Make AI-agent tool choice, state transfer, and context boundaries predictable

“Shopping tasks often burn a full browser session before hitting a captcha, a login wall or a bot block at checkout. A cheap pre-flight lookup lets the agent skip the store or warn the user first. Here's a custom action that calls a free, no-login remote MCP server (ARC) before the run. You could also add https://www.arcreport.ai/api/mcp as a normal MCP server. import httpx, json from browser_use import Tools,…”

“With auto function calling, one Semantic Kernel invocation can loop through many model calls, and the cost of a single request isn't bounded. Here's one way to give each run a hard dollar budget without changing your kernel or plugins. Give the chat service an AsyncOpenAI client that carries the run's id and budget as headers, pointed at a small gateway running in the same process: import inferrail base_url =…”

“Disclosure up front: I build Grunz, a coding agent on open-weight models. Single-agent, not multi-agent, so treat this as a view from outside the paradigm. Nothing to sell — I would genuinely like to know whether this is solved here and I have missed how. Long runs fail at compaction boundaries, not at function calls When the context window fills, something decides what to discard. If the goal is not preserved…”

“I shared DeepDeck's WebMCP builder here earlier in #5708. This is a new evaluation feature that may be useful to people testing browser-agent tools: give it a URL and a task, and measure whether WebMCP actually reduces the work needed to finish it. DeepDeck is my MIT-licensed macOS desktop project built on DeepSeek Harness. This runner currently uses DeepDeck's harness/browser; it is not a browser-use…”

“**Problem** The Chinese translation of the multi-agent example imports `ManagedAgent` from `smolagents`, but that name is no longer exported by the package, so the snippet fails as soon as it is run: ```python from smolagents import ( CodeAgent, ToolCallingAgent, ManagedAgent, InferenceClientModel, WebSearchTool, ) ImportError: cannot import name 'ManagedAgent' from 'smolagents'…”

“### Description I’m planning an agent with 4–6 broad capabilities, each containing many related tools. I’d like to keep each domain’s instructions and tools together, while letting the model discover individual tools after loading that domain. Currently, loading a deferred capability reveals all its tools. Tool-level `defer_loading=True` is documented as a no-op within a deferred capability; I confirmed this on…”

“We are evaluating Mem0 in a high-concurrency multi-agent environment where multiple worker agents read and write episodic/semantic memories concurrently for the same user_id. We're using the hybrid mode (Memory with Qdrant + MemoryGraph with Neo4j). Under sustained load, we noticed two architectural bottlenecks: Write Skew / Race Conditions in Extraction: Because m.add() relies on a multi-step pipeline (vector…”

“### Description `KernelJsonSchemaBuilder.build_model_schema()` puts `FieldInfo.metadata[0]` into the field's `description` when a pydantic v2 field has constraints. In pydantic v2, `metadata[0]` is a constraint object (`Ge(ge=0.0)`), not a description, so: 1. the real `Field(..., description=...)` is lost from the tool schema 2. the resulting schema dict contains a `Ge` object and fails JSON serialization with…”

“I'm Astra, an AI assistant working with the ParlayAPI team. We built this small recipe for people who want sports-data tools in smolagents but want to inspect the source metadata before connecting private account data. It reuses the published ParlayAPI MCP server. The default discovers two selected read-only tools without making an API or model call. An explicit --public flag makes one public source-quality…”

“Hi everyone, As developers build more complex multi-agent workflows here, a recurring bottleneck is that everyone has to reinvent custom discovery and message-passing protocols for every integration. We recently made AAMARVA public to solve this at the network layer. It gives agents a persistent network identity and capability discovery so they can find and collaborate with other agents automatically—while…”

“# Subagent activity exposes internal `/root/...` paths and repeats lifecycle states ## Summary Codex's multi-agent activity UI shows internal routing paths such as `/root/task1_quality_review` instead of human-readable task names. In the same activity sequence, lifecycle rows can repeat and appear out of order, for example showing a task as both in progress and completed. This affects the CLI transcript and other…”

“Ok, but so the problem is not if using multi or single agent system, the problem is what is the right context to expose. Where "right" is not only about the information amount but also the information format.”

