Agent Plugins 1.0 is a packaging format that bundles Agent Skills (markdown instructions) and MCP server configs into a single directory, loadable by ChatGPT, Cursor, GitHub Copilot, Kiro, and VS Code. It's not a new protocol — it's the wrapper above protocols. Vercel initiated it, OpenAI/AWS/Microsoft/Cursor co-authored it, and Google joined on launch day. Anthropic isn't on the governance board, but MCP is a core primitive of the spec.
This site covers MCP thoroughly but has never written about the layer underneath it: when your agent acts for ten thousand end users reading their own Gmail, whose database holds those refresh tokens, who rotates them, who revokes them. Composio is currently the most complete answer — MIT-licensed SDKs, a commercial hosted execution and OAuth layer. It claims 1,000+ toolkits; the managed-auth page actually lists 121 with a Composio OAuth app and 96 that require your own credentials. New pricing effective 2026-08-15: 100K free tool calls, $29/mo Pro. This post takes the authorization model down to an operational level and draws the line between wiring up MCP servers yourself and buying an integration platform.
Chroma's controlled study shows that even when it fits, a full context degrades performance. Coding agent vendors have landed on seven different responses: compact, hand off, prune, defer loading, isolate, train it into the model, or change the unit of work. Amp removed /compact outright, Atlassian argues summarization should be a last resort, and Cursor's A/B test measured a 46.9% token reduction. The three real disagreements come down to what each team is measuring.
CrewAI (GitHub 57.4k stars, MIT, PyPI 11.6M weekly downloads) defines agents by role, goal, and backstory, then groups them into crews for collaboration. Unlike LangGraph's graph-first and MAF's workflow-first approach, CrewAI is team-first — you don't draw nodes and edges, you describe who's on the team and what each person does. It fully removed its LangChain dependency in late 2024 and is now a standalone framework. The commercial side splits into the open-source package and AMP, a managed platform adding visual building, deployment, tracing, and compliance.
Exa turns every indexed web page into an embedding and retrieves by vector similarity instead of keyword matching. Official pricing as checked on 2026-08-21: $7 / 1k requests for /search (first 10 results included), $1 / 1k pages for /contents, $12–15 / 1k for the deep tiers, with $20 in free credits for new accounts. This blog's CLAUDE.md puts Exa first among cloud fetch tools, 16 of its 38 skills reference it directly, and only four existing posts mention it in passing — with zero dedicated posts. This is that post.
Free access is not one model: recurring allowances, balance top-ups, rate-limited access, one-time credits, and self-hosting have different steady-state costs.
LlamaIndex (51,775 GitHub stars, MIT, verified 2026-08-21) has moved its center of gravity from indexing to Workflows: the standalone llama-index-workflows package pulls 2.81M weekly PyPI downloads, more than the 1.97M of the llama-index umbrella package itself. This post covers the core abstractions, the trade-off against hand-rolling a pipeline, and a hands-on test of its defaults on Traditional Chinese text — at the same chunk_size=1024, English fits 4,645 characters and Traditional Chinese only 1,332. Plus one fact you need before choosing: the TypeScript port is archived and unmaintained.
Meilisearch turns application data into fast, typo-tolerant full-text search; the hard parts are index settings, asynchronous tasks, Chinese tokenization, access filters, and tested recovery.
Microsoft merged Semantic Kernel and its own AutoGen into Microsoft Agent Framework, which hit 1.0 GA on 2026-04-02 for .NET and Python (Go is still public preview). The absorbed autogen-agentchat has not shipped since 2025-09-30. But AG2, the fork on the original authors' side, never merged — it shipped 1.0.2 six days ago, and `pip install autogen` gets you AG2, not Microsoft. This post covers MAF's abstractions, the migration clock, and how to read the tangle of names.
Modal is a per-second-billed serverless GPU platform that also treats agent sandboxes as a first-class primitive (company-reported: over 1 billion sandboxes launched, more than a third of revenue). The selection question isn't how convenient it is — it's your GPU utilization. Verified 2026-08-21: Modal's A100 80GB works out to $2.50/hr against RunPod's $1.59/hr for the same card, so above 64% utilization renting your own is cheaper. But on the same day, H100 SXM is $3.95/hr on Modal against $3.99 on Lambda — on that card the premium is gone.
Qdrant is not just a place to store embeddings: define the vector schema, index frequently filtered payload fields, then add dense+sparse queries, tenant boundaries, snapshots, and monitoring to build an operable retrieval service.
Tavily and Exa are cloud-only APIs and can't be self-hosted. What you can assemble instead is SearXNG (269 upstream engines, 82 on by default) plus Crawl4AI (78.8k stars, Apache-2.0), and the ready-made Tavily-compatible wrappers are all still double-digit-star solo projects you should not depend on. But SearXNG has no index of its own, and running it from a datacenter IP gets you empty results — those two facts decide whether self-hosting is worth it.
CS221 is Stanford's course on AI principles and techniques. It lays AI out in four layers — reflex, states, variables and logic — with deep learning as only the lowest one. The executable Python lectures and all eight assignments, with code skeletons and a local grader, are public; lecture recordings, hidden test cases and assignment solutions are not.
CS224N is Stanford's course on natural language processing with deep learning: it goes from word vectors and backpropagation to Transformers, every later lecture builds on Transformers, and the four assignments include writing a decoder-only Transformer yourself. The current slides and assignments are public, and Assignment 3 ships unit tests that run on a laptop; the complete public recordings come from an earlier offering and no longer match the current syllabus.
CS224U is Stanford's natural language understanding course, the follow-on to CS224N: it covers domain adaptation for sentiment analysis, retrieval-augmented in-context learning, advanced behavioral evaluation and analysis methods, with a bake-off attached to each of the three assignments. The material is a public GitHub repo, and the slides and the full recordings of the online version are public too; the data loading in the first assignment needs a fix before it runs in a fresh environment.
CS224V is Stanford's Agentic AI course: it teaches how to constrain LLM agents with formal semantics, knowledge graphs and formal methods so they fabricate less, with two assignments and then a project that carries most of the grade. The fourteen lecture decks, the reading list, the assignment handouts and the open-source systems the course uses (STORM, WikiChat, SUQL, Genie Worksheets) are public; there are no public recordings, the posted slides are deliberately incomplete, and the credentials the assignments need go only to enrolled students.
CS224W is Stanford's machine learning with graphs course: nineteen lectures from node embeddings, GNNs, graph transformers and knowledge graphs to relational deep learning and GNNs with LLMs. The slides, three written homeworks and six Colabs are public and run as downloaded; the public recordings date from 2021, so the newer topics in the second half exist only as slides, and the exam and autograding are out of reach.
CS228 is Stanford's probabilistic graphical models course, in three parts — representation, inference and learning: graphs write down independence assumptions so that exponentially large probability computations become polynomial. What self-learners can get is the 16-chapter public notes, cs228-notes; lecture slides, recordings and the current assignments are not available.
CS25 is Stanford's 1-unit seminar where attendance is the only homework and anyone can audit. Of the nine talks in the Spring 2026 season, the three worth your time are Albert Gu on the inductive biases of SSMs vs Transformers, Charles Frye on serving inference across thousands of GPUs, and Victoria Lin on what native multimodality still hasn't solved.
CS329Z is Stanford's course on engineering AI agents: you first build RAG, tool use and the agent loop by hand without frameworks, then see what frameworks like DSPy abstract away; the two assignments are building an agent harness and designing an agent evaluation. The syllabus, the assigned readings, the first nine slide decks, and the starter code, data and public tests for Assignment 1 are available; the recordings require an enrolled Canvas login.