AI·rete·RAG
Freemium
Teams needing deterministic rules plus RAG explanations
Contextual
PaidSelf-Hosted
Your AI coding agent knows the current code — it has no idea why the webhook signer changed six months ago, or which files will break if you rename that function. Contextual is a local context engine built to hold that institutional memory, on your machine, and surface it through MCP to whatever agent you're already using.
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Summary
The engine indexes your repository — symbols, commits, PR threads, docs — into a local graph stored in ~/.contextual, then keeps it live with a filesystem watcher that reindexes on save and forks the timeline on branch. Semantic search returns not just the matching code, but the commit that changed it, the PR discussion that explains why, and a staleness signal telling you if that context is stale. Zero bytes leave the device; embeddings run on-device with CodeRankEmbed. The wall appears when you need multi-repository memory, team-shared context, or any workflow that requires more than one developer's machine to be in sync — none of that is described in the docs, and the tool is in beta.
Bottom line: Pick Contextual if you are a solo developer who needs an AI agent to recall decisions across a single local codebase; switch to a hosted solution the moment your team needs shared context or you outgrow a single-machine index.
Pricing Plans
Subscription- Price
- $10 /mo
Solo
$10per month
For a single engineer with unlimited local indexing
- Unlimited local projects
- Unlimited files & queries
- MCP stdio + HTTP
- Decisions atlas
- GitHub Community
- Email support
- 14-day free trial
View full pricing on contextuallabs.dev →
Pricing may have changed since last verified. Check the official site for current plans.
Find your fit
Before you choose Contextual
More options in RAG Frameworks. Compare pricing, platforms, and features side by side.
Jylus
Freemium
AI models and agents that require source-backed, up-to-date evidence from changing data
Pros
- On-device embeddings and zero network egress, which means code containing unreleased features, security logic, or proprietary algorithms never leaves the machine — a requirement teams previously could not meet with hosted RAG tools.
- Temporal graph anchors every symbol and PR to the git timeline, so an AI agent can answer 'why was this changed?' rather than just 'what does this do?' — the class of question that previously required manually grepping commit history.
- Filesystem watcher reindexes on save and forks the graph on branch switch, so the context your agent queries reflects the working tree you are actually in, not a stale snapshot from the last manual index run.
- Native MCP integration with Cursor, Claude Desktop, Claude Code, and VS Code, so the engine slots into the editor you already use without writing adapter code or maintaining a side process.
- Single-folder storage and a one-command uninstall mean there is no persistent account state or cloud entanglement — teams evaluating the tool can remove it completely if it does not fit.
Cons
- The architecture is explicitly single-machine: one developer, one local store. The moment a second developer on the same project needs access to the same indexed memory — shared ADRs, shared decision history — the tool has no answer. Teams at that point move to a hosted RAG or knowledge-base solution that supports multi-user access.
- The tool is in v1.0.0 beta; the docs describe no API surface beyond MCP, no programmatic query interface, and no multi-repository support. Teams building internal tooling that needs to query context outside an editor context hit a dead end and fall back to building their own retrieval layer.
- There is no open-source path. The license is proprietary, and the paid tier is required for continued use after the trial period ends. Teams with policy requirements for auditable, self-hostable open-source tooling cannot satisfy those requirements here.
About
- Platforms
- Local / Desktop
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- September 8, 2026
Best For
Who it's for
- Individual developers managing local codebases
- Users needing unlimited on-device indexing
- Projects requiring private, local-first context storage
What it does well
- Indexing local code projects for semantic search
- Maintaining temporal recall of decisions and queries
- Providing context to AI tools via MCP integrations
Integrations
Frequently Asked Questions
- Is Contextual free?
- Contextual is a paid tool ($10 /mo). A 14-day free trial is available.
- Can I self-host Contextual?
- Yes. Contextual supports self-hosting on your own infrastructure.
- What platforms does Contextual support?
- Contextual is available on: Local / Desktop.
Curated lists that include this category
When AI agents lose the story behind the code
Your AI coding agent knows the current code — it has no idea why the webhook signer changed six months ago, or which files will break if you rename that function. Contextual is a local context engine built to hold that institutional memory, on your machine, and surface it through MCP to whatever agent you’re already using.
Local indexing that stays live
The engine indexes your repository — symbols, commits, PR threads, docs — into a local graph stored in ~/.contextual, then keeps it live with a filesystem watcher that reindexes on save and forks the timeline on branch. Semantic search returns not just the matching code, but the commit that changed it, the PR discussion that explains why, and a staleness signal telling you if that context is stale. Zero bytes leave the device; embeddings run on-device with CodeRankEmbed.
Clear limits on scale
The wall appears when you need multi-repository memory, team-shared context, or any workflow that requires more than one dev. The tool is in v1.0.0 beta; the docs describe no API surface beyond MCP, no programmatic query interface, and no multi-repository support. The architecture is explicitly single-machine: one developer, one local store.
Who it is for / who should skip it
Best for individual developers managing local codebases, users needing unlimited on-device indexing, and projects requiring private, local-first context storage. Teams that need shared ADRs or multi-user access should skip it and move to a hosted RAG solution instead. On-device embeddings keep proprietary code private, yet the single-machine design stops short the moment a second developer needs the same indexed memory.
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