Tools for companies that don't need Sourcegraph
The market for tools to support the work of coding agents is exploding. One of the biggest categories is code context. How you feed context about your existing codebase and systems to your coding agent determines what kind of productivity you’re going to get out of it.
Everyday at Sourcegraph we talk to the biggest companies in the world solving the world's hardest coding problems. Increasingly we have been getting questions asking about the new crop of tools that have popped up to address the big code context problems we’ve been solving for decades.
We decided to take this opportunity to share some of the best tools we’ve heard about for companies that don’t need Sourcegraph. And to be clear, most companies don’t need Sourcegraph.
Sourcegraph is only for companies who face “big code” problems. What are “big code” problems you ask?
- Multiple Code Hosts: Sourcegraph customers tend to have multiple code hosts stretched across the organization, often with services or functions that span one of more of these platforms.
- Massive monoliths: Monolith code bases that stretch into the multi-ten figures of lines of code are a special breed of big code. Fitting all that code into a context window is simply not feasible.
- Multiple acquisitions: When you have code that originated at different companies, migrations, batch changes, and compliance become essential functions to keep the company on track.
- Understanding the code you’re generating: Ten thousand engineers generating thousands of lines of code per day is a level of output that has to be experienced to be understood.
These are just a few of the problems that companies who truly need Sourcegraph run into on a daily basis. Our proprietary query syntax and indexing engines that have been hardened over a decade of being the global leader in code search are already trusted by the world's largest software companies.
The best code context tools for companies who don’t need Sourcegraph
For everyone else, there are a wide range of tools that solve some of the individual use cases that Sourcegraph addresses. Here are some of our favorite tools for people who don’t need Sourcegraph:
“Chat with Your Repo” Tools
These tools shine when your codebase is small, modern, and well-structured.
grep.app
What it’s good at:
Blazing-fast regex and keyword search across millions of public GitHub repositories.
Who it’s for:
OSS exploration, security research, pattern-hunting, and answering “has anyone ever done this before?”
repogrep
What it’s good at:
Ultra-fast search across public GitHub repos.
Who it’s for:
OSS exploration, quick “how does React do X?” moments.
ac2.dev
What it’s good at:
Simple repo Q&A and fast lookups.
Who it’s for:
Solo devs or tiny teams.
Repoprompt
What it’s good at:
Stuffing a repo into an LLM prompt.
Who it’s for:
Demos and experiments.
AI IDEs & Copilots
These tools feel magical
Cursor
What it’s good at:
Excellent UX, inline edits, fast iteration.
Who it’s for:
Startups, greenfield projects, product engineers.
GitHub Copilot
What it’s good at:
Autocomplete and inline help.
Who it’s for:
Everyone writing code.
Google Antigravity
What it’s good at:
Agentic workflows and experimental AI IDE concepts.
Who it’s for:
Early adopters, Google-native teams.
Context Layers & MCP Servers
These tools exist to patch missing context into other AI tools.
Unblocked
What it’s good at:
Feeding docs, Slack, and tribal knowledge into AI tools.
Who it’s for:
Teams struggling with knowledge sprawl.
Port of Context
What it’s good at:
MCP execution environments and agent tooling.
Who it’s for:
Agent builders and infra teams.
Augment Code
What it’s good at:
Large token windows and agent-friendly workflows.
Who it’s for:
Teams betting on LLM-native development.
Static Analysis & Refactoring Tools
These tools solve specific problems extremely well.
Moderne
What it’s good at:
Automated large-scale refactors.
Who it’s for:
Planned migrations (JUnit, Java upgrades).
Detail.dev
What it’s good at:
Finding bugs, security issues, and mistakes.
Who it’s for:
Quality-focused teams.
Documentation-First Tools
Dosu
What it’s good at:
Auto-generated docs from code.
Who it’s for:
Teams drowning in stale documentation.
Google Code Wiki
What it’s good at:
Generated wikis tied to repositories.
Who it’s for:
Teams standardizing documentation.
Open Source & Self-Hosted Search
Sourcebot
What it’s good at:
Self-hosted repo search.
Who it’s for:
Cost-sensitive teams.
OpenGrok / Hound / Livegrep
What they’re good at:
Fast text search.
Who they’re for:
Infra teams and power users.
Why are these tools good enough for everyone else?
When you’re a small to medium sized company your engineers can get away will generated context that is non deterministic. You can use open source tools to search a few repositories on github to get context about your code. You can fit the context needed to make a batch change into a single 200k token context window. You don’t need Sourcegraph.
Why this matters for agentic AI
As agentic systems evolve to need things like agent memory, agent skills, agent loops, they all converge on the same hard requirement: reliable, authoritative context.
An agent’s memory is only useful if it’s grounded in the real codebase.
An agent’s skills only work if they can reason across repositories and history.
Agent loops fail when context is partial, stale, or probabilistic.
This is the layer Sourcegraph has spent over a decade building.
Final thoughts
This post isn’t a deep technical breakdown of how we index, query, and reason over code at this scale that deserves its own discussion.
The point here is simpler: most teams don’t need Sourcegraph.
But when software becomes mission-critical, multi-system, and AI-generated at scale, context stops being optional — and that’s the problem we exist to solve.
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