Sourcegraph

Provides Cody, an AI coding assistant that uses repository-wide code search and context to answer questions, generate code, and explain codebases with deep understanding of the full code graph. Cody integrates with VS Code, JetBrains, and web; SOC 2 Type II and ISO 27001 certified; discontinued Cody Free tier in July 2025 to focus on enterprise; strong code-search moat.

Sourcegraph's code-intelligence engine gives AI coding tools the repo-wide context they need to be effective at enterprise scale, making it a critical integration layer for large codebase workflows.

sourcegraph.com Code Search and Intelligence

United Statesprivate

At a glance

Total raised

$223M

last round series d

Headcount

51-200

approximate · public range

Headquarters

San Francisco

United States

Founded

2013

13 years active

Status

Private

Code Search and Intelligence

Latest valuation

$2.6B

Series D · 2021

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Funding history

Total raised

$223M

Rounds tracked

Latest stage

Series D

Latest valuation

$2.6B

Date	Stage	Amount	Valuation	Lead investors
Jul 13, 2021	Series D	$125M	$2.6B	Andreessen Horowitz
Dec 3, 2020	Series C	$50M	—	Sequoia Capital
Jul 28, 2020	Series B	$5M	—	Felicis Ventures
Mar 3, 2020	Series B	$23M	—	Craft Ventures
Oct 6, 2017	Series A	$20M	—	Redpoint Ventures, Goldcrest Capital

Founder backgrounds

Beyang Liu

Co-Founder & CTO (departed)

Studied computer science at Stanford, where he developed a deep interest in compilers and published machine learning research as a member of the Stanford NLP group. Before founding Sourcegraph, worked as an engineer at Palantir Technologies, building large-scale data analysis software for major financial institutions. Co-founded Sourcegraph in 2013 alongside Quinn Slack, initially building a code intelligence and search platform that later evolved into Cody, an AI coding assistant powered by repository-wide code graph context. Liu's background in compilers and static analysis directly informed Sourcegraph's technical approach — combining precise code navigation with large-scale search to ground AI-generated code in a full understanding of existing codebases.

Quinn Slack

Co-Founder & CEO (departed)

Earned a BS in Computer Science from Stanford, then co-founded Blend Labs, an enterprise lending platform that scaled to hundreds of employees. In 2013, co-founded Sourcegraph with Beyang Liu to solve the problem of navigating large codebases — a vision that evolved into Cody, an AI coding assistant powered by repository-wide code search and deep code-graph context. Under his leadership, Sourcegraph raised hundreds of millions in venture funding and was valued at $2.6B. Known for remaining an active, hands-on engineer as CEO — he continued writing code daily and personally shipped production changes — reflecting a belief that deep technical credibility was central to building developer tools. He has since stepped down as CEO and moved on to found Amp, a new venture.

AI bull / bear

Bull case

Sourcegraph's code-search engine is not a feature—it is infrastructure. Cody doesn't just autocomplete; it understands the full code graph across repositories, making it the only AI coding assistant that can reliably answer questions like "where is this function called across every microservice" or "what depends on this schema change." That context moat is years deep and not easily replicated by surface-level LLM wrappers. The July 2025 decision to kill the Cody Free tier signals a company that knows where its value lands: enterprise. SOC 2 Type II and ISO 27001 certifications are table-stakes for large organizations, and Sourcegraph already has them. With $223M raised and a16z anchoring the cap table, the company has the runway and the brand to become the default code-intelligence layer that every enterprise AI workflow plugs into—whether the front-end is Cody, a competitor's IDE plugin, or an internal toolchain. The moat is the graph; the monetization is inevitable.

Bear case

Sourcegraph's last funding round closed in July 2021. Four years without fresh capital at a $2.6 billion peak-2021 valuation raises uncomfortable questions about burn rate, revenue trajectory, and whether the company can raise again without a punishing down round. The code-AI landscape has shifted radically since that Series D. GitHub Copilot, backed by Microsoft's distribution and Azure infrastructure, is embedding deeper codebase awareness with every release. Cursor has captured developer enthusiasm. Codeium and Codium are racing to enterprise feature parity. Meanwhile, Cody's free-tier shutdown risks ceding the grassroots developer mindshare that drives bottom-up enterprise adoption. The code-search moat Sourcegraph spent years building may narrow faster than expected as LLMs improve at context retrieval natively, making specialized graph infrastructure less of a differentiator and more of a cost center. In a consolidating market, being an integration layer can mean being squeezed, not acquired.

Generated by deepseek-v4-pro · May 2, 2026Refreshes quarterly · see disclosures

In the news

The New Stack5/8/2026
“The terminal still matters”: Amp rebuilds its CLI for an agentic future beyond the command line
Amp, spun from Sourcegraph, launches Neo CLI redesigned for autonomous agent workflows and remote control.

Leadership

Quinn Slack

Co-Founder & CEO (departed)

𝕏

Beyang Liu

Co-Founder & CTO (departed)

—

Dan Adler

Chief Executive Officer

in 𝕏

Products

Cody
Cody is an AI-powered coding assistant that uses repository-wide context and code graph intelligence to answer questions, generate code, explain codebases, and automate development tasks. It integrates directly into IDEs like VS Code and JetBrains, supporting features such as autocomplete, chat, test generation, and code editing. By leveraging deep codebase understanding rather than just local file context, Cody provides accurate, context-aware suggestions that help developers navigate large codebases, fix bugs, onboard faster, and improve code quality across enterprise teams.
Code Search
Code Search is Sourcegraph's foundational code intelligence engine that enables developers to search, navigate, and understand code across any language and repository at scale. It supports regex, literal, and structural search patterns across monorepos and multi-repository environments. Code Search provides features like jump-to-definition, find references, documentation hover, and code graph navigation, making it indispensable for large enterprises managing millions of lines of code. It empowers developers to quickly find relevant code, trace dependencies, and refactor with confidence.
Deep Search
Deep Search is Sourcegraph's agentic code search capability that uses AI to interpret natural language queries and understand developer intent, going beyond keyword matching. It combines LLM-powered reasoning with Sourcegraph's code graph to retrieve semantically relevant code, even when the developer doesn't know exact function names or file locations. Deep Search is designed for complex, multi-step code understanding tasks, enabling developers and AI agents to discover relevant code, understand system behavior, and resolve issues across massive, unfamiliar codebases.

Open roles

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