Autonomous AI agents require continuous liquidity to operate. The SVP Bridge enables automated cross-chain fund deposits so agents can rebalance liquidity across chains seamlessly. Read the tech docs: #AI# #DeFiAgents# #SVPChain# #Web3Dev#

Deepagents is becoming a multiplayer harness Auth, memory, etc If you’re building an agent that you want to expose to multiple users in same thread and are thinking about these issues - reach out! Would love to chat

we just changed how deepagents read files! our evals show that this new format reduces edit_file errors by 15% AND total input token usage by 10% the little things really add up w/ agents; we're hard at work on these small but mighty optimizations at @LangChain

opening a bunch of issues against the deepagents repo w/ a brain dump of what's top of mind for me right now wrt agents would love thoughts / comments / questions! featuring topics like browser use, model routing, and code mode!

TL;DR A single Python SDK that lets you swap between DeepAgents, Pydantic AI, Claude Agent SDK, Codex, and OpenCode without rewriting your application code — built around the same query() interface as the Claude Agent SDK. Title: LiteAgents (BerriAI/liteagents) URL: Points 🔀 Switch agent harnesses just by changing the harness parameter 🌐 Supports 8+ model providers via LiteLLM, including OpenAI, Anthropic, Gemini, and Groq 🛠️ Pass typed Python functions and they auto-adapt to each harness's tool schema 💬 LiteAgentClient keeps persistent conversation history across multiple query() calls ⏱️ Optional Temporal integration adds crash recovery, replay, and idempotent tool execution ⚙️ Profiles can be defined in Python, YAML, or JSON 📡 Full async/await and streaming support This could be the end of rewriting your agent code every time you switch harnesses. #AIAgents# #OpenSource#

Are your subagents re-reading the same files the supervisor already read? LangChain just shipped a feature to fix exactly that waste. Title: Organizing Context in a Multi-Agent Harness URL: 📝 Overview The deepagents framework now supports "forked subagents" — you can choose whether a subagent inherits the supervisor's full conversation history or starts from a blank context. ❗ Problem it solves Fully isolated subagents had to redo investigation and context-gathering the supervisor already completed, wasting both tokens and latency. ⚙️ Methodology Two modes are offered: Isolated Mode (fresh context) and Fork Mode (inherits the supervisor's full state). Fork Mode stays cost-efficient thanks to prompt caching. 🔧 Use cases ・Worker agents: use fork to continue fixing work the supervisor started ・Reviewer agents: use isolated for an unbiased evaluation ・Researcher and memory agents also pick modes based on their role 📊 Results No hard benchmark numbers are given, but the post reports reduced duplicate context-gathering and tool calls. I think this is a genuinely new lever for designing subagent roles well. #MultiAgent# #LangChain#

gave a talk "owning your intelligence" - ty @sequoia @sonyatweetybird for having me talked about harnesses and evals and the role they play in owning your intelligence TLDR: > agents = model + harness + context > model - own the weights using something like @FireworksAI_HQ > context - memory needs to be portable > harness - needs to be model agnostic. also needs to be good at bringing right context to llm. "right" context may depend on your use case, which is why an open/configurable harness helps > how to use middleware in langchain/deepagents to configure your harness > how to use langgraph to fully own your cognitive architecture > why evals/obs matters - some quotes from @satyanadella - “Create your private evals, because evals define what “good” looks like inside the organization” - “retain ownership of your organization’s memory, traces, feedbacks, decisions, and institutional context” - “you create your own continuous learning loop (i.e. hill climbing machine) that will allow your AI investments to compound the value of your firm” > how to use harbor for evals > tracing is important > evals + observability only matter so you can set up a data flywheel > data flywheel = run agent -> collect traces -> find interesting traces -> use those to improve > demo of langsmith engine which does exactly this! full video: