Nia – Give Your AI Coder Real Project Context

TL;DR

TL;DR: Nia is a context indexing and retrieval service that gives AI coding agents like Cursor and Claude Code grounded, up-to-date knowledge of your actual codebase and internal docs — eliminating the hallucinations that come from relying on stale public training data.

Source and Accuracy Notes

• Official site: trynia.ai
• HN Launch: Launch HN: Nia (YC S25) — 131 points
• MCP protocol enables broad agent compatibility

What Is Nia?

When you use an AI coding agent on a real project, you have probably run into this: the agent confidently suggests an API that changed two versions ago, uses a framework flag that no longer exists, or references documentation for a library you have already upgraded past. You spend more time correcting it than it would have taken to just write the code yourself.

This is not a model intelligence problem. The model is doing exactly what it was trained to do — retrieve the most statistically likely next token from its training data. The problem is that your codebase, your internal wiki, your specific SDK versions, and your custom internal patterns are not in that training data. General models are trained on public code and documentation that is often months or years old.

Nia (from YC S25) attacks this at the source: it builds a living index of everything that actually matters about your project — your repositories, your internal and external documentation, your SDK pages — and serves it to any connected AI agent as grounded context at the start of each task.

How Nia Works

Under the hood, Nia is an indexing and retrieval service with two interfaces: an MCP server and a REST API. You configure it with a list of sources: GitHub repositories, framework docs, internal wikis, PDF manuals, or any URL. Nia fetches, parses, and chunks that content, then builds several parallel indexes over it.

The first index is semantic: Nia generates embeddings for each chunk so natural language queries like “how do I authenticate with our API?” return relevant results even when the exact wording does not match. The second index is structural: it separately indexes symbols — function names, class names, types, and API endpoints — so queries like “where is the user authentication function defined?” return precise locations. The third is a reference graph that tracks which files import or call which other files and symbols, enabling graph-walk retrieval for queries that span multiple related components.

When an agent calls Nia with a task, it sends a natural language query plus optional hints — the current file path, a stack trace, the active repository. Nia runs a weighted combination of BM25 keyword search, embedding similarity, and graph traversal to rank relevant snippets, then returns them with precise source locations. The agent decides how to use those snippets in its own prompt.

Multi-Agent, Multi-Project Architecture

One Nia deployment can serve multiple agents and multiple projects simultaneously. You can have Cursor, Claude Code, and a browser-based agent all connected to the same Nia instance that knows about your monorepo, your internal wiki, and the external provider docs you depend on.

Nia tracks a session record that logs which sources were consulted and which returned snippets the user accepted. Any MCP client can attach to that session ID and extend it — meaning switching between agents does not discard what has already been discovered. The session carries the context forward.

Keeping indexes fresh is handled by background workers that periodically refetch configured sources, detect changed files or pages, and update only the affected chunks rather than reprocessing everything from scratch.

Setup Workflow

Step 1: Install the Nia CLI

curl -fsSL https://install.trynia.ai | bash

Alternatively, use the npm package:

npm install -g @trynia/cli

Step 2: Initialize a Nia Project

nia init --project myproject
cd myproject

This creates a nia.config.json file where you define your sources.

Step 3: Add Your Codebases and Docs

Edit nia.config.json to register repositories and documentation sources:

{
  "sources": [
    {
      "type": "github",
      "repo": "your-org/your-repo",
      "branches": ["main"]
    },
    {
      "type": "url",
      "url": "https://docs.your-internal-wiki.com/",
      "filter": "*.md"
    },
    {
      "type": "url",
      "url": "https://api.provider.com/docs",
      "priority": "high"
    }
  ],
  "indexes": ["semantic", "symbol", "graph"]
}

Step 4: Index Your Sources

nia index --sources

This fetches and processes all configured sources. The first run may take several minutes for large repositories. Subsequent runs are incremental and much faster.

Step 5: Connect an MCP Client

Nia exposes an MCP server endpoint. Configure your MCP client (Cursor, Claude Code, or any MCP-compatible agent) to connect:

nia mcp start --port 8765

Then in your agent’s MCP configuration file (~/.confignia/mcp.json or equivalent):

{
  "mcpServers": {
    "nia": {
      "command": "nia",
      "args": ["mcp", "connect", "--port", "8765"]
    }
  }
}

Step 6: Start Using Natural Language Queries

Once connected, query your project context directly from the agent:

> Nia: How does our payment webhooks authentication work?
> Nia: What is the latest version of the Stripe SDK we use?
> Nia: Find all places that call the /api/users endpoint

Deeper Analysis

Why This Matters for Agent Reliability

The standard workaround — manually pasting relevant documentation URLs and code snippets into the agent context — works but does not scale. Every new task requires a manual search for the right context, which defeats the purpose of using an agent in the first place. Worse, manual context selection introduces its own bias: you provide what you think is relevant, but you may miss a related function or an updated version of a dependency that changes the approach entirely.