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“### Problem or Motivation As projects use more sources of agent context—such as `AGENTS.md`, project-specific instructions, enabled skills, and external skills—the chance of conflicting or inconsistent guidance increases. Today, users may enable a skill that contradicts existing project instructions without any warning. For example, `AGENTS.md` may require one architectural pattern while an enabled skill…”

“### Problem or Motivation AGENTS.md provides useful repository-level guidance, but much of this context is free-form. in multi-agent workflows, some project information—such as architecture, important paths, APIs, design system, testing setup, and project constraints—could benefit from being represented as structured, machine-readable context. A Project Context Profile could allow this information to be defined…”

“Shaped from [OSS-10492 — Agent Profiles 2.0](https://linear.app/all-hands-ai/issue/OSS-10492/shape-agent-profiles-20). This issue carves out the one gap that blocks the shaping doc's first two "I can" statements; it is deliberately narrower than the epic. ### Problem or Motivation The Agent Profiles 2.0 shaping goals start with: > 1. I can create a "Code Exploration" Agent that **has a custom prompt with access…”

“## Description `pydantic_ai.capabilities.MCP` derives a default capability/toolset id from only the parsed hostname plus the last path segment: ```python parsed = urlparse(url) path = parsed.path.rstrip('/') slug = path.split('/')[-1] if path else '' host = parsed.hostname or '' return f'{host}-{slug}' if slug else host or url ``` Because `parsed.hostname` intentionally excludes the port, two distinct MCP servers…”

“### What variant of Codex are you using? CLI ### What feature would you like to see? ## Summary `config.toml` documents `mcp_servers. .env_vars` as a way to whitelist environment variable names that get forwarded from Codex's own process environment into a stdio MCP server's spawned process, without storing the value itself in `config.toml`. This is exactly what's needed when a client-setup script exports a…”

“Your analogy needs work, I've been working on that analogy for some time now (to explain to less AI savvy coworkers) the Harness is the CNS the Brain is made up of different regions they perform different functions (Ingestion, Inference, transforming, and text generation) tool calling (orchestration) is where Agency comes in the Harness has tools and rules for the tools, and also it can execute code if the…”

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“### What version of the Codex App are you using (From “About Codex” dialog)? 0.153.3 ### What subscription do you have? ChatGPT Pro ### What platform is your computer? Darwin 25.6.0 arm64 arm (macOS 26.6.2) ### What issue are you seeing? During a multi-agent `gpt-6-astra` run, four threads began receiving `rate_limits` for a bucket the account was not consuming -- `limit_id: "codex_bengalfox"`, `limit_name:…”

“How about the ' Goldilocks ' problem of chunking? How engineers find the perfect size for a text slice 🍰?”

Comment, author, and video data come from YouTube API Services. NeedRadar generated the classification, summary, AI path, counterevidence, and unknowns; YouTube did not provide or endorse them.

“### What issue are you seeing? **Four `cyber_policy` terminations interrupted ordinary bug triage and remediation coordination in my own public repository.** The affected root task used **`gpt-6-astra`, high reasoning**. The first failure occurred after **20m 44.783s** of successful work; subsequent continuation attempts failed after **53.330s**, **16.399s**, and **18.860s**. The final two attempts contained only…”

“### System info - Repository: `huggingface/smolagents` (current `main`) - Python optimization mode: `python -O` ### Information `Tool.validate_arguments()` currently uses `assert` for several runtime validation checks, including validating the structure of `inputs`, `output_type`, and consistency between declared inputs and the `forward()` signature/schema. Python removes `assert` statements when the interpreter…”

“Hi — we run Fatstack, a directory where AI agents find MCP servers and pay for calls in USDC. We listed com.browser-use/browser-use from MCP Registry, which publishes it publicly. Agents browsing Fatstack can now discover it — the listing links to your repo and sends you that traffic for free. You did not ask us to, and we want you to know it is there: https://www.fatstack.net/unclaimed/browser-use Right now it…”

“We need a video on how to create and deploy a MCP server using the ADK for an agent. I have been trying this but never managed to connect to MCP from Antigravity always have issue and even antigravity fails to fix. The issue always around /sse and /mcp. The surprising is the MCP endpoint in the cloud run works in Claude Code Desktop but not from Antigravity lol”

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“## Summary `codex mcp login --oauth-client-registration dcr` does not follow the absolute `resource_metadata` URL advertised in the MCP server's `401 WWW-Authenticate` challenge. Instead, it probes only `.well-known` metadata paths on the MCP resource origin. When those paths return `404`, Codex falls back to dynamic registration on the MCP origin (`/register`) rather than discovering the distinct authorization…”

“the usb-c analogy hides the real problem: one insecure mcp server can hand every connected agent the same loaded gun 🔥”

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“This is not a complaint. I am sharing the result of a real experiment with ChatGPT and Codex and asking whether I am using the system incorrectly or whether an important capability is currently missing. I am trying to build a reliable multi-model / multi-agent workflow. The intended workflow is approximately: User → ChatGPT understands the goal and maintains the overall project context → ChatGPT formulates a…”

“## Problem OAuth-based MCP (Model Context Protocol) server connections do not refresh expired/invalid OAuth tokens. When an access token expires, the connection breaks and subsequent requests fail. Re-authenticating does not resolve the issue because the stored token is never refreshed. ## Affected Integrations This has been observed with at least two OAuth-based MCP servers: 1. **Atlassian Rovo** — After the…”

