AI code assistant that finds and explains code instantly
Blackbox is an AI-powered code assistant that lets developers search, autocomplete, and explain code across local repos and public sources. It suits individual engineers and small teams who want fast code lookup, multi-file context, and snippet explanations; pricing starts with a usable free tier and paid Pro plans for heavy usage. For developers wanting an IDE-integrated search-first assistant, Blackbox is a practical, cost-conscious choice.
Blackbox is an AI code assistant that provides contextual code search, autocomplete, and explainability directly in your editor or browser. It indexes local repositories and public sources to let developers find relevant snippets, run smart autocompletions, and get line-by-line explanations. Its key differentiator is editor and browser integrations that surface searchable code answers across projects rather than only single-file LLM completions. Blackbox serves individual developers, open-source contributors, and small engineering teams; it offers a free tier with basic limits and a paid Pro tier for heavier, commercial usage.
Blackbox launched as an AI-native code assistant focused on surfacing existing code and explanations quickly to developers. Founded to bridge code search and generative assistance, it positions itself between traditional IDE autocompletion and full-chat LLM copilots. The core value proposition is searchable, context-aware code answers: instead of generating code from scratch, Blackbox helps you find matching snippets, understand them, and adapt them across repositories. The product emphasizes in-editor workflows and browser snippet grabbing, aiming to reduce time spent hunting through files and docs.
Blackbox's feature set centers on several concrete capabilities. First, Code Search indexes your local projects and public sources and returns ranked code matches with file paths and usage examples. Second, Explain Code offers line-by-line or function-level explanations, attributing the source file and showing surrounding context so you can evaluate correctness. Third, Autocomplete and Snippet Expansion provide multi-line completions seeded from indexed project code, preserving naming and imports from your repo. Fourth, integrations include editor plugins (VS Code, JetBrains) and a browser extension that turns GitHub and Stack Overflow pages into queryable sources. These features combine search, explainability, and context-preserving completions rather than one-off generated outputs.
Blackbox's pricing mixes a free tier and paid subscriptions. The Free plan provides basic usage: limited daily queries, public repo indexing, and browser extension features for personal use. The Pro plan (paid monthly) unlocks private repo indexing, higher query limits, and priority model access for commercial use. Team/Enterprise tiers add centralized billing, SSO, and organization-wide repo indexing with custom limits — these are quoted as custom pricing for larger teams. The exact monthly price for Pro and limits have changed over time; check the billing page for current per-seat costs and the Team/Enterprise contact for volume discounts. The free tier is intentionally usable but restricted to encourage upgrades for private or high-volume workflows.
Developers, maintainers, and engineers use Blackbox for specific workflows that require fast retrieval and comprehension of code. Example users include a backend engineer using Blackbox to find and copy-tested database query patterns across five repos, reducing bug-prone rewrites; and an open-source maintainer using the browser extension to quickly locate relevant implementations on GitHub and craft accurate PR descriptions. Product engineers use it to onboard faster by searching for canonical implementations, while SREs look up runbook code snippets. Compared with a full-chat copilot, Blackbox leans toward searchable code provenance and in-context reuse rather than purely generative synthesis, making it complementary to tools like GitHub Copilot.
Three capabilities that set Blackbox apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Limited daily queries, public repo indexing only, browser extension access | Hobbyists and open-source explorers |
| Pro | $12/month | Private repo indexing, higher query quota, faster model priority | Individual developers needing private use |
| Team | Custom | Org repo indexing, SSO, centralized billing, higher API/usage limits | Engineering teams and small companies |
Copy these into Blackbox as-is. Each targets a different high-value workflow.
You are Blackbox, a code search assistant indexing multiple repositories. Task: locate the three most idiomatic SQL/ORM query snippets that implement a paginated list retrieval by user_id in Python/Django or Node/TypeORM. Constraints: search across indexed repos, prefer tested code (presence of tests or comments), exclude autogenerated files, and return snippets no longer than 12 lines. Output format: JSON array of three objects: {language, label, file_path, repo, lines_snippet, why_this_is_canonical}. Example entry: {language: 'Python', label: 'Django ORM', file_path: '/users/queries.py', lines_snippet: '...'} If fewer than three suitable snippets exist, return the best matches and indicate missing count.
You are Blackbox, a line-by-line explainer for code. Task: explain the provided function (paste code or specify a file path). Constraints: produce a numbered list mapping to each line or logical block; highlight inputs, outputs, side effects, and algorithmic complexity; keep each line explanation <=30 words; include a one-sentence high-level summary at top. Output format: JSON object {high_level_summary, explanations:[{line_number, code, explanation}], suggestions:[short strings]}. Example input: def foo(x): return x+1 -> explanation for each line. Do not modify the code; only annotate and provide optional refactor suggestions.
You are Blackbox, a test generator and project-aware assistant. Task: for the provided function names and repository scope, generate pytest skeleton files with fixtures and two test cases per function (one happy path, one edge case). Constraints: use pytest conventions, include import paths relative to the repo, mock external DB or HTTP calls using pytest-mock, and keep each test file <=120 lines. Output format: JSON array representing a ZIP-style listing [{test_file_path, test_code}]. Example input: ['app/services/user_service.py::get_user_orders'] -> produce tests for that function including fixture scaffolding. If a function uses globals, add fixture placeholders and comments on what to stub.
You are Blackbox, a cross-repo refactor assistant. Task: given an API function name to deprecate (e.g., oldFuncName) and target replacement (newFuncName), find all call sites across indexed repos and produce an upgrade checklist with migration risk, required code edits, configuration changes, and CI/test impact. Constraints: list call_site file paths with line snippets, classify each as 'safe'|'risky'|'breaking' based on signature/behavior changes, and estimate replacement effort (low/medium/high). Output format: JSON {deprecated, replacement, call_sites:[{file, line_number, context_snippet, classification, fix_snippet}], checklist:[strings]}. Also flag external consumers or public API surfaces separately.
You are Blackbox, a security-focused code auditor. Task: scan indexed repositories for uses of eval/exec or dynamic code evaluation patterns and produce a prioritized remediation plan. Multi-step: 1) list findings with file paths, code snippet (<=10 lines), and CVSS-like risk (low/med/high); 2) suggest specific safe replacements or concrete code patches (include example before/after patch); 3) provide a CI check (regex or AST-based) to detect and block new occurrences. Constraints: include impact justification and preferred language-specific fixes (JS: JSON.parse/safe-eval; Python: ast.literal_eval, parsing, or explicit parsers). Output format: JSON {findings:[{file,lines,code_snippet,risk,justification,suggested_patch}], remediation_plan:{ci_check,education_steps,priority_order}}. Include one example finding.
You are Blackbox, a developer tooling expert constructing codemods. Task: produce a reusable jscodeshift codemod that renames a widely-used function oldName to newName across JS/TS repos, updates imports/exports, preserves comments, and optionally adds a deprecation shim. Multi-step deliverables: 1) full codemod script code, 2) test cases showing before/after snippets, 3) roll-back and dry-run command examples, 4) performance considerations and batching strategy for monorepos. Constraints: handle default and named imports, TypeScript type references, and JSX usages. Output format: JSON {script_filename, script_code, tests:[{before,after}], commands:[strings], notes}. Example: rename fetchData to fetchResource.
Choose Blackbox over GitHub Copilot if you prioritize searchable, source-attributed code snippets and multi-repo lookup over generative chat interactions.