# Performance Benchmarks for the Hivemind Graph Module: 25% Cost Reduction on LoCoMo

> Discover Hivemind graph module performance benchmarks: 25% cost reduction on LoCoMo, 1.7x less tokens, and 31% fewer agent turns. Learn how caching boosts efficiency.

- Repository: [Activeloop/hivemind](https://github.com/activeloopai/hivemind)
- Tags: performance
- Published: 2026-06-11

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**The Hivemind graph module reduces API costs by 25%, token usage by 1.7×, and agent turns by 31% on the LoCoMo benchmark by caching structural metadata in a Deeplake-backed virtual file system.**

The graph module in the `activeloopai/hivemind` repository powers the live code-base graph that agents query to answer structural questions like "what calls X?" or "where is Y defined?". These performance benchmarks demonstrate how pre-computed graph lookups replace expensive LLM calls to deliver measurable cost and latency improvements on long-context memory tasks.

## LoCoMo Benchmark Results

The public **LoCoMo** long-context memory benchmark (100 QA pairs, Claude Haiku via `claude -p`, hybrid lexical + semantic retrieval) quantifies the graph module's impact across three critical metrics:

| Metric | Baseline (No Memory) | Hivemind Graph | Improvement |
|--------|---------------------|----------------|-------------|
| **Cost / 100 QA** | $8.94 | **$6.65** | **25% cheaper** |
| **Tokens / question** | 1,700 | **1,008** | **1.7× fewer** |
| **Turns / question** | 8.9 | **6.2** | **31% fewer** |

*Source: Repository README section [Benchmarks](https://github.com/activeloopai/hivemind/blob/main/README.md#benchmarks).*

These figures reflect the entire Hivemind pipeline, but the graph module drives the token and turn reductions by serving pre-computed structural metadata instead of re-parsing source files on each interaction.

## How the Graph Module Achieves These Gains

The performance improvements stem from replacing runtime source analysis with cached graph lookups. Rather than re-deriving context through multiple token-heavy LLM calls, the module retrieves normalized AST data from a local virtual file system.

### Caching and Snapshot Architecture

The graph persists structural metadata in `~/.deeplake/memory/graph/<repo-key>/snapshot.json`, written by [`src/graph/snapshot.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/snapshot.ts) after each session. The [`src/graph/cache.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/cache.ts) module memoizes this snapshot in memory, enabling instantaneous query responses. This architecture eliminates redundant file parsing and minimizes API round-trips.

### Graph Extraction Pipeline

Language-specific extractors in `src/graph/extract/*.ts` parse source files using Tree-sitter to produce a normalized AST. Each extractor contributes nodes, edges, and metadata to a shared schema. After extraction, the `graph-on-stop` hook in [`src/hooks/graph-on-stop.ts`](https://github.com/activeloopai/hivemind/blob/main/src/hooks/graph-on-stop.ts) triggers background rebuilds, respecting rate limits to keep the graph fresh without system overload.

## Key Implementation Files

The graph module's performance characteristics depend on tight integration between these components:

- **[`src/commands/graph.ts`](https://github.com/activeloopai/hivemind/blob/main/src/commands/graph.ts)** – CLI dispatcher that routes sub-commands (`find`, `show`, `layers`, `tour`) to appropriate handlers
- **`src/graph/extract/*.ts`** – Language-specific AST parsers (JavaScript, TypeScript, Go, Rust, Java, Ruby, C, C++)
- **[`src/graph/snapshot.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/snapshot.ts)** – Persists built graphs to the Deeplake virtual file system
- **[`src/graph/cache.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/cache.ts)** – In-memory memoization layer for the latest snapshot
- **[`src/hooks/graph-on-stop.ts`](https://github.com/activeloopai/hivemind/blob/main/src/hooks/graph-on-stop.ts)** – Background build trigger that refreshes the graph after each session
- **`tests/shared/graph/*`** – 79 unit tests covering extraction, cross-file resolution, and query handling (43 + 12 + 24 tests)

## Using the Graph Module

You can interact with the graph through the CLI or programmatically via the Node.js API.

### CLI Commands

The `hivemind graph` command suite provides direct access to the cached graph:

```bash

# Find every occurrence of a symbol named "AuthService"

hivemind graph find AuthService

# Show the definition of a handle returned by find

hivemind graph show <handle>

# Walk the import hierarchy of a file

hivemind graph neighborhood src/auth/auth_service.ts

# Get a high-level architectural view

hivemind graph layers

```

All commands dispatch through [`src/commands/graph.ts`](https://github.com/activeloopai/hivemind/blob/main/src/commands/graph.ts).

### Programmatic Access

Query the graph directly from TypeScript or JavaScript applications:

```typescript
import { queryGraph } from '@deeplake/hivemind';

// Search for a function definition
const results = await queryGraph('find', { pattern: 'handleLogin' });
console.log(results);   // → [{ source_file: 'src/auth/login.ts', line: 42, … }]

```

The `queryGraph` helper proxies to the virtual file system under `~/.deeplake/memory/graph/` and utilizes the cached snapshot managed by [`src/graph/cache.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/cache.ts).

### Custom Extractors

To extend the graph for unsupported languages, implement a custom extractor:

```typescript
// src/graph/extract/my_lang.ts
export async function extractMyLang(file: string): Promise<Node[]> {
  const ast = await parseWithTreeSitter(file, 'my_lang');
  // Convert AST nodes to the shared graph schema
  return convertToGraphNodes(ast);
}

```

Register the extractor in [`src/graph/extract/index.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/extract/index.ts) to include your language in the next background build.

## Summary

- **Cost efficiency**: The graph module cuts API costs by 25% on the LoCoMo benchmark by reducing unnecessary LLM calls.
- **Token reduction**: Pre-computed graph lookups decrease token usage per question from 1,700 to 1,008.
- **Faster resolution**: Agent turns drop by 31% (from 8.9 to 6.2) because structural queries resolve instantly from cache.
- **Robust architecture**: The implementation spans [`src/commands/graph.ts`](https://github.com/activeloopai/hivemind/blob/main/src/commands/graph.ts), [`src/graph/snapshot.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/snapshot.ts), and [`src/graph/cache.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/cache.ts), with 79 unit tests ensuring reliability at scale.

## Frequently Asked Questions

### How does the graph module reduce API costs by 25%?

The module caches structural metadata (functions, classes, calls, imports) in a Deeplake-backed virtual file system at `~/.deeplake/memory/graph/`. When agents query code structure, the system retrieves pre-computed graph data instead of making multiple expensive LLM calls to re-parse source files, directly reducing API consumption.

### What files handle the graph caching mechanism?

The caching layer is implemented in [`src/graph/cache.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/cache.ts), which memoizes the latest snapshot in memory. The snapshot itself is written to disk by [`src/graph/snapshot.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/snapshot.ts) after each session concludes, ensuring subsequent queries serve instantly from local storage rather than rebuilding the graph from scratch.

### Where are the performance benchmarks documented?

The LoCoMo benchmark results are officially documented in the repository's README under the [Benchmarks](https://github.com/activeloopai/hivemind/blob/main/README.md#benchmarks) section. These metrics compare Hivemind with graph enabled against a baseline with no memory system, measured across 100 QA pairs using Claude Haiku.

### How reliable is the graph module for production use?

The core graph files maintain greater than 90% statement coverage through a comprehensive test suite located in `tests/shared/graph/`. With 79 unit tests covering extraction logic, cross-file resolution, and query handling, the module ensures that performance gains scale reliably with real-world agent workloads.