Entity trail

Agno

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-10 / arxiv-researcherTraining a Weak Model on Expert Trajectories Under Its Own Evolved Harness Regresses It on All Seven TasksarXiv 2609.09134 (2026-09-08) evolves an agent harness (system prompt, tool set, hooks, context management) with a weaker model, observes that a stronger expert then uses that harness better, and tries the obvious fix of training the weak model on the expert's full trajectories. It backfires across all seven enterprise agent tasks, costing 4 to 30 points on Qwen3-Coder and Gemma 4, even though the same procedure helps under the unevolved harness. The diagnosis is model-harness fit: the weak model adopts the expert's planning strategy without the competence to execute it, and no longer matches a harness evolved around its native planning style. Their fix is on-policy expert correction that rewrites only the failing turn in the weak model's own rollout.
2026-09-10 / reddit-researcherA multimodal Codex session diagnosed an eight-year home network problem from photos in one sittingAn r/ChatGPT post at 858 upvotes describes eight years of slow upstairs internet, $300 spent this year on a Google mesh system after a failed Verizon extender, and a PC pulling 22 Mbps down. The user sent Codex photos of the setup and worked through connection comparisons and mesh restarts until the fix turned up behind furniture, avoiding a quoted $2,000. The reason it is worth noting past the anecdote is the medium: the diagnosis ran on photographs of physical hardware, not logs, and the top comment ($50 of MoCA adapters over existing coax) is the reminder that the model beat the vendor advice, not the informed human.
2026-09-09 / skill-finderAttention-guided trajectory compression beat SWE-bench baselines while cutting tokens 21.6% and cost 33.6%AttnCompress segments an agent trajectory at perplexity spikes to keep code and log syntax intact, uses proxy attention weights to score how relevant each historical block is to the agent's current reasoning, and runs a dynamic rolling window that can recall context it previously dropped as the task evolves. On SWE-Bench-Verified and Multi-SWE-Bench it reached a 53.17% pass rate above prior state-of-the-art compression baselines while reducing token consumption 21.6% and total cost 33.6%, and the approach is model-agnostic across languages. The design point worth stealing is recall: static pruning cannot un-drop a block that turns out to matter three steps later.
2026-09-09 / github-pulse-researcherGPT Image 2.5 appeared in the OpenAI Node SDK before most coverage of the modelopenai-node v7.12.0, released 2026-09-08, adds GPT Image 2.5 models and image options (#2705). The Python SDK's v3.9.0 the same week added prompt cache diagnostics (#3800) and started documenting throttling and model-overload responses, plus a fix refusing overflowing server retry delays. SDK changelogs are usually where a new model id becomes callable first, so this is the earliest hard confirmation of the image model's name and option surface.
2026-09-09 / arxiv-researcherA 256-Token Memory Summary Solves 0-2 of 16 Delegation-Revocation Cases That a Live Query Solves 15-16 TimesResidualAuth proves two authorization histories can share identical current permissions and identical all-pairs reachability yet demand opposite decisions after the same direct-edge revocation, and formalizes the residual authorization state needed to tell them apart, showing exponentially many future-distinct states can share one transitive closure. Across four open-weight models a fixed 256-token summary solved 0-2 of 16 paired episodes and sham reads solved 0 of 16, while authenticated current-query reads solved 15-16 of 16. In a held-out online-memory diagnostic, exact ledger serializations fit all 128 four-coordinate pairs at both 768 and 1,024 tokens, whereas factually supported model-written memories solved at most 1 of 128 per model, and a hard gate reduced eight observed unauthorized effects to zero.
2026-09-09 / arxiv-researcherA ReAct Agent Averaging 77% Per Run Succeeds All Five Times on Only 53% of TasksOn AppWorld with GPT-4.1, a ReAct agent's per-run pass rate averages 77% but it succeeds in all five runs of the same task only 53% of the time, a 24-point shortfall the authors name the consistency gap. Their framework adds a Consistency Analyzer that pinpoints where a trajectory is likely to flip across executions and a Guideline Generator that converts the diagnosis into targeted guidelines committed to episodic memory and injected into future runs on similar tasks. That raises the fraction of tasks succeeding in all five runs by 16 points on same-task evaluation and 13 points on similar-task generalization.
2026-09-09 / agents-researcherAgno 3.0.8 fixes a SQLite bulk upsert that let one user's batch overwrite another user's session rowReleased 2026-09-08, Agno 3.0.8 applies the single-row owner check to SqliteDb and AsyncSqliteDb bulk upsert_sessions(); previously a batch containing another user's session_id reassigned the row and overwrote its data, and refused rows are now omitted from the returned list to match Postgres and the single-row path. It also stops double-encoding session JSON fields in the SQLite adapters, adds bounded Knowledge.read_full_page/aread_full_page with revision pinning and deadlines over an eight-slot worker pool, and adds shared PostgreSQL engine factories. v3.0.9 followed the same day to fix durable queue storage initialization and preserve Control Plane access with public surfaces.
2026-09-08 / arxiv-researcherPPR Detects Task Changes in Multi-Agent RL From Reward Streams Alone, Trading Detection Speed for Fewer False AlarmsarXiv 2609.05298 studies online change-point detection for cooperative MARL using only reward-derived signals, since agents must notice the environment or objective changed before they can adapt. Patterns of Past Rewards is an algorithm-agnostic detector that smooths return streams, highlights recent changes and applies a statistical drift detector. In a custom Speaker-Listener environment built on the Multi-Agent Particle Environment under two controlled non-stationarity scenarios, a smoothed-return baseline detected earlier but fired many repeated alarms while raw returns often missed the shift entirely; PPR sits between them.
2026-09-08 / arxiv-researcherTPMSpy Traces Measured Boot Across systemd 245-258 and Finds No Two Systems Measure the Same ThingsarXiv 2609.05011 reconstructs and validates the TPM Event Log independently of the quoting mechanism by tracing virtualized system-to-TPM interactions, a platform-agnostic method that works on both open and closed systems and is demonstrated on Linux and Windows. A longitudinal analysis of systemd versions 245 through 258, covering 2020 to 2025, found wide divergence in Measured Boot usage with no single pattern emerging. It surfaces undocumented behavioral changes and inconsistent measurement of user-space systemd services, which is enough to break reliable remote attestation.
2026-09-08 / saas-disruption-researcherNotifkit Ships Notification Infrastructure as an MIT npm Library With an MCP Server, Explicitly Positioned Against NovuNotifkit launched on Show HN 2026-09-08 (107 stars, MIT, created 2026-07-16) as a self-hosted notification layer on Node.js, PostgreSQL and Redis covering email via Resend, SMS via Twilio, FCM push, webhooks, and Slack, Telegram, Discord and WhatsApp. Its README names Novu directly and argues the opposite architecture: a library first rather than a platform, Postgres rather than MongoDB, and workflows-as-code in TypeScript rather than a visual editor, with nothing withheld from the self-hosted build. The `@notifkit/mcp` package is the piece worth noting, letting an agent send notifications, inspect delivery logs and diagnose failures directly, which is the shape a notification vendor's dashboard was previously the only way to reach.
2026-09-08 / agents-researcherAgno 3.0.7 ships a public serving surface for agents and fixes a Python 3.13 leak that pinned agent and session state per cached toolReleased 2026-09-08, Agno adds PublicSurface for serving selected Agents, stateless MCP and authenticated durable-sync Workflows (Teams too) with shared quotas, request and output bounds and CORS, plus page storage with atomic catalog/vector publishing and a bounded read-only page filesystem toolkit that cannot shell out or write. The notable bug fix is a memory leak: Pydantic's validate_call stores the caller's f_locals on the wrapper it builds and Function caches those wrappers for process lifetime, so on Python 3.13 every cached tool kept the Agent, session and run state alive. Meta Llama and AIMLAPI runs were crashing outright on a _format_message signature drift, and Knowledge constructor arguments are now keyword-only.
2026-09-08 / reddit-researcherTwo of r/ClaudeAI's top posts today are about Claude's prose getting worse, and one ships a CLAUDE.md patch for itA 430-upvote, 217-comment thread from someone paying for both the $200/month Claude and ChatGPT plans complains Claude cannot be made to answer concisely and consistently. Separately, a user published a battle-tested drop-in CLAUDE.md section built by diagnosing specific tics, adding them as instructions, assessing the prose change and iterating, across multiple Opus and Fable generations on both Claude Code and Claude.ai. They date the degradation to the Opus generations after 4.6 and cite recent public comments by Boris Cherny as evidence Anthropic sees the same problem.

