authentication runs through a shared session service, but a merged pull request from last month replaced it with token-based auth in the actual code. Search for "how does auth work" in Onyx and you get both the doc and any related text, ranked by relevance. You still have to read them, notice the contradiction, and check the code to see which one reflects reality today.

Unblocked reads the code, the PR history, the conversations around that change, and the doc together, then tells the coding agent the doc is stale and the token-based implementation is current. That reconciliation happens before the context reaches your agent, so the agent works from the answer that matches your codebase rather than one that used to be true.

When should you use both?#

Run both when you have company-wide document search needs and engineering teams running AI coding agents. Point Onyx at your SaaS stack so anyone in sales, support, or operations can ask a question and find the right doc across Slack, Notion, Confluence, and Google Drive. Scope Unblocked to your engineering workflows, where coding agents in Cursor or Claude Code pull reconciled context from code, PRs, and conversations through one MCP server. The two tools cover different questions and rarely overlap.

Skip Unblocked in a dual-tool setup if either caveat applies to you. Below roughly 50 engineers, your codebase and PR history usually aren't dense enough for cross-source synthesis to pay off, and a general search tool plus your agents' native context handling will cover you. On a greenfield codebase, there's little historical context to reconcile yet, so Unblocked has less to draw on. In both cases, keep Onyx for document search and revisit Unblocked once your engineering history grows.

Verdict: who should choose which#

Choose Onyx if your primary need is company-wide document search across SaaS tools like Confluence, Google Drive, and Slack. It's a solid open-source pick for general knowledge search where the reader is a support agent, a PM, or an operations lead looking up a document rather than a coding agent reconciling contradictory sources.

Choose Unblocked if you run an engineering org of roughly 50 or more developers on an established codebase, and your AI coding agents need decision-grade context that reconciles code, PR history, conversations, and docs. Skip it for greenfield projects or small teams, where there isn't enough history to synthesize.

These two tools solve different problems, so the choice isn't strictly either/or. Larger organizations often run both, with Onyx handling broad document search and Unblocked scoped to the engineering workflows where AI agents write code. Match the tool to the question your people are actually asking.

FAQs#

Is Onyx open source? Yes. Onyx ships as an open-source enterprise search and Q&A platform you can self-host, with a paid cloud option. You index SaaS sources like Slack, Confluence, and Google Drive, then query them in a chat interface.

How do the pricing models differ? Onyx offers a free self-hosted tier plus paid cloud plans. Unblocked prices per developer seat as a managed service, since its value scales with the engineers whose code, PRs, and conversations feed the context engine.

Does Unblocked replace Onyx entirely? No. Unblocked handles engineering context for AI coding agents. Onyx covers company-wide document search across departments like sales, HR, and support. Many teams run both, scoping Unblocked to engineering and Onyx to everything else.

What's the minimum team size for Unblocked? Unblocked pays off once you have roughly 50 or more engineers and a codebase with real history. Below that, or on a greenfield project, there aren't enough PRs, conversations, and decisions for the context engine to reconcile.