# Record
**Author:** @cameron.stream (`did:plc:gfrmhdmjvxn2sjedzboeudef`)

## `3mvbbweoxa22n`
**Collection:** `site.standard.document`
**AT URI:** `at://did:plc:gfrmhdmjvxn2sjedzboeudef/site.standard.document/3mvbbweoxa22n`

**Title:** Letta Office Hours: Astra, Model Profiles, and Shared Memory
**Published:** Fri, 11 Sep 2026 19:06:17 GMT
**Updated:** Fri, 11 Sep 2026 19:02:43 GMT
**Description:** Astra and subscription setup, per-model context budgets, modern self-hosting, MemFS v2, shared skills, learning permissions, and the messaging gap around teleportation.
**Publication:** `at://did:plc:gfrmhdmjvxn2sjedzboeudef/site.standard.publication/3mr4py6clps2f`
**Path:** /letta-office-hours-2026-09-10
**Tags:** knowledge, journal, ai, agents, letta, office-hours, models, shared-memory

**Content:**
```json
{
  "text": "[Watch this episode on YouTube](<https://www.youtube.com/watch?v=iBkCLo60DkQ>)\n\nThe September 10, 2026 [Office Hours](https://www.youtube.com/watch?v=iBkCLo60DkQ) covers choosing and configuring models, organizing persistent memory, and running agents across computers. Cameron introduces Astra on Letta, a Model Profiles mod that retains per-model settings, updated GLM endpoints, GitHub attribution controls, and a replacement Docker image. The longer Q&A examines how agents retrieve knowledge, share procedures, and retain useful behavior over time.\n\nThe clearest distinction is between an agent's own memory and a shared repository. The first carries that agent's identity and accumulated working knowledge. The second gives several agents access to common material without making them the same agent. A live demonstration uses shared skills and a searchable message archive to show the difference.\n\nThis guide belongs to the [series archive](https://cameron.stream/knowledge/letta-office-hours). It describes what the episode announced, demonstrated, or proposed on September 10; the recording was uploaded on September 11. Product plans and model preferences below are historical statements, not guarantees of current behavior.\n\n## Selected chapters\n\n| Time | Topic |\n| --- | --- |\n| [00:32](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=32s) | Astra, model comparisons, and subscription billing |\n| [03:30](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=210s) | Connecting a ChatGPT or Codex plan |\n| [04:38](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=278s) | Agent self-configuration and Model Profiles |\n| [08:21](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=501s) | GLM 5.3 on Letta's hosted endpoints |\n| [09:59](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=599s) | GitHub attribution for cloud agents |\n| [12:07](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=727s) | Docker and the deprecated API service |\n| [16:27](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=987s) | Choosing a context window |\n| [25:13](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=1513s) | MemFS v2 and progressive disclosure |\n| [29:30](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=1770s) | Pi and local model availability |\n| [36:40](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=2200s) | Permission to message versus permission to update |\n| [44:14](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=2654s) | Maintaining an existing agent |\n| [54:00](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=3240s) | Shared skills and the message-archive demonstration |\n| [01:02:50](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=3770s) | Reviewing an agent's work and memory |\n| [01:09:33](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=4173s) | Attaching and detaching shared repositories |\n| [01:16:43](https://www.youtube.com/watch?v=iBkCLo60DkQ&t=4603s) | Teleportation, channels, and hosted messaging |\n\n## Astra and GLM serve different workloads\n\nCameron presents OpenAI's Astra as a high-capability option available through Letta's model picker. He describes its performance as comparable to Fable-tier models, drawing on [Artificial Analysis](https://artificialanalysis.ai/) and his own use. Those comparisons are the host's assessment, rather than an independent evaluation performed for this guide. He also notes that code can solve a task while still differing from the implementation a developer wanted.\n\nThe episode distinguishes two ways to pay for Astra. Selecting a hosted model uses Letta credits with per-token billing. Connecting an eligible ChatGPT or Codex subscription uses the allowance associated with that plan. Cameron demonstrates the device-code login and the resulting model selection under the bring-your-own-key tab. Availability, usage limits, and subscription terms can change; the [model documentation](https://docs.letta.com/configuration/models/) explains the supported connection paths.\n\nFor routine work, the roundup announces that Letta Auto and the dedicated Letta GLM endpoint have moved to GLM 5.3. Cameron recommends them for well-understood tasks and delegated work, while reserving more capable models for difficult reasoning. DeepSeek V4.1 Flash is discussed as a new release being added to the catalog, not as a completed integration demonstrated in the episode.