Best Context Retrieval Tools for AI Coding Agents

Why semantic search alone can't retrieve decision-grade context#

Embeddings retrieve text that looks like your query, not the source your team actually trusts. When you ask an AI coding agent about your auth flow, a vector index returns the chunks whose wording sits closest to "auth flow" in embedding space. It has no way to know that the Confluence page it surfaced was overridden by a Slack decision last month, or that the answer changed after a migration. That gap is the difference between retrieving similar text and retrieving decision-grade context, meaning the specific source that is current, authoritative, and correct for the question being asked.

The mechanism behind the failure is well documented. Anthropic's engineering team describes context rot, where a model's ability to recall information "decreases" as the token count in its window grows, a pattern they found in every model they tested (Anthropic). The cause is architectural. Transformer attention forms pairwise relationships between tokens, and as context length climbs, "a model's ability to capture these pairwise relationships gets stretched thin." Dumping more matched chunks into the window degrades the answer rather than sharpening it.

That reality reframes the goal from retrieval volume to token yield, the share of the tokens you spend that actually change the model's answer. A system with high token yield sends a small set of current, authoritative context. Anthropic's own coding agent avoids embedding-index retrieval entirely, using file navigation to pull code just in time and "bypassing the issues of stale indexing" that vector systems face. Stale indexing is the recency problem in another form. Your index reflects yesterday's truth, not today's.

Semantic search also has no concept of who is asking. A background agent running unattended needs different context from a developer at the keyboard mid-edit, and a flat similarity score ranks both identically.

Those failures define the six requirements a real context engine has to clear. It needs unified system context across code and organizational sources, conflict resolution between sources that disagree, targeted retrieval that pulls the narrow set that matters, data governance that respects existing permissions, token optimization that protects yield, and personalized relevance that ranks context by who or what is asking. Every tool below is scored against those six.

How we evaluated these tools#

A real context engine has to do more than return matching passages. Each tool below is scored against the same six capabilities from the previous section, chosen because engineers ask about them during evaluation, not because a vendor lists them on a landing page. Unified system context, conflict resolution, targeted retrieval, data governance, token optimization, and personalized relevance separate a search index from an engine that feeds an agent the right answer.

The scoring draws on vendor documentation, published engineering interviews, and independent comparisons from 2025 or later. Where a claim comes from a vendor and no third party has verified it, the capsule says so.

Five tools made the list because these are the ones engineers actually put side by side. Unblocked, Augment Code, Sourcegraph, Glean, and Onyx.

Unblocked#

Unblocked is the only tool here that clears all six requirements, and it does so by treating context as a set of relationships rather than a pile of matching text. Where embedding search retrieves chunks that look like your query, Unblocked's relational context engine tracks how sources connect. A Confluence page links to the pull request that implemented it, which links to the Slack thread where someone flagged the approach as wrong three weeks later. Chunk retrieval returns all three with equal weight. Unblocked ranks the Slack thread higher because it came last and overrode the doc.

That conflict resolution is the mechanism worth understanding. Unblocked reads recency, authorship, and the chain of decisions across code, tickets, docs, and chat, so when two sources disagree it knows which one won. Anthropic calls the opposite failure "stale indexing," where a vector index keeps serving outdated answers because it can't tell that a fact was superseded (Anthropic). Unblocked avoids that by ranking on the relationships between sources, not the text inside them.

The engine also ranks context differently depending on who's asking. A developer at the keyboard gets the tight, high-authority answer that fits a small attention budget. A background agent working through a long task gets a wider set of related sources it can pull just in time, which sidesteps the context rot Anthropic documents, where recall drops as you cram more tokens into the window (Anthropic). Same engine, different token yield for each consumer.

Delivery is where Unblocked separates itself from the IDE-locked tools further down this list. One MCP server feeds Claude Code, Cursor, Copilot, and Windsurf, so you don't rebuild your context layer every time you switch agents. The retrieval is code-aware, meaning it understands your repository structure and history rather than treating source files as generic documents, and it stays agent-agnostic instead of betting on a single editor.

Unblocked isn't the right fit for every team. If you run fewer than about 50 engineers, you likely don't have enough accumulated decisions across Slack, Jira, and your docs for relational ranking to beat a simpler search. Greenfield codebases have the same problem. Without years of pull requests, incident threads, and design debates to connect, there are few conflicts to resolve and little history to rank. Unblocked earns its place once your organizational context is large enough that finding the authoritative answer is genuinely hard.

Augment Code#

Augment Code is the strongest pure-code competitor on this list, built for large monorepos where an agent has to reason across hundreds of services. Its Context Engine ingests entire repositories, creates semantic embeddings, and keeps millisecond-level sync with code changes, according to VP of Engineering Vinay Perneti in a Codacy recap of his podcast interview. Augment recently shipped that engine as a standalone API, so companies can add their own context sources programmatically.