Nia automates context retrieval at the agent level. The agent itself decides what it needs based on the task, and Nia retrieves the most relevant grounded snippets. This mirrors how a human developer would approach an unfamiliar codebase: skim the relevant files, check the documentation for the specific API call, and build up context before writing code.

The Symbol Index Is the Key Differentiator

Most retrieval-augmented approaches for coding tasks rely on embedding similarity alone — chunk the code, embed it, retrieve by cosine similarity. This works for natural language queries but struggles with precise structural questions like “find the function that handles user login” or “show me the type definition for PaymentIntent.”

Nia’s separate symbol index handles this by parsing code for function definitions, class declarations, type aliases, and exported symbols. Queries that target these structural elements return precise file and line information rather than fuzzy thematic matches. This makes Nia significantly more useful for refactoring tasks, cross-file dependency analysis, and working with large monorepos where semantic similarity alone produces too much noise.

Session Continuity Across Agents

The session record is a subtle but important feature. When you switch from Cursor to Claude Code mid-task, most agent setups lose all accumulated context. Nia’s session continuity means the second agent can see what the first agent already discovered, avoiding redundant lookups and maintaining task coherence across tool switches. For teams where developers use different agents for different purposes, this is a significant workflow improvement.

Practical Evaluation Checklist

• [ ] nia init completes without errors on a clean project
• [ ] GitHub source indexing pulls the correct branches and detects the default branch automatically
• [ ] Symbol index correctly identifies function definitions and type declarations in TypeScript and Python repos
• [ ] Semantic search returns relevant results for natural language queries like “how do I reset a user password?”
• [ ] Graph traversal correctly identifies cross-file dependencies for a given function call
• [ ] MCP connection established with Cursor and natural language queries return grounded results
• [ ] Session continuity works when switching between two MCP clients
• [ ] Incremental re-indexing detects changed files without reprocessing unchanged content
• [ ] Index freshness: configured sources are re-fetched within the expected polling interval

Security Notes

• Nia indexes codebases locally by default; no code leaves your infrastructure unless you explicitly configure a cloud-hosted deployment
• GitHub source integrations use scoped tokens with read-only repository access
• Session records stored locally contain only query metadata, not code content
• If self-hosting Nia, apply standard server hardening: TLS for the MCP endpoint, authentication on the management API, and network segmentation from production services
• Review which sources your Nia instance indexes — internal wikis and private documentation may contain sensitive architectural information that should not be accessible to all agent sessions

FAQ

Q: How is Nia different from just using a better prompt with URLs pasted in?

A: Manual prompt enrichment works for one-off tasks but does not scale and introduces human bias in source selection. Nia automates retrieval at the agent level, so every task gets comprehensive context automatically, and the agent itself decides what is relevant rather than relying on you to guess.

Q: Does Nia work with Copilot or only with Cursor and Claude Code?

A: Nia uses the MCP protocol, so any MCP-compatible client can connect. Copilot in VS Code does not natively support MCP, but VS Code Copilot Chat and JetBrains AI Assistant may have MCP support depending on the plugin version. Check the Nia docs for the latest compatibility list.

Q: How does index freshness work for external documentation?

A: Nia’s background workers periodically re-fetch configured external URLs and compare against the last stored version. If the page content has changed, only the affected chunks are updated. You can configure the polling interval per source — high-priority documentation can be checked hourly, while stable external APIs might be checked weekly.

Q: What happens if Nia retrieves the wrong context snippet?

A: Nia returns snippets with source citations. The agent decides how to use them, and you can inspect which sources were retrieved in the session log. If a snippet is wrong or outdated, you can mark it as rejected in the session, which influences future retrieval rankings.

Q: Can Nia index private GitHub Enterprise repositories?

A: Yes — Nia supports GitHub source integration with any GitHub Enterprise instance, provided you supply an appropriate scoped access token. The token needs read access to the repositories you configure.

Conclusion

Nia addresses the core reliability problem with AI coding agents: they hallucinate not because they are bad models, but because they are working blind against stale public data. By building a living, queryable index of your actual codebase and documentation, Nia gives agents the grounded context they need to make accurate, project-specific decisions.

The multi-agent session continuity is particularly compelling for teams that mix and match tools. If you have been working around AI agent context problems with manual prompt engineering, Nia is worth evaluating as a systematic solution rather than a collection of workarounds.

Start at trynia.ai and connect your first repository — the setup takes under ten minutes for a single repo.