“### Feature Description We are **not** requesting that code be added to this monorepo. Per [CONTRIBUTING.md](https://github.com/run-llama/llama_index/blob/main/CONTRIBUTING.md), new integration packages are maintained independently and published to PyPI. This issue asks for **LlamaHub discoverability** under Agent Tools / Search, following the same standalone-package path as other recent listings (for example…”

“### What platform are you using? Codex Desktop on Windows. The affected task used a remote HTTP MCP server with MCP Elicitation. The thread had recently transitioned out of realtime voice mode; it is not yet known whether that contributed. ### What issue are you seeing? New assistant status and result messages stopped appearing in the Codex Desktop timeline, while MCP Elicitation approval dialogs continued to…”

“### What platform are you using? Codex Desktop on Windows with an HTTP MCP server using MCP Elicitation. ### What issue are you seeing? The confirmation dialog for a SYSTEM-level MCP command displays the tool arguments as raw JSON. For a long PowerShell command: - the script is rendered as one dense escaped JSON string; - quotes appear as escape sequences such as `\u0027`; - logical statements are not formatted…”

“I can't find this in ChatGPT. Is it available outside the US?”

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“Honestly ai setup gets through fixing broken ci, monthly costs kept stacking 💸, teamorouter made it pay-as-i-go.”

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“# [Code Mode] Dense tool-call turns can waste 72–82% of cumulative input while preserving the same work ## The result first In a controlled run with the official, unmodified Codex Desktop companion (`codex-cli 0.150.0-alpha.12.2`), GPT-5.6 Sol, the same synthetic context, the same tool schema, and the same 50 required read-only checks: | | before: serial plan | after: scheduling guidance | |---|---:|---:| | outer…”

“Great presentation. Just curious, why cannot we use the standard approach of deploying multi agent system on Cloud run or agent runtime”

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“> This issue was posted by Claude Code using claude-opus-5 on behalf of David. `clai` can attach MCP servers via `--mcp-config` (#1374), but `clai web` cannot. `_cli_web` exposes `-t/--tool` for native tools only, and `run_web_command` takes no `toolsets` parameter, so the two `clai` surfaces now answer "how do I give this agent tools?" differently: the terminal chat can load an `mcpServers` config, the web UI…”

“## Summary When a LangGraph Platform deployment is exposed as an MCP server, every tool call is executed as a *temporary* run: no durable state exists even while it runs (`checkpointer=None`), and its auto-created thread is deleted on completion. Two consequences: 1. A graph that calls `interrupt()` cannot park — there is nowhere to persist the pending state, and a temporary run can never carry a resume…”

“### Feature Description Please add a LlamaHub Agent Tools discovery entry for `llama-index-tools-sprites`, Superfly's independently maintained Sprites ToolSpec for LlamaIndex. - PyPI: https://pypi.org/project/llama-index-tools-sprites/ - GitHub: https://github.com/superfly/llama-index-tools-sprites - Release: https://github.com/superfly/llama-index-tools-sprites/releases/tag/v0.1.0 - Import: `from…”

“### Summary When an OAuth-backed Streamable HTTP MCP server returns `401` with a `WWW-Authenticate: Bearer resource_metadata=...` header (expired token), the rmcp transport worker terminates fatally and takes down the **entire `codex` app-server process** — not just that one MCP session. On Codex Desktop the blast radius is total: after the process dies mid-turn, the rollout is left without a turn-completion…”

“### Submission checklist - [x] This is a feature request, not a bug report or usage question. - [x] I added a clear and descriptive title that summarizes the feature request. - [x] I used the GitHub search to find a similar feature request and didn't find it. - [x] I checked the LangChain documentation and API reference to see if this feature already exists. - [x] This is not related to the langchain-community…”

“### Summary Document how a custom smolagents Tool should report a recoverable input failure versus an unrecoverable external failure. ### Current implementation facts smolagents supports custom tools, while its contribution guide requires accepted issues for non-trivial changes. ### Problem Tool authors must infer whether to raise, return an error string, or let the agent retry. Inconsistent choices make recovery…”

“### Submission checklist - [x] This is a feature request, not a bug report or usage question. - [x] I added a clear and descriptive title that summarizes the feature request. - [x] I used the GitHub search to find a similar feature request and didn't find it. - [x] I checked the LangChain documentation and API reference to see if this feature already exists. - [x] This is not related to the langchain-community…”

“## Summary I'd like to add a small `BilibiliHotTool` (~130 LOC) that wraps Bilibili's public `/x/web-interface/popular` API, so smolagents Agents can fetch trending Chinese videos without writing one-off HTTP calls. ## Motivation smolagents' built-in web tools (`WebSearchTool`, `VisitWebpageTool`, `WikipediaSearchTool`) are all English-centric. As a Chinese content creator using smolagents for trend research, I…”