**This post is for the technical builders** For those who dont use tooling -- its the actual key for enterprise use cases and its the biggest differentiator, even internally, as you chase big projects / clients. We have set up abilities using tools, middleware and external database that you wouldn't even believe and they are very high ticket (think north of $50k), I suggest its something to take a look at if you haven't before So, as we build alongside you for fulfillment we noticed some limitations that I think needed to be addressed. So, here is the basis of those additional functionalities: post / put / patch / get / delete, execution type (proxy, direct, workflow execution), headers, timeout MS, flattened tool menu to a page not a popup Proxied tool calls is your traditional tool call for middleware as you see it today. Output is wrapped in an args object with a meta_data object. These are best suited for middleware usage (make, n8n, buildship, etc) because not only do you have your agent's output but you have location id, contact id, etc. Direct tool calls are exactly that - we dont wrap it in anything - we just send the raw request exactly as you have configured it. This is for when you want to attach to something like an MLS, or niche API directly. Cofigure headers (variable friendly), configure body parameters (and soon query parameters), and the http type. We'll send the request on your behalf just as you would anywhere and return the direct output. Workflow execution is for the internal workflow engine we are adding so we can supply you with a middleware if you dont already have one, or looking for something more native to our functinoality. more to come, happy thursday

Can Assistable reply to emails now with the customer replied trigger and active tag? Anyone having success here?

I’m running into an issue where the assistant pulls scheduling info from a contact, but after a database reactivation it starts mixing things up and returning incorrect times. Is there a way to automatically clear a specific contact’s conversation history? Keeping all the old context is actually hurting the workflow, and I’m already seeing it negatively affect my accounts. Ideally, I’d like to trigger this with something like a webhook in a workflow or a workflow action that clears the conversation history for that contact. Thanks @Jorden Williams, @Mike Copeland

This is me (clone), calling me

So, when using your own openai key (and even us as a business), you notice with agent stack (tools, prompt, convo history, RAG, etc) it starts to stack up quick - especially if you have a really involved process. We implemented a token optimization model before our chat completions to ensure you get the cost savings and ill share some data at the end :) So, we are now truncating and summarizing conversation history - we noticed there are large chat completeions coming through with 300-400+ message histories. This becomes expensive overtime if its a lead you've been working or following up with for a while engaging in conversation, so we are reducing that number and summarizing the history to ensure the intelligence stays the same but the token consumption goes way down (98% decrease on larger runs) Another thing we are doing is truncating large tool call outputs within the window that are not relevant to the current task - meaning, if there are tool calls with large outputs (like get_availability), if they are not relevant to the current task at hand, we truncate the response to show the agent that the action happened but the context is shorter. This saw a huge reduction in token consuption as well (96% decrease on larger runs) Here is the before and after, this is the same exact conversation history, assistant ID, tools, custom fields, knowledge base, etc - but see the speed and cost difference and the output was the exact same message: Differences: - 35 seconds faster - 95.95% cheaper ---- Before: "error_type": null, "usage_cost": { "notes": null, "tokens": { "output": 211, "input_total": 175948, "input_cached": 0, "input_noncached": 175948 }, "total_cost": 0.353584, "model_normalized": "gpt-4o", "models_encountered": [ "gpt-4o" ], "price_used_per_million": { "input": 2.5, "cached_input": 1.25, "output": 10 }, "error_message": null, "run_time_seconds": 32.692, "returned_an_error": false, After: "run_time_seconds": 2.618, "returned_an_error": false,