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STACK REVIEWS

The tools we use, rated honestly.

LLM frameworks, vector databases, and agent platforms, reviewed by the engineers who deploy them in production. We name what works and what we'd avoid.

3.5/5 · 11+ projects

LangChain review

LangChain is useful again after its v1 refocus, but we still treat it as a high-level agent and integration layer, not the place to hide core product logic. It is strongest when a team wants provider flexibility, standard message/tool abstractions, and a fast path to a working agent loop. For durable, auditable production agents, we still push the state machine into LangGraph or ordinary application code.

4/5 · 9+ projects

Pinecone review

Pinecone remains the safest managed vector database choice when the team wants retrieval to be someone else's operational problem. Its best fit is production RAG where uptime, scaling, backup posture, and indexing behavior matter more than database portability. The tradeoff is cost and lock-in: once metadata design, namespaces, and retrieval behavior are tuned around Pinecone, moving away is real work.

4.5/5 · 8+ projects

LangGraph review

LangGraph is our default choice for production agents that need explicit state, durable execution, streaming, checkpoints, and human review. It is lower-level than LangChain, which is the point: production agent behavior should be inspectable instead of hidden inside a one-call abstraction. The cost is extra architecture work up front, but that cost is cheaper than debugging a runaway agent loop after launch.

4.5/5 · 6+ projects

Claude Agent SDK review

Claude Agent SDK is the strongest vendor-specific agent SDK when the job resembles Claude Code: inspect a codebase, run commands, edit files, and work through a task loop. It is not a neutral agent framework; its value comes from exposing Claude Code's agent loop in Python and TypeScript. Use it for developer automation and coding-agent workflows, not for general customer-facing agents where model portability, predictable cost, and strict sandboxing dominate.

4.5/5 · 7+ projects

Qdrant review

Qdrant is our preferred open-source vector database when filtering, tenant boundaries, and retrieval control matter more than managed convenience. The Rust core, payload filtering model, quantization options, and hybrid query features make it a serious production choice. The tradeoff is ownership: if you self-host it, you own capacity planning, backups, upgrades, and query tuning.

4/5 · 4+ projects

Weaviate review

Weaviate is strongest when vector search, keyword search, reranking, and RAG workflow features need to live close together. It is more opinionated than Qdrant and less plug-and-play than Pinecone, but it gives teams a broad search platform rather than just a vector index. We choose it when hybrid retrieval and schema-driven data modeling matter; we avoid it when the product only needs a small vector sidecar.

4/5 · 12+ projects

LlamaIndex review

LlamaIndex is still the best specialized framework for document-heavy RAG, ingestion, parsing, retrieval, and context assembly. It is not our default for general agent orchestration, but it is usually the fastest path from messy documents to a usable retrieval layer. The production risk is over-adopting the framework: keep ingestion, retrieval evaluation, and source lineage explicit so the app can evolve beyond the first RAG prototype.

4.5/5 · 8+ projects

Vercel AI SDK review

Vercel AI SDK is the best TypeScript-first toolkit for shipping AI product interfaces: streaming text, tool calls, structured output, provider routing, and React/Next.js chat UX. It should not own your agent business logic or long-running state, but it is excellent at the edge between model output and user experience. We use it when the hard problem is product polish, streaming ergonomics, and provider flexibility in a web app.

4/5 · 14+ projects

pgvector review

pgvector is the right answer when vector search is a feature of your Postgres application, not the center of your retrieval business. It keeps embeddings close to relational data, transactions, backups, access control, and existing operational habits. It stops being the right answer when vector search needs independent scaling, deep hybrid search, multi-tenant retrieval tuning, or massive ANN workloads.

4/5 · 9+ projects

MLflow review

MLflow has become much more relevant for AI engineering because tracing, evaluation, prompt/version management, and production monitoring now matter as much as classic experiment tracking. It is strongest for teams that need one open platform spanning ML models, LLM apps, and agents. It is heavier than purpose-built LLM observability tools, but that weight can be a strength in enterprises already using Databricks or MLflow governance patterns.

4.5/5 · 18+ projects

Cohere review

Cohere is most valuable in production RAG as an enterprise retrieval-quality vendor, especially for embeddings and reranking. We rarely choose it because it has the flashiest chat model; we choose it when semantic relevance, multilingual search, or reranking quality is worth paying for. The risk is treating Rerank like magic: it improves ordering, but it cannot recover documents the retriever never found.

4/5 · 7+ projects

Mistral review

Mistral is best understood as a serious enterprise AI platform with strong open-model roots, not just a cheap OpenAI alternative. We use it when deployment control, European vendor posture, model choice, or on-prem/private deployment options matter. It is less automatic as a default app model than OpenAI or Anthropic, but it belongs on the shortlist for enterprise, sovereignty, and custom-model work.

4.5/5 · 11+ projects

Modal review

Modal is one of the best Python-first ways to run AI compute without becoming an infrastructure team. It shines for bursty GPU inference, batch jobs, fine-tuning experiments, sandboxes, and internal ML services where per-second serverless economics beat idle GPU ownership. It is less ideal for always-hot, ultra-low-latency services where dedicated infrastructure or a managed inference provider may be cheaper and more predictable.

4/5 · 9+ projects

Together AI review

Together AI is a strong default for managed open-model inference when teams want fast access to a broad model library, fine-tuning, dedicated endpoints, and GPU clusters without operating the stack themselves. It is especially useful when cost, model optionality, and open-source model access matter. It is not a universal replacement for frontier APIs: quality, latency, and reliability must be evaluated per model and endpoint type.

3.8/5 · 3+ projects

DSPy review

DSPy is the strongest framework we have used for turning prompt work into an optimization problem, but it is not a general replacement for LangGraph, LlamaIndex, or direct model APIs. Use it when the quality bottleneck is measurable prompt behavior and you have a real development set, a metric, and time to run optimizer experiments. Skip it when you need a full production orchestration layer, TypeScript-first product plumbing, or a team that is still learning the basics of LLM evaluation.