Sourcegraph connects code search to changes across many repositories

Sourcegraph gives engineering organizations a searchable, indexed view of their code across repositories and code hosts. It combines exact search and navigation with AI investigation through Deep Search and coordinated execution through Agentic Batch Changes. Its strongest use is work whose difficult part is finding every affected system and tracking the whole change, rather than writing an isolated patch in one familiar repository.

01 / ProductThree connected jobs in one code platform

The current platform¹ is organized around understanding code, overseeing it and evolving it. Code Search finds explicit patterns and symbols. Deep Search investigates natural-language questions and presents supporting sources. Agentic Batch Changes uses that context to scope and execute work across repositories. Code Insights and Monitoring add ways to observe patterns and changes over time.

Code Search³ supports literal, keyword and regular-expression queries, filtering by properties such as paths and languages. It also offers commit and diff search, search contexts and cross-repository navigation. Precise navigation can use SCIP semantic indexes, with other search-based methods when those are unavailable. The practical value is knowing which kind of result you are examining: an exact text match, a symbol relationship or an inferred answer.

Deep Search⁴ adds an agent that investigates code and history and cites its evidence. Its Evaluator can execute code in a sandbox to aggregate or join results. This supports questions such as which repositories still use an old interface and how their usage differs. A natural-language answer can help explain the inventory; the underlying searches remain important when completeness matters.

02 / AudienceWho needs more than repository-local context

Sourcegraph fits organizations with many services, a large monorepo or software spread across several code hosts. A platform engineer planning a runtime migration needs the targets, their owners, the relevant build files and the exceptional cases. A security engineer investigating an unsafe pattern needs to know whether a result is exhaustive within the indexed scope. Both jobs begin before any code is changed.

The product can also help engineers joining unfamiliar systems. Instead of asking several senior people where a particular behavior is implemented, a newcomer can follow searches, definitions and cited investigations. The value depends on connected repositories being the ones that actually matter. An elegant answer over half the estate does not become organization-wide knowledge simply because it appears in a central interface.

For a developer mainly changing code in a familiar workspace, the Cursor blueprint offers a more direct editor comparison. The Augment Code blueprint is relevant when evaluating agent context for a substantial codebase. Sourcegraph’s case is particularly strong when the same inventory and cross-repository evidence must serve several tools and engineering teams.

03 / WorkflowA proposed workflow for retiring an old runtime

Imagine an organization retiring an unsupported runtime version across its services. This is a proposed evaluation, not a measured migration. Begin by defining what counts as a target: production services, build images, CI workflows, development containers and deployment templates. Exclude archived examples only when the owner confirms they are outside the release process. Otherwise an apparently unused file may still generate a live deployment.

Use Code Search to find explicit version strings and image references. Then ask Deep Search to explain how those values reach production and identify shared templates that may affect several services. Save the exact queries and the repository revisions used. The output should be an inventory with evidence, owner, proposed action and exception reason. That record makes later claims about completion inspectable.

Choose a small group of representative repositories for the first change: a normal service, one with a custom build and one that depends on a shared template. Ask Agentic Batch Changes⁵ to plan the update with those targets. The product describes using deterministic scripts for mechanical edits and coding agents where judgment is needed, executing work in isolated containers and tracking changesets with CI feedback.

Keep the acceptance criteria specific. The service should build under the new runtime, preserve its public behavior and produce the expected deployment artifact. A version replacement that passes a unit suite but breaks the production image is incomplete. Ask the agent to identify failed checks and the modifications made to resolve them. Review the first changesets before applying the same plan to the larger inventory.

Track at least four states: identified, patch prepared, merged and deployed. A merged changeset is a useful engineering milestone, but deployment may happen separately. After rollout, rerun the original search and reconcile remaining matches against the exception list. A stale test fixture and a service still deploying the old runtime should not be counted the same way. The outcome is a defensible migration record rather than a large number of open pull requests.

04 / PricingPricing combines an enterprise contract with AI usage

The enterprise pricing page lists code search, AI search, APIs, CLI and MCP access alongside administration and support. It also says AI credits are pooled and do not expire monthly. That makes the buying discussion different from choosing a small fixed allowance for each editor user. Ask how active people, connected agents and recurring investigations contribute to the quoted allocation.

Agentic Batch Changes advertises outcome pricing: a changeset that the team does not merge is not charged under that described model. This aligns the unit with one observable result, but it does not make every merged changeset equally valuable. A mechanical one-line edit and a difficult migration can represent very different work. Confirm the unit price, any volume agreement and how generated changesets are grouped before forecasting a large program.

For an internal business case, include the work already spent creating spreadsheets of repositories, repeating searches and chasing owners. Also include index maintenance and integration setup. The useful comparison is the cost of completing and proving a migration with the existing process versus the proposed system. Vendor demonstrations and illustrative examples are not independent evidence of that savings for your estate.

05 / DistinctionsWhat stands out in a large engineering organization

Sourcegraph’s indexed foundation can serve humans and coding agents. A shared code search capability helps a developer verify an AI answer and can provide the same repository context to tools through MCP. That makes context a maintained engineering resource instead of something each new agent session has to rediscover independently.

The distinction between exact and agentic search is also useful. An engineer can start with a known unsafe function name, use exact results to establish a candidate set and ask Deep Search to interpret why those call sites exist. Neither mode eliminates the other. The explicit search is helpful for repeatable inventory; the investigation helps explain relationships and edge cases that a string match alone cannot settle.

Coordinated change management is a further step beyond generating code. Agentic Batch Changes describes publishing and tracking changesets across code hosts, using CI feedback and allowing review before merge. The benefit is organizational: the migration lead can follow the whole program and its exceptions. A capable agent in a single repository does not by itself provide that program-level view.

06 / QuestionsQuestions to settle before relying on the index

Which repositories, branches and revisions are searchable? Establish an inventory of connected hosts and compare it with the systems listed by service owners. Check whether generated code, large files or restricted repositories affect the particular investigation. Coverage is a property of the configured index and permissions, not a promise that every answer sees every byte of company software.

How will you verify absence? If a search returns no instances of an old runtime, confirm the query syntax and scope against a known example. Look for indirect configuration, image tags or shared templates that do not contain the obvious version string. Negative results are useful only when the search would have found the thing if it were present.

Who accepts cross-team changes? A central migration owner can coordinate the work, but individual service owners may know compatibility constraints that are missing from code. Agree on review routes, rollout windows and exceptions before publishing broadly. The first pilot should expose those coordination needs while the number of proposed changes remains small.

07 / DecisionThe decision starts with a trustworthy inventory

Choose Sourcegraph when understanding the complete code estate and coordinating repeatable change are recurring problems worth maintaining infrastructure for. Its search, investigation and execution capabilities form a coherent path from a question to a tracked migration. Start by proving that the inventory is accurate, then evaluate the work it enables.

Keep the saved searches, cited answers, selected pilot changesets and exception register together. Measure reviewer corrections, time to accepted change and unresolved targets as well as AI consumption. That evidence shows whether the platform improves this organization’s ability to change software at scale.