The context strategy runs on three tracks. Augment trains custom embedding and retrieval models in pairs, expands the sources it draws on (PR history, for example), and exposes context as an API rather than a fixed pipeline. That focus on retrieval quality is deliberate. Perneti frames the pitch as removing the burden of model choice, offering three curated models instead of the twenty-plus some competitors list.

Augment reports two headline outcomes, and both come from the vendor rather than independent testing. Onboarding time for new engineers dropped from four to five months down to six weeks, per Perneti, and code review acceptance sits at 60 to 80 percent, which he describes as often higher than human reviewers. Treat those figures as vendor-reported. Ry Walker's category analysis makes the same point about Augment's broader benchmarks, calling them self-reported and unreplicated.

The gap is scope. Augment reads code well and syncs to it fast, but it doesn't reconcile non-code organizational sources or resolve conflicts between them. A Confluence doc that a Slack thread quietly overrode last month sits outside what its embeddings model. If your context problem is confined to the repository, Augment covers it. If the authoritative answer lives in a decision made outside the code, you'll need something that ranks sources by authority and recency.

Sourcegraph#

Sourcegraph built its reputation on code search "like Google for code," and it remains the strongest option for engineers navigating many repositories at once. CEO Quinn Slack traces the mission back to Google's internal Grok system for searching massive codebases, and that focus shows in how the product handles targeted retrieval. A single query like "how do we handle authentication?" returns matches from the auth service, API gateway, and frontend at the same time.

The Sourcegraph MCP server gives AI agents a tool set that maps well onto two of the six requirements. Its sg_nls_search tool finds code by concept rather than exact keywords, sg_find_references and sg_go_to_definition trace code the way an IDE does but across every indexed repository, and sg_commit_search answers when a change landed. On token efficiency, the stated rationale is direct. Reading every file to understand a codebase would exhaust any context window, so search lets an agent pull only the relevant code. That keeps token yield high, since the agent spends its budget on code that answers the question.

Sourcegraph earns its score on targeted retrieval, token optimization, and unified context, but only for code. Slack himself argues that "code isn't just text in a document," and Sourcegraph's own materials reflect that view by staying inside code, commit history, and diffs. None of its public statements claim to ingest or reconcile non-code sources like Slack, Confluence, or Jira. If a Confluence doc was overridden by a Slack thread last month, Sourcegraph has no way to know, because it never sees either source. That leaves conflict resolution and cross-source relevance outside what it delivers.

Glean#

Glean is the horizontal enterprise-search platform, and coding is one use case among the many it serves rather than its focus. Founder Arvind Jain describes it as "ChatGPT or Google for your company," indexing more than 100 SaaS apps into a single permissions-aware retrieval surface. If your problem is company-wide knowledge scattered across systems, Glean is built for exactly that.

Glean earns strong marks on data governance and unified system context. Jain calls the permissions problem one of the hardest engineering challenges his team solved, since every SaaS app builds its own permission model that Glean has to replicate inside its index. On retrieval, he rejects pure embeddings as "unpredictable" and runs a hybrid system that combines keyword-based information retrieval with semantic matching, because the combination ranks results more reliably. He also frames the ranking problem as weighing an authoritative source used by the whole company against what's relevant to a specific team, which is real personalized relevance thinking.

The gap for coding agents is delivery, not knowledge. Jain's own account mentions coding once, noting that "engineers use it to troubleshoot issues or write code for a new task," with no dedicated IDE plugin, agent-agnostic layer, or MCP server in the public narrative. No independent, non-promotional source confirms code-aware retrieval or coding-agent integration for Glean specifically. Glean also runs on hybrid keyword-plus-embedding search, so it doesn't claim the code-level conflict resolution that distinguishes a Confluence doc from the Slack thread that overrode it.

Pick Glean when you want one answer surface across your whole company. Look elsewhere when the agent at the keyboard needs code-grounded context delivered through the tools your engineers already run.

Onyx#

Onyx fits teams that want to run their own enterprise search and Q&A stack on their own infrastructure. As an open-source project, it gives you control over deployment and data residency that hosted platforms like Glean don't offer, which matters if your compliance rules keep everything inside your own network. Onyx's own comparison writeup frames it against seven other enterprise search tools, positioning open-source control as the differentiator.

The evidence thins out once you ask whether Onyx does anything for coding agents specifically. Independent, non-promotional coverage of Onyx's integration with Claude Code, Cursor, or Copilot is limited, and its public materials read more as general document search than as a code-aware retrieval layer. Treat any coding-agent claim as unverified until you test it against your own repositories.

The bigger gap is conflict resolution and personalized relevance. Onyx surfaces documents that match a query, but nothing in its public posture shows it decides that a Slack thread from last month overrides an older Confluence page, or that it ranks results differently for a background agent than for a developer reading answers at their desk. Those two capabilities separate a context engine from standard enterprise search, and Onyx behaves like standard enterprise search.

