Google's recent File Search API launch made waves: free storage, free query-time embeddings, and a fully managed RAG pipeline. On the surface, it looks like the perfect solution for giving AI agents memory. Upload some PDFs, ask Gemini questions, and you're done.
But here's the problem: real agents don't just read static files. They listen to audio. They watch videos. They monitor Slack channels. They ingest emails, pull from GitHub, and synthesize information across dozens of live data sources. They need multimodal understanding, continuous ingestion, and model flexibility — none of which Google File Search provides.
If you're evaluating Google File Search vs. Graphlit, this guide will show you exactly where Google's file-centric approach breaks down — and why Graphlit's semantic memory platform is built for the real world.
Table of Contents
- TL;DR — Quick Feature Comparison
- Understanding the Platforms
- The File-Only Trap: What Google File Search Can't Do
- Ingestion: Manual Uploads vs. Live Orchestration
- Multimodal Limitations: No Audio, No Video Intelligence
- LLM Lock-In: Gemini-Only vs. Model Flexibility
- Search and Retrieval: Vector-Only vs. Knowledge Graphs
- Agent Infrastructure: Retrieval vs. Orchestration
- Developer Experience and APIs
- Use Cases: When to Choose Each
- Final Verdict
TL;DR — Quick Feature Comparison
Understanding the Platforms
What is Google File Search?
Google File Search is a managed RAG solution built into the Gemini API. It abstracts away the complexity of traditional RAG pipelines — chunking, embedding generation, vector storage, and retrieval — and packages it as a single API call.
The workflow is simple:
- Create a
FileSearchStore - Upload files (PDFs, DOCX, code, etc.)
- Query with
generateContent()using Gemini - Get responses with automatic citations
It's positioned as a replacement for DIY RAG stacks, and for file-based retrieval with Gemini, it works well. But that's where it stops.
What is Graphlit?
Graphlit is an semantic memory platform powered by the Model Context Protocol (MCP). It's not just a RAG layer — it's a complete platform for giving AI agents persistent, multimodal memory across all their information sources.
Graphlit handles:
- Live data ingestion from 30+ connectors (Slack, Gmail, GitHub, RSS, YouTube, etc.)
- Multimodal processing (audio transcription, video understanding, image OCR)
- Knowledge graph construction (entity extraction, relationship modeling, graph traversal)
- Multi-LLM support (OpenAI, Anthropic, Google, Meta, custom models)
- Agent workflows (conversation management, tool calling, MCP integration)
- Continuous synchronization (automated feeds, real-time updates)
Where Google File Search is a retrieval tool, Graphlit is an agent infrastructure platform.
The File-Only Trap: What Google File Search Can't Do
Google File Search is built for one use case: static file retrieval. If your agent's knowledge comes entirely from uploaded documents, it might be enough. But real-world agents need far more:
❌ No Audio Transcription
Your team records meetings, customer calls, and podcasts. Google File Search cannot transcribe or ingest audio. You'd need to manually transcribe, save as text, and upload — defeating the purpose of automation.
Graphlit: Automatically transcribes audio files and audio feeds (podcasts, meeting recordings) with speaker diarization and timestamp metadata.
❌ No Video Understanding
Videos contain rich information: visual context, spoken dialogue, on-screen text. Google File Search cannot process video — it only sees static documents.
Graphlit: Extracts audio, transcribes dialogue, runs OCR on frames, and indexes video content as searchable, citable knowledge.
❌ No Image Intelligence
Images with embedded text (screenshots, infographics, slides) are invisible to Google File Search. No OCR, no visual understanding.
Graphlit: Runs OCR on images, extracts text from PDFs with embedded images, and makes visual content searchable.
❌ No Live Data Sources
Agents need to pull from Slack conversations, GitHub issues, email threads, RSS feeds, and social media. Google File Search requires manual file export and upload for every update.
Graphlit: Connects directly to 30+ live sources with automated polling or webhooks, continuously syncing new information without manual intervention.
❌ No Structured Data Ingestion
What about your CRM data, database exports, or API responses? Google File Search can't ingest structured JSON or CSV and understand relationships.
Graphlit: Ingests structured data, extracts entities and relationships, and links them into a queryable knowledge graph.
💡 Key Point: Google File Search works for static document collections. Real agents need live, multimodal, structured memory — which requires a platform, not just a file upload API.
Ingestion: Manual Uploads vs. Live Orchestration
Google File Search requires developers to:
- Export data from sources (Slack, email, GitHub, etc.)
- Convert to supported file formats (PDF, DOCX, TXT, etc.)
- Upload via API or resumable upload endpoint
- Re-upload manually whenever content changes
This works for one-time document ingestion. It fails catastrophically for agents that need current information.
Graphlit provides:
- 30+ pre-built connectors: Slack, Discord, Gmail, GitHub, Jira, Linear, RSS, YouTube, web pages, and more
- Automated feed synchronization: Continuous polling or webhook-based updates
- Incremental ingestion: Only processes new/changed content
- File ingestion too: Supports manual uploads when needed
- Multi-format support: Documents, audio, video, images, structured data, web pages
If your agent monitors a Slack channel for customer feedback, Graphlit ingests new messages automatically. With Google File Search, you'd need to manually export Slack history and re-upload it daily — an untenable workflow.
Multimodal Limitations: No Audio, No Video Intelligence
Google File Search supports 100+ file types — but all of them are static documents or code. It has no multimodal understanding:
Audio Blindness
- ❌ Cannot transcribe audio files
- ❌ Cannot ingest podcasts or recordings
- ❌ Cannot process meeting audio
- ❌ Cannot extract speaker metadata
Graphlit: Transcribes audio with speaker diarization, timestamps, and keyword extraction. Agents can answer "What did Sarah say about pricing in the Q3 planning call?"
Video Blindness
- ❌ Cannot process video files
- ❌ Cannot extract audio from video
- ❌ Cannot run OCR on video frames
- ❌ Cannot understand visual context
Graphlit: Extracts audio tracks, transcribes dialogue, runs OCR on key frames, and indexes video as searchable content with citations.
Image Blindness
- ❌ Cannot extract text from images (no OCR)
- ❌ Cannot process screenshots
- ❌ Cannot understand visual diagrams
Graphlit: Runs OCR on images, extracts embedded text from PDFs, and makes visual content queryable.
This isn't a minor limitation — it's a fundamental architectural gap. Modern agents operate in multimodal environments. Restricting them to text-only documents is like building a search engine that can't index videos.
LLM Lock-In: Gemini-Only vs. Model Flexibility
Google File Search is Gemini-only. You cannot use it with:
- OpenAI's GPT-4 or o1
- Anthropic's Claude
- Meta's Llama
- Custom fine-tuned models
- Local models (Ollama, LM Studio)
If you've built your agent stack on OpenAI or Anthropic, you'll need to rewrite your entire application to use Google File Search. If Gemini's pricing changes, you're locked in. If Google deprioritizes Gemini (as they've done with past products), you're stuck.
Graphlit is model-agnostic:
- Works with any LLM (Model Context Protocol)
- Supports OpenAI, Anthropic, Google, Meta, and custom models
- Allows per-conversation model selection
- No vendor lock-in — switch models at any time
This flexibility is critical for production systems where LLM landscape shifts rapidly. Betting on a single vendor's API is a liability, not a feature.
Search and Retrieval: Vector-Only vs. Knowledge Graphs
Google File Search uses semantic vector search:
- Embeds file chunks with Gemini Embedding model
- Performs vector similarity search
- Returns matching chunks with citations
This works for similarity-based retrieval but lacks relational understanding. If your agent needs to answer "Who worked with Alice on the Q4 roadmap?" or "What changed between the January and March proposals?", vector search alone won't cut it.
Graphlit combines:
- Semantic vector search (like Google File Search)
- Keyword search with BM25 ranking
- Knowledge graph traversal (entity-aware retrieval)
- Relationship queries ("Find content where Alice and Bob collaborated")
- Temporal filters ("Show me discussions before the product launch")
Graphlit automatically extracts entities (people, organizations, places, events) and models relationships. Agents can query not just by similarity, but by who, what, when, and how things connect.
