Sourcegraph
Sourcegraph is a code intelligence platform that enables developers to search, write, and understand code across large-scale codebases by integrating insights directly into their development environments.[1] Founded in 2013 by Quinn Slack and Beyang Liu, the company is headquartered in San Francisco, California, and operates as an all-remote organization.[2][3] The platform offers universal code search, semantic code navigation, and AI-powered agents to automate routine development tasks, helping teams at enterprises accelerate software building.[4] Key features include rapid querying of billions of lines of code, batch code changes, and contextual insights to improve developer productivity.[5] Sourcegraph's mission focuses on industrializing software development, making coding more accessible and efficient for organizations worldwide.[1] Since its inception, Sourcegraph has grown through multiple funding rounds, raising $125 million in a Series D led by Andreessen Horowitz in 2021 at a $2.625 billion valuation, with total funding exceeding $223 million from investors including Sequoia Capital, Redpoint Ventures, and Craft Ventures.[6][7] It serves major customers such as Uber, Atlassian, Amazon, PayPal, and GE, supporting their efforts to manage complex, distributed codebases.[8] In recent developments, Sourcegraph has emphasized AI agents to handle repetitive tasks, further enhancing its role in enterprise software workflows.[9]Company Overview
Founding and Mission
Sourcegraph was founded in 2013 in San Francisco by Quinn Slack, who serves as CEO, and Beyang Liu, the CTO.[5][10] The two met while working as engineers at Palantir Technologies, where they encountered significant challenges in navigating and searching large, complex codebases without the advanced tools they had experienced earlier in their careers.[5] Liu, who had previously engineered at Google and grown accustomed to its robust internal code search capabilities, found the absence of similar functionality outside such environments particularly frustrating, highlighting a broader gap in developer tools for effective code discovery.[5] This experience directly inspired the company's initial focus on developing a universal code search tool designed to transcend simple keyword matching.[5] Instead, Sourcegraph aimed to incorporate semantic understanding, allowing developers to perform precise queries, cross-reference symbols across repositories, and gain deeper insights into code structure and usage.[11] The goal was to democratize access to powerful code intelligence, enabling teams at any organization to explore and comprehend vast codebases efficiently, much like the internal tools at leading tech firms.[5] Over the years, Sourcegraph's mission has evolved from providing foundational code search capabilities in its early days to "industrializing software development with AI agents" by 2025.[4] This shift emphasizes leveraging AI to automate routine developer tasks—such as code reviews, testing, and migrations—thereby enhancing productivity and allowing engineers to focus on high-value creative work.[12] A key milestone in this progression was the 2016 release of its core search engine as an open-source, self-hosted tool, which empowered developers to deploy and customize the platform independently for immediate use in their workflows.Leadership and Operations
Sourcegraph's leadership team, as of 2025, is led by cofounders Quinn Slack as CEO and Beyang Liu as CTO, both of whom maintain active roles in steering the company's strategic direction and technical vision.[1] Other key executives include Erika Rice Scherpelz as Head of Engineering, Dan Adler as VP of Operations, and Carly Jones as VP of People and Talent, reflecting a structure that emphasizes engineering excellence, operational efficiency, and human resources to support scalable growth.[1][13] This C-suite composition underscores the founders' ongoing involvement, drawing from their backgrounds in software engineering and entrepreneurship to guide product innovation and market expansion.[14] The company employs approximately 209 people in 2025, operating as a distributed organization with a core presence in San Francisco and remote workers spanning multiple global locations.[15] This structure fosters a flexible work environment, accommodating international talent while maintaining cohesion through asynchronous collaboration tools tailored for developers. Sourcegraph's headquarters, established in San Francisco, California, since its founding in 2013, is located at 400 Montgomery Street, 6th floor, serving as the central hub for executive functions and key engineering teams.[2] Operationally, Sourcegraph delivers its platform through a hybrid model that includes both cloud-based SaaS offerings and self-hosted deployments, allowing enterprises to choose configurations that align with their security and scalability needs.[16] The company remains committed to open-source principles by maintaining portions of its core codebase as open source and actively contributing to developer communities through tools, documentation, and events that promote code intelligence best practices.[17] This approach not only enhances community engagement but also reinforces Sourcegraph's position as a collaborative force in software development ecosystems.[18]Historical Development
Inception and Early Milestones
