Open ETF Agent
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Headlines · 15
- ETF Agent details its Deep Research engineering and tests
ETF Agent developers say they implemented Deep Research inside the existing Native Research Loop rather than creating a separate agent, adding authorized resource tiers, runtime-aware delivery guidance, versioning and recovery. Paired Standard/Deep experiments showed that both modes could run research and deliver formal answers, but fewer calls did not consistently translate into better accuracy, speed or cost. The developers explicitly say this engineering implementation is not a feature of the current public Open ETF Agent, and the findings are vendor-reported.
掘金 · 人工智能 · 🔥 5 - How Agents Resume Work With Context, Memory, and Checkpoints
A tutorial distinguishes chat history, context, long-term memory, checkpoints, and execution records by the information each preserves. Using a synthetic Atlas Notes document-comparison scenario, it explains how an agent can continue research, carry saved preferences into later requests, and distinguish an unknown tool result from an action that never occurred. Open ETF Agent v0.26.0 examples demonstrate memory changes, compaction and rereading, and cross-process recovery.
掘金 · 人工智能 · 🔥 5 - How Tool, Function Calling, MCP, and Skill Work Together
A tutorial uses a synthetic document-comparison scenario to show how Tool, Function Calling, MCP, and Skill participate in one assistant workflow. It describes tools as executable capabilities, function calling as a structured way for a model to request a tool, MCP as a connection to external services and their tools, and a Skill as reusable task guidance and resources. The runnable example starts a standard-input/output MCP process to retrieve synthetic pages, then saves a report. It also shows how to inspect call parameters, paired results, action records, and generated artifacts. Because the demo uses preset model actions, it does not establish whether a model can independently select the correct Skill or tool.
掘金 · 人工智能 · 🔥 5 - Tutorial: Tracing Model Inputs Through Prompts, RAG, and Memory
This tutorial explains how prompts, context, retrieval-augmented generation, and long-term memory contribute different elements to a model’s input. Using synthetic Atlas Notes documents and Open ETF Agent v0.26.0, it shows how to diagnose an incorrect answer by checking whether the task was specified clearly, the required source text reached the request, retrieval selected suitable material, and recalled memory remained applicable. The Open ETF Agent example uses preset actions to simulate model selection, so it does not establish how a real model performs.
掘金 · 人工智能 · 🔥 5 - How an Agent Client Presents Research and Citations
An article on ETF Agent client architecture explains how an Agent client should map user actions, concurrent runs, real-time events, historical reads, and citations. It emphasizes that receiving an SSE string does not prove that a research result was rendered correctly. Proposed acceptance scenarios cover misrouted responses, stale revisions, premature saves, permission errors, and citations linked to the wrong version. The article also points to Open ETF Agent’s local example for reading saved reports and sources, but states that the example was not rerun during its preparation and that it is not a complete product client.
掘金 · 人工智能 · 🔥 0 - How ETF Agent Keeps Atomic Reports from Losing Their Identity
A Juejin AI engineering article describes a case where a same-day lightweight report replaced a formal portfolio report at the same date-based path, leaving the earlier run’s history pointing to different but still valid JSON. It attributes the problem to mismatched report identities, output paths, and discovery rules rather than model hallucination. The described private fix routes lightweight reports to a separate lightweight/ subdirectory and restricts formal consumers to the approved report root.
掘金 · 人工智能 · 🔥 0 - An Evidence Contract for AI Agent Engineering Claims
A technical article argues that a complete explanation and a public repository do not by themselves validate an AI agent’s claimed capabilities. Using Open ETF Agent v0.5 as a case study, the author maps technical claims to implementation locations, fixed source versions, runnable targets, verification receipts, and narrowly defined behavior. The article also explains how structured coverage checks distinguish verification of existing records from an actual rerun of a bounded target. The v0.5 example covers explicit saving, revising, and forgetting with synthetic inputs; it does not demonstrate a complete account-level memory service or automatic semantic learning.
掘金 · 人工智能 · 🔥 0 - ETF Agent Maps the RAG Evidence-Delivery Chain
An ETF Agent engineering report organizes RAG into four interacting chains: knowledge construction, online retrieval, evidence consumption and delivery, and evaluation and governance. In the checked private knowledge path, lexical retrieval can use BM25 when the PostgreSQL pg_search extension, a BM25 index, and configuration are available; otherwise it uses PostgreSQL full-text search. Code support alone does not show that any particular query used BM25. The public Open ETF Agent repository offers a smaller teaching implementation with restricted lexical retrieval, original-page readback, and source-linked reporting on synthetic documents, rather than the full private architecture. An RQ-08 evaluation fixed three source versions to one candidate set, compared the ranking of 306 real indexed chunks, and observed actual Knowledge calls. However, the chunks lacked complete manual relevance labels, and some reads confirmed only that a location was found. The results therefore do not establish improved real Recall or end-to-end financial-answer accuracy.