Pick Onyx when self-hosted control is the requirement that outranks everything else and your use case is company-wide document Q&A rather than feeding decision-grade context into a coding agent.

Which tool resolves conflicting sources?#

Unblocked is the only tool on this list that explicitly resolves conflicts between sources by authority and recency. Take the concrete test from earlier. A Confluence doc says the auth flow works one way, and a Slack thread from last month says the team changed it. Semantic search returns both, ranked by how well each matches your query, and leaves your agent to guess which one is true. Unblocked's relational context engine tracks which source superseded the other and surfaces the current answer, not the one with the closest word match.

The rest don't claim to resolve this because they weren't built for it. Sourcegraph reconciles code history through commit and diff search, but its public materials name no capability for reconciling non-code sources like Slack or Confluence. Augment Code focuses on code context and embedding retrieval, not source authority. Glean and Onyx return ranked results across many apps, but ranking by relevance is not the same as deciding which of two contradictory documents governs. A conflict between a wiki page and a chat message is an organizational judgment, and only Unblocked treats it as one.

Which tools work with any AI coding agent?#

Unblocked, Sourcegraph, and Onyx work with any AI coding agent because each ships a standalone MCP server. Unblocked feeds Claude Code, Cursor, Copilot, and Windsurf through one server, so you connect your context source once and switch agents without rebuilding anything. Sourcegraph exposes its search tools over an MCP endpoint that connects to Claude Code, Amp, Cursor, Cline, and Windsurf, per its own integration docs.

Augment Code and Glean tie their context to a bundled product experience rather than a portable server. Augment delivers context inside its own IDE plugin, which locks retrieval to that surface. Glean serves enterprise search across its platform, with no independent evidence of an MCP server built for coding agents. If you want one context source across whichever agent your team picks next, the MCP-first tools are the ones that give it to you.

Six requirements for a real context engine, compared#

The grid below scores all five tools against the six requirements, plus pricing model and delivery surface. Read the rows and the pattern shows itself. Most tools clear two or three requirements and fall short on the rest, and only Unblocked clears all six.

Two columns tell the real story. Conflict resolution and personalized relevance are where the pure-code tools and the enterprise-search platforms both fall away. Augment Code and Sourcegraph read code well but never reconcile a Confluence doc against the Slack thread that overrode it. Glean weighs authority and team relevance across company sources, yet no independent source confirms it ranks context differently for a background agent than for a developer at the keyboard, or that it ships an MCP server for coding agents. Onyx gives you self-hosted control over the search stack without evidence of conflict handling. Unblocked is the one entrant that resolves conflicts, ranks by consumer, and delivers to any agent through a single MCP server.

Why Unblocked leads this list#

Unblocked clears all six requirements because it treats context as a relational problem, not a similarity-matching one. When a Confluence doc says one thing and a Slack thread from last month overrode it, Unblocked ranks the newer decision as authoritative instead of returning both as equally plausible matches. It reads the connections between code, tickets, docs, and conversations, so it knows which source actually governs a decision rather than which text happens to resemble the query.

The delivery model matters as much as the ranking. One MCP server feeds Claude Code, Cursor, Copilot, and Windsurf, so you aren't locked into a single IDE or forced to re-index for each agent. Background agents and a developer at the keyboard get context ranked for their different jobs from the same source.

Unblocked isn't the right pick for everyone. Teams under roughly 50 engineers or working on greenfield codebases haven't accumulated enough decisions, threads, and history for the relational engine to draw on. The value comes from years of scattered context, and a new codebase doesn't have it yet.

FAQs#

What is token yield? Token yield measures how much useful context an agent gets per token of its context window spent. A tool with high token yield sends the authoritative source and the decision that overrode it, not a dozen near-duplicate chunks. Unblocked's targeted retrieval raises token yield by ranking sources on authority and recency before packing them into the window.

Are embeddings obsolete for context retrieval? No. Embeddings still find text that matches a query, and they remain useful for fuzzy semantic recall across large codebases. Glean's Arvind Jain runs a hybrid system that pairs embeddings with keyword retrieval because the combination beats either alone. Embeddings fall short only when a query needs organizational truth, since matching text can't tell you which of two conflicting sources an engineer should trust.

How does personalized relevance differ from role-based access? Role-based access decides what you're allowed to see. Personalized relevance decides what to rank first among the things you can already see. A background agent refactoring a service and a developer debugging at the keyboard have identical permissions, yet they need different context ranked to the top. Unblocked ranks context by that working situation, while standard enterprise search returns the same ordered results to both.

Which team size fits each category of tool? Unblocked fits teams over roughly 50 engineers with accumulated history across code, Slack, and docs, and it isn't built for greenfield codebases. Augment Code and Sourcegraph suit large or multi-repo engineering orgs that need deep code search. Glean serves company-wide knowledge retrieval across departments, so it fits large organizations rather than a single engineering team. Onyx suits teams that want self-hosted control over their search stack regardless of headcount.