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

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.

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 after each session. The 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 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 – 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 – Persists built graphs to the Deeplake virtual file system
• src/graph/cache.ts – In-memory memoization layer for the latest snapshot
• 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:

# 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.

Programmatic Access

Query the graph directly from TypeScript or JavaScript applications:

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.

Custom Extractors

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

// 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 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, src/graph/snapshot.ts, and 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, which memoizes the latest snapshot in memory. The snapshot itself is written to disk by 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 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.

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