掘金 · 人工智能 · 🔥 0 - ETF Agent vs. General Agent Frameworks
An engineering analysis compares ETF Agent with Pi, Claude Agent SDK, and LangGraph across execution, permissions, recovery, artifact delivery, and financial research responsibilities. It does not publish a scorecard because the four systems were not tested with the same models, data windows, budgets, and question sets. Drawing on the frameworks’ official mechanisms and checked ETF Agent implementation evidence, the analysis identifies financial methods, provenance, permissions, result identity, and verifiable delivery as the system’s distinctive strengths. It argues that its custom runtime should continue to be tested against the mature alternatives rather than treated as necessary simply because it exists.
掘金 · 人工智能 · 🔥 0 - What Changes When AI Agents Become Long-Running Assistants
掘金 · 人工智能 argues that agent engineering differs from conventional backend development because a model may choose its next action from newly observed material, adding a semantically uncertain decision stage. Using an Open ETF Agent v0.19.0 teaching case, the article compares tool contracts, permissions, state, recovery, testing, and delivery, then draws on a private research application to examine asynchronous delivery, human replies, memory consumption, and daily replay. It explicitly says these examples do not demonstrate real investment-research or automated-trading capability.
掘金 · 人工智能 · 🔥 0 - ETF Agent Skills Architecture Separates Methods from Execution
ETF Agent authors describe an architecture in which Skills define reusable research methods while models select and synthesize them, the Host controls capabilities and source authorization, deterministic programs handle calculations, and the Runtime manages budgets, execution, and recovery. The article also covers progressive loading, differing Skill visibility for Entry, Main, and Child agents, receipt-based verification, version governance, and a reproducible public v0.2 demo. The internal fund-research bundle and private-portfolio workflow remain vendor-described, while the public implementation supports only a narrower synthetic-guide example.
掘金 · 人工智能 · 🔥 0 - Open ETF Agent v0.7 details durable control-event handoffs
A report examines how Open ETF Agent v0.7 separates reliable delivery of control events from confirmation that their intended business effects occurred. It describes the public SQLite implementation’s Claim/Fence leases, receipt reconciliation with the original Owner, unknown outcomes, cancellation, and restart recovery. The public slice covers a narrower path for a single Run and does not include the original system’s multi-Owner control-event bus. The report also cautions that not every target, production deployment, or cross-process failure has received unified end-to-end validation.
掘金 · 人工智能 · 🔥 0 - ETF Agent’s Approach to Versioned Artifacts and Targeted Sharing
An ETF Agent engineering article uses a teaching scenario to explain how an AI agent can treat a generated report as a versioned Artifact rather than simply a filename and URL. The proposed record binds a file to a fixed content digest, owner, generating task, source and retention information. The save process distinguishes not-yet-committed, committed and unknown states, while downloads check the current visitor’s session, object ownership and sharing record. The article contrasts account-specific sharing with Amazon S3 presigned URLs and notes that the public Open ETF Agent v0.15.2 teaching version supports fixed artifacts and local read-only viewing, but not complete multi-account sharing. The account-specific sharing behavior is described in the article, but the implementation details were not independently verified; the described commit behavior is specific to local file storage and does not establish atomicity across arbitrary database and object-storage combinations.
掘金 · 人工智能 · 🔥 0 - ETF Agent adds a Workspace for long-term, cross-run research
The ETF Agent author describes a Research Workspace that organizes long-term goals and links prior Runs, Publications, evidence versions, permissions, and research progress without replacing the existing execution engine. Its P0 implementation has been merged into the private codebase, where controlled tests used synthetic sources and scripted model responses to consume exact historical Publication versions across independent Runs. The feature remains disabled by default, full type checking and formal acceptance are unfinished, and the public Open ETF Agent repository does not include this complete module.
掘金 · 人工智能 · 🔥 0 - A Goal-Oriented Framework for AI Agent Context Governance
The article synthesizes Anthropic’s context-engineering guidance into a framework for organizing AI-agent context around goals, evidence, permissions, memory, exploration, and progress. Using a synthetic fund-comparison scenario, it argues that context should preserve source boundaries and support the next useful judgment rather than simply expand the context window. It also distinguishes model judgments from actual tool execution records and describes memory operations including writing, recall, correction, expiry, and forgetting.
掘金 · 人工智能 · 🔥 0
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