Sourcegraph originated in 2013 as an informal side project initiated by founders Quinn Slack and Beyang Liu, who were developers grappling with inefficient code navigation in expansive codebases during their time at companies like Palantir.[5] Inspired by internal tools such as Google's Code Search, they aimed to create a universal solution for browsing and understanding code without the limitations of proprietary systems.[5] This early effort focused on building a personal tool to streamline code exploration, laying the groundwork for broader accessibility. The project transitioned to a public open-source code search tool in 2016, marking its formal launch with features emphasizing speed and semantic intelligence, including jump-to-definition, find-references, and symbol search across public repositories.[19] It rapidly attracted individual developers and small teams, with tens of thousands of users praising its IDE-like experience for navigating global codebases like the Go repository, which enhanced productivity in reading and understanding unfamiliar code.[19] Between 2017 and 2018, Sourcegraph expanded into enterprise capabilities, introducing features for large-scale code modifications—such as early refactoring tools across multiple repositories—and securing its first major customer win with Uber, followed by adoption at other tech firms like Dropbox and Lyft.[20] These developments addressed growing demands from organizations with distributed codebases, shifting from a cloud-based model to self-hosted deployments to meet security needs.[5] A key early challenge was scaling search functionality for massive repositories containing millions of lines of code, which strained performance in large organizations with hundreds of developers and thousands of repositories.[21] Sourcegraph tackled this through iterative open-source enhancements, such as improved result filtering, pagination for large sets, and optimized repository counting in the 2018 release (version 2.9), enabling reliable searches across tens of thousands of repositories.[21]Growth and Key Expansions
In 2019 and 2020, Sourcegraph transitioned toward a commercial enterprise focus, securing $23 million in Series B funding in March 2020 to expand its universal code search capabilities for large organizations.[22] This period also saw the launch of key integrations, including a native collaboration with GitLab announced in November 2019, enabling code navigation and intelligence directly within GitLab instances.[23] Building on its early open-source roots, these developments positioned Sourcegraph as a scalable tool for enterprise codebases. Further funding of $5 million in July 2020 and $50 million in Series C in December 2020 supported enhancements like batch changes for automated code migrations, emphasizing DevOps efficiency.[24][25] By 2021, Sourcegraph achieved unicorn status through a $125 million Series D funding round in July, valuing the company at $2.6 billion and fueling rapid adoption among major enterprises.[5] This growth enabled service to high-profile customers such as Uber and Lyft, where the platform facilitated large-scale code searches across complex repositories.[18] The funding accelerated product maturation, with user base expansion reflecting increased demand for code intelligence in distributed development environments. In 2023, Sourcegraph began integrating AI elements, with early versions of Cody, its AI coding assistant, rolling out in June with capabilities for autocompletions and multi-repository context awareness, marking the onset of AI-driven productivity tools.[26] These innovations expanded Sourcegraph's role beyond search into intelligent automation, supporting developers in tasks like code generation and insight extraction. In 2024 and 2025, Sourcegraph pivoted heavily toward AI, with Cody achieving general availability in December 2023 and receiving major updates throughout 2024, including support for advanced models like Claude 3[27] and Vertex AI.[28] The introduction of agentic tools, such as Amp in 2025—a coding agent for autonomous reasoning and code editing—further solidified this shift, becoming publicly available without waitlist in October 2025.[29] In November 2025, Amp received updates improving context handling by replacing compaction with a 'handoff' mechanism.[30] Revenue doubled to $50 million in 2025 from $31 million the prior year, driven by AI adoption.[15] Key partnerships, including with Google Cloud in 2025 for accelerating AI model integration like Gemini, enhanced deployment options for enterprise AI workflows.[31]Products and Services
Code Search Platform
Sourcegraph's code search platform provides universal search capabilities across codebases, enabling developers to query code semantically and structurally regardless of programming language or repository boundaries. It supports searches using regular expressions (RE2 syntax), structural patterns such as symbol types (e.g.,type:symbol for functions or variables), and diff-specific queries (e.g., type:diff to examine commit changes or modified lines). These mechanics allow querying across multiple repositories and languages, with filters for repositories (repo:), files (file:), languages (lang:), and revisions (revision:), facilitating precise location of code elements in diverse environments.[32]
Key navigation features include jump-to-definition, which allows users to navigate directly to a symbol's definition by clicking on it or using a dedicated button, and find references, which displays all occurrences of a symbol including definitions and implementations in a popover view. These features operate via search-based heuristics for immediate usability or precise code intelligence for accuracy, supporting navigation in large-scale codebases and monorepos without requiring local clones. Repository management is optimized for monorepos through efficient indexing that handles billions of lines of code, with real-time updates enabling searches across vast repositories.
The platform deploys in self-hosted configurations using Docker Compose for single-node setups or Kubernetes with Helm for scalable, multi-node environments, alongside Sourcegraph Cloud as a fully managed option. Hybrid deployments combine these for flexibility, and it integrates with code hosts like GitHub to index both private and public repositories seamlessly.[33]
Common use cases include onboarding new developers by quickly locating and understanding codebase elements, such as mapping specific functions or events; auditing code for issues like security vulnerabilities or unversioned references across files; and refactoring through diff searches to track schema changes or propagate updates without relying on a full IDE.[34]