The RAG Grep Tool
Run fast literal and regex search over an indexed repository snapshot so an agent can find precise strings, symbols, and patterns — the exact-match complement to semantic retrieval.
Your answer exists — retrieval just can’t find it
Private knowledge is only useful if an agent can retrieve exactly the right piece and ground its answer in it. Keyword search misses, hosted RAG can’t touch sensitive data, and ungrounded models make things up.
Keyword search misses
The right content is phrased differently than the query.
Ungrounded answers
Without retrieval, models invent instead of cite.
Scale hides signal
The best chunk is buried among thousands of near-matches.
Hosted RAG is off-limits
Sensitive knowledge can’t go to a third-party index.
RAG Grep, without the risk
Capability
What it does
Exact-match search across an indexed repository.
it runs literal or regex search across an indexed repository snapshot and returns matching lines with paths.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
it searches the governed index rather than live disk, returning precise matches with context, so exact-string retrieval is fast, repeatable, and scoped to what the agent may see.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
Indexing and retrieval run on-premise or in your sovereign cloud, scoped per tenant and audit-logged, so even sensitive knowledge is searchable and citable without any of it leaving your perimeter.
Per-tenant, logged
Parameters
The rag.grep tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: false Optional Treat the pattern as a regular expression.
default: 100 Optional Maximum matches to return.
How the RAG Grep tool works in practice
RAG Grep is a semantic search & rag tool you assign to a VDF AI agent. It runs literal or regex search across an indexed repository snapshot and returns matching lines with paths. Its hallmarks — Exact, Regex, Indexed — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, it searches the governed index rather than live disk, returning precise matches with context, so exact-string retrieval is fast, repeatable, and scoped to what the agent may see. It expects repo and pattern as required inputs, so calls are explicit and easy to audit. Every call is scoped to the requesting tenant and written to an audit log, so the capability is safe to run inside a regulated, on-premise environment — the same governance model behind every VDF AI tool.
Teams reach for RAG Grep when they need to handle precise lookup, pattern audits, and hybrid retrieval. It rarely works alone — pair it with RAG Symbol Graph, Github Semantic Search, and Hybrid Search to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where RAG Grep pays back
Precise lookup
Find an exact symbol or config key across a repo.
Pattern audits
Locate every use of a risky pattern by regex.
Hybrid retrieval
Combine exact grep with semantic search for recall and precision.
Grounding
Give a code agent exact references to reason over.
Assigned to agents, orchestrated as networks
On VDF AI, an industry’s use cases map to agents, and you assign tools like this one to those agents. Compose multiple agents into a governed, on-premise network.
What changes after you assign it
Questions about the RAG Grep tool
What is the RAG Grep tool?
It runs literal or regex search across an indexed repository snapshot and returns matching lines with paths. Assigned to a VDF AI agent, it runs under role-based policy with full audit logging so the capability is safe to use in production.
How is this different from semantic code search?
RAG grep matches exact strings and patterns; semantic search matches meaning. Agents often use both together.
Does it search live files?
No. It searches a governed, indexed snapshot, so results are repeatable and access-controlled.
What inputs does the RAG Grep tool need?
It requires repo and pattern, and optionally accepts is_regex and max_results. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with RAG Grep?
RAG Grep is commonly assigned alongside RAG Symbol Graph, Github Semantic Search, and Hybrid Search. On VDF AI you compose several tools and agents into a single governed, on-premise network.
Does it run on-premise?
Yes. Like every VDF AI tool, it can run on-premise or in your sovereign cloud, scoped per user and audit-logged, so your data never leaves your perimeter.
How do agents use it?
You assign the tool to an agent under a role-based policy; the agent calls it as one step in a task, and several agents and tools can be orchestrated together as a governed VDF AI Network.
Assign RAG Grep to these agents
These VDF AI agents can be assigned this tool. Open an agent to see the full toolkit it can run.
Tools that work well alongside this one
Where this tool delivers value
Put RAG Grep to work
See the RAG Grep tool assigned to an agent and orchestrated in a governed, on-premise network.