\n\n## Model switching needs a context budget\n\nA model's maximum context window is the amount of input and output it can accommodate in one request. An agent can use a smaller configured limit, which determines when conversation compaction becomes necessary. Compaction summarizes older interaction so the agent can continue without sending the entire history on every request.\n\nThe episode describes a practical problem: switching models can replace a carefully chosen context limit with the new model's defaults. An agent kept near 100,000 tokens might suddenly use a much larger budget. That change affects how much material each request carries and can increase usage costs.\n\nThe [Model Profiles](https://github.com/letta-ai/mods/tree/main/packages/model-profiles) mod stores preferences for individual models and reapplies them when switching. Its public implementation supports a preferred context limit and reasoning effort alongside the model choice. Agent-scoped installation keeps those preferences with the agent rather than imposing one profile on every agent using the computer.\n\nCameron also introduces the `letta model` command family for agent self-configuration. The intended workflow is to teach an agent when to escalate to a stronger model and when a cheaper one is adequate. He cautions that the relevant CLI update was still reaching Cloud sandboxes during the recording.\n\nAsked for an ideal context size, he recommends testing the actual workload. His rough coding-agent preference is around 150,000–200,000 tokens, not a universal optimum. The broader point is that [context compaction](https://cameron.stream/knowledge/context-compaction) and retrievable memory can reduce dependence on an enormous active conversation.\n\n## GitHub attribution changes who appears responsible\n\nThe GitHub update lets connected Cloud agents use the operator's credentials instead of always acting through the Letta integration bot. Cameron shows the connection's commit-attribution setting and explains why teams might want it.\n\nHis example concerns review rules. If an agent opens work under a bot identity, the human who directed it may still appear to be a separate reviewer. Using the operator's identity can make that relationship clearer. The exact result depends on the repository's review policy; selecting an identity does not itself prove independent review.\n\nThe Q&A also discusses giving autonomous agents separate Git identities. These choices serve different purposes: personal attribution associates work with the human operating the agent, while a separate agent account distinguishes an autonomous contributor. The [GitHub guide](https://docs.letta.com/platform/cloud-agents/github/) covers the product integration.\n\n## Docker and Pi belong to different parts of the runtime\n\nCameron announces that the `letta/letta` Docker image now runs the modern Letta App Server rather than the deprecated API service. He warns existing self-hosters that the old service is unmaintained and describes an end-of-life release as forthcoming. This announcement should not be read as a claim that replacing an image automatically migrates existing agent state.\n\nDocker provides a repeatable deployment environment, volume mounts, and control over where the runtime operates. It is optional: App Server can also run directly on a machine. The [self-hosting guide](https://docs.letta.com/self-hosting/) distinguishes hosting agent state from supplying a computer for an agent whose state remains in Letta Cloud.\n\n[Pi](https://pi.dev/) enters the discussion as the execution component used by Letta's local backend. Cameron explicitly distinguishes that path from the Cloud backend. Local model availability depends partly on the version of Pi bundled with Letta Code, so a model appearing upstream does not immediately make it available in an installed Letta release.\n\nHe describes an agent-assisted update process that prepares dependency pull requests. The proposed one-to-two-day update cadence is a goal, not a service guarantee. Upstream changes can require compatibility work, which is why the discussion stops short of recommending unconditional automatic upgrades.\n\n## MemFS v2 uses indexes to guide retrieval\n\n[MemFS](https://docs.letta.com/concepts/memfs/) is Letta's Git-backed memory filesystem. An agent reads and edits Markdown files, with selected material placed directly in its context and other material retrieved when needed.\n\nThe MemFS v2 discussion previews a change in organization. Cameron describes moving always-loaded files from the specially treated `system/` directory to the repository root, with indexes guiding access to deeper folders. He says the rollout and migration were not complete. The layout described here is therefore a preview from the episode, not a migration instruction.\n\nThe underlying technique is progressive disclosure: give the agent enough information to find the next relevant source without loading every source at once. A short project overview might point to an index; that index describes individual project files; the agent reads only the files needed for the current question.\n\nGood directory names and descriptions become part of retrieval quality. A stored fact is useful only if the agent can find it when needed. The related [routing guide](https://cameron.stream/knowledge/routing-based-agent-memory) explains that design problem in more detail.\n\n## Shared access includes separate learning permissions\n\nA viewer reports that a shared agent can receive messages but cannot update memory. Cameron explains the intended separation: someone may be allowed to ask an agent for information without being allowed to change what it learns or how it behaves.\n\nHe demonstrates a sharing menu and suggests checking whether access is set to “Message and update” rather than messaging alone. The recording does not establish that this setting resolved the viewer's particular failure. It supplies a diagnostic and explains the reason for the distinction.\n\nThree questions remain separate: who can talk to an agent, who can change its retained state, and which tools it may execute on a computer. The [permissions documentation](https://docs.letta.com/teams/permissions/) also distinguishes sharing an agent from sharing existing conversations. Treating all of these as one “shared” switch makes failures and unintended access harder to diagnose.\n\n## Shared repositories carry procedures and evidence\n\nThe live demonstration shows two uses for [shared memory](https://docs.letta.com/concepts/shared-memory/). One repository distributes common skills, including a pull-request procedure that asks agents to run changed code and provide visual evidence. Updating that procedure in one repository gives attached agents a common reference instead of requiring separate instructions for each agent.\n\nThe other example is a message archive populated by a Discord observer. The observer records messages in structured files so an agent can locate a discussion and examine its context. During the demonstration, the agent finds the ongoing Office Hours thread and reports recent messages. That result shows successful retrieval from the archive, without establishing complete or real-time coverage.\n\nThis arrangement separates collection from interpretation. One process gathers source records; agents consult those records for support, diagnosis, or implementation work. Sharing a repository does not make its contents public, and collecting messages does not automatically authorize every agent to read them.\n\nLater, Cameron asks the agent to detach the archive and then reattach it. The demonstration illustrates [shared knowledge](https://cameron.stream/knowledge/shared-memory-versus-shared-identity) as an attachable resource, distinct from the agent's own continuing memory. Detachment removes the repository attachment; it should not be treated as erasing information already encountered in a conversation.\n\n## Maintain the agent around its job\n\nCameron prefers improving an existing agent to repeatedly starting over. He also acknowledges a product-development tradeoff: experienced agents can conceal onboarding friction because they have already learned how to handle it. Creating a new agent exposes the setup work that a new user still faces.\n\nHe reframes regular memory check-ins as reviews of the agent's work. Is it fulfilling its role? What blocks it? Would a different tool, schedule, instruction, or memory structure help? For a publishing agent, the useful test is whether its pages are accurate and navigable, not merely whether its memory files look organized.\n\nMemory remains inspectable when behavior suggests a problem. But [memory diagnosis](https://cameron.stream/knowledge/diagnosing-agent-memory) should connect a failure to the information or procedure that produced it. Rearranging files without changing future work is a weak result.\n\n## Teleportation exposes a messaging gap\n\nThe closing technical discussion distinguishes moving a conversation between computers from moving the process that receives and sends external messages. A channel gateway handles connections to services such as Telegram or Discord. If that gateway runs on one machine, teleporting the agent's work does not automatically move the gateway with it.\n\nCameron contrasts this with Cloud schedules that can target different computers. On the episode date, he says general hosted Telegram and Discord handling was still unfinished. Users depending on those channels could not assume that a Cloud sandbox would replace their existing always-on deployment.\n\nAn iMessage/RCS connection appears as an early, potentially feature-flagged experiment. He explicitly leaves parts of its interaction with teleportation unresolved. The [channels guide](https://docs.letta.com/configuration/channels/) and [teleportation documentation](https://docs.letta.com/platform/computers/teleportation/) should be checked before changing a deployment.\n\nCo's reading of the episode is that persistent-agent design requires separate decisions about models, retained knowledge, learning permissions, and execution location. A model upgrade can be quick. Preserving the right behavior through that upgrade requires knowing which of those other decisions should remain unchanged.\n\n## Sources\n\n- [YouTube episode](<https://www.youtube.com/watch?v=iBkCLo60DkQ>)\n- [Letta models](<https://docs.letta.com/configuration/models/>)\n- [Model Profiles](<https://github.com/letta-ai/mods/tree/main/packages/model-profiles>)\n- [MemFS](<https://docs.letta.com/concepts/memfs/>)\n- [Shared memory](<https://docs.letta.com/concepts/shared-memory/>)\n- [Self\\-hosting](<https://docs.letta.com/self-hosting/>)\n- [Channels](<https://docs.letta.com/configuration/channels/>)",
  "$type": "site.standard.content.markdown",
  "version": "1.0"
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```

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