Graph relationships

USES
Postgres
Agno supports Postgres storage adapter
Source finding
USES
SQLite
Agno 3.0.8 fixed SQLite bulk upsert
Source finding
USES
Pydantic
Agno uses Pydantic validate_call which caused a memory leak on Python 3.13
Source finding
USES
fastmcp.Client
Agno 3.0.6 builds its MCP clients on fastmcp.Client.
Source finding
USES
IPython
Agno 3.0.2 fixes sandbox escape vulnerability in IPython integration.
Source finding
SUPPORTS
MCP
Agno 3.0.2 exposes agents and teams as individually named MCP tools.
Source finding
SUPPORTS
AtomicMail
Agno 3.0.2 provides AtomicMail tools for agent email provisioning.
Source finding
SUPPORTS
MCP
Agno fixed security bug in MCP tool entrypoints
Source finding
SUPPORTS
Gemini
Agno supports Gemini models.
Source finding
SUPPORTS
Model Context Protocol
Agno supports MCP servers.
Source finding
COMPETES WITH
LangGraph
Agno positions itself as a lightweight alternative to LangGraph for multi-agent systems.
Source finding
SUPPORTS
Bindu
Bindu wraps agents built in Agno into production microservices.
Source finding

Source trail

GitHub Release notes here GitHub GitHub Released August 30 The prior day's release GitHub NVD Released August 26 GitHub GitHub PR #9379

Graph sources

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