✅ Example: "What did Sarah discuss with the engineering team about API pricing?" — Graphlit understands
Sarah(person),engineering team(organization), andAPI pricing(topic) and returns contextually linked results. Google File Search only matches text similarity.
Agent Infrastructure: Retrieval vs. Orchestration
Google File Search provides retrieval. That's it. No conversation management, no tool calling, no workflow automation.
To build a functional agent, you still need:
- Conversation state management
- Multi-turn dialogue handling
- Tool/function calling infrastructure
- Authentication and user isolation
- Observability and logging
- Rate limiting and error handling
You're responsible for building all of this yourself.
Graphlit provides the full stack:
- Conversation management: Multi-turn dialogues with context persistence
- MCP tool integration: Agents can call external tools (APIs, databases, services)
- Workflow automation: Real-time hooks when content is ingested or updated
- User isolation: Per-user knowledge graphs and conversation history
- Multi-tenant architecture: Secure, isolated data per customer
- Observability: Built-in logging, monitoring, and debugging tools
Google File Search is a component. Graphlit is a platform.
Developer Experience and APIs
Google File Search
- REST API for file management
uploadToFileSearchStore()for ingestiongenerateContent()for querying- Python, JavaScript, REST clients
- Managed infrastructure on Google Cloud
The API surface is small because the functionality is narrow. You get retrieval and citations — nothing more.
Graphlit
- Unified GraphQL API for ingestion, query, and chat
- SDKs for Python, JavaScript/TypeScript, C#
- UI dashboard for monitoring and testing
- MCP integration for tool calling
- Workflow APIs for automation
- Fully managed, auto-scaling infrastructure
Graphlit's API is broader because it handles the entire agent lifecycle, not just file retrieval.
Use Cases: When to Choose Each
Choose Google File Search If:
- You only need static document retrieval
- Your use case is Gemini-specific (no model flexibility needed)
- You have no audio, video, or live data requirements
- You're building a simple QA bot over a fixed document set
- You want free storage and don't mind Google ecosystem lock-in
Choose Graphlit If:
- You need live data sources (Slack, GitHub, email, RSS, etc.)
- You require multimodal processing (audio transcription, video understanding, OCR)
- You want LLM flexibility (OpenAI, Anthropic, Google, custom models)
- You're building production agents with conversation management and tool calling
- You need knowledge graphs for entity extraction and relationship modeling
- You want continuous synchronization without manual re-uploads
- You're building multi-tenant SaaS with per-user isolation
Final Verdict: File Search is a Starting Point, Not a Platform
Google File Search is a welcome addition to the RAG ecosystem — it removes friction for Gemini-based file retrieval and offers attractive free-tier pricing. For simple document QA, it's a solid choice.
But it's not an agent platform. It's a file upload API with semantic search.
Real agents need:
- ✅ Multimodal understanding (audio, video, images)
- ✅ Live data connectors (Slack, GitHub, email, feeds)
- ✅ Model flexibility (OpenAI, Anthropic, Google, custom)
- ✅ Knowledge graphs (entity extraction, relationship modeling)
- ✅ Agent workflows (conversations, tools, automation)
- ✅ Continuous synchronization (no manual re-uploads)
Graphlit provides all of this.
If you're building agents that operate in the real world — agents that listen, watch, monitor, and synthesize information across dozens of sources — you need infrastructure, not just a file upload endpoint.
That's why we built Graphlit.
Explore Graphlit Features:
- Data Connectors - 30+ connectors for Slack, Gmail, GitHub, and more
- Multimodal Processing - Audio transcription, video understanding, OCR
- Building Knowledge Graphs - Automatic entity extraction and relationship modeling
- Complete Guide to Search - Hybrid search deep dive
- Agent Workflows - Conversation management and MCP tool integration
Learn More:
Agents need more than file search. They need orchestration. We built the platform.