Built for multi-million line codebases and complex architectures. AI that actually understands your code — powering search, reviews, and automation across your entire stack.
PMs and support get answers without interrupting engineers. Reviews run automatically. Processes that used to take days happen in minutes.
The bottleneck isn't writing code anymore — it's understanding it.
In small repos, "search a few strings and read a snippet" often works. In enterprise codebases it creates a predictable failure: tools claim understanding too early—and the plan, review, or fix is built on partial context.
Instead of searching text, Probe queries your codebase the way you'd query a database — with structure, precision, and speed.
Full query syntax with boolean operators, phrase matching, and field-specific search. Works out of the box; optional indexing for larger repos.
Probe understands code structure — functions, classes, scopes, call paths. It returns the smallest useful slice with clear boundaries, not arbitrary line ranges.
Works with language servers for go-to-definition and references. Reads go.mod, package.json, Cargo.toml — understands what your project actually depends on.
Built in Rust with SIMD optimizations — fast enough to search million-line repos locally. Runs entirely on your machine as a single binary. Retrieval runs locally; you control what context is sent to the model.
From product and support to engineering leadership, Probe turns codebases into shared, reliable knowledge.
As a Product Manager, Probe helps me to understand the true behaviour of the software so that I can go beyond the documentation and validate edge case scenarios and answer "what if?" questions as I develop the product roadmap and define new capabilities. This saves a lot of time and disruption to the development teams, as I can provide more fully formed and reasonable proposals to them without having to ask them to check my understanding of the code every five minutes.
I'm using Probe Labs tools daily as a technical lead, and they've been adopted across marketing, sales, documentation, product, delivery, and engineering. Probe is useful in every part of the SDLC, and it helps us understand complex features across multi-project dependency chains quickly and efficiently. The YAML-based automation makes it easy to wire in tools like JIRA, Zendesk, and GitHub for agentic flows that actually work from day one.
In 30 minutes, we'll show you exactly how Probe empowers your entire organization to get answers without waiting for engineering.
This will be an engineer-to-engineer meeting. No sales fluff.
Engineers, leaders, and product teams all get value — in different ways. See what Probe delivers for your role.
Three things that work right now: chat with your entire codebase, intelligent code reviews that scale, and workflow automation that runs itself.
Real value, not demos. Pick any of these and have something running before your next meeting.
Get enterprise-grade code understanding in your existing workflow. Probe auto-detects Claude Code and Codex auth, or works with any LLM API. One command - your AI actually understands your architecture.
claude mcp add probe -- npx -y @probelabs/probe@latest agent --mcp# Opens a web browser with the Probe agent UI
npx -y @probelabs/probe-chat@latest --webDirect entry points for teams, tools, and automation.
Ask questions across large codebases with grounded context.
Chat with code →Issue + PR automation inside GitHub Actions.
GitHub Assistant →Shared context for PMs, support, and engineering.
Team chat →Shell-first pipelines for large repos and automation.
CLI workflows →Visor workflows for real product + engineering processes.
Process automation →Integrations with Claude Code, Codex, and IDEs.
Editor integrations →Custom tooling on top of Probe context.
Build tools →Programmatic workflows in JavaScript.
Node.js SDK →CI is great for building, testing, and deploying code. Visor is built for something different: running agent workflows across tools and teams—where you need explicit control flow, validated outputs, and observable runs.
Steps, prompts, schemas, routing, outputs—all defined in YAML. No hidden glue, no magic.
AI + GitHub + HTTP + shell + MCP tools + memory in one workflow.
Enforce structured outputs. Stable results for PR comments, reports, and downstream systems.
OpenTelemetry traces + log correlation built in. Debug agent workflows like any production system.
Bounded retries, deterministic routing, approval gates. Predictable behavior, not improvisation.
Fixtures and mocks validate behavior before production. Run the same workflow locally, in CI, or via API.
Same foundation for everyone: pipelines (not prompts), scoped context, validated outputs, and observable runs.
Make AI execution predictable across teams—without forcing one workflow on everyone.
Engineering leadership →Ship "paved roads" for agent workflows: permissions, templates, audit trails, and standards.
Platform teams →Get answers grounded in your stack—and turn repeatable work into versioned pipelines.
Engineers →Onboard faster to unfamiliar codebases with context packs + repeatable workflow templates.
Agencies →Resolve tickets and write specs using source-of-truth context—without blocking engineering.
Product & Support →Not sure? Browse workflow examples →
Run it in your environment, bring your model, and audit every workflow like infrastructure.
No vendor lock-in: open-source core + open standards + deploy anywhere.
Prompts are instructions. Pipelines are defined systems: explicit steps, constrained tools, validated outputs, retries, and feedback loops—so behavior is repeatable and governable.
Model outputs can vary. The workflow doesn't have to. Visor makes execution deterministic through constraints, schemas, validation, retries, and auditability—so outcomes are governable.
Yes. Deploy in your environment and choose the LLM per workflow. You can switch models without rewriting your entire workflow strategy.
Open-source core, deploy anywhere, model-agnostic execution, and open standards like OpenTelemetry for observability.
Because it's hard to trust, debug, and govern. Many specialized agents inside explicit pipelines are predictable and scalable.
No. Probe retrieves the right context deeply across enterprise codebases and docs, and Visor turns that context into validated, observable execution.
If you're moving toward agent-first development, the question isn't whether you'll automate. It's whether your automation will be repeatable, inspectable, and safe—with clear boundaries, validations, and audit trails.
Prefer bottom-up adoption? Start with one workflow, run it on-prem, then expand into a library.