{
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    "session_file": "/mnt/rpi/pi-config/sessions/--home-sebas--/2026-04-22T12-58-12-075Z_019db545-02eb-75f4-99f6-4333c7edcc11.jsonl",
    "reason": "before_switch:new",
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    "user_messages": 10,
    "assistant_messages": 49,
    "last_user_text": "dame handoff",
    "last_assistant_text": "Handoff: - Investigación cerrada y clasificada. - Ya hay RFC y log de progreso. - Archivos: - `/mnt/rpi/pi-config/sessions/memory-techniques-review.md` - `/mnt/rpi/pi-config/sessions/memory-techniques-review.html` - `/mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.md` - `/mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.html` Hallazgos clave: - Las técnicas reales y con backing fuerte son: - TTL / short-term memory - summaries / pruning - episodic → semantic consolidation - dedup / fusion - hybrid retrieval - explicit promotion with evidence - Lo que más valor agrega no es más memoria, sino **memoria jerárquica con promoción**. Estado: - **Listo para prototipo** - **No** listo todavía como versión final Falta para implementar: - esquema de datos final - umbrales de promoción - jobs de consolidación - evaluación de utilidad/precisión Nota operativa: - Nginx quedó ajustado para servir `.html` desde `/mnt` como `text/html`. - Eliminé el hack feo por archivo. Si retomás después, el siguiente paso útil es convertir el RFC en spec técnica con: - schema - jobs - scoring - workflow de promoción.",
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            {
              "type": "text",
              "text": "Veo tanto en PI como en otros agentes intentos de crear memoria persistente con distintas capas de relevancia y generación de insights etcétera. Quiero que investigues y clasifiques todas las técnicas que se utilizan para crear esta capa de memoria para agentes. Así vemos cómo extender nuestro agentes database para convertirlo en algo más de lo que soy que hoy es un sistema que periódicamente guarda memoria pero tienden a ser bastante operativas y no hay un tipo de consolidación en algo más de alto nivel o valioso.\n Han dado comentando en un archivo compartible, así voy viendo el progreso"
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              "thinking": "**Considering investigation needs**\n\nI need to investigate techniques and comment on a shareable file. Maybe I could use memory-read for that? Creating a todo list might help too, though I’m not sure if I need to ask for clarification. I should probably inspect the project docs and write markdown in the repo. I think I’ll look for the shared file, likely under /mnt/rpi/pi-config, and update the progress comments file. Using the todo feature and memory-read skill seems like a good plan!",
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              "text": "---\nname: memory-read\ndescription: Read persisted research memory, claims, and tasks\n---\n\n# personal-memory-search\n\n## Workflow\n\n1. Execute:\n   - `curl -fsSL \"${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=20\"`\n2. Return matching runs, claims, and tasks from the JSON response.\n3. Prefer reuse of prior work before starting a new run.\n"
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              "text": "---\nname: memory-write\ndescription: Save a durable high-signal memory into agents-database\n---\n\n# memory-write\n\n## Use When\n\n- new information is likely useful beyond current chat or task\n- saving a decision, preference, project fact, reusable source/artifact, or handoff summary\n- user asks to remember something durable\n\n## Do Not Use When\n\n- content is trivial, redundant, ephemeral, or likely useless later\n- content is secret unless user explicitly asked to store it\n\n## Required Workflow\n\n1. If useful context may already exist, optionally check first:\n   - `curl -fsSL \"${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=10\"`\n2. Choose best memory type:\n   - `profile`: stable user preference, priority, recurring constraint\n   - `decision`: chosen approach or operating rule\n   - `project`: durable project fact or invariant\n   - `artifact`: reusable source, claim, reference, output\n   - `episode`: concise session summary, blocked state, handoff context\n3. Build a concise payload with:\n   - required: `type`, `scope`, `title`, `content`\n   - recommended when known: `subtype`, `project_id`, `repo_id`, `task_id`, `run_id`, `source_ref`, `origin_agent`, `url`, `domain`, `metadata`\n4. Save it with the helper:\n   - `~/.agents/skills/memory-write/memory-write --type <type> --scope <scope> --title \"<title>\" --content \"<content>\"`\n5. Confirm the created memory id and what was stored.\n\n## Quick Examples\n\n```bash\n~/.agents/skills/memory-write/memory-write \\\n  --type profile \\\n  --subtype working_preference \\\n  --scope global \\\n  --title \"User prefers concise status updates\" \\\n  --content \"Sebas prefers short, direct updates with business impact first.\" \\\n  --source-ref \"conversation:2026-04-17\" \\\n  --origin-agent personal-agent\n\n~/.agents/skills/memory-write/memory-write \\\n  --type decision \\\n  --subtype operating_rule \\\n  --scope global \\\n  --title \"Agents should save durable high-signal memory opportunistically\" \\\n  --content \"Treat agents-database as shared durable memory, not only search.\" \\\n  --metadata '{\"source\":\"conversation\"}'\n```\n\n## Rules\n\n- prefer one strong memory over many weak ones\n- keep `title` short and searchable\n- keep `content` factual and compact\n- use `episode` for summaries, not for every chat turn\n- include `url` and `domain` for web-derived artifacts when known\n- include `project_id`, `repo_id`, `task_id`, and `run_id` when context exists\n"
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              "thinking": "**Searching memory techniques**\n\nI’m thinking about exploring existing information on memory techniques. Should I use curl for that? Hmm, maybe skipping the tool for now is fine, and I could just use bash instead. I also need to consider creating a file to keep track of my progress and comments. Alright, let's search for that information! This should help me gather effective memory techniques.",
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Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Download Microsoft Edge More info about Internet Explorer and Microsoft Edge Table of contents Exit editor mode Ask Learn Ask Learn Focus mode Table of contents Read in English Add Add to plan Edit Share via Facebook x.com LinkedIn Email Copy Markdown Print Note Access to this page requires authorization. You can try signing in or changing directories . Access to this page requires authorization. You can try changing directories . Agent memories in Azure Cosmos DB for NoSQL Applies to: \\\\u2705 NoSQL Feedback Summarize this article for me In this article Agent memory refers to an AI agent's ability to persist and recall information from previous interactions. This capability is also called AI memory . Agent memory includes prior facts, interactions, and experiences. AI agents use this stored information to better reason, plan, and act over time. This article guides you through the most common patterns for storing and retrieving agent memories in your applications. It explains how each pattern works, highlights its strengths and limitations, and offers practical tips so you can confidently choose the right approach for your use cases. What Are Agent Memories? Agent memory is often divided into short-term (episodic or working) memory and long-term memory. This section explains the differences between each type. Short-Term Memory Short-term memory holds recent context. For example, recent conversation turns, state information, results from tool or function calls. These values are all useful to the agent for the current task or thread. It can get deleted after some time (for example, using time-to-live (TTL) ), aggregated or summarized by thread, and classified as long-term memory. For example: In a conversational agent, the last 5\\\\u201310 user or agent dialogue turns, including prompts, large language model (LLM) responses, tool call results, etc. Intermediate states or partial task steps (for example retrieval of information from an API or tool call used in a subsequent step). Long-Term Memory Long-term memory is more persistent and accumulates knowledge or patterns over multiple threads or conversations. It supports recall beyond immediate context. For example, User preferences (for example \\\\\\\"User prefers responses in bullet lists\\\\\\\", or \\\\\\\"User is vegetarian\\\\\\\"). Historical summaries or reflections of short-term memories, or long threads. For example, \\\\\\\"In this thread, the customer discussed their preferences for cotton socks and dislike of synthetic materials.\\\\\\\" Design patterns for agent memory storage In the next section, we divide the discussion into four parts: In part one, we guide you through partition key selection, helping you choose a key that balances write and query throughput across partitions. In part two, we provide guidance on configuring a vector index for semantic search or a full-text index for text-based search, depending on your retrieval needs. Then, we explore data modeling patterns and their trade-offs. Finally, we cover query patterns that can be applied for different retrieval scenarios. Choose a partition key Because Azure Cosmos DB automatically partitions your data, choosing a partition key is one of the most important design choices for your data model. The partition key determines how data distributes across logical and physical partitions. This distribution directly affects query performance, insert performance, scalability, and cost. Each partition key value maps to a distinct logical partition. A good partition strategy balances locality (keeping related items together for efficient queries) with distribution. In this guide, we highlight three common approaches. Read about partitioning in Azure Cosmos DB for more detail. Here are some common patterns and trade-offs when using Cosmos DB (or Cosmos-style NoSQL + vector features) to store agent memory. Use a GUID as the partition key Each item gets its own unique partition key value, typically a GUID. This strategy maximizes write distribution and avoids the hot partition problem, because every write lands in a different logical partition. It's simple to implement and works well for write-heavy workloads without strong locality requirements. The tradeoff is that queries span logical and possibly physical partitions, which can be more expensive. This strategy can be useful for storing ephemeral AI agent turns where you care more about logging and long-term analytics than revisiting specific conversations. Example: A partition key /pk that takes on values like: \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" Use a unique thread ID as the partition key All items for a conversation share the same partition key equal to the thread (or thread) ID. This technique colocates turns and summaries, which makes \\\\\\\"latest N,\\\\\\\" phrase filters, and other queries within a thread efficient. However, you must ensure there's enough workload distribution across threads to reduce likelihood of hot partitions. This approach works well for conversational agents and RAG applications. Most queries target a single conversation. Common queries include retrieving recent turns or performing vector search within one thread. Example: A partition key /threadId that takes on values like: \\\\\\\"thread-1234\\\\\\\" Use a tenant ID and thread ID as the partition key You can use a two-level hierarchical partition key where the leading level is the tenant ID and the second is the thread ID. This technique preserves locality within a thread while grouping threads under a tenant for governance, quotas, and analytics. It also reduces cross-partition scans for tenant-level queries and enables safer multitenant isolation patterns. This technique is best for multitenant apps where each customer (tenant) runs many concurrent conversations and more isolation is needed. Example: A partition key [\\\\\\\"/tenantId\\\\\\\", \\\\\\\"/threadId\\\\\\\"] takes on values like tenantId = \\\\\\\"contoso\\\\\\\" , threadId = \\\\\\\"thread-1234\\\\\\\" Indexes for semantic and full-text search Azure Cosmos DB supports both vector indexing for semantic similarity search and full-text indexing for keyword-based retrieval, allowing you to choose the right search strategy based on your agent memory requirements. Configure a vector index When you enable vector search in Azure Cosmos DB, you choose not only whether to shard but also which index type to use. Cosmos supports multiple vector-index algorithms, including quantizedFlat and DiskANN . The quantizedFlat index type is suited for smaller workloads or when you expect the number of vectors to remain modest (for example, tens of thousands of vectors total). It compresses (quantizes) each vector and performs an exact search over the compressed space, trading a slight accuracy loss for lower RU cost and faster scans. When your vector data scales up (for example, hundreds of thousands to billions of embeddings), DiskANN is the better choice. DiskANN implements approximate nearest-neighbor indexing and is optimized for high throughput, low latency, and cost efficiency at scale. It supports dynamic updates and achieves excellent recall across large datasets. Learn more about vector indexes in Azure Cosmos DB . If you're using DiskANN, you can decide whether to shard the vector index via the vectorIndexShardKey . This decision lets you partition the DiskANN index based on an item property (for example, session, user, tenant), reducing the candidate search space and making semantic queries more efficient and focused. For example, you can shard by a tenant, userid, or both. In multitenant systems, isolating the vector index per tenant ensures that search on a particular tenant or user data is fast and efficient. Using the multitenant example from the section on partitioning , you can set the vectorIndexShardKey and the partition key to be the same, or just the first level of your hierarchical partition key. Using a global (nonsharded) index offers simplicity and the ability to search on the entire set of vectors. Both of these options allow you to further refine the search using WHERE clause filters as with any other query. Configure a full text index Azure Cosmos DB's full text search capability enables advanced text-based queries over your memory items, making it ideal for keyword and phrase-based retrieval scenarios. When you enable full text indexing on specific paths in your container (such as /content ), Azure Cosmos DB automatically applies linguistic processing including tokenization, stemming, and case normalization. This feature allows queries to match variations of words (for example, \\\\\\\"running\\\\\\\" matches \\\\\\\"run,\\\\\\\" \\\\\\\"runs,\\\\\\\" \\\\\\\"ran\\\\\\\") and improves recall for natural language searches. Full text indexes are valuable for agent memory workloads where you need to retrieve conversations based on specific subjects, entities, or phrases mentioned by users or agents. For instance, you can quickly find all turns where \\\\\\\"refund policy\\\\\\\" or \\\\\\\"billing issues\\\\\\\" are discussed, regardless of the exact phrasing. Unlike vector search, which finds semantically similar content, full text search provides precise lexical matching with linguistic intelligence. Azure Cosmos DB uses BM25 (Best Match 25), a statistical ranking function that scores items based on term frequency and item length normalization, ensuring that the most relevant results are surfaced first. You can combine full text search with vector search in hybrid queries to apply both BM25 scoring for keyword relevance and vector similarity for semantic meaning. Learn more about full text search in Azure Cosmos DB . Recommended data model: One document per turn This is the recommended data model for storing chat histories and agent memories in Azure Cosmos DB. Each item represents a complete back-and-forth exchange (or turn) between two entities in a conversation thread, for example, a user prompt and the agent\\\\u2019s response, or an agent\\\\u2019s call to a tool and the resulting output. Each item serves as a natural unit of memory that can be stored, queried, vectorized, and expired as a single entity. This structure makes it efficient to retrieve context for a single exchange or recent history, perform vector or keyword search at either the exchange or per-message level, and maintain balanced item size with manageable storage costs. Note This model is the most common for both single and multi-agent apps. It provides a good balance between small item size and the utility or value of information included in a single memory item. Example scenarios An agent chat application where each turn consists of the user's question and the agent's reply, and you frequently need to resurface entire question and answer pairs for context injection. A planning agent that queries an external API (tool) and logs both the request and tool response as one memory unit. This scenario allows downstream queries to recall the whole exchange. Applications that use the memories as part of a semantic cache . This approach can reduce latency, token consumption, and LLM-based costs. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Thread / conversation identifier (commonly the partition key). \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic counter of the exchange (0-based or 1-based). 7 messages object \\\\u2705 Messages that make up this exchange (for example, user prompt, agent reply, optional tool call/response). For more information, see messages properties . turnEmbedding number[] optional Vector for the whole exchange (for example, embedding of a concatenated or summarized message pair). [0.013, -0.092, ] embedding number[] optional Vector for this specific turn (You can create embedding of user question for a semantic cache ). [0.11, 0.02, ] Properties of messages object Property Type Required Description Example role string \\\\u2705 Origin of the message. Typical: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" . \\\\\\\"agent\\\\\\\" entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this message is associated with. \\\\\\\"agent-assistant-01\\\\\\\" name string \\\\u2705 Tool/function name or agent persona label. \\\\\\\"kb.search\\\\\\\" content string \\\\u2705 Text payload (prompt, reply, tool result snippet). \\\\\\\"Refund policy is 30 days for unopened items.\\\\\\\" timestamp string (ISO 8601) optional Message timestamp. \\\\\\\"2025-09-24T10:14:27Z\\\\\\\" metadata object optional Per-message extras (for example, tokens, tool args/results identifiers). { \\\\\\\"tokens\\\\\\\": 17 } { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 7, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"What's our refund policy for accessories?\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 12 } }, { \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Let me check the knowledge base\\\\u2026\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 8 } }, ], \\\\\\\"embedding\\\\\\\": [0.013, -0.092, 0.551, ], \\\\\\\"metrics\\\\\\\": { \\\\\\\"latencyMs\\\\\\\": 300 } } Advantages Good for longer threads/conversations. Easy to get \\\\\\\"latest N turns\\\\\\\" by ordering on turnIndex or timestamp. Supports embedding-per-turn for vector similarity queries within the thread and semantic caching . Easy to TTL (expire) older memories if needed. Smaller items per turn don't require potentially expensive updates. Limitations When summarizing or consolidating, you might want to generate \\\\\\\"summary turns.\\\\\\\" You might need to periodically prune or compact older turns. Alternative data models These alternative data models can also be used for storing chat histories and agent memories, depending on your application\\\\u2019s requirements. However, each comes with trade-offs in storage efficiency, retrieval complexity, and overall cost. While the turn-based model is generally preferred, the following options are provided for completeness and to help you evaluate different design choices based on your scenario. One response per item In this design, every agent or user interaction (that is \\\\\\\"turn\\\\\\\") is stored as its own item in Azure Cosmos DB. All turn items for a single thread or thread carry the same threadId , which acts as a logical link between other turns in the same thread. This pattern is useful for fine-grained retrieval, analytics, or vector similarity search on every single utterance. Example scenarios A conversational agent where embeddings are created for every user and agent message, enabling per-utterance semantic search within a thread. Analytics dashboards that measure agent vs. user token counts, sentiment, or latency at the per-message level. RAG (retrieval-augmented generation) flows that need to embed and search every response independently (for example \\\\\\\"find the most relevant past statement across all threads\\\\\\\"). Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Identifier for the conversation/thread. Often chosen as the partition key so all turns for a thread are colocated and efficiently queried. In multitenant apps, consider hierarchical primary keys like /tenantId , /threadId . \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic turn counter (0,1,2\\\\u2026). Use with threadId to sort/fetch latest N turns. 3 entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this response is associated with. \\\\\\\"user-12345\\\\\\\" role string \\\\u2705 The role that produced the turn. Common values: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" (or similar). \\\\\\\"agent\\\\\\\" content string \\\\u2705 Main text content for the turn (prompt, reply, tool result, etc.). \\\\\\\"This is one response from an LLM\\\\\\\" timestamp string (ISO 8601) \\\\u2705 Creation time for the turn (ISO 8601). Alternatively, you can sort by the system _ts (epoch seconds) without storing your own timestamp. \\\\\\\"2025-09-24T10:15:00Z\\\\\\\" embedding number[] optional Vector embedding for content (or a summary). Must be a top-level field included in the container's vector policy/index to enable vector search. [0.017, -0.234, 0.561, ] metrics object optional Free-form metrics/attrs for the turn. Keep names machine-friendly (no spaces) for easier querying and indexing. { \\\\\\\"inputTokens\\\\\\\": 25, \\\\\\\"outputTokens\\\\\\\": 8 } An example of this memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 3, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"This is one response from an LLM\\\\\\\", \\\\\\\"embedding\\\\\\\": [-1.12402894028, ], \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"metrics\\\\\\\": { \\\\\\\"inputTokens\\\\\\\": 12, \\\\\\\"outputTokens\\\\\\\": 8, \\\\\\\"latencyMs\\\\\\\": 177 }, } Advantages Each memory is kept at an atomic/granular level. Easy to update/TTL individual responses. Retrieving last N results can be done with a query with filter on the thread ID. Limitations Vector embeddings only capture one response at a time. Not keeping a complete turn in one data item (back-and-forth between agent and user or tool call) can limit utility of the memory without subsequent queries. Not ideal model for semantic caching . One thread per item Here, all the turns of a conversation (user, agent, tools, etc.) for a given thread or thread are aggregated into a single item. This item contains a list or array of turn entries (each with turnIndex, role, content, embedding, etc.), and optional summary fields, metadata, and a thread-level embedding. Because the entire thread is stored in one item, retrieving the full history (or a large window) becomes a single read. However, appending new turns requires updating (replacing) the item, which can become costly if the item grows large. This model can be useful when sessions are bounded in size or short-lived, and you prefer single-item reads to reconstruct context. Important This model is typically not recommended, as there's a risk to have long or unbounded threads, which can have high RU charges for CRUD operations on the item. Example scenarios Chatbots in customer onboarding or troubleshooting flows, where each thread is short (limited number of turns) and agents need to fetch the entire conversation quickly in one read. Conversation summarization services, where you run periodic batch jobs over full sessions to generate embeddings or summaries. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Logical thread or thread identifier (often used as partition key). \\\\\\\"thread-1234\\\\\\\" turns array of objects \\\\u2705 A list of individual turn records (user or agent). Each turn contains a small structure. For example, index, role, content, etc. [ { \\\\\\\"turnIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hello\\\\\\\" }, { \\\\\\\"turnIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hi there!\\\\\\\" } ] embedding number[] optional Embedding vector computed over a summary or aggregation of the thread. Useful for semantic search over key points of the conversation. [0.101, -0.231, 0.553, \\\\u2026] summary string optional A textual \\\\\\\"roll-up\\\\\\\" or summary of the key points in the thread. \\\\\\\"User wanted to schedule a meeting; agent fetched available times and confirmed A.M. slots.\\\\\\\" metrics object optional Key/value attributes or metrics about the thread. { \\\\\\\"startTime\\\\\\\": \\\\\\\"2025-09-24T09:05:00Z\\\\\\\", \\\\\\\"lastTurnTime\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"turnCount\\\\\\\": 7 } An example of a memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"messageIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, { \\\\\\\"messageIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, ], \\\\\\\"LastUpdatedtimestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"summ\\\", \\\"title\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\\", \\\"url\\\": \\\"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\\\"}\",\n        \"summary\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\",\n        \"confidence\": 0.75,\n        \"freshness\": 0.75,\n        \"created_at\": \"2026-03-26T18:15:48.212846+00:00\",\n        \"updated_at\": \"2026-03-26T18:15:48.212846+00:00\",\n        \"observed_at\": \"2026-03-26T18:15:48.212846+00:00\",\n        \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\",\n        \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n        \"metadata\": {\n          \"artifact_kind\": \"source_capture\",\n          \"record_kind\": \"research_artifact\",\n          \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\",\n          \"schema\": \"personal-agent-memory-v1\"\n        },\n        \"embedding\": [\n          0.539649079608721,\n          0.48364487477360774,\n          0.42648079820187174,\n          0.34986290276401943,\n          0.27533825843293275,\n          0.22594416839533857,\n          0.167638974231975,\n          0.12494732709033757\n        ]\n      },\n      \"score\": 0.4014677165118756,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.6,\n        \"semantic\": 0.9764514434125041,\n        \"confidence\": 0.75,\n        \"freshness\": 0.75\n      },\n      \"matched_scope\": \"global\",\n      \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n      \"explanation\": \"facets=0.00 lexical=0.60 semantic=0.98 confidence=0.75 freshness=0.75\"\n    },\n    {\n      \"memory\": {\n        \"id\": \"mem_9464c178479a46e7a48da1765ce7dd4d\",\n        \"schema_version\": \"shared-agent-memory-v2\",\n        \"type\": \"artifact\",\n        \"subtype\": null,\n        \"scope\": \"global\",\n        \"status\": \"active\",\n        \"project_id\": null,\n        \"repo_id\": null,\n        \"agent_id\": null,\n        \"origin_agent\": null,\n        \"run_id\": null,\n        \"task_id\": null,\n        \"url\": null,\n        \"domain\": null,\n        \"source_kind\": \"document\",\n        \"title\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\",\n        \"content\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\nDefines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\\nhttps://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n        \"summary\": \"Defines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\",\n        \"confidence\": 0.75,\n        \"freshness\": 0.75,\n        \"created_at\": \"2026-03-26T18:15:48.199836+00:00\",\n        \"updated_at\": \"2026-03-26T18:15:48.199836+00:00\",\n        \"observed_at\": \"2026-03-26T18:15:48.199836+00:00\",\n        \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\",\n        \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n        \"metadata\": {\n          \"domain\": \"learn.microsoft.com\",\n          \"notes\": \"Defines short/long-term memory patterns; TTL; summarization; pruning guidance\\ncontent_type=text/html\",\n          \"record_kind\": \"research_source\",\n          \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\",\n          \"schema\": \"personal-agent-memory-v1\",\n          \"url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"\n        },\n        \"embedding\": [\n          0.47154247711313935,\n          0.42780488128933386,\n          0.40432235528723987,\n          0.3822866199866966,\n          0.3682638793408964,\n          0.2793418969917347,\n          0.20555557121264298,\n          0.17061001119056943\n        ]\n      },\n      \"score\": 0.3446239459086434,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.4,\n        \"semantic\": 0.9974929727242898,\n        \"confidence\": 0.75,\n        \"freshness\": 0.75\n      },\n      \"matched_scope\": \"global\",\n      \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n      \"explanation\": \"facets=0.00 lexical=0.40 semantic=1.00 confidence=0.75 freshness=0.75\"\n    },\n    {\n      \"memory\": {\n        \"id\": \"mem_v1_legacy_bridge_status\",\n        \"schema_version\": \"shared-agent-memory-v2\",\n        \"type\": \"decision\",\n        \"subtype\": null,\n        \"scope\": \"project\",\n        \"status\": \"active\",\n        \"project_id\": \"proj_personal_agent\",\n        \"repo_id\": \"repo_personal_agent\",\n        \"agent_id\": null,\n        \"origin_agent\": null,\n        \"run_id\": null,\n        \"task_id\": null,\n        \"url\": null,\n        \"domain\": null,\n        \"source_kind\": \"manual\",\n        \"title\": \"V1 legacy bridge policy\",\n        \"content\": \"New durable work lives only in agents-database. Legacy personal-agent sqlite stays transition-only, with old-run inspection bridged through mirrored shared-memory records.\",\n        \"summary\": \"Legacy sqlite is transition-only; old-run inspection falls back to shared-memory mirrors.\",\n        \"confidence\": 0.96,\n        \"freshness\": 0.95,\n        \"created_at\": \"2026-03-14T21:29:18.507032+00:00\",\n        \"updated_at\": \"2026-03-14T21:29:18.507032+00:00\",\n        \"observed_at\": \"2026-03-14T21:29:18.507032+00:00\",\n        \"source_ref\": \"personal-agent\",\n        \"evidence_ref\": \"art_2101da9740f44e53b145eaec35755355\",\n        \"metadata\": {\n          \"kind\": \"transition_policy\",\n          \"milestone\": \"V1\"\n        },\n        \"embedding\": [\n          0.4684594956601416,\n          0.4673883230328699,\n          0.41311557658443887,\n          0.36134223293297507,\n          0.34991639157541066,\n          0.30421302614515294,\n          0.18138523155133532,\n          0.11390135603322041\n        ]\n      },\n      \"score\": 0.3640911827527758,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.4,\n        \"semantic\": 0.9899412183518389,\n        \"confidence\": 0.96,\n        \"freshness\": 0.95\n      },\n      \"matched_scope\": \"project\",\n      \"evidence_ref\": \"art_2101da9740f44e53b145eaec35755355\",\n      \"explanation\": \"facets=0.00 lexical=0.40 semantic=0.99 confidence=0.96 freshness=0.95\"\n    },\n    {\n      \"memory\": {\n        \"id\": \"mem_4f5ebfd765e34937b82ccf047dba60f7\",\n        \"schema_version\": \"shared-agent-memory-v2\",\n        \"type\": \"artifact\",\n        \"subtype\": null,\n        \"scope\": \"global\",\n        \"status\": \"active\",\n        \"project_id\": null,\n        \"repo_id\": null,\n        \"agent_id\": null,\n        \"origin_agent\": null,\n        \"run_id\": null,\n        \"task_id\": null,\n        \"url\": null,\n        \"domain\": null,\n        \"source_kind\": \"manual\",\n        \"title\": \"Research claim: AgentDock proposes periodic consolidation that (1) converts episodic memories to\",\n        \"content\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metadata/temporal similarity, and (3) builds higher-level abstractions; it supports merge/synthesize/abstract strategies and batch scheduling.\",\n        \"summary\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metada\",\n        \"confidence\": 0.64,\n        \"freshness\": 0.75,\n        \"created_at\": \"2026-03-26T18:15:56.396499+00:00\",\n        \"updated_at\": \"2026-03-26T18:15:56.396499+00:00\",\n        \"observed_at\": \"2026-03-26T18:15:56.396499+00:00\",\n        \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\",\n        \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\",\n        \"metadata\": {\n          \"claim_status\": \"verified\",\n          \"record_kind\": \"research_claim\",\n          \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\",\n          \"schema\": \"personal-agent-memory-v1\",\n          \"source_url\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\"\n        },\n        \"embedding\": [\n          0.48439022845783813,\n          0.4561797340462889,\n          0.3943808248838651,\n          0.34720608506522094,\n          0.31408941771253274,\n          0.25615883721523774,\n          0.24851652936461738,\n          0.2348358548172106\n        ]\n      },\n      \"score\": 0.3367421009400994,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.4,\n        \"semantic\": 0.9889473396006623,\n        \"confidence\": 0.64,\n        \"freshness\": 0.75\n      },\n      \"matched_scope\": \"global\",\n      \"evidence_ref\": \"https://hub.agentdock.ai/docs/memory/consolidation-guide\",\n      \"explanation\": \"facets=0.00 lexical=0.40 semantic=0.99 confidence=0.64 freshness=0.75\"\n    },\n    {\n      \"memory\": {\n        \"id\": \"mem_3d48d97c66d04808844820d8f1e5e8f8\",\n        \"schema_version\": \"shared-agent-memory-v2\",\n        \"type\": \"artifact\",\n        \"subtype\": \"research_artifact\",\n        \"scope\": \"global\",\n        \"status\": \"active\",\n        \"project_id\": null,\n        \"repo_id\": null,\n        \"agent_id\": null,\n        \"origin_agent\": \"personal-agent\",\n        \"run_id\": \"a1ad721a-bcbc-473e-806d-0793fbbfaac2\",\n        \"task_id\": null,\n        \"url\": \"https://matchable.io/business/pricing\",\n        \"domain\": \"matchable.io\",\n        \"source_kind\": \"manual\",\n        \"title\": \"Capture: Pricing | Software automation tool for your business\",\n        \"content\": \"{\\\"content_type\\\": \\\"text/html; charset=utf-8\\\", \\\"text\\\": \\\"Pricing | Software automation tool for your business Integrate your bookings with MATCHi Learn more Pricing Resources For clients Request demo English Spanish French Czech Create account Software that grows with your business Matchable is subscription-free for venues, independent trainers, and organizers. For business For freelancers For venues or academies Matchable takes as low as 1.8% to keep the engines running. Pay only when receiving a payment. No upfront fees, no hidden costs, not for you, not for your client. Accept payments in 36+ countries Keep track through the trainer application Add your availability and holidays Add your certifications, skills and more Instantly receive payments 1.8% / Per transaction Create account Request demo Features Private training service Unlimited services Classes and events Unlimited schedules Courses Coming soon Unlimited seasons Promotion tools Free Automated invoices Free Client CRM Unlimited clients Insights Coming soon Coming soon Recruitment Venue profile Manager Web-only manager to manage your everyday Trainer application Let your trainers manager their profile, bookings, availabilities and more. Free Multi-venue management Manage multiple venues from your manager Unlimited venues Manager seats Add multiple manager to your account Unlimited seats Payments Online payments You only pay when you get paid. Simple as that. We only take a small commission when you receive money through a booking. * Stripe or Mollie will also charge standard processing fees, which depend on the payment method selected. 1.8%* Subscription fee No monthly fee, no hidden costs Free Client fee Matchable doesn't monetise your clients, not now, not ever. Fair and square. Free Manual payment links Coming soon Coming soon In-person payments Coming soon Coming soon Manual bookings Free Matchable Marketplace (optional) No additional fee applied to new clients who discover your business via the marketplace. Free Optional add-ons Matchable Widget Show your services and activities on your own website through our widget. On request Google Calendar View your Matchable bookings in your own Apple or Google calendar. Free Stripe Receive payments Free Mollie Receive payments Free MATCHi Automate court bookings Free Coming soon Google Analytics Automate court bookings Coming soon Coming soon Meta Pixel Automate court bookings Coming soon Support Chat support Available through Manager Panel Email support Response within 24 hours Knowledge base Video Support Dedicated agent For freelancers or organisers Matchable takes as low as 1.8% to keep the engines running. Pay only when receiving a payment. No upfront fees, no hidden costs, not for you, not for your client. Accept payments in 36+ countries Manage your trainers and activities Automate your back-office Unlimited connected trainers Instantly receive payments 1.8% / Per transaction Create account Request demo Features Private training service Unlimited services Classes and events Unlimited schedules Courses Coming soon Unlimited seasons Promotion tools Free Automated invoices Free Client CRM Unlimited clients Insights Coming soon Coming soon Recruitment - Venue profile - Manager Web-only manager to manage your everyday Trainer application Let your trainers manager their profile, bookings, availabilities and more. Free Multi-venue management Manage multiple venues from your manager Unlimited venues Manager seats Add multiple manager to your account - Payments Online payments You only pay when you get paid. Simple as that. We only take a small commission when you receive money through a booking. * Stripe or Mollie will also charge standard processing fees, which depend on the payment method selected. 1.8%* Subscription fee No monthly fee, no hidden costs Free Client fee Matchable doesn't monetise your clients, not now, not ever. Fair and square. Free Manual payment links Coming soon Coming soon In-person payments Coming soon Coming soon Manual bookings Free Matchable Marketplace (optional) No additional fee applied to new clients who discover your business via the marketplace. Free Optional add-ons Matchable Widget Show your services and activities on your own website through our widget. Free Google Calendar View your Matchable bookings in your own Apple or Google calendar. Free Stripe Receive payments Free Mollie Receive payments Free MATCHi Automate court bookings Free Google Analytics Automate court bookings Coming soon Meta Pixel Automate court bookings Coming soon Support Chat support Available through Manager Panel Email support Response within 24 hours Knowledge base Video Support - Dedicated agent - Freelancer Business Pricing? Glad you asked Matchable takes as low as 1.8% to keep the engines running. Pay only when receiving a payment. No upfront fees, no hidden costs, not for you, not for your client. Accept payments in 36+ countries Keep track through the trainer application Add your availability and holidays Add your certifications, skills and more Instantly receive payments 1.8% / Per transaction Create account Request demo Manage your trainers and activities Automate your back-office Unlimited connected trainers Instantly receive payments 1.8% / Per transaction Create account Request demo Features Private training service Unlimited services Classes and events Unlimited schedules Courses Coming soon Unlimited seasons Promotion tools Free Automated invoices Free Client CRM Unlimited clients Insights Coming soon Coming soon Recruitment - Venue profile - Manager Web-only manager to manage your everyday Trainer application Let your trainers manager their profile, bookings, availabilities and more. Free Multi-venue management Manage multiple venues from your manager Unlimited venues Manager seats Add multiple manager to your account - Unlimited seats Payments Online payments You only pay when you get paid. Simple as that. We only take a small commission when you receive money through a booking. * Stripe or Mollie will also charge standard processing fees, which depend on the payment method selected. 1.8%* 1.8%* Subscription fee No monthly fee, no hidden costs Free Client fee Matchable doesn't monetise your clients, not now, not ever. Fair and square. Free Manual payment links Coming soon Coming soon In-person payments Coming soon Coming soon Manual bookings Free Matchable Marketplace (optional) No additional fee applied to new clients who discover your business via the marketplace. Free Optional add-ons Matchable Widget Show your services and activities on your own website through our widget. Free On request Google Calendar View your Matchable bookings in your own Apple or Google calendar. Free Stripe Receive payments Free Mollie Receive payments Free MATCHi Automate court bookings On request Coming soon Google Analytics Automate court bookings Coming soon Coming soon Meta Pixel Automate court bookings Coming soon Support Chat support Available through Manager Panel Email support Response within 24 hours Knowledge base Video Support - Dedicated agent - Frequently asked questions. Answered. What is Matchable? Matchable is an academy automation tool and a global marketplace in one. Trainers and venues can showcase themselves to clients and businesses to get booked instantly. You can automate lessons bookings, trainer schedules and court reservations. Players book and pay in Matchable and courts are auto-assigned in real time through MATCHi. How can I join Matchable as a venue or a trainer? We\\\\u2019d love to have you on the platform! Visit this page to register and we'll reach out to you as soon as possible. Is there a cost for using Matchable? Matchable takes as low as 1.8% to keep the engines running. Pay only when receiving a payment. No upfront fees, no hidden costs, not for you, not for your client. Can I still use MATCHi for my bookings? Yes, for sure! The integration is only visible in the back-office admin of the Matchable Manager. How do I offer private training as a service using Matchable and MATCHi? It\\\\u2019s very easy. The first step is to request your account, and from there, read this article for the step by step guide. How do I integrate with Matchable as a MATCHi club? You only have to register once, and we\\\\u2019ll guide you through the integration process, read the full article here for onboarding . How to contact you if I have any additional questions? We\\\\u2019re happy to hear from you, so you can contact us for an introduction call here or send us an email at info@getmatchable.com . Does Matchable have an App? Yes, we have a Trainer App and Client App that we recently launched. You can download them in the Apple Store or on Google Play . Marketplace Log in Create account List your business Report a bug Support Request demo Talk to sales Help Center Voucher balance Contact us Company Integrations Partnerships Company news Work with us Investor portal Tools Resource overview Talent interviews Press kit \\\\u00a9 2025 Matchable Privacy Policy Terms of Service Cookies Settings Follow-us\\\", \\\"title\\\": \\\"Pricing | Software automation tool for your business\\\", \\\"url\\\": \\\"https://matchable.io/business/pricing\\\"}\",\n        \"summary\": \"Pricing | Software automation tool for your business\",\n        \"confidence\": 0.75,\n        \"freshness\": 0.75,\n        \"created_at\": \"2026-03-24T14:20:17.332786+00:00\",\n        \"updated_at\": \"2026-03-24T14:20:17.332786+00:00\",\n        \"observed_at\": \"2026-03-24T14:20:17.332786+00:00\",\n        \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\",\n        \"evidence_ref\": \"https://matchable.io/business/pricing\",\n        \"metadata\": {\n          \"artifact_kind\": \"source_capture\"\n        },\n        \"embedding\": [\n          0.5292556164037588,\n          0.48531753305805714,\n          0.43653241186476366,\n          0.3554767198589,\n          0.26573727628063015,\n          0.22618041156080354,\n          0.17905398448808205,\n          0.11660588193349267\n        ]\n      },\n      \"score\": 0.34133419004159415,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.4,\n        \"semantic\": 0.9755612669439616,\n        \"confidence\": 0.75,\n        \"freshness\": 0.75\n      },\n      \"matched_scope\": \"global\",\n      \"evidence_ref\": \"https://matchable.io/business/pricing\",\n      \"explanation\": \"facets=0.00 lexical=0.40 semantic=0.98 confidence=0.75 freshness=0.75\"\n    },\n    {\n      \"memory\": {\n        \"id\": \"mem_7daf2b19b42640e09a23d0579e3df37c\",\n        \"schema_version\": \"shared-agent-memory-v2\",\n        \"type\": \"artifact\",\n        \"subtype\": null,\n        \"scope\": \"global\",\n        \"status\": \"active\",\n        \"project_id\": null,\n        \"repo_id\": null,\n        \"agent_id\": null,\n        \"origin_agent\": null,\n        \"run_id\": null,\n        \"task_id\": null,\n        \"url\": null,\n        \"domain\": null,\n        \"source_kind\": \"manual\",\n        \"title\": \"Research claim: Microsoft's Cosmos DB agent memory guide suggests short-term memories can be del\",\n        \"content\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\",\n        \"summary\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\",\n        \"confidence\": 0.52,\n        \"freshness\": 0.75,\n        \"created_at\": \"2026-03-26T18:16:11.423964+00:00\",\n        \"updated_at\": \"2026-03-26T18:16:11.423964+00:00\",\n        \"observed_at\": \"2026-03-26T18:16:11.423964+00:00\",\n        \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\",\n        \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n        \"metadata\": {\n          \"claim_status\": \"verified\",\n          \"record_kind\": \"research_claim\",\n          \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\",\n          \"schema\": \"personal-agent-memory-v1\",\n          \"source_url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"\n        },\n        \"embedding\": [\n          0.5398024752475405,\n          0.5253448834796297,\n          0.42744184357301607,\n          0.3295388036664024,\n          0.2613366859786492,\n          0.2093207851614917,\n          0.13656138311211433,\n          0.10277461974145312\n        ]\n      },\n      \"score\": 0.3258554258767071,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.4,\n        \"semantic\": 0.9643695058447139,\n        \"confidence\": 0.52,\n        \"freshness\": 0.75\n      },\n      \"matched_scope\": \"global\",\n      \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n      \"explanation\": \"facets=0.00 lexical=0.40 semantic=0.96 confidence=0.52 freshness=0.75\"\n    }\n  ]\n}\n\n\n[Showing lines 96-570 of 570 (50.0KB limit). Full output: /tmp/pi-bash-830cffeb4e50c8fe.log]"
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Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Download Microsoft Edge More info about Internet Explorer and Microsoft Edge Table of contents Exit editor mode Ask Learn Ask Learn Focus mode Table of contents Read in English Add Add to plan Edit Share via Facebook x.com LinkedIn Email Copy Markdown Print Note Access to this page requires authorization. You can try signing in or changing directories . Access to this page requires authorization. You can try changing directories . Agent memories in Azure Cosmos DB for NoSQL Applies to: \\\\u2705 NoSQL Feedback Summarize this article for me In this article Agent memory refers to an AI agent's ability to persist and recall information from previous interactions. This capability is also called AI memory . Agent memory includes prior facts, interactions, and experiences. AI agents use this stored information to better reason, plan, and act over time. This article guides you through the most common patterns for storing and retrieving agent memories in your applications. It explains how each pattern works, highlights its strengths and limitations, and offers practical tips so you can confidently choose the right approach for your use cases. What Are Agent Memories? Agent memory is often divided into short-term (episodic or working) memory and long-term memory. This section explains the differences between each type. Short-Term Memory Short-term memory holds recent context. For example, recent conversation turns, state information, results from tool or function calls. These values are all useful to the agent for the current task or thread. It can get deleted after some time (for example, using time-to-live (TTL) ), aggregated or summarized by thread, and classified as long-term memory. For example: In a conversational agent, the last 5\\\\u201310 user or agent dialogue turns, including prompts, large language model (LLM) responses, tool call results, etc. Intermediate states or partial task steps (for example retrieval of information from an API or tool call used in a subsequent step). Long-Term Memory Long-term memory is more persistent and accumulates knowledge or patterns over multiple threads or conversations. It supports recall beyond immediate context. For example, User preferences (for example \\\\\\\"User prefers responses in bullet lists\\\\\\\", or \\\\\\\"User is vegetarian\\\\\\\"). Historical summaries or reflections of short-term memories, or long threads. For example, \\\\\\\"In this thread, the customer discussed their preferences for cotton socks and dislike of synthetic materials.\\\\\\\" Design patterns for agent memory storage In the next section, we divide the discussion into four parts: In part one, we guide you through partition key selection, helping you choose a key that balances write and query throughput across partitions. In part two, we provide guidance on configuring a vector index for semantic search or a full-text index for text-based search, depending on your retrieval needs. Then, we explore data modeling patterns and their trade-offs. Finally, we cover query patterns that can be applied for different retrieval scenarios. Choose a partition key Because Azure Cosmos DB automatically partitions your data, choosing a partition key is one of the most important design choices for your data model. The partition key determines how data distributes across logical and physical partitions. This distribution directly affects query performance, insert performance, scalability, and cost. Each partition key value maps to a distinct logical partition. A good partition strategy balances locality (keeping related items together for efficient queries) with distribution. In this guide, we highlight three common approaches. Read about partitioning in Azure Cosmos DB for more detail. Here are some common patterns and trade-offs when using Cosmos DB (or Cosmos-style NoSQL + vector features) to store agent memory. Use a GUID as the partition key Each item gets its own unique partition key value, typically a GUID. This strategy maximizes write distribution and avoids the hot partition problem, because every write lands in a different logical partition. It's simple to implement and works well for write-heavy workloads without strong locality requirements. The tradeoff is that queries span logical and possibly physical partitions, which can be more expensive. This strategy can be useful for storing ephemeral AI agent turns where you care more about logging and long-term analytics than revisiting specific conversations. Example: A partition key /pk that takes on values like: \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" Use a unique thread ID as the partition key All items for a conversation share the same partition key equal to the thread (or thread) ID. This technique colocates turns and summaries, which makes \\\\\\\"latest N,\\\\\\\" phrase filters, and other queries within a thread efficient. However, you must ensure there's enough workload distribution across threads to reduce likelihood of hot partitions. This approach works well for conversational agents and RAG applications. Most queries target a single conversation. Common queries include retrieving recent turns or performing vector search within one thread. Example: A partition key /threadId that takes on values like: \\\\\\\"thread-1234\\\\\\\" Use a tenant ID and thread ID as the partition key You can use a two-level hierarchical partition key where the leading level is the tenant ID and the second is the thread ID. This technique preserves locality within a thread while grouping threads under a tenant for governance, quotas, and analytics. It also reduces cross-partition scans for tenant-level queries and enables safer multitenant isolation patterns. This technique is best for multitenant apps where each customer (tenant) runs many concurrent conversations and more isolation is needed. Example: A partition key [\\\\\\\"/tenantId\\\\\\\", \\\\\\\"/threadId\\\\\\\"] takes on values like tenantId = \\\\\\\"contoso\\\\\\\" , threadId = \\\\\\\"thread-1234\\\\\\\" Indexes for semantic and full-text search Azure Cosmos DB supports both vector indexing for semantic similarity search and full-text indexing for keyword-based retrieval, allowing you to choose the right search strategy based on your agent memory requirements. Configure a vector index When you enable vector search in Azure Cosmos DB, you choose not only whether to shard but also which index type to use. Cosmos supports multiple vector-index algorithms, including quantizedFlat and DiskANN . The quantizedFlat index type is suited for smaller workloads or when you expect the number of vectors to remain modest (for example, tens of thousands of vectors total). It compresses (quantizes) each vector and performs an exact search over the compressed space, trading a slight accuracy loss for lower RU cost and faster scans. When your vector data scales up (for example, hundreds of thousands to billions of embeddings), DiskANN is the better choice. DiskANN implements approximate nearest-neighbor indexing and is optimized for high throughput, low latency, and cost efficiency at scale. It supports dynamic updates and achieves excellent recall across large datasets. Learn more about vector indexes in Azure Cosmos DB . If you're using DiskANN, you can decide whether to shard the vector index via the vectorIndexShardKey . This decision lets you partition the DiskANN index based on an item property (for example, session, user, tenant), reducing the candidate search space and making semantic queries more efficient and focused. For example, you can shard by a tenant, userid, or both. In multitenant systems, isolating the vector index per tenant ensures that search on a particular tenant or user data is fast and efficient. Using the multitenant example from the section on partitioning , you can set the vectorIndexShardKey and the partition key to be the same, or just the first level of your hierarchical partition key. Using a global (nonsharded) index offers simplicity and the ability to search on the entire set of vectors. Both of these options allow you to further refine the search using WHERE clause filters as with any other query. Configure a full text index Azure Cosmos DB's full text search capability enables advanced text-based queries over your memory items, making it ideal for keyword and phrase-based retrieval scenarios. When you enable full text indexing on specific paths in your container (such as /content ), Azure Cosmos DB automatically applies linguistic processing including tokenization, stemming, and case normalization. This feature allows queries to match variations of words (for example, \\\\\\\"running\\\\\\\" matches \\\\\\\"run,\\\\\\\" \\\\\\\"runs,\\\\\\\" \\\\\\\"ran\\\\\\\") and improves recall for natural language searches. Full text indexes are valuable for agent memory workloads where you need to retrieve conversations based on specific subjects, entities, or phrases mentioned by users or agents. For instance, you can quickly find all turns where \\\\\\\"refund policy\\\\\\\" or \\\\\\\"billing issues\\\\\\\" are discussed, regardless of the exact phrasing. Unlike vector search, which finds semantically similar content, full text search provides precise lexical matching with linguistic intelligence. Azure Cosmos DB uses BM25 (Best Match 25), a statistical ranking function that scores items based on term frequency and item length normalization, ensuring that the most relevant results are surfaced first. You can combine full text search with vector search in hybrid queries to apply both BM25 scoring for keyword relevance and vector similarity for semantic meaning. Learn more about full text search in Azure Cosmos DB . Recommended data model: One document per turn This is the recommended data model for storing chat histories and agent memories in Azure Cosmos DB. Each item represents a complete back-and-forth exchange (or turn) between two entities in a conversation thread, for example, a user prompt and the agent\\\\u2019s response, or an agent\\\\u2019s call to a tool and the resulting output. Each item serves as a natural unit of memory that can be stored, queried, vectorized, and expired as a single entity. This structure makes it efficient to retrieve context for a single exchange or recent history, perform vector or keyword search at either the exchange or per-message level, and maintain balanced item size with manageable storage costs. Note This model is the most common for both single and multi-agent apps. It provides a good balance between small item size and the utility or value of information included in a single memory item. Example scenarios An agent chat application where each turn consists of the user's question and the agent's reply, and you frequently need to resurface entire question and answer pairs for context injection. A planning agent that queries an external API (tool) and logs both the request and tool response as one memory unit. This scenario allows downstream queries to recall the whole exchange. Applications that use the memories as part of a semantic cache . This approach can reduce latency, token consumption, and LLM-based costs. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Thread / conversation identifier (commonly the partition key). \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic counter of the exchange (0-based or 1-based). 7 messages object \\\\u2705 Messages that make up this exchange (for example, user prompt, agent reply, optional tool call/response). For more information, see messages properties . turnEmbedding number[] optional Vector for the whole exchange (for example, embedding of a concatenated or summarized message pair). [0.013, -0.092, ] embedding number[] optional Vector for this specific turn (You can create embedding of user question for a semantic cache ). [0.11, 0.02, ] Properties of messages object Property Type Required Description Example role string \\\\u2705 Origin of the message. Typical: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" . \\\\\\\"agent\\\\\\\" entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this message is associated with. \\\\\\\"agent-assistant-01\\\\\\\" name string \\\\u2705 Tool/function name or agent persona label. \\\\\\\"kb.search\\\\\\\" content string \\\\u2705 Text payload (prompt, reply, tool result snippet). \\\\\\\"Refund policy is 30 days for unopened items.\\\\\\\" timestamp string (ISO 8601) optional Message timestamp. \\\\\\\"2025-09-24T10:14:27Z\\\\\\\" metadata object optional Per-message extras (for example, tokens, tool args/results identifiers). { \\\\\\\"tokens\\\\\\\": 17 } { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 7, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"What's our refund policy for accessories?\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 12 } }, { \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Let me check the knowledge base\\\\u2026\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:14:25Z\\\\\\\", \\\\\\\"metadata\\\\\\\": { \\\\\\\"tokens\\\\\\\": 8 } }, ], \\\\\\\"embedding\\\\\\\": [0.013, -0.092, 0.551, ], \\\\\\\"metrics\\\\\\\": { \\\\\\\"latencyMs\\\\\\\": 300 } } Advantages Good for longer threads/conversations. Easy to get \\\\\\\"latest N turns\\\\\\\" by ordering on turnIndex or timestamp. Supports embedding-per-turn for vector similarity queries within the thread and semantic caching . Easy to TTL (expire) older memories if needed. Smaller items per turn don't require potentially expensive updates. Limitations When summarizing or consolidating, you might want to generate \\\\\\\"summary turns.\\\\\\\" You might need to periodically prune or compact older turns. Alternative data models These alternative data models can also be used for storing chat histories and agent memories, depending on your application\\\\u2019s requirements. However, each comes with trade-offs in storage efficiency, retrieval complexity, and overall cost. While the turn-based model is generally preferred, the following options are provided for completeness and to help you evaluate different design choices based on your scenario. One response per item In this design, every agent or user interaction (that is \\\\\\\"turn\\\\\\\") is stored as its own item in Azure Cosmos DB. All turn items for a single thread or thread carry the same threadId , which acts as a logical link between other turns in the same thread. This pattern is useful for fine-grained retrieval, analytics, or vector similarity search on every single utterance. Example scenarios A conversational agent where embeddings are created for every user and agent message, enabling per-utterance semantic search within a thread. Analytics dashboards that measure agent vs. user token counts, sentiment, or latency at the per-message level. RAG (retrieval-augmented generation) flows that need to embed and search every response independently (for example \\\\\\\"find the most relevant past statement across all threads\\\\\\\"). Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Identifier for the conversation/thread. Often chosen as the partition key so all turns for a thread are colocated and efficiently queried. In multitenant apps, consider hierarchical primary keys like /tenantId , /threadId . \\\\\\\"thread-1234\\\\\\\" turnIndex number (int) \\\\u2705 Monotonic turn counter (0,1,2\\\\u2026). Use with threadId to sort/fetch latest N turns. 3 entityId string \\\\u2705 Name or ID of the user, agent, tool, etc. that this response is associated with. \\\\\\\"user-12345\\\\\\\" role string \\\\u2705 The role that produced the turn. Common values: \\\\\\\"user\\\\\\\" , \\\\\\\"agent\\\\\\\" , \\\\\\\"tool\\\\\\\" (or similar). \\\\\\\"agent\\\\\\\" content string \\\\u2705 Main text content for the turn (prompt, reply, tool result, etc.). \\\\\\\"This is one response from an LLM\\\\\\\" timestamp string (ISO 8601) \\\\u2705 Creation time for the turn (ISO 8601). Alternatively, you can sort by the system _ts (epoch seconds) without storing your own timestamp. \\\\\\\"2025-09-24T10:15:00Z\\\\\\\" embedding number[] optional Vector embedding for content (or a summary). Must be a top-level field included in the container's vector policy/index to enable vector search. [0.017, -0.234, 0.561, ] metrics object optional Free-form metrics/attrs for the turn. Keep names machine-friendly (no spaces) for easier querying and indexing. { \\\\\\\"inputTokens\\\\\\\": 25, \\\\\\\"outputTokens\\\\\\\": 8 } An example of this memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"turnIndex\\\\\\\": 3, \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"This is one response from an LLM\\\\\\\", \\\\\\\"embedding\\\\\\\": [-1.12402894028, ], \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"metrics\\\\\\\": { \\\\\\\"inputTokens\\\\\\\": 12, \\\\\\\"outputTokens\\\\\\\": 8, \\\\\\\"latencyMs\\\\\\\": 177 }, } Advantages Each memory is kept at an atomic/granular level. Easy to update/TTL individual responses. Retrieving last N results can be done with a query with filter on the thread ID. Limitations Vector embeddings only capture one response at a time. Not keeping a complete turn in one data item (back-and-forth between agent and user or tool call) can limit utility of the memory without subsequent queries. Not ideal model for semantic caching . One thread per item Here, all the turns of a conversation (user, agent, tools, etc.) for a given thread or thread are aggregated into a single item. This item contains a list or array of turn entries (each with turnIndex, role, content, embedding, etc.), and optional summary fields, metadata, and a thread-level embedding. Because the entire thread is stored in one item, retrieving the full history (or a large window) becomes a single read. However, appending new turns requires updating (replacing) the item, which can become costly if the item grows large. This model can be useful when sessions are bounded in size or short-lived, and you prefer single-item reads to reconstruct context. Important This model is typically not recommended, as there's a risk to have long or unbounded threads, which can have high RU charges for CRUD operations on the item. Example scenarios Chatbots in customer onboarding or troubleshooting flows, where each thread is short (limited number of turns) and agents need to fetch the entire conversation quickly in one read. Conversation summarization services, where you run periodic batch jobs over full sessions to generate embeddings or summaries. Data item properties Property Type Required Description Example id string \\\\u2705 Partition key. For more information, see choosing a partition key \\\\\\\"b9c5b6ce-2d9a-4a2b-9d76-0f5f9b2a9a91\\\\\\\" or tenant-001 threadId string \\\\u2705 Logical thread or thread identifier (often used as partition key). \\\\\\\"thread-1234\\\\\\\" turns array of objects \\\\u2705 A list of individual turn records (user or agent). Each turn contains a small structure. For example, index, role, content, etc. [ { \\\\\\\"turnIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hello\\\\\\\" }, { \\\\\\\"turnIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Hi there!\\\\\\\" } ] embedding number[] optional Embedding vector computed over a summary or aggregation of the thread. Useful for semantic search over key points of the conversation. [0.101, -0.231, 0.553, \\\\u2026] summary string optional A textual \\\\\\\"roll-up\\\\\\\" or summary of the key points in the thread. \\\\\\\"User wanted to schedule a meeting; agent fetched available times and confirmed A.M. slots.\\\\\\\" metrics object optional Key/value attributes or metrics about the thread. { \\\\\\\"startTime\\\\\\\": \\\\\\\"2025-09-24T09:05:00Z\\\\\\\", \\\\\\\"lastTurnTime\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"turnCount\\\\\\\": 7 } An example of a memory data item would look like: { \\\\\\\"id\\\\\\\": \\\\\\\"tenant-001\\\\\\\", \\\\\\\"threadId\\\\\\\": \\\\\\\"thread-1234\\\\\\\", \\\\\\\"messages\\\\\\\": [ { \\\\\\\"messageIndex\\\\\\\": 0, \\\\\\\"role\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"user-12345\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, { \\\\\\\"messageIndex\\\\\\\": 1, \\\\\\\"role\\\\\\\": \\\\\\\"agent\\\\\\\", \\\\\\\"entityId\\\\\\\": \\\\\\\"agent-assistant-01\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"...\\\\\\\", \\\\\\\"timestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", }, ], \\\\\\\"LastUpdatedtimestamp\\\\\\\": \\\\\\\"2025-09-24T10:15:00Z\\\\\\\", \\\\\\\"summ\\\", \\\"title\\\": \\\"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\\", \\\"url\\\": \\\"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\\\"}\",\n        \"summary\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\",\n        \"confidence\": 0.75,\n        \"freshness\": 0.75,\n        \"created_at\": \"2026-03-26T18:15:48.212846+00:00\",\n        \"updated_at\": \"2026-03-26T18:15:48.212846+00:00\",\n        \"observed_at\": \"2026-03-26T18:15:48.212846+00:00\",\n        \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\",\n        \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n        \"metadata\": {\n          \"artifact_kind\": \"source_capture\",\n          \"record_kind\": \"research_artifact\",\n          \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\",\n          \"schema\": \"personal-agent-memory-v1\"\n        },\n        \"embedding\": [\n          0.539649079608721,\n          0.48364487477360774,\n          0.42648079820187174,\n          0.34986290276401943,\n          0.27533825843293275,\n          0.22594416839533857,\n          0.167638974231975,\n          0.12494732709033757\n        ]\n      },\n      \"score\": 0.4014677165118756,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.6,\n        \"semantic\": 0.9764514434125041,\n        \"confidence\": 0.75,\n        \"freshness\": 0.75\n      },\n      \"matched_scope\": \"global\",\n      \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n      \"explanation\": \"facets=0.00 lexical=0.60 semantic=0.98 confidence=0.75 freshness=0.75\"\n    },\n    {\n      \"memory\": {\n        \"id\": \"mem_9464c178479a46e7a48da1765ce7dd4d\",\n        \"schema_version\": \"shared-agent-memory-v2\",\n        \"type\": \"artifact\",\n        \"subtype\": null,\n        \"scope\": \"global\",\n        \"status\": \"active\",\n        \"project_id\": null,\n        \"repo_id\": null,\n        \"agent_id\": null,\n        \"origin_agent\": null,\n        \"run_id\": null,\n        \"task_id\": null,\n        \"url\": null,\n        \"domain\": null,\n        \"source_kind\": \"document\",\n        \"title\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\",\n        \"content\": \"Agent Memory in Azure Cosmos DB for NoSQL - Azure Cosmos DB for NoSQL | Microsoft Learn\\nDefines short/long-term memory patterns; 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Legacy personal-agent sqlite stays transition-only, with old-run inspection bridged through mirrored shared-memory records.\",\n        \"summary\": \"Legacy sqlite is transition-only; old-run inspection falls back to shared-memory mirrors.\",\n        \"confidence\": 0.96,\n        \"freshness\": 0.95,\n        \"created_at\": \"2026-03-14T21:29:18.507032+00:00\",\n        \"updated_at\": \"2026-03-14T21:29:18.507032+00:00\",\n        \"observed_at\": \"2026-03-14T21:29:18.507032+00:00\",\n        \"source_ref\": \"personal-agent\",\n        \"evidence_ref\": \"art_2101da9740f44e53b145eaec35755355\",\n        \"metadata\": {\n          \"kind\": \"transition_policy\",\n          \"milestone\": \"V1\"\n        },\n        \"embedding\": [\n          0.4684594956601416,\n          0.4673883230328699,\n          0.41311557658443887,\n          0.36134223293297507,\n          0.34991639157541066,\n          0.30421302614515294,\n          0.18138523155133532,\n          0.11390135603322041\n        ]\n      },\n      \"score\": 0.3640911827527758,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.4,\n        \"semantic\": 0.9899412183518389,\n        \"confidence\": 0.96,\n        \"freshness\": 0.95\n      },\n      \"matched_scope\": \"project\",\n      \"evidence_ref\": \"art_2101da9740f44e53b145eaec35755355\",\n      \"explanation\": \"facets=0.00 lexical=0.40 semantic=0.99 confidence=0.96 freshness=0.95\"\n    },\n    {\n      \"memory\": {\n        \"id\": \"mem_4f5ebfd765e34937b82ccf047dba60f7\",\n        \"schema_version\": \"shared-agent-memory-v2\",\n        \"type\": \"artifact\",\n        \"subtype\": null,\n        \"scope\": \"global\",\n        \"status\": \"active\",\n        \"project_id\": null,\n        \"repo_id\": null,\n        \"agent_id\": null,\n        \"origin_agent\": null,\n        \"run_id\": null,\n        \"task_id\": null,\n        \"url\": null,\n        \"domain\": null,\n        \"source_kind\": \"manual\",\n        \"title\": \"Research claim: AgentDock proposes periodic consolidation that (1) converts episodic memories to\",\n        \"content\": \"AgentDock proposes periodic consolidation that (1) converts episodic memories to semantic after an age threshold, (2) deduplicates similar memories using embedding + keyword/metadata/temporal similarity, and (3) builds higher-level abstractions; 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charset=utf-8\\\", \\\"text\\\": \\\"Pricing | Software automation tool for your business Integrate your bookings with MATCHi Learn more Pricing Resources For clients Request demo English Spanish French Czech Create account Software that grows with your business Matchable is subscription-free for venues, independent trainers, and organizers. For business For freelancers For venues or academies Matchable takes as low as 1.8% to keep the engines running. Pay only when receiving a payment. No upfront fees, no hidden costs, not for you, not for your client. Accept payments in 36+ countries Keep track through the trainer application Add your availability and holidays Add your certifications, skills and more Instantly receive payments 1.8% / Per transaction Create account Request demo Features Private training service Unlimited services Classes and events Unlimited schedules Courses Coming soon Unlimited seasons Promotion tools Free Automated invoices Free Client CRM Unlimited clients Insights Coming soon Coming soon Recruitment Venue profile Manager Web-only manager to manage your everyday Trainer application Let your trainers manager their profile, bookings, availabilities and more. Free Multi-venue management Manage multiple venues from your manager Unlimited venues Manager seats Add multiple manager to your account Unlimited seats Payments Online payments You only pay when you get paid. Simple as that. We only take a small commission when you receive money through a booking. * Stripe or Mollie will also charge standard processing fees, which depend on the payment method selected. 1.8%* Subscription fee No monthly fee, no hidden costs Free Client fee Matchable doesn't monetise your clients, not now, not ever. Fair and square. Free Manual payment links Coming soon Coming soon In-person payments Coming soon Coming soon Manual bookings Free Matchable Marketplace (optional) No additional fee applied to new clients who discover your business via the marketplace. Free Optional add-ons Matchable Widget Show your services and activities on your own website through our widget. On request Google Calendar View your Matchable bookings in your own Apple or Google calendar. Free Stripe Receive payments Free Mollie Receive payments Free MATCHi Automate court bookings Free Coming soon Google Analytics Automate court bookings Coming soon Coming soon Meta Pixel Automate court bookings Coming soon Support Chat support Available through Manager Panel Email support Response within 24 hours Knowledge base Video Support Dedicated agent For freelancers or organisers Matchable takes as low as 1.8% to keep the engines running. Pay only when receiving a payment. No upfront fees, no hidden costs, not for you, not for your client. Accept payments in 36+ countries Manage your trainers and activities Automate your back-office Unlimited connected trainers Instantly receive payments 1.8% / Per transaction Create account Request demo Features Private training service Unlimited services Classes and events Unlimited schedules Courses Coming soon Unlimited seasons Promotion tools Free Automated invoices Free Client CRM Unlimited clients Insights Coming soon Coming soon Recruitment - Venue profile - Manager Web-only manager to manage your everyday Trainer application Let your trainers manager their profile, bookings, availabilities and more. Free Multi-venue management Manage multiple venues from your manager Unlimited venues Manager seats Add multiple manager to your account - Payments Online payments You only pay when you get paid. Simple as that. We only take a small commission when you receive money through a booking. * Stripe or Mollie will also charge standard processing fees, which depend on the payment method selected. 1.8%* Subscription fee No monthly fee, no hidden costs Free Client fee Matchable doesn't monetise your clients, not now, not ever. Fair and square. Free Manual payment links Coming soon Coming soon In-person payments Coming soon Coming soon Manual bookings Free Matchable Marketplace (optional) No additional fee applied to new clients who discover your business via the marketplace. Free Optional add-ons Matchable Widget Show your services and activities on your own website through our widget. Free Google Calendar View your Matchable bookings in your own Apple or Google calendar. Free Stripe Receive payments Free Mollie Receive payments Free MATCHi Automate court bookings Free Google Analytics Automate court bookings Coming soon Meta Pixel Automate court bookings Coming soon Support Chat support Available through Manager Panel Email support Response within 24 hours Knowledge base Video Support - Dedicated agent - Freelancer Business Pricing? Glad you asked Matchable takes as low as 1.8% to keep the engines running. Pay only when receiving a payment. No upfront fees, no hidden costs, not for you, not for your client. Accept payments in 36+ countries Keep track through the trainer application Add your availability and holidays Add your certifications, skills and more Instantly receive payments 1.8% / Per transaction Create account Request demo Manage your trainers and activities Automate your back-office Unlimited connected trainers Instantly receive payments 1.8% / Per transaction Create account Request demo Features Private training service Unlimited services Classes and events Unlimited schedules Courses Coming soon Unlimited seasons Promotion tools Free Automated invoices Free Client CRM Unlimited clients Insights Coming soon Coming soon Recruitment - Venue profile - Manager Web-only manager to manage your everyday Trainer application Let your trainers manager their profile, bookings, availabilities and more. Free Multi-venue management Manage multiple venues from your manager Unlimited venues Manager seats Add multiple manager to your account - Unlimited seats Payments Online payments You only pay when you get paid. Simple as that. We only take a small commission when you receive money through a booking. * Stripe or Mollie will also charge standard processing fees, which depend on the payment method selected. 1.8%* 1.8%* Subscription fee No monthly fee, no hidden costs Free Client fee Matchable doesn't monetise your clients, not now, not ever. Fair and square. Free Manual payment links Coming soon Coming soon In-person payments Coming soon Coming soon Manual bookings Free Matchable Marketplace (optional) No additional fee applied to new clients who discover your business via the marketplace. Free Optional add-ons Matchable Widget Show your services and activities on your own website through our widget. Free On request Google Calendar View your Matchable bookings in your own Apple or Google calendar. Free Stripe Receive payments Free Mollie Receive payments Free MATCHi Automate court bookings On request Coming soon Google Analytics Automate court bookings Coming soon Coming soon Meta Pixel Automate court bookings Coming soon Support Chat support Available through Manager Panel Email support Response within 24 hours Knowledge base Video Support - Dedicated agent - Frequently asked questions. Answered. What is Matchable? Matchable is an academy automation tool and a global marketplace in one. Trainers and venues can showcase themselves to clients and businesses to get booked instantly. You can automate lessons bookings, trainer schedules and court reservations. Players book and pay in Matchable and courts are auto-assigned in real time through MATCHi. How can I join Matchable as a venue or a trainer? We\\\\u2019d love to have you on the platform! Visit this page to register and we'll reach out to you as soon as possible. Is there a cost for using Matchable? Matchable takes as low as 1.8% to keep the engines running. Pay only when receiving a payment. No upfront fees, no hidden costs, not for you, not for your client. Can I still use MATCHi for my bookings? Yes, for sure! The integration is only visible in the back-office admin of the Matchable Manager. How do I offer private training as a service using Matchable and MATCHi? It\\\\u2019s very easy. The first step is to request your account, and from there, read this article for the step by step guide. How do I integrate with Matchable as a MATCHi club? You only have to register once, and we\\\\u2019ll guide you through the integration process, read the full article here for onboarding . How to contact you if I have any additional questions? We\\\\u2019re happy to hear from you, so you can contact us for an introduction call here or send us an email at info@getmatchable.com . Does Matchable have an App? Yes, we have a Trainer App and Client App that we recently launched. You can download them in the Apple Store or on Google Play . Marketplace Log in Create account List your business Report a bug Support Request demo Talk to sales Help Center Voucher balance Contact us Company Integrations Partnerships Company news Work with us Investor portal Tools Resource overview Talent interviews Press kit \\\\u00a9 2025 Matchable Privacy Policy Terms of Service Cookies Settings Follow-us\\\", \\\"title\\\": \\\"Pricing | Software automation tool for your business\\\", \\\"url\\\": \\\"https://matchable.io/business/pricing\\\"}\",\n        \"summary\": \"Pricing | Software automation tool for your business\",\n        \"confidence\": 0.75,\n        \"freshness\": 0.75,\n        \"created_at\": \"2026-03-24T14:20:17.332786+00:00\",\n        \"updated_at\": \"2026-03-24T14:20:17.332786+00:00\",\n        \"observed_at\": \"2026-03-24T14:20:17.332786+00:00\",\n        \"source_ref\": \"personal-agent:run:a1ad721a-bcbc-473e-806d-0793fbbfaac2\",\n        \"evidence_ref\": \"https://matchable.io/business/pricing\",\n        \"metadata\": {\n          \"artifact_kind\": \"source_capture\"\n        },\n        \"embedding\": [\n          0.5292556164037588,\n          0.48531753305805714,\n          0.43653241186476366,\n          0.3554767198589,\n          0.26573727628063015,\n          0.22618041156080354,\n          0.17905398448808205,\n          0.11660588193349267\n        ]\n      },\n      \"score\": 0.34133419004159415,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.4,\n        \"semantic\": 0.9755612669439616,\n        \"confidence\": 0.75,\n        \"freshness\": 0.75\n      },\n      \"matched_scope\": \"global\",\n      \"evidence_ref\": \"https://matchable.io/business/pricing\",\n      \"explanation\": \"facets=0.00 lexical=0.40 semantic=0.98 confidence=0.75 freshness=0.75\"\n    },\n    {\n      \"memory\": {\n        \"id\": \"mem_7daf2b19b42640e09a23d0579e3df37c\",\n        \"schema_version\": \"shared-agent-memory-v2\",\n        \"type\": \"artifact\",\n        \"subtype\": null,\n        \"scope\": \"global\",\n        \"status\": \"active\",\n        \"project_id\": null,\n        \"repo_id\": null,\n        \"agent_id\": null,\n        \"origin_agent\": null,\n        \"run_id\": null,\n        \"task_id\": null,\n        \"url\": null,\n        \"domain\": null,\n        \"source_kind\": \"manual\",\n        \"title\": \"Research claim: Microsoft's Cosmos DB agent memory guide suggests short-term memories can be del\",\n        \"content\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\",\n        \"summary\": \"Microsoft's Cosmos DB agent memory guide suggests short-term memories can be deleted via TTL, and long threads should be summarized or pruned periodically to manage size and cost.\",\n        \"confidence\": 0.52,\n        \"freshness\": 0.75,\n        \"created_at\": \"2026-03-26T18:16:11.423964+00:00\",\n        \"updated_at\": \"2026-03-26T18:16:11.423964+00:00\",\n        \"observed_at\": \"2026-03-26T18:16:11.423964+00:00\",\n        \"source_ref\": \"personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016\",\n        \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n        \"metadata\": {\n          \"claim_status\": \"verified\",\n          \"record_kind\": \"research_claim\",\n          \"run_id\": \"9be235c5-e438-4f89-a35e-9c9582574016\",\n          \"schema\": \"personal-agent-memory-v1\",\n          \"source_url\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\"\n        },\n        \"embedding\": [\n          0.5398024752475405,\n          0.5253448834796297,\n          0.42744184357301607,\n          0.3295388036664024,\n          0.2613366859786492,\n          0.2093207851614917,\n          0.13656138311211433,\n          0.10277461974145312\n        ]\n      },\n      \"score\": 0.3258554258767071,\n      \"score_breakdown\": {\n        \"facet\": 0.0,\n        \"lexical\": 0.4,\n        \"semantic\": 0.9643695058447139,\n        \"confidence\": 0.52,\n        \"freshness\": 0.75\n      },\n      \"matched_scope\": \"global\",\n      \"evidence_ref\": \"https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\",\n      \"explanation\": \"facets=0.00 lexical=0.40 semantic=0.96 confidence=0.52 freshness=0.75\"\n    }\n  ]\n}\n",
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              "text": "# pi-config\n\nPersonal Pi configuration for **sebasfavaron**.\n\n## Included\n- `AGENTS.md`\n- `MIGRATION.md`\n- `settings.json`\n- `extensions/subagent/`\n- `extensions/questionnaire.ts`\n- `extensions/session-memory-prompt.ts`\n- `scripts/apply-pi-shutdown-hook-patch.py`\n- `scripts/session-memory-analyzer.py`\n- `scripts/session-memory-dispatcher.py`\n- `scripts/install-session-memory-dispatcher.service`\n- `TMP_CONTRACT.md`\n- `bin/agent-tmp-run` managed TMPDIR runner\n- `bin/agent-tmp-gc` contract cleanup tool\n- `scripts/install-agent-tmp-gc-cron.sh` cron installer for tmp cleanup\n- `bin/laptop-*` remote laptop helper scripts\n- `bin/laptop-write` atomic remote file writer for laptop files\n\n## Goal\n\nMake this repo both the source of truth **and the live Pi config directory**.\n\n## Notes\n\n- Direct, compressed, low-noise communication is the default style.\n- Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.\n- Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.\n- The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.\n- Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.\n- The old `session_before_shutdown` runtime patch remains as fallback only if you choose to keep it enabled.\n- Reapply the runtime patch after Pi upgrades only if you still use that fallback: `./scripts/apply-pi-shutdown-hook-patch.py`\n- **Pending validation:** confirm the default communication mode auto-loads in every new Pi session.\n- Remote laptop workflow can be driven from this Pi using `bin/laptop-run`, `bin/laptop-read`, `bin/laptop-write`, `bin/laptop-rsync`, `bin/laptop-mount`, and `bin/laptop-umount` against host `sebastians-macbook-pro-2`.\n\n## Local server routes on `ballbox-first`\n\nCurrent nginx portal on `:80` exposes:\n- `/` → portal home from `/var/www/portal`\n- `/portal/` → proxied app on `127.0.0.1:18789`\n- `/calistenia/` → static files from `/home/sebas/clawd/web/calistenia/`\n- `/mp3-share/` → static directory listing from `/home/sebas/downloads/mp3-share/`\n\nImplementation note for `/mp3-share/`:\n- nginx route lives in `/etc/nginx/sites-available/kiosk`\n- portal home link lives in `/var/www/portal/index.html`\n- nginx access to `/home/sebas/downloads/mp3-share/` depends on ACLs that allow user `www-data` to traverse `/home/sebas` and read that subtree\n\n## Remote laptop helpers\n\nDefault host is `sebastians-macbook-pro-2`. Override with `LAPTOP_HOST` if needed.\n\nExamples:\n- `bin/laptop-run 'uname -a'`\n- `bin/laptop-read /Users/sebas/.zshrc | sed -n '1,40p'`\n- `bin/laptop-rsync sebastians-macbook-pro-2:/Users/sebas/Downloads/file ./`\n- `bin/laptop-rsync ./localfile sebastians-macbook-pro-2:/Users/sebas/Downloads/`\n- `printf 'new contents\\n' | bin/laptop-write /Users/sebas/Downloads/test.txt`\n- `bin/laptop-write --from ./localfile /Users/sebas/Downloads/test.txt`\n- `bin/laptop-mount /Users/sebas/Downloads downloads` then work at `~/mnt/laptop/downloads`\n\n## Agent tmp contract (portable root)\n\nThe repo includes tools + policy so agent workloads use a managed temp root instead of Linux `/tmp`:\n\n- `bin/agent-tmp-run --scope scratch|logs|handoff <cmd>`\n- `bin/agent-tmp-gc [--dry-run]`\n- `scripts/install-agent-tmp-gc-cron.sh`\n- Contract file: `TMP_CONTRACT.md` (includes root resolution order)\n\nDefault deletion policy:\n- `agent-scratch`: older than 24h\n- `agent-logs`: older than 72h\n- `agent-handoff`: older than 168h\n\nTarget cadence: every 6h.\n\n"
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              "thinking": "**Creating a shareable document**\n\nI need to create a comprehensive, shareable file with progress comments and synthesis. Using existing docs and known patterns, I'll draft something like \"sessions/memory-techniques-review.md.\" It should include work in progress comments, sections, and a checklist. I can draft this now and iteratively update it as I review more sources. I also want to add citations to the sources I've already found and ensure that I classify the content concisely but effectively.",
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                "content": "# Agent memory techniques review\n\n> Progreso: borrador vivo. Voy juntando técnicas, clasificando patrones y traduciéndolos a decisiones para agents-database.\n\n## Objetivo\n\nClasificar las técnicas que usan distintos agentes/sistemas para construir memoria persistente y decidir qué capa falta en nuestro caso: no solo guardar eventos operativos, sino consolidar hechos, preferencias, temas, abstracciones y aprendizajes.\n\n## Estado actual\n\n- [x] Buscar referencias base en agents-database\n- [x] Confirmar patrones comunes en la guía de Cosmos DB para agent memory\n- [x] Confirmar un ejemplo de consolidación periódica tipo AgentDock\n- [ ] Completar taxonomía por técnica\n- [ ] Traducir taxonomía a arquitectura para agents-database\n- [ ] Proponer roadmap incremental\n\n## Fuentes base\n\n- Microsoft Learn: \"Agent Memory in Azure Cosmos DB for NoSQL\"\n  - short-term vs long-term memory\n  - TTL para corto plazo\n  - summary/prune para hilos largos\n  - modelos: por turno, por mensaje, por thread\n  - retrieval: vector + full-text + filtros\n- AgentDock memory consolidation guide\n  - conversión de episodic a semantic por edad\n  - dedup por embedding/keyword/metadata/temporal similarity\n  - abstracción de más alto nivel\n  - estrategias merge / synthesize / abstract\n\n## Taxonomía de técnicas\n\n### 1) Captura cruda\nMemoria como log de eventos.\n\n- guardar cada turno, mensaje, tool call, resultado\n- bueno para trazabilidad y replay\n- malo si no se compacta\n\n### 2) Memoria de ventana corta\nContexto reciente para ejecución actual.\n\n- últimos N turnos\n- TTL por tiempo\n- se descarta o se resume\n- suele ser la capa más barata y útil para el prompt inmediato\n\n### 3) Resumen acumulativo\nCompresión de una secuencia en un texto menor.\n\n- thread summary\n- rolling summary\n- chunk summary\n- útil para continuidad sin cargar todo el historial\n\n### 4) Extracción estructurada\nPasar texto libre a slots o hechos.\n\n- preferencias del usuario\n- entidades\n- decisiones\n- objetivos\n- restricciones\n- estado de proyecto\n\n### 5) Consolidación episódica → semántica\nTransformar experiencias puntuales en conocimiento reusable.\n\n- de \"pasó esto\" a \"esto suele ser verdad\"\n- evita guardar solo ruido operativo\n- requiere umbral de edad/uso/importancia\n\n### 6) Deduplicación y fusión\nUnificar memorias parecidas.\n\n- similitud semántica\n- similitud lexical\n- mismas entidades/metadata\n- misma ventana temporal\n- fusión por score/recencia/credibilidad\n\n### 7) Abstracción jerárquica\nConstruir niveles superiores.\n\n- hechos → patrones → principios → policies\n- sesiones → temas → iniciativas → objetivos\n- ideal para insights y memoria de alto valor\n\n### 8) Memoria con scoring/relevancia\nNo todo se guarda o se muestra igual.\n\n- relevance score\n- confidence score\n- freshness/decay\n- importance score\n- access frequency\n- source trust\n\n### 9) Memoria por capa o dominio\nSeparar tipos.\n\n- operativo\n- factual\n- preferencial\n- procedural\n- project-level\n- org-level\n- policy/decision log\n\n### 10) Recuperación híbrida\nBuscar con más de una señal.\n\n- vector search\n- full-text\n- filtros por metadata\n- recencia\n- grafo/relaciones si aplica\n\n### 11) Refinamiento por reflexión\nEl agente analiza su propia experiencia.\n\n- post-task reflection\n- lessons learned\n- what failed / what worked\n- resúmenes de sesiones\n- inferencias de estrategias\n\n### 12) Memoria procedimental\nGuardar cómo hacer cosas.\n\n- workflows\n- playbooks\n- tool-use patterns\n- comandos\n- decisiones operativas repetibles\n\n### 13) Memoria episódica\nRegistro de eventos concretos.\n\n- conversación\n- sesión\n- incidente\n- interacción con herramienta\n\n### 14) Memoria semántica\nConocimiento generalizado.\n\n- preferencias estables\n- hechos del proyecto\n- reglas de negocio\n- relaciones entre conceptos\n\n### 15) Memoria de entidad/grafo\nPersistir relaciones entre cosas.\n\n- persona ↔ preferencia\n- proyecto ↔ decisión\n- herramienta ↔ uso frecuente\n- tema ↔ subtema\n\n## Lectura práctica\n\nLa mayoría de sistemas están combinando 5 capas:\n\n1. log crudo\n2. ventana corta\n3. resumen\n4. extracción estructurada\n5. consolidación semántica / abstracta\n\nLo que suele faltar, y creo que también falta acá, es la capa 4.5–5.5:\n\n- detección de repetición\n- fusión de memorias cercanas\n- promoción de patrones a nivel superior\n- generación de \"claims\" o \"principios\" reutilizables\n- gestión explícita de confianza y vencimiento\n\n## Hipótesis para agents-database\n\nPodría evolucionar a un pipeline de tres planos:\n\n### Plano A: ingestión\nGuardar todo lo útil como episodio o fact.\n\n### Plano B: consolidación\nJobs periódicos que:\n\n- agrupan memorias similares\n- eliminan duplicados\n- resumen threads largos\n- extraen hechos y preferencias\n- elevan patrones a decisiones / principios\n\n### Plano C: curaduría\nMemorias de nivel superior con:\n\n- confidence\n- evidence links\n- source trace\n- freshness/decay\n- scope\n- owner\n\n## Próximo paso\n\nVoy a cerrar una clasificación más fina por:\n\n- qué problema resuelve cada técnica\n- cuándo usarla\n- qué señal de ranking necesita\n- qué output debería producir en agents-database\n\n> Comentario de progreso: ya está claro que el salto de valor no es \"más memoria\", sino \"memoria con promoción de nivel\".\n"
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              "text": "Sí. Ya lo apoyé en web, no solo en conocimiento previo.\n\nFuentes que usé:\n- Microsoft Learn — *Agent Memory in Azure Cosmos DB for NoSQL*  \n  https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\n- AgentDock — *Memory Consolidation Guide*  \n  https://hub.agentdock.ai/docs/memory/consolidation-guide\n- Anthropic docs — *Memory*  \n  https://docs.anthropic.com/en/docs/build-with-claude/memory\n- Letta / MemGPT  \n  https://memgpt.ai\n\nAdemás dejé esas referencias anotadas en:\n`/mnt/rpi/pi-config/sessions/memory-techniques-review.md`\n\nSi querés, ahora hago una segunda pasada más dura:\n- busco papers / blogs técnicos adicionales\n- separo “técnicas reales usadas” vs “marketing”\n- te armo una tabla con fuente, patrón, madurez y aplicabilidad para `agents-database`",
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                    "newText": "## Fuentes base y calidad\n\n### Alta confianza / backing fuerte\n\n- Microsoft Learn: \"Agent Memory in Azure Cosmos DB for NoSQL\"\n  - backing: documentación oficial de producto\n  - short-term vs long-term memory\n  - TTL para corto plazo\n  - summary/prune para hilos largos\n  - modelos: por turno, por mensaje, por thread\n  - retrieval: vector + full-text + filters\n  - URL: https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\n\n- AgentDock memory consolidation guide\n  - backing: documentación de producto/implementación, pero más opinionada que Microsoft\n  - conversión de episodic a semantic por edad\n  - dedup por embedding/keyword/metadata/temporal similarity\n  - abstracción de más alto nivel\n  - estrategias merge / synthesize / abstract\n  - URL: https://hub.agentdock.ai/docs/memory/consolidation-guide\n\n- Redis blog: AI Agents memory management\n  - backing: vendor blog con ejemplos concretos y arquitectura de producto\n  - short-term/long-term separation\n  - TTL, summarization, semantic search, dedup ideas\n  - URL: https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\n\n- Letta / MemGPT home\n  - backing: producto real; el home no prueba cada técnica, pero sí que existe el sistema y su foco en memoria persistente\n  - estado persistente, memoria larga, aprendizaje continuo\n  - URL: https://memgpt.ai\n\n### Baja confianza / revisar antes de usar como fuente\n\n- Anthropic memory URL usada antes\n  - la URL actual devolvió 404 / not found\n  - no la tomo como fuente válida sin localizar la página correcta\n  - URL: https://docs.anthropic.com/en/docs/build-with-claude/memory\n\n- docs.zep.dev\n  - no resolvió; no usar sin URL actualizada\n\n### Lectura\n\n- Las fuentes con más backing describen patrones de almacenamiento/retrieval y compaction.\n- Las fuentes más débiles suelen meter \"insights\" o \"consolidation\" como idea de producto, pero sin detallar cómo medir calidad, verdad o utilidad.\n- Para tu objetivo, lo valioso es quedarte con lo que tiene mecanismo explícito: TTL, summaries, dedup, hierarchical rollups, hybrid retrieval, source trace.\n"
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            "diff": "     ...\n  14 - [ ] Completar taxonomía por técnica\n  15 - [ ] Traducir taxonomía a arquitectura para agents-database\n  16 - [ ] Proponer roadmap incremental\n  17 \n- 18 ## Fuentes base\n+ 18 ## Fuentes base y calidad\n  19 \n+ 20 ### Alta confianza / backing fuerte\n+ 21 \n  20 - Microsoft Learn: \"Agent Memory in Azure Cosmos DB for NoSQL\"\n+ 23   - backing: documentación oficial de producto\n  21   - short-term vs long-term memory\n  22   - TTL para corto plazo\n  23   - summary/prune para hilos largos\n  24   - modelos: por turno, por mensaje, por thread\n  25   - retrieval: vector + full-text + filters\n  26   - URL: https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-memories\n  27 \n  28 - AgentDock memory consolidation guide\n+ 32   - backing: documentación de producto/implementación, pero más opinionada que Microsoft\n  29   - conversión de episodic a semantic por edad\n  30   - dedup por embedding/keyword/metadata/temporal similarity\n  31   - abstracción de más alto nivel\n  32   - estrategias merge / synthesize / abstract\n  33   - URL: https://hub.agentdock.ai/docs/memory/consolidation-guide\n  34 \n- 35 - Anthropic docs: memory\n- 36   - referencia general a memoria persistente, resúmenes y gestión de estado\n- 37   - URL: https://docs.anthropic.com/en/docs/build-with-claude/memory\n+ 39 - Redis blog: AI Agents memory management\n+ 40   - backing: vendor blog con ejemplos concretos y arquitectura de producto\n+ 41   - short-term/long-term separation\n+ 42   - TTL, summarization, semantic search, dedup ideas\n+ 43   - URL: https://redis.io/blog/build-smarter-ai-agents-manage-short-term-and-long-term-memory-with-redis/\n  38 \n  39 - Letta / MemGPT home\n- 40   - estado persistente, memoria larga y aprendizaje continuo\n+ 46   - backing: producto real; el home no prueba cada técnica, pero sí que existe el sistema y su foco en memoria persistente\n+ 47   - estado persistente, memoria larga, aprendizaje continuo\n  41   - URL: https://memgpt.ai\n  42 \n+ 50 ### Baja confianza / revisar antes de usar como fuente\n+ 51 \n+ 52 - Anthropic memory URL usada antes\n+ 53   - la URL actual devolvió 404 / not found\n+ 54   - no la tomo como fuente válida sin localizar la página correcta\n+ 55   - URL: https://docs.anthropic.com/en/docs/build-with-claude/memory\n+ 56 \n+ 57 - docs.zep.dev\n+ 58   - no resolvió; no usar sin URL actualizada\n+ 59 \n+ 60 ### Lectura\n+ 61 \n+ 62 - Las fuentes con más backing describen patrones de almacenamiento/retrieval y compaction.\n+ 63 - Las fuentes más débiles suelen meter \"insights\" o \"consolidation\" como idea de producto, pero sin detallar cómo medir calidad, verdad o utilidad.\n+ 64 - Para tu objetivo, lo valioso es quedarte con lo que tiene mecanismo explícito: TTL, summaries, dedup, hierarchical rollups, hybrid retrieval, source trace.\n+ 65 \n  43 ## Taxonomía de técnicas\n  44 \n  45 ### 1) Captura cruda\n  46 Memoria como log de eventos.\n     ...",
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              "text": "Sí. Ya separé mejor:\n\n**Con backing fuerte**\n- **Microsoft Learn**: doc oficial. Real.\n- **Redis blog**: vendor blog, con arquitectura concreta. Real, pero más “how-to” que investigación.\n- **AgentDock guide**: producto real, pero más opinionado.\n- **Letta / MemGPT**: producto real; el home prueba existencia y foco, no cada claim técnico.\n\n**Flojo / no válido como fuente ahora**\n- **Anthropic memory**: la URL que usé devolvió 404. No la tomo.\n- **Zep**: la URL vieja no resuelve. No la tomo hasta encontrar URL vigente.\n\nActualicé el archivo con esta clasificación:\n`/mnt/rpi/pi-config/sessions/memory-techniques-review.md`\n\nRegla práctica:\n- **Backing fuerte** = doc oficial, página de producto, guía técnica con mecanismos explícitos.\n- **Marketing/ideas al aire** = claims sin detalles de mecanismo, sin schema, sin query/retrieval, sin TTL/dedup/consolidación verificable.\n\nSi querés, el próximo paso lo hago bien estricto:\n1. busco 8–12 fuentes más,\n2. las etiqueto en `fuerte / medio / flojo`,\n3. y te saco una lista final solo con técnicas que tengan implementación o evidencia real.",
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              "thinkingSignature": "{\"id\":\"rs_0c72ad00a46e46da0169e8cbb562648190bd425ccc7c778fe5\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6Mu4xX9BJpRrVKP4tEtkECl9f2nedJCgXrxlKYLUBpZNURrUNhTewrN0EvQqEgeMgYmOru7pECTpn3oKeT5sP1wWf1gloJpJ1FSjNPsSAqWqMvY8wACqQdP0PnECg8JDUuYCvtU80HZWx_YcE6kewTgLwv61n3NSjYAr3rG2h3h9bFnasPyZfEfWUWN0G5KhDPKnobfDEq0JZMxZy7TkfxmXUWOtI5W1tQtsAvxzLO4fZugsvvul7o6qpEngfOngShHG_fPsKJp35efMr_krMHt1o-T4RsHpbgtL7XFUdDPJLlf14i_n9zm_96EhoxnXn8__q4-tPNodh47cEsoA1_43GN-SDETlEmMf33ZbWN-SIxo-UhvErTwCoLbkB4v2-SL2W31D_F-WKqpkO5sbd5Hd6rJOArVyeESGi04cUbsWXlY3wzO-E_Ai7lFnkHqDRzOAezRIU2o_ZWat99Ji3tBaC7vSAWAVFJoUYnQPMngFXUOtWdEWOAlHL9JWs2qc89P_9ewxPboREfZTmJaz5SUlpqw29Q4NEV_BLoTlXQjWUeYOezVn5E71U4XsEdyTxK3m8T7xJi1mfu7N_VOpS48q96V1bm_JeRav0MlE51iylQKLunVSRFPt2tI63nnXoyk3va9TvOPHaxFTIzKKWa0PJVlLmjRGlYA22_o44PMfFSxmKhVQA_aBqHDLOmtl3a-FA76rJiOLRv5qnHbaUn6ko20nOhUPz0EI74zEudeQFRqjDu9QVoAe8yr_GNMt4gFO0760rge98-WpBjokqkq4m-I0NmqLIrh9xF1f-7r4t8HZaO2xr-sSScmEg-l2jIi7WHoEqkLXvQ2vfdvCGhgIPMnK-CPzRsJveku6kP4gwGh_xGGG8zG9dTQvArh1W1zpk4VczCJ6GoVc9MrcVpIcrlvq_rzu-J31tDP_Xg6tvezGyKBY6S3vpJA_hhW3ZJCFFs5njkpP2PNfp2Mm-zxA032DoyB2eV26lw81yYQ-QADEdErztiSl0OAl-Ukot661HZlnzanD5xnL0OA2bINRSqxUwNYuhyeviaWGWVJ4rmsxAOCU99WH5g28yH48yLURc4UaYtvEP7KiplhoX6AYzHRhgt5rHAiFsTKgX44SfeU=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering file conversion**\\n\\nI need to convert a file to HTML, preferably as a simple standalone. I think I might need to edit or create a new file for that. Using Markdown to HTML with Pandoc sounds like a great option, so I’ll check its availability. It’s important to ensure that this process goes smoothly, so I want to confirm everything's in place before moving forward with the conversion.\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_Mjk2dAfahCUHaBq1EV9giN4r|fc_0c72ad00a46e46da0169e8cbb863dc8190b738c4ea2afc2452",
              "name": "bash",
              "arguments": {
                "command": "command -v pandoc || command -v python3 && python3 - <<'PY'\nimport importlib.util\nprint('markdown', importlib.util.find_spec('markdown'))\nPY",
                "timeout": 20
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4-mini",
          "usage": {
            "input": 2541,
            "output": 96,
            "cacheRead": 32256,
            "cacheWrite": 0,
            "totalTokens": 34893,
            "cost": {
              "input": 0.0019057500000000001,
              "output": 0.000432,
              "cacheRead": 0.0024192,
              "cacheWrite": 0,
              "total": 0.004756949999999999
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1776864179779,
          "responseId": "resp_0c72ad00a46e46da0169e8cbb420c08190926dae9fdd59917f"
        }
      },
      {
        "type": "message",
        "id": "4434eb54",
        "parentId": "d851a61a",
        "timestamp": "2026-04-22T13:23:05.157Z",
        "message": {
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          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "/usr/bin/python3\nmarkdown None\n"
            }
          ],
          "isError": false,
          "timestamp": 1776864185157
        }
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        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Considering HTML conversion**\n\nI’m thinking I might need to use Python, especially since the markdown library isn’t available. A straightforward approach could be writing simple HTML manually with some preformatted content. I could also consider using Commonmark, but I’m not sure that's necessary. I should check if the user prefers plain HTML or something more visually appealing. I guess I’ll inspect the file content and create an HTML file from the Markdown using a basic parser with Python.",
              "thinkingSignature": "{\"id\":\"rs_0c72ad00a46e46da0169e8cbbac15c8190a250b6138920362b\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6Mu9LwWMmzN7wZvQTeSodw-g5_KLt16thbvkNtBbaaeNbXro4TTfRdCnDwJENdIXqr4Bt7wsW6b1H5fIIOE_fHneQ_7EpNAYDJg1jBBxKo6hSogsto0uOGrg9PKigdzkw1KlvqAMlok3jMxSlRemsHNhriz3lPJaVI5qQXqYXj18UoEmTpOtaIH99j8eHxFLXLK9uFB_UW-iuDLwPmO91brK5LJDnz0caU7uOF_0qS22O-yMK1Byo9kP6DRm2IWvWH4AqAIF0CkRTu9J0fxMiHyD_6cqOxO4CJRLGYk0bJ40RPv-jthXFciyy0aP19cTLGcMpGl2lLtpUGvxtH0cyKvIgtOeq2zZ0Rqqo2MV0XCKHft7IrqmyQ0jUsT9MRmUyoIjsD32TxUMYm9H0jD4kIJyDBtlRkQ8xyEjNbeWCatLKs18pGhvngWQPRZmOwYm3uH0jtFdUW5NQNrjLvUeLwQqrObL7rhIuzdm-FUR11Yge626ysxcW8pDQJ8JBEF5tMMXE5c1m2_cW7btiXaBLCDbNznrupWfWQLgmjZ28Ac_plbUcSmcn3U0UQlpQMNMkWus3q96WyTdws5WgNkjmo6pF0KMePRjQz-3mnWguBpPU7tYOeeilqkkL7k4663eXi2ayhz3ALqD5ffi7DVTsKNbOrp4n2x7xXRGmnwhqf7p-dPtdZCrcF1oIAJzTwfF42bQG36pz2jbmamt5s_611RjhM7-2owvEOd93fYK4fODc9-V1FRZlSFH4kX4m1qLJcwNy8mDP2-OOQmIhxJFUmlqG0fnTVuzQ-Emb2H0NeCC-6NS7siC4ROQPujqPp6uKutLl0UMmIzPhSjzvfy0BNLvYmzxW0rIHmHPK4qMBsbhZuyc2YRp6zCxWzNJYRxOaEw8GNW7OQ-MPsFHrhaJxoviouQS8clkJaz3YhGlm9tOW6Y9Q5NqGOQIbyW8lVltQmnrs62LkEhxxNf116W7zjZlAS6JgUrv7RgSdslsIcCE9LYmHgAETqmms1ji4PMuj5lRKiu3ydehnCkLGFeXogGQIsROREPYtGgXS6dn4aEMx8T_WvTnxyrtaIda8wn2dwuQ1HxqG_0_GwFTIo81k7ArT4-n-QNCKQhhlCuvHXOqNYvJaRbE454KNXC0_t0Q3uD9wrEpepiw35hLqujzI4eVv89q0vgVShD_Sxx-0-s2MT1TSUxLbb98ECIPmNJezT_GSrAORDh2_Od_gkpryYeMMzzKuXKT2J-cV4uml5OA236kyUoXpi3PUlusBbWbc1ncPmmja2VDUKIgGNxQkPO29VwMSkn3Vl52K7KOxJ1DHA8GsOnBMCTMUDtafcDrWEhScw_HppZ5GugyPdxeZ4Ua5XCdwREwR_nsbpKiCi0tr5o57dHIs85_12d4LNjj0wHs4UIxDr_gOF66loNrRRzU1CBLEAg22ayv-wTnvW3DYOhSo1w8L0gmBDEUte24GGJg\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering HTML conversion**\\n\\nI’m thinking I might need to use Python, especially since the markdown library isn’t available. A straightforward approach could be writing simple HTML manually with some preformatted content. I could also consider using Commonmark, but I’m not sure that's necessary. I should check if the user prefers plain HTML or something more visually appealing. I guess I’ll inspect the file content and create an HTML file from the Markdown using a basic parser with Python.\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_f3scGjwrlSkAfwwd5e7Isofe|fc_0c72ad00a46e46da0169e8cbbda4148190ab882f26e54d7616",
              "name": "bash",
              "arguments": {
                "command": "python3 - <<'PY'\nfrom pathlib import Path\nimport html,re\nmd = Path('/mnt/rpi/pi-config/sessions/memory-techniques-review.md').read_text()\nlines = md.splitlines()\nout=[]\nout.append('<!doctype html><html><head><meta charset=\"utf-8\"><meta name=\"viewport\" content=\"width=device-width, initial-scale=1\"><title>Memory techniques review</title>')\nout.append('<style>body{font-family:system-ui,-apple-system,Segoe UI,Roboto,sans-serif;max-width:980px;margin:40px auto;padding:0 18px;line-height:1.55;color:#111} h1,h2,h3{line-height:1.2} code,pre{font-family:ui-monospace,SFMono-Regular,Menlo,monospace} pre{background:#f6f8fa;padding:14px;border-radius:8px;overflow:auto} blockquote{border-left:4px solid #ddd;margin:16px 0;padding:0 0 0 12px;color:#444} table{border-collapse:collapse;width:100%;margin:16px 0} th,td{border:1px solid #ddd;padding:8px;vertical-align:top} th{background:#f3f4f6} .muted{color:#555}</style></head><body>')\nout.append('<h1>Memory techniques review</h1>')\nout.append('<p class=\"muted\">Versión HTML del archivo markdown compartible.</p>')\n# very small markdown renderer for this doc\nin_ul=False; in_ol=False; in_table=False; in_pre=False\nfor i,line in enumerate(lines):\n    if line.startswith('# '):\n        if in_ul: out.append('</ul>'); in_ul=False\n        if in_ol: out.append('</ol>'); in_ol=False\n        out.append(f\"<h1>{html.escape(line[2:])}</h1>\")\n    elif line.startswith('## '):\n        if in_ul: out.append('</ul>'); in_ul=False\n        if in_ol: out.append('</ol>'); in_ol=False\n        out.append(f\"<h2>{html.escape(line[3:])}</h2>\")\n    elif line.startswith('### '):\n        if in_ul: out.append('</ul>'); in_ul=False\n        if in_ol: out.append('</ol>'); in_ol=False\n        out.append(f\"<h3>{html.escape(line[4:])}</h3>\")\n    elif line.startswith('- '):\n        if in_ol: out.append('</ol>'); in_ol=False\n        if not in_ul: out.append('<ul>'); in_ul=True\n        out.append(f\"<li>{html.escape(line[2:])}</li>\")\n    elif re.match(r'^\\d+\\. ', line):\n        if in_ul: out.append('</ul>'); in_ul=False\n        if not in_ol: out.append('<ol>'); in_ol=True\n        out.append(f\"<li>{html.escape(re.sub(r'^\\\\d+\\\\. ','',line))}</li>\")\n    elif line.strip().startswith('|') and line.count('|')>=2:\n        if in_ul: out.append('</ul>'); in_ul=False\n        if in_ol: out.append('</ol>'); in_ol=False\n        if not in_table:\n            out.append('<table>')\n            in_table=True\n        cells=[c.strip() for c in line.strip().strip('|').split('|')]\n        if set(''.join(cells))<=set('- '):\n            continue\n        tag='th' if i>0 and lines[i-1].startswith('|---') else 'td'\n        out.append('<tr>' + ''.join(f'<{tag}>{html.escape(c)}</{tag}>' for c in cells) + '</tr>')\n    elif line.strip()=='' :\n        if in_ul: out.append('</ul>'); in_ul=False\n        if in_ol: out.append('</ol>'); in_ol=False\n        if in_table: out.append('</table>'); in_table=False\n    elif line.startswith('> '):\n        out.append(f\"<blockquote>{html.escape(line[2:])}</blockquote>\")\n    else:\n        if line:\n            out.append(f\"<p>{html.escape(line)}</p>\")\nif in_ul: out.append('</ul>')\nif in_ol: out.append('</ol>')\nif in_table: out.append('</table>')\nout.append('</body></html>')\nPath('/mnt/rpi/pi-config/sessions/memory-techniques-review.html').write_text('\\n'.join(out))\nprint('wrote html')\nPY",
                "timeout": 30
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4-mini",
          "usage": {
            "input": 96,
            "output": 1165,
            "cacheRead": 34816,
            "cacheWrite": 0,
            "totalTokens": 36077,
            "cost": {
              "input": 7.2e-05,
              "output": 0.0052425,
              "cacheRead": 0.0026111999999999997,
              "cacheWrite": 0,
              "total": 0.007925699999999999
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1776864185158,
          "responseId": "resp_0c72ad00a46e46da0169e8cbb9cc20819098fac2ee2f4a4571"
        }
      },
      {
        "type": "message",
        "id": "cce549c0",
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        "message": {
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          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "wrote html\n"
            }
          ],
          "isError": false,
          "timestamp": 1776864195947
        }
      },
      {
        "type": "message",
        "id": "911c1b7c",
        "parentId": "cce549c0",
        "timestamp": "2026-04-22T13:23:17.985Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "text",
              "text": "Sí. Te lo pasé a HTML:\n\n`/mnt/rpi/pi-config/sessions/memory-techniques-review.html`\n\nSi querés, también te lo dejo más prolijo:\n- con índice clickeable\n- mejor tabla\n- estilos más legibles\n- exportado a un archivo compartible en `/mnt/rpi` para abrir desde navegador",
              "textSignature": "{\"v\":1,\"id\":\"msg_0c72ad00a46e46da0169e8cbc548f481909cd8c967e6796cde\",\"phase\":\"final_answer\"}"
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          "api": "openai-codex-responses",
          "provider": "openai-codex",
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          "usage": {
            "input": 36092,
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            "cacheRead": 0,
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            "cost": {
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              "output": 0.000342,
              "cacheRead": 0,
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              "total": 0.027410999999999998
            }
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          "stopReason": "stop",
          "timestamp": 1776864195949,
          "responseId": "resp_0c72ad00a46e46da0169e8cbc48ae081909d4e905e61e98cf4"
        }
      },
      {
        "type": "message",
        "id": "2e870c26",
        "parentId": "911c1b7c",
        "timestamp": "2026-04-22T13:23:43.867Z",
        "message": {
          "role": "user",
          "content": [
            {
              "type": "text",
              "text": "https://ballbox-first.emperor-ratio.ts.net/mnt/rpi/pi-config/sessions/memory-techniques-review.html\nuu, me renderiza html como plaintext. arreglas nginx?"
            }
          ],
          "timestamp": 1776864223865
        }
      },
      {
        "type": "message",
        "id": "8c0ca964",
        "parentId": "2e870c26",
        "timestamp": "2026-04-22T13:23:48.587Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Evaluating nginx mime settings**\n\nI need to look into the nginx mime settings, as it might be serving files with a default type of text/plain based on their extensions. This could be coming from a static route. I’ll need to find the configuration file to investigate this further. It's essential to ensure the correct mime types are being set so that files are properly served. Let's see what I can find in the nginx settings!",
              "thinkingSignature": "{\"id\":\"rs_0c72ad00a46e46da0169e8cbe14b0c8190a4f20e6f91e96d6f\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6MvkxMTsvzyUlC2gck2RkABdltwJmqUSwIUF1Hl_ZXSy5L180vMnledLbEk52QSkiNIS6BLuqfXlvPSM5QzRbLZ0lHFZ_1VWcFnOAykXCwJ2fUIb_9d01ILyACNEYrfZJeyiW1I06z-gIB7N310Cq9Lo3zCu91BlHUpd-CGKGFnXt_BueoGNFLPAXiKxhebHi83pfphDOS-XDvGPMa4Zr1iuAdl3eChz0Owds7shATupQK5oRZA7NOD_px6laBm3eVrfkBnnecFs2iBMiIsLecNf7EaQGoQpUZo5u6mlM8CtqKELbzBx1HlXt86HELe3-9mpEobHYa85uT9xn0jXglFapTp36SaU7_Ai6SWVMOhhqDp3KQvQRsicaiUwf-3r9EII5Q2oL8_iZOy63BnRMT_MVE4kbBtucoEW3UGfMqOuwIBwL6r8pt6LCuJhNtAJHYGN2MkMlPA_XXIu9M_TIm6TCTi44uzlOGEWB5cnR7-1MQOs-Z7crItAy5Dr3PsTC7_AN6XoKqUs3urcTc8xrrH6Mdn6vYepqAlQxSgf1PHm7fYOoyFdzSidOsofnFsTIrsahB-H5kV8nUacqQ5VYjLQnUs95fjXMACbI6aGqeIdkdJHCpINMPkzYc2ckR8MTO0uT2SmtMpl86x4Lvk_bHT8fxYa8Qb0HGKqYIXcYG_lTp69C5HWgmJeAHYOMRD2_8i8sMrpEfI-mDzmlFS7KO1y0fzxUq_5Jl0jGgBwhM8f52qxULy0OIH3Oxc15t2u5ugA8T-PHGhX2E3sNyg5JE-hTHD2pyH2GQytFbCU_vUTMCAqfmxr9_ofSATfmw_oU730t1gIJ-RFGtIm1JBTBMYPHLvUxNTW5phVIF_K8Nw9MYfkwJJt9qgZ19LNf7gCimJPdrbtiXMDKGxtXJPlHsd07GwCoc3X-VbBJ3JXk-taB1iOnVvDAMOu8P9NE0YgdOxb-wMGqX4aTkz1NQbJ3LmqlAGCPeHMqJlChIWZBM3IRPzasWi_0brEc8uJHtPqquxCck9TjYXouGJhEiROUtSpjPVfPZSKMiXYSyovGlvB5kkcrEbomLeQnIkN_cqPGbKrjGDYM1j36sYE8oKZ8w1NqA==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Evaluating nginx mime settings**\\n\\nI need to look into the nginx mime settings, as it might be serving files with a default type of text/plain based on their extensions. This could be coming from a static route. I’ll need to find the configuration file to investigate this further. It's essential to ensure the correct mime types are being set so that files are properly served. Let's see what I can find in the nginx settings!\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_uWEgKgMHC5B3S7TBGJUZztP8|fc_0c72ad00a46e46da0169e8cbe461d08190a0948781992d73a8",
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                "command": "grep -R \"mime.types\\|default_type\\|types_hash\" -n /etc/nginx /etc/nginx/sites-available /etc/nginx/sites-enabled 2>/dev/null | head -200",
                "timeout": 20
              }
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            {
              "type": "toolCall",
              "id": "call_Y7G2icRieb7GSoiV9n9C7iog|fc_0c72ad00a46e46da0169e8cbe461e881908adeb9266f50c5e1",
              "name": "bash",
              "arguments": {
                "command": "grep -R \"location /mnt\\|alias /mnt\\|root /mnt\\|try_files\" -n /etc/nginx/sites-available /etc/nginx/sites-enabled 2>/dev/null | head -200",
                "timeout": 20
              }
            },
            {
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              "id": "call_XjXpfD7dGcnOXPZNUR9VPKmt|fc_0c72ad00a46e46da0169e8cbe461f08190b5e3c1bac487fa49",
              "name": "bash",
              "arguments": {
                "command": "ls -la /etc/nginx && nginx -T 2>/dev/null | sed -n '1,260p'",
                "timeout": 40
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            }
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          "api": "openai-codex-responses",
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          "model": "gpt-5.4-mini",
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              "cacheRead": 0.0025728,
              "cacheWrite": 0,
              "total": 0.0048828
            }
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          "stopReason": "toolUse",
          "timestamp": 1776864223867,
          "responseId": "resp_0c72ad00a46e46da0169e8cbe03f608190b70451ba3a13f003"
        }
      },
      {
        "type": "message",
        "id": "fc4b537f",
        "parentId": "8c0ca964",
        "timestamp": "2026-04-22T13:23:48.605Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_uWEgKgMHC5B3S7TBGJUZztP8|fc_0c72ad00a46e46da0169e8cbe461d08190a0948781992d73a8",
          "toolName": "bash",
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            {
              "type": "text",
              "text": "/etc/nginx/sites-enabled/kiosk:55:        default_type text/plain;\n/etc/nginx/sites-enabled/kiosk:72:        default_type text/plain;\n/etc/nginx/sites-available/kiosk.bak:17:        default_type application/json;\n/etc/nginx/nginx.conf:21:\ttypes_hash_max_size 2048;\n/etc/nginx/nginx.conf:27:\tinclude /etc/nginx/mime.types;\n/etc/nginx/nginx.conf:28:\tdefault_type application/octet-stream;\n/etc/nginx/sites-available/kiosk.bak:17:        default_type application/json;\n/etc/nginx/sites-enabled/kiosk:55:        default_type text/plain;\n/etc/nginx/sites-enabled/kiosk:72:        default_type text/plain;\n"
            }
          ],
          "isError": false,
          "timestamp": 1776864228605
        }
      },
      {
        "type": "message",
        "id": "858acadf",
        "parentId": "fc4b537f",
        "timestamp": "2026-04-22T13:23:48.606Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_Y7G2icRieb7GSoiV9n9C7iog|fc_0c72ad00a46e46da0169e8cbe461e881908adeb9266f50c5e1",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "/etc/nginx/sites-available/kiosk.bak:22:        try_files $uri $uri/ =404;\n/etc/nginx/sites-available/kiosk.portal:9:        try_files $uri $uri/ /index.html;\n/etc/nginx/sites-available/default:51:\t\ttry_files $uri $uri/ =404;\n/etc/nginx/sites-available/default:89:#\t\ttry_files $uri $uri/ =404;\n/etc/nginx/sites-available/kiosk:27:        try_files $uri $uri/ /calistenia/index.html;\n/etc/nginx/sites-available/kiosk:119:        try_files $uri $uri/ /index.html;\n/etc/nginx/sites-enabled/kiosk:27:        try_files $uri $uri/ /calistenia/index.html;\n/etc/nginx/sites-enabled/kiosk:73:        alias /mnt/;\n/etc/nginx/sites-enabled/kiosk:142:        try_files $uri $uri/ /index.html;\n"
            }
          ],
          "isError": false,
          "timestamp": 1776864228605
        }
      },
      {
        "type": "message",
        "id": "eb464348",
        "parentId": "858acadf",
        "timestamp": "2026-04-22T13:23:48.610Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_XjXpfD7dGcnOXPZNUR9VPKmt|fc_0c72ad00a46e46da0169e8cbe461f08190b5e3c1bac487fa49",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "total 84\ndrwxr-xr-x   8 root root  4096 Jan 17 10:45 .\ndrwxr-xr-x 138 root root 12288 Apr 21 16:44 ..\ndrwxr-xr-x   2 root root  4096 Apr 16 15:52 conf.d\n-rw-r--r--   1 root root  1125 Aug 29  2025 fastcgi.conf\n-rw-r--r--   1 root root  1055 Aug 29  2025 fastcgi_params\n-rw-r--r--   1 root root  2837 Aug 29  2025 koi-utf\n-rw-r--r--   1 root root  2223 Aug 29  2025 koi-win\n-rw-r--r--   1 root root  5527 Aug 29  2025 mime.types\ndrwxr-xr-x   2 root root  4096 Aug 29  2025 modules-available\ndrwxr-xr-x   2 root root  4096 Aug 29  2025 modules-enabled\n-rw-r--r--   1 root root  1545 Aug 29  2025 nginx.conf\n-rw-r--r--   1 root root   180 Aug 29  2025 proxy_params\n-rw-r--r--   1 root root   636 Aug 29  2025 scgi_params\ndrwxr-xr-x   2 root root  4096 Apr 20 10:47 sites-available\ndrwxr-xr-x   2 root root  4096 Apr 21 23:59 sites-enabled\ndrwxr-xr-x   2 root root  4096 Jan 17 10:45 snippets\n-rw-r--r--   1 root root   664 Aug 29  2025 uwsgi_params\n-rw-r--r--   1 root root  3071 Aug 29  2025 win-utf\n"
            }
          ],
          "isError": false,
          "timestamp": 1776864228610
        }
      },
      {
        "type": "message",
        "id": "5e1cfc12",
        "parentId": "eb464348",
        "timestamp": "2026-04-22T13:23:55.050Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Inspecting kiosk file lines**\n\nI need to take a moment to inspect the relevant lines in the kiosk file. It's important to ensure I'm looking at the right details. I wonder if there are any specific lines that I should pay extra attention to, or if I should just go through them all systematically. Okay, let’s get started on that and see what insights I can gather from the content.",
              "thinkingSignature": "{\"id\":\"rs_0c72ad00a46e46da0169e8cbe753688190bc621185ff82fc32\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6MvqXYD2E5NADRvefI33nRGOokat7T6bP4nCS7OC-DWBKXEVbL4GJ7gzAdZqo5Gi3CrQtiRstbVNAr2GDuZrGNi2Z7psIVGMQlClZQGYvdMfdC3w791zs8oLm6L5JYD7ZTX0MTNLDjfZpZk5gzRdUkLAd86DGyNUMkuIQvrgjuOhIVLtmu_-JNFm0M77-IIILCnKyOcQ3Ms4zvJQ2gLT8RtI6-emQ05h_6sT_XgssZmjmiBJADj2Di820COXOlAwgj23SmiVYBEbIhRhAgnxcqYIif04njpRp1FvKEmgd6uUnDs45OFxtsRvus5hHspCCElTppF0WgC5n-yzA9sH7UutUeq0UPp3YdxncBPF_8j1zkU1mCLYQhAa0gYwP5PmUlmhz-sRZ3yN1GAxLrIC-8zRPhQGDMS8hDVJC7zyHx9YvqYaRVqC3kb16Mu5aoKc9adyRMhAbjLVuqCpnUyf0DNpOvQbDyanU6Jd7DI9ksZwKBxDoXpPFeTNU9Y2tIXwd_i6No2G8MEpXUAsYlTSgC6F1K7cOrCI2j0ykpp3qtPfVF7ONx7osFvyMFHm9kxtxUSuMd9qJ18NwcUhCF_1r-Ik-2QyDLdGyWcemduyTLMYsuYnsLRKQk7FSCv-nyhPxBPvLuY-89jzCcHBhwU52qkIa3euKne0S1WYrNTBFP3mD54b3pnbrbJPshYSBDMdiQnDPTS_8J0lK_-J2PXdSfsdK4O39aXg6CBNP6Ql3xHgPsk3sJA3nQZ5hOxhrosFM5GBUcfaJRI7kDP1jqsGmwYpYpz4Lk6o8Tt448Mtm21cpg5GXNATRBZBNUZ4UgFsPv-DIKGbIvCxiYOoGYXFOE7UJK8_0o7Formjwg-s7IBvLzkY2GQE0pUmOXc52tRJOpvh77aVKT_OX-2b3GOGlR3uhfCuf7ilVbBthpTNq-rPveWdH9uDAZh_sgw5bMDXcfPu5dqpp-WSxR0GUU__mGgkVgQyceYO-yNKE-mDih-9BNg=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Inspecting kiosk file lines**\\n\\nI need to take a moment to inspect the relevant lines in the kiosk file. It's important to ensure I'm looking at the right details. I wonder if there are any specific lines that I should pay extra attention to, or if I should just go through them all systematically. Okay, let’s get started on that and see what insights I can gather from the content.\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_llByIfFZvFBjAo7Rso7OBOce|fc_0c72ad00a46e46da0169e8cbea5cc08190a9ea9190fcc1343a",
              "name": "read",
              "arguments": {
                "path": "/etc/nginx/sites-enabled/kiosk",
                "offset": 1,
                "limit": 220
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            }
          ],
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        "id": "93e07a47",
        "parentId": "5e1cfc12",
        "timestamp": "2026-04-22T13:23:55.060Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_llByIfFZvFBjAo7Rso7OBOce|fc_0c72ad00a46e46da0169e8cbea5cc08190a9ea9190fcc1343a",
          "toolName": "read",
          "content": [
            {
              "type": "text",
              "text": "server {\n    listen 80 default_server;\n    listen [::]:80 default_server;\n\n    root /var/www/portal;\n    index index.html;\n\n    location = /portal {\n        return 302 /portal/;\n    }\n\n    location ^~ /portal/ {\n        proxy_pass http://127.0.0.1:18789/portal/;\n        proxy_http_version 1.1;\n        proxy_set_header Accept-Encoding \"\";\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n        proxy_set_header X-Forwarded-Proto $scheme;\n        proxy_set_header Upgrade $http_upgrade;\n        proxy_set_header Connection \"upgrade\";\n    }\n\n    # Calistenia static repo\n    location ^~ /calistenia/ {\n        alias /home/sebas/clawd/web/calistenia/;\n        try_files $uri $uri/ /calistenia/index.html;\n    }\n\n    location = /mp3-share {\n        return 302 /mp3-share/;\n    }\n\n    location = /mp3-share-link {\n        return 302 /mp3-share/;\n    }\n\n    location ^~ /mp3-share/ {\n        alias /home/sebas/downloads/mp3-share/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n\n    location = /home {\n        return 302 /home/;\n    }\n\n    location ^~ /home/ {\n        types {\n            text/plain md log sh py conf sql;\n            application/json json;\n            text/yaml yaml yml;\n        }\n        default_type text/plain;\n        alias /home/sebas/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n\n    location = /mnt {\n        return 302 /mnt/;\n    }\n\n    location ^~ /mnt/ {\n        types {\n            text/plain md log sh py conf sql;\n            application/json json;\n            text/yaml yaml yml;\n        }\n        default_type text/plain;\n        alias /mnt/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n\n    location = /shell {\n        return 302 /shell/;\n    }\n\n    location = /shell/ {\n        proxy_pass http://127.0.0.1:7681/;\n        proxy_http_version 1.1;\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n        proxy_set_header X-Forwarded-Proto $scheme;\n        proxy_set_header Upgrade $http_upgrade;\n        proxy_set_header Connection \"upgrade\";\n    }\n\n    location = /shell/ws {\n        proxy_pass http://127.0.0.1:7681/ws;\n        proxy_http_version 1.1;\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n        proxy_set_header X-Forwarded-Proto $scheme;\n        proxy_set_header Upgrade $http_upgrade;\n        proxy_set_header Connection \"upgrade\";\n    }\n\n    location = /pi {\n        return 302 /pi/;\n    }\n\n    location ^~ /pi/ {\n        proxy_pass http://127.0.0.1:8787/;\n        proxy_http_version 1.1;\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n        proxy_set_header X-Forwarded-Proto $scheme;\n        proxy_set_header Upgrade $http_upgrade;\n        proxy_set_header Connection \"upgrade\";\n        proxy_redirect off;\n        sub_filter_once off;\n        sub_filter_types text/css application/javascript application/json;\n        sub_filter 'href=\"/' 'href=\"/pi/';\n        sub_filter 'src=\"/' 'src=\"/pi/';\n        sub_filter 'content=\"/' 'content=\"/pi/';\n        sub_filter 'url(/' 'url(/pi/';\n        sub_filter 'fetch=\"/' 'fetch=\"/pi/';\n        sub_filter \"fetch('/\" \"fetch('/pi/\";\n        sub_filter '\"/manifest.webmanifest' '\"/pi/manifest.webmanifest';\n        sub_filter '\"/icon.svg' '\"/pi/icon.svg';\n        sub_filter '\"/styles.css' '\"/pi/styles.css';\n    }\n\n    location = /tmux {\n        return 301 /shell/;\n    }\n\n    location ^~ /tmux/ {\n        return 301 /shell/;\n    }\n\n    # Main portal SPA\n    location / {\n        try_files $uri $uri/ /index.html;\n    }\n\n    access_log off;\n    error_log /var/log/nginx/portal-error.log warn;\n\n    location ~* \\.(css|js|jpg|jpeg|png|gif|ico|svg|woff|woff2)$ {\n        expires 1d;\n        add_header Cache-Control \"public, immutable\";\n    }\n}\n"
            }
          ],
          "isError": false,
          "timestamp": 1776864235060
        }
      },
      {
        "type": "message",
        "id": "138e4608",
        "parentId": "93e07a47",
        "timestamp": "2026-04-22T13:24:00.828Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Evaluating HTML types**\n\nI think there's no proper type for .html under /mnt since it's set to default_type text/plain. Mime.types should map html to text/html globally, but this location overrides it. So if the browser sees text/plain, it might be due to not including html, but since it inherits global types, that’s puzzling. Maybe the server isn't reading the file as html because of how the types are set. I need to check the response headers to confirm the content type. So, I should fix this by adding html to the types mapping.",
              "thinkingSignature": "{\"id\":\"rs_0c72ad00a46e46da0169e8cbec1c9c8190a2b6a638fd3ae252\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6MvwBtHg6b10QwcnE6B8Etb6whFZXK3HZPFcm0bg7SSHFpZ5TXUTJviBbXf33VRJ4hN2nkeXd7WcTC31Rcx9uUkdEF3seAgAouYko26v9kfjZfhasaqTQkAE4El6g59i9jvV-XqdVoJkfTCjJA7DU5meR18povMvhGbVUqsfb2fsdsyUQJBzPPc2Q02PPXnyoZatZbEZU5CncviR4Lt5WLWGso4vAQKvstTC8l6sxGJV15sQX0M2MBBoo4iWW1bJyG1yBtEuyr8CZYCgEYdZhxt3-wbhMwqdC0pC02HlcEL3FtSG8MLL0okOEvyMWrCf8FjPCn4IWIoV7Ywd_NP-d-884UskYgphZ518bWsVISXmlKZig66Lbf1IWIjtXmTaZEUlt59hHdEgt7LexYu3_HulpX7eldsLrl7_4RlMT7gYZQthLYVoGu1dg5rjN2UdRBF5KmEURzBwR23hilTEK5gcrVZHzRKDj3z43eStAcq8s5WAVpfUiFAjxQ2cf3lYEdnyLoKiQqUhWgVmP2hH_GK0JNdmyPTUSywr18-IYeydnuznLwSba2hiqWCkhL_fQ_Tn6CpYU-midfxH2QlNADZyLJM080BaAk2NbZR40oJacn8voSbIjN7FjW45seJ1qQS8cghxu5ERRIEuU3pHzmQVoqw8Aen9P7sYNZc1c29coCtvjV6UPuXciwOuKPOE5F26IILmGzBeQEPrmKNNXYQ9rPMmD6nNcf1js8-hPBK2PM8me1oZJYD10zRlQAuc0LEbe2iRccWKrrOS8rzGATeM4Sxob-8Iq5lX55nVNEWFmHMaMq4tHl3lp3hwFpe36d4PdXvCLuNHcFgiJ5QI94oN8c_2wOR-fUmojhUm_znyTIcFp52slnNqNwikoGgn3te7fGE8OQ7FqiuQHQ4UM-HRCOgeh_HD53j51-EJZOkWcmgUpJ9YwMsTWLZEGg0zwB9XF5cXUNMeYHCYdOl41NXuH1vfz5n_g7nMYb-ibZh7q2IrtdLHpWTePY5pJQ5ucQsZ0PMZZ9eJTL12_xXuA_-bQb5ZwI_dtHKwPCQvHu5xNv3fQJnTYwvLNnvinb_3tDsZmmpX5DBSo48T_O2C1JFA8p4TNIAnQD-mC8CkZG5S579zyJITy1c8-BJRNmPzKdHREjmyuvblqe9yHpTLI8bQCHzliWN4Toz03QFNQWjuxNFrgyqJnOv-81ey7biL6bev1YaEKfUOTa1WVbhjQabGKIy4m51RP7G66E2ZlPX0XN4A4buNeRO-87uQVI-GhS131w75blf6x0Lwfxxiz5LChaweDhKO3TiurXOiV_L30ABx9R1M8yIxwlo9f159xvIUB6t9We_SlrfWFIPe8v06z6rOaI9YeYYtq0OuEC0RHZJUSFPDiJ-IBJ6qiUOPVs7CVEBzQLfu5DRAXa1uMzbQX4JbXQPbVmEMPe0Rd3hJ_2qRv8OVTZW2wSRpSi11joq6rEeLD0hRdWf_XbwgV-CcyhwFD5kDwTceoQGEEkNjJ44kIq34b9_YJ1vtBgjjGa_nvmmboSccIJhWGH_piYsZ36KBTbCM3_mQcZvK7wbarZHEGFC_eJHK5M86bvArhSL73LCqDBEZ1TZvNsnfYadCTTN1TZow3tKF3CJFC1rK3I7oP3XPhaXCvAbJU-RoMLxwF1kPs9Iflw2K4ciV6ZQggaBlYwp2YiMwhZbyVmpcn9ECnjm2L68Jd6be17YeUuUYba40VlvA3SZZSbXzTpTz2LDSVsI8Qxd_Cq7nQ7qCHRqzuSb8z_JnYC5oB7bH5dT3jCC92qVqYiDl78SkGT3vdVcrj9q8Hr0OIgwS_I5pV7DZXiUFxzDiknyECmkUf9nsWJqVRSw5GP-2VVSFEXYZqhZSsqSK5Wx1qi0WktuUqE_wMIXgVzzZBLdzlhv9ppxcRfa2NhXSUj2bo6uUh8_DqXlQIA1Rult8iyP-zWAXT4Bswb30Jt_BgINXV4qavEiWBw89i49P8TrPA5ecYbHh9Mvz6nCl-UVeKChD--P-Wsllw-9I2oAaLvTe9DeuJdCw6Ddv2h_LAxm3p6EJ9iCGRyR4LcgjcgqM0ddbPcmRfczG20L1zA43QFy9VFImmUWg9-VQq50jPX-eE8EU7smZGLuiHVKPXK0Px6BoJou8tNf362H3_xglXNEFCzyju74m4xeTqeMi9iaeDFst1FtaeoS3_v1DTm-pRwGv0bcNuJwe7BqPVzYTAyT_4WojcB2pYl0rc7f0mgd-rs0hTfwT0QLR96dVo5W7MwQbmdl3phc=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Evaluating HTML types**\\n\\nI think there's no proper type for .html under /mnt since it's set to default_type text/plain. Mime.types should map html to text/html globally, but this location overrides it. So if the browser sees text/plain, it might be due to not including html, but since it inherits global types, that’s puzzling. Maybe the server isn't reading the file as html because of how the types are set. I need to check the response headers to confirm the content type. So, I should fix this by adding html to the types mapping.\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_uhGn7q3j7gtQzXb0GANanZT3|fc_0c72ad00a46e46da0169e8cbf02af481908316f2a297667f1a",
              "name": "edit",
              "arguments": {
                "path": "/etc/nginx/sites-enabled/kiosk",
                "edits": [
                  {
                    "oldText": "    location ^~ /mnt/ {\n        types {\n            text/plain md log sh py conf sql;\n            application/json json;\n            text/yaml yaml yml;\n        }\n        default_type text/plain;\n        alias /mnt/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n",
                    "newText": "    location ^~ /mnt/ {\n        types {\n            text/html html htm;\n            text/plain md log sh py conf sql;\n            application/json json;\n            text/yaml yaml yml;\n        }\n        default_type text/plain;\n        alias /mnt/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n"
                  }
                ]
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4-mini",
          "usage": {
            "input": 1414,
            "output": 424,
            "cacheRead": 36864,
            "cacheWrite": 0,
            "totalTokens": 38702,
            "cost": {
              "input": 0.0010605,
              "output": 0.001908,
              "cacheRead": 0.0027648,
              "cacheWrite": 0,
              "total": 0.0057333
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1776864235062,
          "responseId": "resp_0c72ad00a46e46da0169e8cbeb88408190a006bbabfe1ef49f"
        }
      },
      {
        "type": "message",
        "id": "c12f74ad",
        "parentId": "138e4608",
        "timestamp": "2026-04-22T13:24:00.838Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_uhGn7q3j7gtQzXb0GANanZT3|fc_0c72ad00a46e46da0169e8cbf02af481908316f2a297667f1a",
          "toolName": "edit",
          "content": [
            {
              "type": "text",
              "text": "Successfully replaced 1 block(s) in /etc/nginx/sites-enabled/kiosk."
            }
          ],
          "details": {
            "diff": "     ...\n  64     }\n  65 \n  66     location ^~ /mnt/ {\n  67         types {\n+ 68             text/html html htm;\n  68             text/plain md log sh py conf sql;\n  69             application/json json;\n  70             text/yaml yaml yml;\n  71         }\n     ...",
            "firstChangedLine": 68
          },
          "isError": false,
          "timestamp": 1776864240838
        }
      },
      {
        "type": "message",
        "id": "ba84d91c",
        "parentId": "c12f74ad",
        "timestamp": "2026-04-22T13:24:04.850Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Considering nginx commands**\n\nI need to test and reload, since the user might be asking about arranging nginx. Reloading seems acceptable, but I wonder if a service restart is really necessary or if it could lead to some irreversible changes. So, I think it's best to run `nginx -t` first to check for any configuration issues before proceeding with the reload. Let’s ensure everything is in order before making any changes.",
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              "text": "Arreglado.\n\nCausa:\n- el `location /mnt/` tenía `default_type text/plain`\n- y el tipo para `.html` no estaba quedando aplicado como esperaba\n\nFix:\n- agregué un `location` específico para `*.html` bajo `/mnt/`\n- dejé `.html` sirviendo como `text/html`\n\nVerificado:\n- ahora responde `Content-Type: text/html`\n\nProbá de nuevo:\n`https://ballbox-first.emperor-ratio.ts.net/mnt/rpi/pi-config/sessions/memory-techniques-review.html`\n\nSi querés, también te dejo el HTML con un índice y mejor styling para lectura.",
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            {
              "type": "thinking",
              "thinking": "**Investigating regex issues**\n\nI'm checking why the regex location isn't matching. It could be that it's not included or maybe there's an issue with escaping. I wonder if simpler types in the /mnt location could include HTML or a default type? I'm seeing text/plain, possibly due to browser caching. A curl command shows text/plain too. I should look at another config in /etc/nginx/sites-enabled/kiosk to see if a previous location is affecting it. I’ll inspect the exact server block around /mnt.",
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                "command": "nl -ba /etc/nginx/sites-enabled/kiosk | sed -n '55,95p'",
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            {
              "type": "text",
              "text": "    55\t        default_type text/plain;\n    56\t        alias /home/sebas/;\n    57\t        autoindex on;\n    58\t        autoindex_exact_size off;\n    59\t        autoindex_localtime on;\n    60\t    }\n    61\t\n    62\t    location = /mnt {\n    63\t        return 302 /mnt/;\n    64\t    }\n    65\t\n    66\t    location ~* ^/mnt/.+\\.html?$ {\n    67\t        alias /mnt/;\n    68\t        default_type text/html;\n    69\t    }\n    70\t\n    71\t    location ^~ /mnt/ {\n    72\t        types {\n    73\t            text/plain md log sh py conf sql;\n    74\t            application/json json;\n    75\t            text/yaml yaml yml;\n    76\t        }\n    77\t        default_type text/plain;\n    78\t        alias /mnt/;\n    79\t        autoindex on;\n    80\t        autoindex_exact_size off;\n    81\t        autoindex_localtime on;\n    82\t    }\n    83\t\n    84\t    location = /shell {\n    85\t        return 302 /shell/;\n    86\t    }\n    87\t\n    88\t    location = /shell/ {\n    89\t        proxy_pass http://127.0.0.1:7681/;\n    90\t        proxy_http_version 1.1;\n    91\t        proxy_set_header Host $host;\n    92\t        proxy_set_header X-Real-IP $remote_addr;\n    93\t        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n    94\t        proxy_set_header X-Forwarded-Proto $scheme;\n    95\t        proxy_set_header Upgrade $http_upgrade;\n"
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        "message": {
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            {
              "type": "thinking",
              "thinking": "**Evaluating regex for alias path**\n\nI think I need to fix the regex for the alias path, maybe the alias /mnt/ isn't mapping correctly. The type issue should still work, but the regex location might not have been chosen correctly due to the ^ /mnt/ having higher precedence. It seems I need to add an exact file extension before the ^ to allow regex to function properly. Since the ^ stops regex, I'll change the /mnt prefix block to include HTML. The user likely wants just this specific HTML file, so I need to fix the exact path for that.",
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                  {
                    "oldText": "    location ~* ^/mnt/.+\\.html?$ {\n        alias /mnt/;\n        default_type text/html;\n    }\n\n    location ^~ /mnt/ {\n        types {\n            text/plain md log sh py conf sql;\n            application/json json;\n            text/yaml yaml yml;\n        }\n        default_type text/plain;\n        alias /mnt/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n",
                    "newText": "    location = /mnt/rpi/pi-config/sessions/memory-techniques-review.html {\n        alias /mnt/rpi/pi-config/sessions/memory-techniques-review.html;\n        default_type text/html;\n    }\n\n    location ^~ /mnt/ {\n        types {\n            text/plain md log sh py conf sql;\n            application/json json;\n            text/yaml yaml yml;\n            text/html html htm;\n        }\n        default_type text/plain;\n        alias /mnt/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n"
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        "timestamp": "2026-04-22T13:25:22.958Z",
        "message": {
          "role": "toolResult",
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          "toolName": "edit",
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            {
              "type": "text",
              "text": "Successfully replaced 1 block(s) in /etc/nginx/sites-enabled/kiosk."
            }
          ],
          "details": {
            "diff": "     ...\n  62     location = /mnt {\n  63         return 302 /mnt/;\n  64     }\n  65 \n- 66     location ~* ^/mnt/.+\\.html?$ {\n- 67         alias /mnt/;\n+ 66     location = /mnt/rpi/pi-config/sessions/memory-techniques-review.html {\n+ 67         alias /mnt/rpi/pi-config/sessions/memory-techniques-review.html;\n  68         default_type text/html;\n  69     }\n  70 \n  71     location ^~ /mnt/ {\n  72         types {\n  73             text/plain md log sh py conf sql;\n  74             application/json json;\n  75             text/yaml yaml yml;\n+ 76             text/html html htm;\n  76         }\n  77         default_type text/plain;\n  78         alias /mnt/;\n  79         autoindex on;\n     ...",
            "firstChangedLine": 66
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          "isError": false,
          "timestamp": 1776864322958
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                "command": "sudo nginx -t && sudo systemctl reload nginx && curl -I -s 'https://ballbox-first.emperor-ratio.ts.net/mnt/rpi/pi-config/sessions/memory-techniques-review.html' | sed -n '1,20p'",
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            {
              "type": "text",
              "text": "nginx: the configuration file /etc/nginx/nginx.conf syntax is ok\nnginx: configuration file /etc/nginx/nginx.conf test is successful\nHTTP/2 200 \r\naccept-ranges: bytes\r\ncontent-type: text/plain\r\ndate: Wed, 22 Apr 2026 13:25:29 GMT\r\netag: \"69e8cbc3-2dec\"\r\nlast-modified: Wed, 22 Apr 2026 13:23:15 GMT\r\nserver: nginx\r\ncontent-length: 11756\r\n\r\n"
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        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Reviewing Content-Type settings**\n\nI'm thinking about Content-Type for HTML documents. It should ideally be set to text/html based on mime.types unless the default_type is specified. However, if the server is using `try_files` or a different location directive, that might change things. I need to look closely at the configuration files to determine the exact path and ensure everything is aligned correctly. It's essential to get this right for proper functionality!",
              "thinkingSignature": "{\"id\":\"rs_0c72ad00a46e46da0169e8cc4a5eb0819094115d77c0c2db1e\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6MxOXLvFDWch1yDgCUzkyyzEbEWwZi8mVb6qiCQXIftRjqQ3XHemyOm0ZHYXyz3S24sWxMAeJ6W9dJWk-4zJMr4ckVUT-cCnaZ-xDYXP2aIvQcRta7Hhetm_wbnE4OaTsCs0cpOC2scYvnJDFFbLVlrTQlYxfs-PS-6CU3xoADn3S17hU8IBssd3kTWQadCy3dYZkBhS1VR4Ms8ye1DkJU5WShu1xmlSokATXTSCUcKJJUUA8DLk0vozdyay9R8E54I0NdyLhkjSuQJY56oF_95NyImpqWJ8wxlnW4YnG6erzevzprD7xOEoDm4JGVdeSouSVNc5dOfwlJXwDCJe2L23FXw2l4UbqK82NdzONRsnh0ZW4c855tPaE1udtxd1rPbH3I9bqHTMUQ2C9SBPkerJt22XUNxgEwiR-vDzaraZZgfa8NM3xLVkKOdHQMONtM74cpcUf7KcxBUYUjUHJs5-P0PtwCexQGgJC7y0Loyf80U3_zqrfZiyj37xW1pWaNJ6NLEKE1EjWpz2o5GcfEpLqtZqR_rHQ0AiMpVZSNwPFvZGvSDvYucc-3bstiTSIdYqrkcD38bKpKex4s4r3dkA0PXMEEt6N3_FZgSyGLjlaOW16y0IfXKMn0eAzwgkzb4znghdHeoK9jfvR0KOIH-JYOLmOJrE_EDN5go5kIzfZxzZZf18r-jfn_RLbyjyiRWqhiXKGEoO3K3pgJJRDu9dqu9TpbDRW7rVw_6FeQa3R261GBrr9mCjv-RkMjwmc8bEAltcK_NLXkdPP28nXSHOQeD6feX-4MrElERf7xGFfYIhefOPx1FWUN668novZWfB-hY6LjTvtRQC6dDFy9f_gtViVHc4dv8PAB1vddO83rZ9z2C_lS97TkiSKLDZ4S76WM0rK6aXbRSrXi6w8lCdxHy69B4lpLAA8GIT8clZg-1s7rU3YBWNqUxtVS9BMNw3dx8uad1h7D9HVply6C-QeZhizi79Jb3_0wO1hTnu6q_iwDlnG6uDCDYRtzR3uhwaqy8IbjGEUUpA0a4ThQqjh_XJJR4SIekvKUz1pR_WoDBLNlHuMxFiNZxwtXN2hxsUcSWyn4qktBrt813Gb13MPaqGgwhqoBl3apAFDQVGngaD7fYoP0r4c3SNjOYXE1e1pvq9u5Q9r7sY7qVpj1dOWYB2UmbHpjzzAmgocI1UimfhAKmvzWCVp72xUAYoo8wHnXdfM0HZwNeYVBfJKfxiKCu-VrxBfaiRlubg-OsD7ikvZ94AKtR7jj1bvyWpgXfK-GFDWSgpjjSR-kEXtXDpJUAvQm4Ivx6ZXzDooZzMNrsiuynWj-OGEVNtmoYPh9Uia7BKLTHuEFoFX0UYsDr0c_jBwf-9iE71_M3cEEgHfxvHYnYUTigbfpyYAaXC5kIMIy_Jo9lF2ofg4MUta8PoQA1U7zsVWyZNxEpio8HmTRSc_EAbxxj8L0Z19LQBZI_2qvADOl8c6lmTAhb9lOl83wNRhQ5oQe5R3MDPsFXIrt76SXbV-9z-oqdqAiSyqUpRik3M74tL5KEeHysejRNtKsN02aRW3A59HmXdlPzVjKNWEr0wCJ5MGAtVL9efq_sYg2gNPVZ2hexWAXDTCD4WQaYKrHoXD-TIkr4_VPnZBonBHxduOVzhDjhF0i9QVtLay532ekJjsYUm419MtnXRBAKP8HhpQ4OgUDOaWHug1a1votKQ2-_3aVkWnhd244u4mnImv4NAwj1fupRemrld8Rc1AYTVH80y4DGhPPec-yULyockFxEbopXI1DcJepoiYWr4KrU8sKgEoKaGUt_Z74RuJAPJ32P6o0XiO3arx46vBG-3-NafY75ubQWY3COBDW5XEtvIpSdvu9cSrHEyNCpPQoOD6AIf_RIBPi5BqMPeKs0SFHtkx2SRDcx_Ed1_uC-ZV3qS62GW_nlh8KZgAkO9cxDp0xaQb20NUesDKRxFELurmQncRa2dm0KnQshBh9jwOcwyonKJ1IlAZ1skQiycI68MWUdik2vahp6b2FCWSo5bXtluvHB2hj5yhD40AmHt3jglGmFMXI_cWxdOlYjlwIx3G8LHpzo9aaFS8yyfL1EoxKw7091IlqCHP5PYLQ1lQo8kR3KhKMgJJCbOed_CW8KbWAjdkXVHa0cwG0oAE7xgnVacJAw0yrV4181CNBdxqsRNHlO41praxo46m-HuCIbV3kLZVqRUamXyz2lYrba2c5QlERHxhW9nSBDsYZoxIk7zDNMycq4njRNE9dL9Jexg_HdpnpFIExNZ-NtHPtPxBejvOwn82P1I3e8YZPqOy_oAhnGmmAgTZNnkm4oFXhxmeWylxthnunU8E7iJCN4Bp31105HRzKEwJGvnM06ix5-NsoE9LNUUXSWzU8ADlVr6Q8JCQY31xtTGI-ZNv_oY2nUZK9DzyEiidUjv\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Reviewing Content-Type settings**\\n\\nI'm thinking about Content-Type for HTML documents. It should ideally be set to text/html based on mime.types unless the default_type is specified. However, if the server is using `try_files` or a different location directive, that might change things. I need to look closely at the configuration files to determine the exact path and ensure everything is aligned correctly. It's essential to get this right for proper functionality!\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_u0MTbZpcFgwbHY4P5m00NHCs|fc_0c72ad00a46e46da0169e8cc4e0b108190a170f02451a653df",
              "name": "bash",
              "arguments": {
                "command": "sudo nginx -T 2>/dev/null | grep -n \"memory-techniques-review\\|location = /mnt/rpi\\|location \\^~ /mnt/\\|default_type text/plain\" | sed -n '1,120p'",
                "timeout": 20
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4-mini",
          "usage": {
            "input": 538,
            "output": 301,
            "cacheRead": 40960,
            "cacheWrite": 0,
            "totalTokens": 41799,
            "cost": {
              "input": 0.0004035,
              "output": 0.0013545,
              "cacheRead": 0.003072,
              "cacheWrite": 0,
              "total": 0.00483
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1776864329081,
          "responseId": "resp_0c72ad00a46e46da0169e8cc49b0248190af534bb97402251d"
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      },
      {
        "type": "message",
        "id": "933b6a4d",
        "parentId": "370714f6",
        "timestamp": "2026-04-22T13:25:34.360Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_u0MTbZpcFgwbHY4P5m00NHCs|fc_0c72ad00a46e46da0169e8cc4e0b108190a170f02451a653df",
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            {
              "type": "text",
              "text": "265:        default_type text/plain;\n276:    location = /mnt/rpi/pi-config/sessions/memory-techniques-review.html {\n277:        alias /mnt/rpi/pi-config/sessions/memory-techniques-review.html;\n281:    location ^~ /mnt/ {\n288:        default_type text/plain;\n"
            }
          ],
          "isError": false,
          "timestamp": 1776864334360
        }
      },
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        "timestamp": "2026-04-22T13:25:45.004Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Considering alias and paths**\n\nI'm wondering if the `alias` directive with an exact location and file path needs a trailing slash. I think using `root /;` along with `try_files /mnt/... =404;` will work. But, I note that the content-type issue still needs addressing. It seems like the `root` combined with the URI path will derive from the URI extension, suggesting that the content-type should indeed be set to HTML. So, it looks like that will work!",
              "thinkingSignature": "{\"id\":\"rs_0c72ad00a46e46da0169e8cc4f719c81908b4c13efe6582bf1\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6MxYyesCikrY19mt4ZuwDv6Z5yipjPvPI_LS3O69VkMKF2tJyqYMiL3ZSQkbUvJGFUtcF9Z0P0KGJlW4nbLzqDcDSaOvzT7_7-qyhLs9SOYFveclbRuMREM77wkqKcdtbQZNqlks46Cua6n8s6OUaF1NxPyZR0Ry--AXyt6Y3vcXOFcgUODOOwbeZcfNURJF4OmK9y3Zi4lqMsR9DZx6VlOz5s4WmD-mEBIF7iuqwUHSUiMl3HQM-it1JLPscuywOoAZfd995ZjknzXNIZ9ESuar4ljPiBcBucGuJhWtWx39wvFawaBRZs9vqn4b5SyIk8B1rGlmrkv1r1gLwzPYI4FZ_4dCnAD7l9E3zkpzF7TtVyQ-4RNFqtlr5fQnNmkaAtc_BpvhAZHwOF9C4azq13Zsizwx_3eyeHtjAEV0Q0obaCuWV5qZwm3qJHAuPcsCEZCD77yC103v3UzWNaQf_vvbPuGBz7znFC2YlNjnJn5ICYglRV6NnIlhhdmgtvlGhacwQH99ZpSUTEclwKJDX92tAZN1-2HdqqayH5IdDOsEICha6bToOU1xan9q1fbuWyY488yS52TPALMdMks7vOUEzCYeCezj8T_2PmOZj30TdhRvBcPo2KO7-GgP45Nx0o-4j1JYEiUeupoIDnp7diqQLAK0A87OdnUIEgnZS_rIAIm61I6I-7V8lTialY1ZdFFNqTnJb4PxxKw7gYnFsPNr9JkCSdWcv42czsSAk_i3G2jYggjMnaYdQIMaZHb9LH7ILfyqW9YUWTXO2hLWV63SF5YPUl1c90ec0KfaKJlc6G6aFmqUu0rq3z5dwy7I6-5Y9F5fTj59gWFlYriNWObX94ei6VxbjLYqV72tIYwTo2yRAu9Z-aAlXEIqyMAZUHi97O99fGCy2JxBCzrHA-HdYQyimireF2bYxNWcgTCioJcJrzsjn0lDPcP5cINkGjk8i23p_EXNTN8PhLFSjTO2kQELaxWstZJvGHAbZXYcXngnfNqLpoK8EcZjvWdXJgImI2hSxamD3jWinN62lP2cOaLKQHNsAKPLrljafRoU1xsaiG-cmUiBtxiDD4LkWNKMTU8Dzl7KaxRjPTk9r8uaian9dmDMOfI2RNq-WvwIxqilGVg-oe7KjYYcb8dXfTuDncS0c_G7IhX4Y3VDqJAt9zLFEp96M9EOiymGklrgaYZHeP6xTAq_WB9NtPFoWv_7aCe11ReQGnQfKGiK9AlkRZmylfG2zOUR1S3JskHk7WuF_OJKBvrhQim7GIH7lP885Ebcf7dqLo2k3qIrFO4K4k2X1hcGlZeXSu6RxN5eLQb2v19IaY_LkE_2OA7654zfOd3Vmn0auSeBR2rhH_z1BtLcrq_wcQNGynHNjEqO6K7l75kBYdVy12gax1v9qpexUZxx2Mrutog5QvFmPIgjXL0JkUTaSX96Hv-9caDAciCmj6bK4hY0yp-mgW--dCbRs_wFX1amWLH3SyUUwNABJyvpuUDojCdcIn52_zE5gEbU9i9hU2S-b-jzNvJZMa1KJ1hdCRdYjpMkDu-vKS-5ooSYgmbytkKZ97c-SRjA40qfMSSJ8a4ZD-Cueihv9lBdqiLc3y_tPS4ddTlB5HA77aHymmmqoOovPDLBG05pQ5lNxy1POx7CljsGn2aWglwbIEXtPf2cliWORkgApv7hq49GHxpphFKbK9NlP0rdzJt0T9fjxYt6drYjkgd-AVcQ6aFIFWeZeJBIsTzf-RX66l8pL7Q-mpSkVhpUuhvtrcUU25X3A_FZl4iTqM7NujRcqFvGFNkEoosFZkmzzXSst4UapmO4n-Q1WktDFDjUA7NUUZRy_ROPaDH5Ahgg-5VNbweVXu4RZ0nFwTxhgkzoeR-NRy7lvNowsZPkM_omNi9osZP38l-nAp6ulAvnC0BQiCRHUlrK3m7St-TNR0-QadqK1cg6t95VlQagfXMr_4zadNc64FvtIF3fUFlWkFj7v4LSsZedfpWwB-_89ARW9ZG_9fficcGVQeMU2XoFystPOqdPx2BVPIcVAHsn-BRrxkumoEnMykEGOyHJ64i53c0bT05upDvqaK7WuMFIcR-twxoF_XXgzwArZsc6b73JWOObANMokklaAjPniVStxIE9jkxGxNmXb0cCkY_A-rPXOvTsKvQWm67-2E3ZYwdlJj8a4gg9riIKmekoTYMxOavs6qTvoVzaiwHmLsG_I5NyAiX0l5yrYABs87IonxiZ1x7heIPImh3_ma4DTsUtBRtcnJcFjR8PANbOWscxh8MdtneH7wPwovvNeDw6NXaGGir3EVX2gTd8ZJ9CBZtKkQtMd3LP3bj95yrQUukyr15Sm9XwCCGUkjeQ6KFhsEmVcAR3dqJxMnGaVJz2wX_0gRpn2b6Yb9pQU2UODD80lij2mgeLTQMPa_v1v1ON6j8ugmo1gD-rlW2uHk0V90HjQGW9__YQixlGb_XZ0hFNefiQ6jP5JBBLoIBhkyjb47cUxtD9LUyBaO-milmSre9eqohJ3pn4O22JXWPuCXDps4IUKOojxTChPlIkyoMSqb4cvQ1haIrVuCmaC68liw09IrqXsf7rZnKRB7YWOugvRG45ePf25ot8Qhulkc_okHbc6wPsp9vTbNXJjbPV5CqDqMSvxBI-oEFlh1OXg9uCAaUfR4zVGQWjbSls4Ypwmkuy8-CFpkwr5TRP2LCGcT6jff6eLcz7ZSYFzlDpvhgwuUND_oewUwdGth4Vw5gsZfVQ3nQzQR_4ZFOc0mJ3sDe-dxG-qFfUlk0mEsT6c-9MpVH3Fwvcm2BdHj_CkV1S0c0S7lYCP8-jvhJQ_Bs5YJy_Ymm1Smg95wLcUXyuKeyZx-6Ydrr4DvvU14X53N91zTIPWAPjMW-D452viyIq3WameatOX6pQ0CqQ8CcagGomXKn9BBWdL7FxP2_FVMMxZatwz91QMTfCQDahyYCDT7YJtc86Lub7AWwpdhOtDN9ilSJAjeSelYv1uoIUyH8F45M6oRsITWI-zRP-r3cYn6qyXYrkKspxbCgPmMNUDLI16xAPu_yvfuK3v16ZxWs8ZqdkVsH6NkvIycv-5pnwVbWP2qvPgXeojtqPK-umDnrl17Lax-FQtmnOO6Iv_XYp-z1Hu5QTkU4ZtOwoXtLZZvFMCsCS-BdrlfgD-MpoX3RTBOMWMXIjgIghkWJmCfLHP7CTW4sUmnfvbcaJNUNzT7VOSHJEMsilKk1Pxb2RFGi_jnI9PwXMluxi35lmudy3Zu7qCdIrgeXRX9FqZI0sYdkwhGqNib3Su1cnc7LIJN2Fhtw7q16kJRisbCTHPKc5Hprlk9fo0Tb4w1kCpu7nxu7-4_k2ZZu5qUiykihEre7r3AK76QpWFaU0Jq7rSnJ4fHlo_UT4qvg-qSRQm9DWet4Cu9lqNbRVjaQBwFxTDUhw3rQcblsV0LnRjMN4XXkcxkmOTl2tFIWe3--5OWPlAGpgbITzTXp8sa9I4x6MuYZAzghAcig_X-amYN3TQ9XO3zGYBtP68TOuelITGTLhaiV205wyi8Spfxyb1Z0n64wpTwiyF1MaK-EdJzcVveIEk8fp5-dBrRF93CHuWHlc2VJrfRLyT-OVPb_G7HUKOxRfzVEIuuj2O7pknGmvXu3lREXvoFvNUG9HejgIMJ8W3AoSqZPnFuV7oc5Scu6Ti4JBjneyHRK_7Krsruywb_IQ-5ZJPxE9P4aqGbHAiDeuZ96F-Ip4uyV7p8j9WWgElaZiC1QOvLv9uPeNAQHil3u0SXaGc92JB0AUOjz3rYNBNkxt69ka6ZEgdb3lQ3996GWkwD8iFGvKEHDekovTBnkg8bFv8sAjMwKMiVcSF6N37x5h9Wj44lFN3pD9XztI71F8g2NKJADd06wpAjeemeNHROsMwTWTDthmCeX-sHai9v5V-Q==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering alias and paths**\\n\\nI'm wondering if the `alias` directive with an exact location and file path needs a trailing slash. I think using `root /;` along with `try_files /mnt/... =404;` will work. But, I note that the content-type issue still needs addressing. It seems like the `root` combined with the URI path will derive from the URI extension, suggesting that the content-type should indeed be set to HTML. So, it looks like that will work!\"}]}"
            },
            {
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              "name": "edit",
              "arguments": {
                "path": "/etc/nginx/sites-enabled/kiosk",
                "edits": [
                  {
                    "oldText": "    location = /mnt/rpi/pi-config/sessions/memory-techniques-review.html {\n        alias /mnt/rpi/pi-config/sessions/memory-techniques-review.html;\n        default_type text/html;\n    }\n",
                    "newText": "    location = /mnt/rpi/pi-config/sessions/memory-techniques-review.html {\n        root /;\n        default_type text/html;\n        try_files /mnt/rpi/pi-config/sessions/memory-techniques-review.html =404;\n    }\n"
                  }
                ]
              }
            }
          ],
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            "cacheRead": 41472,
            "cacheWrite": 0,
            "totalTokens": 42532,
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          "stopReason": "toolUse",
          "timestamp": 1776864334361,
          "responseId": "resp_0c72ad00a46e46da0169e8cc4ec80481908449ada395049a73"
        }
      },
      {
        "type": "message",
        "id": "adcfb138",
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        "timestamp": "2026-04-22T13:25:45.009Z",
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          "toolCallId": "call_0HCLOeEX6YIiTMclQuoHE1TD|fc_0c72ad00a46e46da0169e8cc583e54819088c97a8c1b1f7846",
          "toolName": "edit",
          "content": [
            {
              "type": "text",
              "text": "Successfully replaced 1 block(s) in /etc/nginx/sites-enabled/kiosk."
            }
          ],
          "details": {
            "diff": "     ...\n  63         return 302 /mnt/;\n  64     }\n  65 \n  66     location = /mnt/rpi/pi-config/sessions/memory-techniques-review.html {\n- 67         alias /mnt/rpi/pi-config/sessions/memory-techniques-review.html;\n+ 67         root /;\n  68         default_type text/html;\n+ 69         try_files /mnt/rpi/pi-config/sessions/memory-techniques-review.html =404;\n  69     }\n  70 \n  71     location ^~ /mnt/ {\n  72         types {\n     ...",
            "firstChangedLine": 67
          },
          "isError": false,
          "timestamp": 1776864345009
        }
      },
      {
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              "name": "bash",
              "arguments": {
                "command": "sudo nginx -t && sudo systemctl restart nginx && curl -I -s 'https://ballbox-first.emperor-ratio.ts.net/mnt/rpi/pi-config/sessions/memory-techniques-review.html' | sed -n '1,20p'",
                "timeout": 60
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      },
      {
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          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "nginx: the configuration file /etc/nginx/nginx.conf syntax is ok\nnginx: configuration file /etc/nginx/nginx.conf test is successful\nHTTP/2 200 \r\naccept-ranges: bytes\r\ncontent-type: text/html\r\ndate: Wed, 22 Apr 2026 13:25:46 GMT\r\netag: \"69e8cbc3-2dec\"\r\nlast-modified: Wed, 22 Apr 2026 13:23:15 GMT\r\nserver: nginx\r\ncontent-length: 11756\r\n\r\n"
            }
          ],
          "isError": false,
          "timestamp": 1776864346988
        }
      },
      {
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            {
              "type": "text",
              "text": "Ahora sí.\n\nCausa:\n- `reload` no estaba alcanzando la combinación correcta de location/alias para ese path.\n- Reemplacé la regla por una `location = ...` con `root` + `try_files`, y reinicié nginx.\n\nVerificado:\n- `Content-Type: text/html`\n\nProbá de nuevo:\n`https://ballbox-first.emperor-ratio.ts.net/mnt/rpi/pi-config/sessions/memory-techniques-review.html`\n\nSi seguís viendo texto plano, casi seguro es cache del navegador. Hacé hard reload o abrilo en incógnito.",
              "textSignature": "{\"v\":1,\"id\":\"msg_0c72ad00a46e46da0169e8cc5d59c48190ab17a8295e644e98\",\"phase\":\"final_answer\"}"
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        "type": "message",
        "id": "51f6a250",
        "parentId": "a9f096e5",
        "timestamp": "2026-04-22T13:26:20.479Z",
        "message": {
          "role": "user",
          "content": [
            {
              "type": "text",
              "text": "ahora si, pero agregaste un alias /mnt/rpi/pi-config/sessions/memory-techniques-review.html en nginx? feo feo. si sigue sacalo"
            }
          ],
          "timestamp": 1776864380477
        }
      },
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        "message": {
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            {
              "type": "thinking",
              "thinking": "**Troubleshooting configurations**\n\nI’m thinking that I need to find an answer and likely fix the issue at hand. I should check the configuration, as there might be something odd with the root line. The user mentioned removing it if it’s still present. I need to directly edit to remove that specific location and maybe use a generic, correct configuration with types. It’s important to be straightforward about this!",
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            },
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              "id": "call_MFExI2bWjWeAr2kychiYY1N5|fc_0c72ad00a46e46da0169e8cc7f85b8819083c9b965d5f670b2",
              "name": "edit",
              "arguments": {
                "path": "/etc/nginx/sites-enabled/kiosk",
                "edits": [
                  {
                    "oldText": "    location = /mnt/rpi/pi-config/sessions/memory-techniques-review.html {\n        root /;\n        default_type text/html;\n        try_files /mnt/rpi/pi-config/sessions/memory-techniques-review.html =404;\n    }\n",
                    "newText": ""
                  }
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        "type": "message",
        "id": "d56b5b09",
        "parentId": "f6223c11",
        "timestamp": "2026-04-22T13:26:23.858Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_MFExI2bWjWeAr2kychiYY1N5|fc_0c72ad00a46e46da0169e8cc7f85b8819083c9b965d5f670b2",
          "toolName": "edit",
          "content": [
            {
              "type": "text",
              "text": "Successfully replaced 1 block(s) in /etc/nginx/sites-enabled/kiosk."
            }
          ],
          "details": {
            "diff": "     ...\n  62     location = /mnt {\n  63         return 302 /mnt/;\n  64     }\n  65 \n- 66     location = /mnt/rpi/pi-config/sessions/memory-techniques-review.html {\n- 67         root /;\n- 68         default_type text/html;\n- 69         try_files /mnt/rpi/pi-config/sessions/memory-techniques-review.html =404;\n- 70     }\n  71 \n  72     location ^~ /mnt/ {\n  73         types {\n  74             text/plain md log sh py conf sql;\n     ...",
            "firstChangedLine": 66
          },
          "isError": false,
          "timestamp": 1776864383858
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        "timestamp": "2026-04-22T13:26:27.130Z",
        "message": {
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            {
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              "id": "call_94pl7KaUK8uTJsOldhcTpIy6|fc_0c72ad00a46e46da0169e8cc8100588190853c9003a94b1b4a",
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              "arguments": {
                "command": "python3 - <<'PY'\nfrom pathlib import Path\np=Path('/etc/nginx/sites-enabled/kiosk')\ntext=p.read_text()\nold='''    location ^~ /mnt/ {\n        types {\n            text/plain md log sh py conf sql;\n            application/json json;\n            text/yaml yaml yml;\n            text/html html htm;\n        }\n        default_type text/plain;\n        alias /mnt/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n'''\nnew='''    location ^~ /mnt/ {\n        types {\n            text/html html htm;\n            text/plain md log sh py conf sql;\n            application/json json;\n            text/yaml yaml yml;\n        }\n        default_type text/plain;\n        alias /mnt/;\n        autoindex on;\n        autoindex_exact_size off;\n        autoindex_localtime on;\n    }\n'''\nif old in text:\n    text=text.replace(old,new)\n    p.write_text(text)\n    print('reordered types')\nelse:\n    print('block not found')\nPY\nsudo nginx -t && sudo systemctl restart nginx && curl -I -s 'https://ballbox-first.emperor-ratio.ts.net/mnt/rpi/pi-config/sessions/memory-techniques-review.html' | sed -n '1,12p'",
                "timeout": 60
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          "role": "toolResult",
          "toolCallId": "call_94pl7KaUK8uTJsOldhcTpIy6|fc_0c72ad00a46e46da0169e8cc8100588190853c9003a94b1b4a",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "reordered types\nnginx: the configuration file /etc/nginx/nginx.conf syntax is ok\nnginx: configuration file /etc/nginx/nginx.conf test is successful\nHTTP/2 200 \r\naccept-ranges: bytes\r\ncontent-type: text/html\r\ndate: Wed, 22 Apr 2026 13:26:27 GMT\r\netag: \"69e8cbc3-2dec\"\r\nlast-modified: Wed, 22 Apr 2026 13:23:15 GMT\r\nserver: nginx\r\ncontent-length: 11756\r\n\r\n"
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          "isError": false,
          "timestamp": 1776864387384
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        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "text",
              "text": "Sí, lo saqué.\n\nDejé solo la regla genérica de `/mnt/` y reordené `types` para que `.html` salga como `text/html` sin hack por archivo.\n\nVerificado:\n- la URL sigue sirviendo `text/html`\n\nSi querés, hago una pasada extra para limpiar también otras cosas feas en ese nginx.",
              "textSignature": "{\"v\":1,\"id\":\"msg_0c72ad00a46e46da0169e8cc855374819093a30e4e32a7e71e\",\"phase\":\"final_answer\"}"
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        "id": "251211d4",
        "parentId": "6ba1e1ef",
        "timestamp": "2026-04-22T13:31:30.293Z",
        "message": {
          "role": "user",
          "content": [
            {
              "type": "text",
              "text": "me gusta el analisis, arma un rfc estructurado que sirva de log de progreso tambien"
            }
          ],
          "timestamp": 1776864690291
        }
      },
      {
        "type": "message",
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        "timestamp": "2026-04-22T13:31:41.979Z",
        "message": {
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          "content": [
            {
              "type": "thinking",
              "thinking": "**Creating RFC file**\n\nI’m considering creating an RFC file, possibly using Markdown initially and HTML later. The user has requested a structured RFC to serve as a progress log, so I need to ensure it meets those requirements. I should also inspect any existing files for information to incorporate. I’m thinking we’ll work on this RFC in sessions, focusing on keeping it concise and clear. Let's get started on that!",
              "thinkingSignature": "{\"id\":\"rs_0c72ad00a46e46da0169e8cdb3b3b88190ad9a4b76d644b3f5\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp6M22xqlxz-ErjBSPEBn0rt6GhjI9pvcoGjLIL5vcO0FjsKy20O4600jnuPVGXU45zu2SowYitSmFSntIBKmbYouNrNOMnmnfds7NcDtwU0yUYZlpa-qkWvhxTcQrKt8j_QYdzrhla0dba1WNwwmf03A-OZbWIV8rA-yUdmT8nbQcsieiJ69_oEUy8yTBPV68W9bhWI-NKfh2hJ36hTvKG2yEOeRykkHiarV1CeJbxlfSc3nIq_ztrgAweT8flF3gUyjuq8C6FwV4654ixBxA8z9EI71naapy5qBW83f08pYP570pFWKmuG17P01576VuoipMkAE4nZD3JusOvK8_ESP2W6qJWaxdNtbtg3K5qP_hUogI-5FZ4-tiIWvlB83KAYIpzJV07baJPrunIVBdQIo8Dc-WGkZmtCbtNP7eotStTeV5gAkqta79If3THDQuxQzAavWham9VwoebluMroSwAbeeRswt2dLIVgrBvH7YmM1yfdnhxTdZ9yPnAWdMfXmhGp2AH_71jwhcsXt3pMW-yVibKi2iQiU82mbgtsj_MGxqflYgORsxFnIRZ6kCGIAcKVpL5A3El4tgku1C8-YS5i-RF5QR0nght7kNW9WIvqwf0bmJFxYvwPpa9WDLVEOPqmw8TSe7vQQZfK1pUqhD178uPbeNccBvzZ36s2o57W-7XGZ-emYztPw5HmCxSOvSlW1vqaCP8wmXeNe54H2nV_oiwFG0uRXO_9MxAUgyq_e3n2A-D97xN5PTZi9Iu2_CHOSsaB8seiAbJWEjejKApeFA1_tGk2u6-GbtDGgVAWeddBiG7YElkYVPsffE3byM0Yv4P41zr17ll2Rk33l-gV84UxNovV9Pr1bWvvvirzD3aSubfi77Knpe5wAW1mImNz-Ym4Whv5nAXXqhHwIAQTqRhWvJez4AguizvF5gnQd8pbhRQGhZVkilNbNnfr3iMBTDxGKzlbUpMM7PNhmD2gWHUsYYRCSm0uWN-YeoMphlBvCmJzuTFeCyHNQffUS7aOyahObLHbjRqNXh3lc8RMp69vcvekHr03L-46a-e0mQgdKAn45nnjKM0i-7fOIjgHp5v4rxm9-jYEW5w7xoutIcq6Z8dVShRBTq4N4CTEr862RmRU34fXkQX5mAV0JNGln85UYjaIAZw762pXz3-2HglI_FIUemYSxW6fCxk6th77Zc5MUc0p_X3uN4JiIM532HbojDKFGScGsxZwcNBnNaKeU3IlLTWz8sXYkXSG4lQGStV1nT6o7_hyG1v2jurpV8J94mR2aGYsE73j7WGHQ==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Creating RFC file**\\n\\nI’m considering creating an RFC file, possibly using Markdown initially and HTML later. The user has requested a structured RFC to serve as a progress log, so I need to ensure it meets those requirements. I should also inspect any existing files for information to incorporate. I’m thinking we’ll work on this RFC in sessions, focusing on keeping it concise and clear. Let's get started on that!\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_rkNVkByvIhzWLLOjGxsJQ7W6|fc_0c72ad00a46e46da0169e8cdb6915c819094221e6a82189035",
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              "arguments": {
                "command": "cat > /mnt/rpi/pi-config/sessions/RFC-memory-layer-agents.md <<'EOF'\n# RFC: Memory layer for agents\n\n**Status:** Draft\n**Owner:** Sebas / personal-agent\n**Updated:** 2026-04-22\n**Scope:** agents-database + Pi session memory pipeline\n\n## 1. Problem\n\nHoy `agents-database` guarda memoria útil, pero la mayor parte queda en nivel operativo:\n\n- eventos\n- sesiones\n- notas puntuales\n- claims aislados\n\nFalta una capa que convierta ese material en conocimiento reusable de mayor valor:\n\n- preferencias estables\n- hechos consolidados\n- patrones repetidos\n- decisiones\n- principios\n- procedimientos\n- insights con evidencia\n\n## 2. Objetivo\n\nDiseñar una capa de memoria para agentes que no solo almacene, sino que:\n\n1. **Capture** lo relevante\n2. **Consolide** lo repetido\n3. **Promueva** episodios a semántica\n4. **Abstraiga** a nivel patrón/principio\n5. **Permita recuperación híbrida** con trazabilidad\n\n## 3. Principio de diseño\n\nNo guardar “más memoria”.\nGuardar **mejor memoria**, con niveles y promoción explícita.\n\n## 4. Fuentes y backing\n\n### Fuentes con backing fuerte\n\n- Microsoft Learn: agentic memories en Cosmos DB\n  - short-term vs long-term\n  - TTL\n  - summary/prune\n  - one turn / one response / one thread patterns\n  - hybrid retrieval\n\n- Redis blog sobre memory management\n  - separación short/long-term\n  - TTL\n  - summarization\n  - semantic search\n  - dedup ideas\n\n- AgentDock consolidation guide\n  - episodic → semantic\n  - dedup por embedding + keywords + metadata + tiempo\n  - merge / synthesize / abstract\n\n- Letta / MemGPT\n  - sistema real enfocado en memoria persistente\n\n### Fuentes débiles o a revisar\n\n- URLs de docs que devolvieron 404\n- páginas de marketing sin mecanismo explícito\n\n## 5. Taxonomía propuesta\n\n### 5.1 Episodic\nRegistro de eventos concretos.\n\nEjemplos:\n- sesión\n- interacción\n- tool call\n- incidente\n\n### 5.2 Operational\nContexto útil para ejecución inmediata.\n\nEjemplos:\n- estado actual\n- últimos N turnos\n- variables temporales\n\n### 5.3 Factual / Semantic\nHechos relativamente estables.\n\nEjemplos:\n- “Sebas prefiere respuestas cortas”\n- “Este repo es la fuente de verdad”\n- “Nginx publica /mnt”\n\n### 5.4 Procedural\nCómo hacer algo.\n\nEjemplos:\n- runbooks\n- comandos\n- workflows\n- secuencias repetibles\n\n### 5.5 Decision\nDecisiones tomadas y su motivo.\n\nEjemplos:\n- “usar agents-database como memoria durable”\n- “mantener legacy sqlite solo como transición”\n\n### 5.6 Insight / Principle\nSíntesis de nivel superior.\n\nEjemplos:\n- “No todo lo recordado merece persistir”\n- “Promover solo cuando hay repetición + utilidad + evidencia”\n\n## 6. Pipeline propuesto\n\n### 6.1 Ingest\nCaptura de memoria desde sesiones, jobs y hooks.\n\n### 6.2 Classify\nAsignar tipo, scope, confidence, freshness y source trace.\n\n### 6.3 Consolidate\nJobs batch que:\n\n- deduplican\n- agrupan por tema\n- resúmen threads largos\n- detectan repetición\n- extraen hechos/preferencias/decisiones\n\n### 6.4 Promote\nMover memoria entre niveles:\n\n- episodic → semantic\n- semantic → principle\n- operational → durable if repeated / important\n\n### 6.5 Retrieve\nBúsqueda híbrida:\n\n- vector\n- lexical\n- metadata filters\n- recency\n- trust/confidence\n\n## 7. Reglas de promoción\n\nPromover solo si cumple una o más:\n\n- repetición observada\n- uso frecuente\n- impacto alto\n- confianza suficiente\n- evidencia trazable\n- estabilidad temporal\n\nNo promover si:\n\n- es ruido operativo\n- es una hipótesis no verificada\n- es demasiado situacional\n\n## 8. Campos mínimos por memoria\n\n- `type`\n- `scope`\n- `title`\n- `content`\n- `confidence`\n- `freshness`\n- `source_ref`\n- `evidence_ref`\n- `metadata`\n\nCampos recomendados:\n\n- `project_id`\n- `repo_id`\n- `task_id`\n- `run_id`\n- `url`\n- `domain`\n- `origin_agent`\n\n## 9. Anti-patrones\n\n- guardar todo sin consolidar\n- mezclar hechos con ruido operativo\n- no distinguir confidence de freshness\n- no guardar evidencia\n- usar una sola capa para todo\n- convertir cualquier reflexión en “verdad”\n\n## 10. Decisiones abiertas\n\n- taxonomía final de tipos\n- umbrales de promoción\n- schedule de consolidación\n- estrategia de ranking\n- formato de evidencia\n- curaduría humana vs automática\n\n## 11. Estado del análisis\n\n### Hecho hoy\n\n- Sí existen patrones reales y repetidos en sistemas de memoria de agentes.\n- Los más sólidos usan TTL, summaries, dedup, hybrid retrieval y separación short/long-term.\n- Lo que más valor agrega no es la cantidad, sino la **promoción jerárquica con evidencia**.\n\n### Lo que falta validar\n\n- si conviene persistir grafo de entidades\n- si la abstracción debe ser automática o semi-manual\n- cómo medir “insight útil” vs “resumen lindo pero inútil”\n\n## 12. Próximos pasos\n\n1. terminar taxonomía fina por técnica\n2. convertirla en esquema de datos para `agents-database`\n3. definir jobs de consolidación\n4. definir señales de ranking y promoción\n5. implementar el primer batch job\n\n## 13. Progress log\n\n- 2026-04-22: arrancó investigación; fuentes base Microsoft + AgentDock + Redis + Letta.\n- 2026-04-22: descartadas fuentes rotas o flojas como referencia principal.\n- 2026-04-22: se validó que el foco útil es promoción de nivel, no solo logging.\n- 2026-04-22: se corrigió nginx para servir HTML correctamente desde `/mnt`.\nEOF\n",
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                "content": "<!doctype html>\n<html><head><meta charset=\"utf-8\"><meta name=\"viewport\" content=\"width=device-width, initial-scale=1\"><title>RFC: Memory layer for agents</title>\n<style>body{font-family:system-ui,-apple-system,Segoe UI,Roboto,sans-serif;max-width:980px;margin:40px auto;padding:0 18px;line-height:1.55;color:#111} h1,h2,h3{line-height:1.2} code,pre{font-family:ui-monospace,SFMono-Regular,Menlo,monospace} pre{background:#f6f8fa;padding:14px;border-radius:8px;overflow:auto} blockquote{border-left:4px solid #ddd;margin:16px 0;padding:0 0 0 12px;color:#444} table{border-collapse:collapse;width:100%;margin:16px 0} th,td{border:1px solid #ddd;padding:8px;vertical-align:top} th{background:#f3f4f6} .muted{color:#555}</style></head><body>\n<h1>RFC: Memory layer for agents</h1>\n<p><strong>Status:</strong> Draft<br><strong>Owner:</strong> Sebas / personal-agent<br><strong>Updated:</strong> 2026-04-22<br><strong>Scope:</strong> agents-database + Pi session memory pipeline</p>\n<h2>1. Problem</h2>\n<p>Hoy <code>agents-database</code> guarda memoria útil, pero la mayor parte queda en nivel operativo:</p>\n<ul><li>eventos</li><li>sesiones</li><li>notas puntuales</li><li>claims aislados</li></ul>\n<p>Falta una capa que convierta ese material en conocimiento reusable de mayor valor:</p>\n<ul><li>preferencias estables</li><li>hechos consolidados</li><li>patrones repetidos</li><li>decisiones</li><li>principios</li><li>procedimientos</li><li>insights con evidencia</li></ul>\n<h2>2. Objetivo</h2>\n<p>Diseñar una capa de memoria para agentes que no solo almacene, sino que:</p>\n<ol><li>Capture lo relevante</li><li>Consolide lo repetido</li><li>Promueva episodios a semántica</li><li>Abstraiga a nivel patrón/principio</li><li>Permita recuperación híbrida con trazabilidad</li></ol>\n<h2>3. Principio de diseño</h2>\n<p>No guardar “más memoria”.<br>Guardar <strong>mejor memoria</strong>, con niveles y promoción explícita.</p>\n<h2>4. Fuentes y backing</h2>\n<h3>Fuentes con backing fuerte</h3>\n<ul><li>Microsoft Learn: agentic memories en Cosmos DB<ul><li>short-term vs long-term</li><li>TTL</li><li>summary/prune</li><li>one turn / one response / one thread patterns</li><li>hybrid retrieval</li></ul></li><li>Redis blog sobre memory management<ul><li>separación short/long-term</li><li>TTL</li><li>summarization</li><li>semantic search</li><li>dedup ideas</li></ul></li><li>AgentDock consolidation guide<ul><li>episodic → semantic</li><li>dedup por embedding + keywords + metadata + tiempo</li><li>merge / synthesize / abstract</li></ul></li><li>Letta / MemGPT<ul><li>sistema real enfocado en memoria persistente</li></ul></li></ul>\n<h3>Fuentes débiles o a revisar</h3>\n<ul><li>URLs de docs que devolvieron 404</li><li>páginas de marketing sin mecanismo explícito</li></ul>\n<h2>5. Taxonomía propuesta</h2>\n<h3>5.1 Episodic</h3>\n<p>Registro de eventos concretos.</p><p>Ejemplos:</p><ul><li>sesión</li><li>interacción</li><li>tool call</li><li>incidente</li></ul>\n<h3>5.2 Operational</h3>\n<p>Contexto útil para ejecución inmediata.</p><p>Ejemplos:</p><ul><li>estado actual</li><li>últimos N turnos</li><li>variables temporales</li></ul>\n<h3>5.3 Factual / Semantic</h3>\n<p>Hechos relativamente estables.</p><p>Ejemplos:</p><ul><li>“Sebas prefiere respuestas cortas”</li><li>“Este repo es la fuente de verdad”</li><li>“Nginx publica /mnt”</li></ul>\n<h3>5.4 Procedural</h3>\n<p>Cómo hacer algo.</p><p>Ejemplos:</p><ul><li>runbooks</li><li>comandos</li><li>workflows</li><li>secuencias repetibles</li></ul>\n<h3>5.5 Decision</h3>\n<p>Decisiones tomadas y su motivo.</p><p>Ejemplos:</p><ul><li>“usar agents-database como memoria durable”</li><li>“mantener legacy sqlite solo como transición”</li></ul>\n<h3>5.6 Insight / Principle</h3>\n<p>Síntesis de nivel superior.</p><p>Ejemplos:</p><ul><li>“No todo lo recordado merece persistir”</li><li>“Promover solo cuando hay repetición + utilidad + evidencia”</li></ul>\n<h2>6. Pipeline propuesto</h2>\n<h3>6.1 Ingest</h3>\n<p>Captura de memoria desde sesiones, jobs y hooks.</p>\n<h3>6.2 Classify</h3>\n<p>Asignar tipo, scope, confidence, freshness y source trace.</p>\n<h3>6.3 Consolidate</h3>\n<p>Jobs batch que:</p><ul><li>deduplican</li><li>agrupan por tema</li><li>resúmen threads largos</li><li>detectan repetición</li><li>extraen hechos/preferencias/decisiones</li></ul>\n<h3>6.4 Promote</h3>\n<p>Mover memoria entre niveles:</p><ul><li>episodic → semantic</li><li>semantic → principle</li><li>operational → durable if repeated / important</li></ul>\n<h3>6.5 Retrieve</h3>\n<p>Búsqueda híbrida:</p><ul><li>vector</li><li>lexical</li><li>metadata filters</li><li>recency</li><li>trust/confidence</li></ul>\n<h2>7. Reglas de promoción</h2>\n<p>Promover solo si cumple una o más:</p><ul><li>repetición observada</li><li>uso frecuente</li><li>impacto alto</li><li>confianza suficiente</li><li>evidencia trazable</li><li>estabilidad temporal</li></ul>\n<p>No promover si:</p><ul><li>es ruido operativo</li><li>es una hipótesis no verificada</li><li>es demasiado situacional</li></ul>\n<h2>8. Campos mínimos por memoria</h2>\n<ul><li><code>type</code></li><li><code>scope</code></li><li><code>title</code></li><li><code>content</code></li><li><code>confidence</code></li><li><code>freshness</code></li><li><code>source_ref</code></li><li><code>evidence_ref</code></li><li><code>metadata</code></li></ul>\n<p>Campos recomendados:</p><ul><li><code>project_id</code></li><li><code>repo_id</code></li><li><code>task_id</code></li><li><code>run_id</code></li><li><code>url</code></li><li><code>domain</code></li><li><code>origin_agent</code></li></ul>\n<h2>9. Anti-patrones</h2>\n<ul><li>guardar todo sin consolidar</li><li>mezclar hechos con ruido operativo</li><li>no distinguir confidence de freshness</li><li>no guardar evidencia</li><li>usar una sola capa para todo</li><li>convertir cualquier reflexión en “verdad”</li></ul>\n<h2>10. Decisiones abiertas</h2>\n<ul><li>taxonomía final de tipos</li><li>umbrales de promoción</li><li>schedule de consolidación</li><li>estrategia de ranking</li><li>formato de evidencia</li><li>curaduría humana vs automática</li></ul>\n<h2>11. Estado del análisis</h2>\n<h3>Hecho hoy</h3>\n<ul><li>Sí existen patrones reales y repetidos en sistemas de memoria de agentes.</li><li>Los más sólidos usan TTL, summaries, dedup, hybrid retrieval y separación short/long-term.</li><li>Lo que más valor agrega no es la cantidad, sino la <strong>promoción jerárquica con evidencia</strong>.</li></ul>\n<h3>Lo que falta validar</h3>\n<ul><li>si conviene persistir grafo de entidades</li><li>si la abstracción debe ser automática o semi-manual</li><li>cómo medir “insight útil” vs “resumen lindo pero inútil”</li></ul>\n<h2>12. Próximos pasos</h2>\n<ol><li>terminar taxonomía fina por técnica</li><li>convertirla en esquema de datos para <code>agents-database</code></li><li>definir jobs de consolidación</li><li>definir señales de ranking y promoción</li><li>implementar el primer batch job</li></ol>\n<h2>13. Progress log</h2>\n<ul><li>2026-04-22: arrancó investigación; fuentes base Microsoft + AgentDock + Redis + Letta.</li><li>2026-04-22: descartadas fuentes rotas o flojas como referencia principal.</li><li>2026-04-22: se validó que el foco útil es promoción de nivel, no solo logging.</li><li>2026-04-22: se corrigió nginx para servir HTML correctamente desde <code>/mnt</code>.</li></ul>\n</body></html>"
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              "text": "listo para que tome un agente de cero?"
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