Nia vs Exa Code: Why Indexing Beats Search for AI Agents

Exa recently launched Exa Code, a context API that searches across GitHub repos, docs, and Stack Overflow to provide code snippets. It’s a step forward for coding agents, but it fundamentally approaches the problem differently than Nia.

The core difference: Exa searches the web. Nia indexes your sources.

This distinction matters more than it might seem.

The Architecture Gap

Exa Code is a single endpoint:

response = requests.post("https://api.exa.ai/context", json={
    "query": "how to use React hooks",
    "tokensNum": 5000
})

You send a query, you get snippets back. That’s it. No persistence, no customization, no control over what sources are searched.

Nia is a context layer with 10+ specialized tools:

• search — semantic search across your indexed sources
• nia_grep — regex pattern matching in code
• nia_read — read specific files with line numbers
• nia_explore — browse repository file structures
• nia_research — autonomous deep research (quick, deep, oracle modes)
• context — save and share findings across sessions and agents

The difference isn’t just feature count. It’s the fundamental model: Nia gives you deterministic access to specific sources while Exa gives you probabilistic search results.

You Choose What Gets Indexed

With Exa, you query their search index and hope it contains what you need. With Nia, you explicitly index:

• GitHub repositories — your private repos, specific open source projects
• Documentation sites — any docs site, scraped and chunked properly
• Research papers — arXiv papers for academic context
• Local folders — private data, chat history, databases
• Packages — 3,000+ pre-indexed packages from PyPI, NPM, Crates.io, Go

This matters because context quality depends on source quality. When you’re migrating from one API version to another, you want the agent to reference the exact documentation for both versions, not whatever Stack Overflow answers happen to rank highly.

Beyond Code: Use Cases Exa Can’t Handle

Exa Code is built for one thing: code snippets from public sources. Nia’s indexing model enables entirely different use cases:

• Index the Cursor forum to answer questions about Cursor-specific issues
• Index Paul Graham’s essays to build a personalized writing assistant
• Index Naval’s archive to create an agent with his mental models
• Index healthcare documentation for domain-specific medical agents
• Index your company’s internal wiki for onboarding agents

Nia’s /explore page shows the community contributing sources across domains. It’s not just code anymore.

Context That Persists

Exa is stateless. Every request starts fresh.

Nia maintains context across sessions:

# Save context from one session
context(action='save', title='API migration plan',
        content='Findings about v2 to v3 migration...')

# Retrieve it in another session, even from a different agent
context(action='retrieve', context_id='...')

This enables multi-agent workflows where different tools (Cursor, Claude Code, browser agents) share discoveries. One agent investigates an issue, saves findings. Another agent picks up where it left off.

The Benchmark Results

We ran a benchmark focused on newly released and beta features—the exact scenario where LLMs fail because features shipped after training cutoff.

Tool	Hallucination Rate
Nia Oracle	Lowest
Exa	Higher
Brave Search	Higher
Perplexity	Higher
Baseline (no context)	Highest

The methodology: strict LLM-as-a-Judge evaluation, temperature=0.0 for reproducibility, categorizing errors as invented_method, wrong_parameter, or outdated_api.

Example failure case: Anthropic’s token counting API.

Brave Search returned code using client.tokens.count_tokens(). The actual API is client.messages.count_tokens(). Search-based approaches found plausible-looking but wrong code.

Nia retrieved the correct endpoint from the indexed SDK because we had the actual source indexed, not search results about it.

Auto-Sync vs Point-in-Time

Exa searches whatever is in their index at query time. If docs updated yesterday, you might get stale results.

Nia’s background workers continuously monitor indexed sources, detect changes, and reindex incrementally. Your context stays current without manual intervention.

This matters because many “hallucinations” are actually the model quoting valid documentation for the wrong version.

Pricing Comparison

Exa Code: Per-query pricing based on tokens returned.

Nia:

• Free tier: 50 queries/month, 3 indexing jobs
• Pro ($15/mo): 1,000 queries, 50 indexing jobs
• Startup ($50/engineer/mo): 5,000 queries, context sharing, 100 Oracle calls

For teams doing serious agent work, Nia’s model means you pay for the capability, not per-snippet.

When to Use Each

Use Exa Code if:

• You need quick, one-off code snippets
• You’re building a simple coding assistant without specific source requirements
• Stateless search results are acceptable

Use Nia if:

• You need deterministic access to specific sources
• You’re building agents that require up-to-date documentation
• You want context to persist across sessions
• You need to index private repos, internal docs, or non-code sources
• You’re building team workflows with shared context

Conclusion

Exa Code is a good search API. Nia is a context layer.

Search gives you what’s popular. Indexing gives you what’s correct.

For agents that need to generate working code against specific libraries, frameworks, and APIs, the distinction between “found some snippets” and “retrieved the exact source” is the difference between code that runs and code that hallucinates.

Nia is available at trynia.ai.