@lowercasebryan

Ever wanted a model that finds exact matches in massive datasets at lightning speed? This one uses FAISS for ultra-fast similarity search, perfect for retrieval tasks. It's a game changer for search and recommendation systems.

Time travel, built into the Deep Agents runtime, allows you to explore alternative agent trajectories. You can fork from any checkpoint in an agent run and experiment with different agent context.

Excited to share that Harvey was used to prepare for argument before the Supreme Court. We partnered with @neal_katyal to build Harvey Moot, which draws on historical questioning patterns, rulings, and opinions to simulate argument with each of the Supreme Court Justices. Neal used Harvey Moot to prepare for and win a landmark case this term. We're now rolling out Harvey Moot to our law school partners, so every law student can practice argument before the Supreme Court like Neal did.

My deep-dive analysis of @harvey's new Legal Agent Benchmark: Model Evaluation - First: this is a *model*, not a harness benchmark. The harness is very simple. No special system prompt for legal. Standard bash, read, write, edit, glob, grep tools. A few skills for dealing with files. - This is a tricky design decision. You want to isolate model evaluation. But if the harness sways too from what you would actually use in production, eval results may not carry over. I think wise overall to simplify. Tasks and Evaluation - All tasks are one turn. No compaction or context engineering built into harness. Simplicity of single turn arguably a feature as a starting point even if in real world users are likely to ask follow ups and refinements. - Evaluation criteria very interesting and well designed. All judgement is put into detailed criteria sets per task. Effectively unit tests. I.e. "Pass if memo identifies inconsistent publication count, Fail if not". The judge itself is dumb. Takes final input and criteria and returns pass fail - A task only passes if all criteria pass. Makes sense for legal work. But there is post run visibility to see Task N passed 18/20 criteria etc. - Most notable here: the benchmark's quality is capped by the task criteria text. Poorly specified or missing criteria could tank the trustworthiness of the entire benchmark. Presumably they had heavy expert input on these criteria. Environment Accuracy - The benchmark is high-quality but small scale. This is a big area for improvement imo. But its tremendously hard to build accurate synthetic legal matters at scale. - Each task is based in a matter (court case). The matters have documents, emails, spreadsheets, and power-points. - Docs per matter median: 7, P95: 14. This is much smaller than in real world. Emails even worse. Total token size per matter ~= 60k median, 120k P90. Again very small. - That being said the content is extremely high quality. This is actually much more important than total size anyways for this use case. After a threshold you get into harness, not model evalution. - But there is a local maximum risk. This tests whether a model has strong built in legal knowledge work capabilities. It does not test a model's ability to search and synthesize huge amounts of data, which is equally important in law. Engineering tricks - Everything is parallelized within reason (caps to avoid rate limits). - Streaming utilized to prevent timeouts - Secure sandbox document parsing implementation - Overall very well designed. Few small things would be nice to add, for example, if agent stops, reason is not logged right now (context limit hit? timeout? failure?). Utility - The most practical application of LAB is for model evaluation on legal knowledge work - However, you could also repurpose this benchmark as a means of benchmarking different harnesses. One might keep the model constant and instead iterate on the harness to a get an idea of what matters in legal. To make this really robust it would be important to have some matters with real-world scale context. Some things harness engineers might experiment with: - Vectorize all documents and give agent a semantic search tool - Legal specific system prompt - Encouragement to use grep in parallel to search documents without reading entire file into context - Compare performance of embedding based rag vs just grep - Pre-load short summaries of each doc in context - Introducing subagent spawning to read docs in separate context - Cross reference resolution prompting or tool ("as defined in Section 3.2..") - Code interpreter to handle xlxs files But again this is not meant to be a harness benchmark. Overall this is a very high quality benchmark. It is much harder to get together a high quality environment of underlying data, tasks, and expected outputs in knowledge / legal work than it is for coding. The design decisions around judging are very smart. I think this will be enormously useful for the legal AI community.

Today we're announcing the Legora aOS™. It's something we've been building toward for three years, and I think it's the most important thing we've ever shipped. The legal industry has had AI that assists with individual tasks. What it hasn't had is AI that drives entire work products from start to finish. The Legora aOS changes that. It's a single connected system – matter intake, research, drafting, review, service delivery – orchestrated by the new Legora Agent, running continuously, grounded in your organization’s own knowledge. The legal teams who use it won't just be faster. They'll operate at a scale that simply wasn't possible before. We've spent three years being told the legal industry moves too slowly to change. We've also spent three years watching it change faster than almost anyone predicted. The best time in history to be a lawyer starts today. @WeAreLegora is built to be the partner that makes it possible. Read the full announcement: legora.com/newsroom/legor…

We’ve been working closely with the @harvey team on the launch of the Legal Agent Benchmark, a product focused on evaluating how open-weight models perform on long-horizon, real-world legal tasks. Check it out:

Swapping between open models is as easy as changing the model string! Try in deepagents today ⬇️

one of the features i'm most excited about in our upcoming langgraph release is delta channels! the langgraph runtime lets you "checkpoint" agent progress at every step (model call, tool call, hooks). the problem, though, is that checkpoints bloat quickly when context is long! delta channel mitigates this with diff based storage from checkpoint to checkpoint. with delta channels, you still have a full history of agent progress, the only diff (haha get it) is the storage format. in-depth blog coming soon, but in the meantime, try it out and lmk what you think! #deltachannel-beta" target="_blank" rel="nofollow noopener">docs.langchain.com/oss/python/lan…

CopilotKit just gave the agent stack its third open-source layer. > MCP lets agents use tools > A2A lets agents talk to other agents > AG-UI lets agents work with people inside software

I detected a bad Agent action, what do I do about it? this is pretty much the main question that will power the future’s Human+Agent driven improvement loops Gather data -> Mine Errors -> Find out which piece(s) of the agent is contribute to this behavior -> Apply Fix -> Test -> Loop The most important boundary in agents is the context window, it’s the box on which all LLM computation actually happens. The first thing you want to try is optimizing context engineering. No model can solve an issue without the necessary information From there work backwards all the way to swapping out or adding a model or The loop is driven by running agents, Tracing + Monitoring them, and gathering feedback to classify, understand, fix, and test errors at scale Every piece of data an Agent produces is a potential avenue to improve it, the dream is to help every team turn that data into actionable edits to improve agents over time and at scale

deepagents you can run with a "virtual filesystem" lets do lots of great context engineering tricks, without requiring an actual sandbox environment!

Want to run the same harness across multiple interfaces? Try ACP. Deep Agents ships with it out of the box.

agent observability is great. but in order to use it to power an agent improvement loop, you need to be collecting (and even generating) feedback data inside your agent observability platform

No longer are Fleet agents constrained to a single model. With multi-model support, you can build more efficient agents at scale langchain.com/langsmith/fleet

@hthieblot Self funded, $50k in so far, backing myself.

You just became a VC. You’ve got $1M to deploy. Who gets your money? Tag them. Or back yourself.

Build agents with LangChain + @browserbase. Give your Deep Agents search, fetch, and browser subagents to access the full web. All with full observability with the Browserbase dashboard.

one future trend i'm very excited by: models getting good enough where they can power agents that browse the web deepagents + @browserbase is a glimpse of that future See the full example here: github.com/browserbase/in…

Claude Security is now in public beta, built into Claude Code on the web. Point it at a repo, get validated vulnerability findings, and fix them in the same place you're already writing code claude.com/product/claude…

Should you use a sandbox for your agent? @ListenLabs Co-Founder & CTO @florian_jue shared what can go wrong on the Max Agency podcast hosted by @hwchase17 .