For engineering teams adopting AI coding agents, Sourcegraph is a code intelligence platform that indexes and searches your entire codebase, giving AI coding assistants the context they need to write and review code accurately.
Presence & Market Position
Tracked · below panel floor — earned mentions are accruing toward the next scored window.
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Sourcegraph positions itself as the code-intelligence layer beneath both engineers and AI coding agents — indexing entire codebases so agents work from full repository context instead of fragments, and framing SOC 2 compliance and zero data retention as what separates it from consumer-grade coding assistants. Its footprint centers on the large enterprise engineering orgs it names as customers, and public discussion of the platform is still early relative to that footprint, with the visible record so far tracking product and documentation expansion rather than a wide open debate about the category.
Claude Code
Codex
Cursor
Lovable
ReplitStripe, Reddit, Canva, MongoDB, HubSpot, Dropbox, Blackstone, Midjourney, Indeed, MathWorks
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Business Profile
Founded in 2013, Sourcegraph has grown into a code-intelligence platform used by engineering teams inside large organizations including Stripe, Reddit, and MongoDB, with roughly 148 employees and $248M raised to date. The company operates fully remote, across time zones and continents. In December 2025 it spun its AI coding agent, Amp, out into a separate entity, narrowing Sourcegraph's own focus back to codebase search, indexing, and the context layer that other AI agents draw on — including a July 2026 public beta of Agentic Batch Changes for large-scale, agent-driven code changes.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Sourcegraph ships an MCP server that exposes its code-intelligence layer directly to AI agents — search, file navigation, go-to-definition, commit history, and Code Finder, an agentic search tool that resolves multi-step lookups in a single call rather than leaving an agent to hunt through files. The MCP works across multiple agent platforms rather than locking into one. Agentic Batch Changes, a public beta for AI-driven large-scale code changes, extends that build-out from retrieval into code modification.
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