RAG Frameworks With an API

As of June 2026, AIDiveForge tracks 14 rag frameworks with an api. Curated rag frameworks with an api tracked by AIDiveForge. Listings are verified against each tool's live website and re-checked regularly.

Last updated June 9, 2026 · 14 tools

1. 

   1. AgentRecall

   AgentRecall is a memory layer that gives AI agents persistent context across sessions — so a support agent recalls a customer's past issue, a sales agent remembers where a deal stalled, and a coding assistant doesn't ask you to re-explain your architecture for the third time. The vendor describes a retrieval-and-storage infrastructure that indexes memories and surfaces relevant ones at query time, rather than stuffing the full conversation history into every prompt. The cloud tier caps at 1,000 stored memories, which is adequate for prototyping but a ceiling teams hit in production. Self-hosting under the MIT license removes that ceiling and keeps data inside your own infrastructure — the tradeoff is that you own the ops. API access covers JavaScript and Python environments.

   Paid
2. 

   2. Cognita

   An open-source RAG framework for building and deploying scalable retrieval-augmented generation applications.

   Free
3. 

   3. Dify

   Open-source LLM app development platform combining AI workflow, RAG pipeline, agent capabilities, model management, observability features and more.

   Paid
4. 

   4. Elysia

   An open-source framework that spins up an end-to-end agentic RAG application with just two terminal commands.

   Free
5. 

   5. HarvestGuard

   The system fuses live satellite vegetation indices, rainfall anomaly data, and WFP food security indicators, then routes that combined signal through Claude to produce country-level crop failure risk assessments. Docker handles deployment; an Anthropic API key handles the inference. For an NGO standing up a proof-of-concept or a research institution prototyping AI plus Earth observation, the architecture is legible and the cost surface is clear — you pay for API calls, not a platform license. The wall appears when you need operational guarantees: this is a single-maintainer GitHub project with one star, no issue history, and no documented accuracy benchmarks against historical famine events. Teams that need auditable model provenance or SLA-backed uptime will hit that ceiling fast.

   FreeOpen Source
6. 

   6. Honcho

   Every message written to Honcho triggers automatic reasoning via the vendor's Neuromancer model, which learns user psychology and behavioral patterns rather than just indexing text. The `context()` call returns a curated summary plus conversation history shaped to a token budget you set — the vendor claims 60–90% token reduction versus naive retrieval. Multi-participant sessions model each peer separately, so a group conversation doesn't collapse everyone's state into one blob. The ceiling appears when you need reasoning beyond user memory — Honcho does not run tasks, make decisions, or coordinate agents; it only informs them. Teams building full autonomous pipelines still wire Honcho into a separate orchestration layer.

   PaidOpen Source
7. 

   7. LanceDB

   Open-source embedded vector database for multimodal AI with billion-scale search on Lance columnar format.

   Paid
8. 

   8. local-deep-research

   The tool autonomously plans and executes multi-step research tasks: it queries sources, follows citations, synthesizes findings, and returns results with full attribution — all without a cloud handoff. The vendor reports ~95% on SimpleQA benchmarks using models like Qwen3-27B on a single RTX 3090, which gives you a concrete hardware target. It pulls from 10+ search backends including arXiv, PubMed, and private document collections. Where it breaks: running capable local models demands real GPU headroom, and teams without that hardware will either throttle to weaker models or route queries to cloud LLMs — at which point the privacy guarantee depends entirely on which cloud endpoint they configure. The 109 open issues and 210 open pull requests on GitHub signal an active but fast-moving codebase; production stability requires version pinning.

   FreeOpen Source
9. 

   9. Memori

   The vendor states Memori classifies each chat turn into facts, preferences, rules, and summaries, then pulls targeted snippets at recall time rather than re-injecting full history. On the LoCoMo benchmark, the docs report 81.95% accuracy while cutting token usage by 95% versus full-context retrieval — a meaningful number if your cost problem is upstream of the model choice. The memory graph shows how entities connect across sessions, and every recall result ships with lineage explaining why that snippet was included, which matters when an enterprise audit asks why the agent said what it said. The ceiling appears when your retrieval logic needs fine-grained control the SDK's zero-configuration defaults don't expose — teams at that point are writing wrapper logic to compensate. Self-hosted deployment is available, so organizations with data-residency requirements are not locked into the cloud path.

   Paid
10. 

    10. OpenRAG

    OpenRAG is a modular framework for exploring Retrieval-Augmented Generation (RAG) techniques, built for transparency and rapid experimentation to develop document-grounded AI systems—fully ready for production-scale deployment. It uses Ray to parallelize chunking, embedding, and ingestion across CPUs and GPUs, enabling fast, scalable processing of large document sets, and can be deployed seamlessly on Kubernetes for distributed, production-grade workloads. Advanced loaders like Docling and Marker parse complex layouts with OCR-enhanced PDFs, and chunk contextualization significantly boosts retrieval relevance. The platform ships with fully OpenAI-compatible chat API for seamless integration with tools like LangChain, OpenWebUI, or N8N—no adapter work required. Built-in clustering auto-generates synthetic QA datasets from your indexed documents, and a local LLM scores each query-chunk pair to help you tune retrieval before production. Two friction points surface at scale: in collaborative systems where documents update hourly, embeddings are recomputed every time by vLLM, which is computationally expensive, and admin users cannot grant access to partitions they were not explicitly given access to—the admin role does not override partition-level access restrictions.

    Free
11. 

    11. RAGFlow

    Open-source RAG engine with deep document understanding, hybrid search, and agentic workflow orchestration.

    PaidOpen Source
12. 

    12. Supermemory

    Supermemory wraps memory, retrieval, user profiling, data connectors, and document extraction into one API so your agent doesn't reassemble context from scratch on every request. The retrieval layer claims sub-300ms latency using hybrid search with reranking, and the memory layer maintains a knowledge graph that merges contradictions and evolves facts over time rather than appending chunks blindly. Connectors to Slack, Notion, Drive, Gmail, GitHub, and S3 sync automatically — no ETL pipeline to maintain. The core memory engine is proprietary and hosted-only; self-hosting requires an enterprise agreement, so teams with strict data residency requirements hit a wall before they ship.

    PaidOpen Source
13. 

    13. Unabyss

    The scraped page content provided does not match the tool described in the structured data: the page describes 'Spotter,' a travel-identification app, not the context-infrastructure layer attributed to Unabyss. No production details, integration specifics, API behavior, or access-control mechanics for the named tool can be sourced from the provided content. Any description of how the tool retrieves context, gates permissions, or connects to Cursor and Claude Code would be fabricated. What the validator context does confirm: the tool is a passive retrieval and permission-gating system, not an agent — it feeds context to external tools rather than executing tasks on its own.

    Paid
14. 

    14. VideoDB

    VideoDB ingests video from YouTube, S3, URLs, and RTSP/RTMP streams, then produces a continuous AI context stream — transcripts, visual scene indexes, audio summaries, and triggered alerts — with the vendor citing roughly two seconds of processing latency. Agents downstream query that structure instead of wrestling with raw frames or bloated context windows. The pattern holds well for single-stream use cases: a meeting copilot, a screen-aware pair programming agent, a security monitor flagging sensitive content. Where you hit friction is multi-stream scale and anything requiring on-premise data residency — the platform is cloud-only, with no self-hosted option. Teams with strict data sovereignty requirements end up re-evaluating before they ship.

    Paid

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