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arize-phoenix @arize-phoenix.bsky.social · 9h

Benchmarking AI agents & tool use with Harbor + Arize Phoenix. Join us Oct 8 at 11am PT / 2pm ET to learn how to compare agents or models over the same task set, separate behavioral scores from infrastructure failures, and inspect ATIF traces. luma.com/arizeai-ben...

luma.com

Benchmarking AI agents & tool use with Harbor and Arize Phoenix · Luma

When you change an agent's model, prompt, tools, or environment, how do you know whether the new version performs better? Production traces show what happens…

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arize-phoenix @arize-phoenix.bsky.social · 23/09/2026

Not all tokens cost the same. Cache writes have an upfront cost, but they save you money on subsequent turns. Premature or inefficient compaction can cause cache misses that add both latency and cost. In Phoenix, you can search LLM calls across a turn and see how context evolves.

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arize-phoenix @arize-phoenix.bsky.social · 22/09/2026

Webinar coming up: benchmarking AI agents & tool use with Harbor and Arize Phoenix. Learn how to compare agents and models on the same tasks, diagnose regressions, and inspect scores, errors, and traces. Oct 8 · 11am PT / 2pm ET Save your spot: luma.com/arizeai-ben...

luma.com

Benchmarking AI agents & tool use with Harbor and Arize Phoenix · Luma

When you change an agent's model, prompt, tools, or environment, how do you know whether the new version performs better? Production traces show what happens…

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arize-phoenix @arize-phoenix.bsky.social · 22/09/2026

When conducting experiments, it might be important to keep track of the data being fed into the agent using a Git like version control system - especially if you are collaborating with others. Phoenix now has bulk editing for datasets so that you can track the dataset patches.

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arize-phoenix @arize-phoenix.bsky.social · 18/09/2026

TypeSafe came out of stealth this week with Jev, the first System One Model: a frontier model that never generates prose. Unstructured state in, typed probabilistic decisions out. 70 to 500 ms, 0% type errors, calibrated probabilities on every answer, $0.042/MTok in and output free.

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arize-phoenix @arize-phoenix.bsky.social · 17/09/2026

“Did the agent actually do what the user asked?” 👀 Phoenix now has docs for built-in evals that help answer that from real traces: ✅ completeness 🔎 retrieval relevance 🧭 hallucination 🛡️ toxicity 🔒 PII detection Start here: arize.com/docs/phoeni...

arize.com

SDK Eval Metrics - Phoenix

Ready-to-use evaluation metrics for measuring LLM application quality

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arize-phoenix @arize-phoenix.bsky.social · 15/09/2026

Phoenix now supports Google's Agent Development Kit for Java.

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arize-phoenix @arize-phoenix.bsky.social · 10/09/2026

OpenInference now supports tracing Agno teams. Teams are a way to coordinate groups of agents to solve complex tasks. Thank you to our OSS contributors that helped add tracing and session support!

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arize-phoenix @arize-phoenix.bsky.social · 10/09/2026

Computer-use is a new frontier that many agents are trying to tackle now. With the release of models like Astra, the capabilities of agents is ever expanding. Thanks to an OSS community member PR, you now can trace the computer use of your OpenAI agents! pypi.org/project/ope...

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arize-phoenix @arize-phoenix.bsky.social · 08/09/2026

Phoenix now supports Meta's Muse Spark 1.3. Muse Spark 1.3 landed last week in the same 48 hours as Astra and Fable, with no blog post, and it deserves more attention than it got.

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arize-phoenix @arize-phoenix.bsky.social · 04/09/2026

Phoenix 20.5 is out, and the part I keep showing people is small but a huge time saver: PXI can now file the GitHub issue for the bug it just found.

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arize-phoenix @arize-phoenix.bsky.social · 03/09/2026

We think MiniMax is underrated for agentic workloads, and the reason is architectural. The M-series is sparse MoE: M2 routes to ~10B of 230B parameters per forward pass, and M3 to ~23B of 428B. That is why M2 fits on 4xH100s at FP8 and why list pricing sits around $0.30/$1.20 per million tokens.

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arize-phoenix @arize-phoenix.bsky.social · 03/09/2026

Arize Phoenix now supports Z.ai and GLM-5.3. GLM-5.3 uses the same base as GLM-5.2. Every gain came from post-training. Z.ai scaled up long-horizon task environments (some representing days of real engineering work) and trained on them with their open-source slime RL framework.

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arize-phoenix @arize-phoenix.bsky.social · 01/09/2026

Claude Fable 5.1 and Mythos 5.1 are the same underlying model at two safeguard levels. Fable 5.1 is generally available everywhere (API, AWS, GCP, Azure) as claude-fable-5-1, while Mythos 5.1 is restricted to vetted US organizations via cyber/life-sciences verification programs.

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arize-phoenix @arize-phoenix.bsky.social · 01/09/2026

The prime directive of the Phoenix project is to go from bad agent behavior to fix as quickly as possible. Introducing GitHub integration. Ask PXI to find problematic traces and automatically create and de-duplicate issues so your engineers and agents can quickly find a fix.

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arize-phoenix @arize-phoenix.bsky.social · 01/09/2026

Fixed APIs answer fixed questions. Agents ask questions nobody anticipated.

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arize-phoenix @arize-phoenix.bsky.social · 29/08/2026

This week we make moving from questions to answers faster. • Smarter PXI workflows complete multi-step UI tasks now run via a JavaScript sandbox so the assistant can steer the UI for you via code. • Retrieval Relevance: Evaluate retrievals from vector search, tools, MCP, web search, or SQL.

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arize-phoenix @arize-phoenix.bsky.social · 26/08/2026

The model you requested might not be the model that responds.

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arize-phoenix @arize-phoenix.bsky.social · 20/08/2026

Did you know that your browser might have an LLM built right into it? With the Prompt API, you can take advantage of local models like developer.chrome.com/docs/ai/pro...). Cool right? Well now you also can get a nice streaming UI to chat with these models, built right into Arize Phoenix.

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arize-phoenix @arize-phoenix.bsky.social · 07/08/2026

4 instrumentors shipped today thanks to our cracked OSS community. 🤖 AG2: multi-agent conversations, group chats, and tool calls ⚡ Together AI: fast inference across OSS models 🧠 Cohere: enterprise chat, search, and RAG 🦙 Ollama: local models galore arize.com/docs/phoeni...

arize.com

Integrations - Phoenix

Connect Phoenix with your favorite AI frameworks, LLM providers, and tools

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arize-phoenix @arize-phoenix.bsky.social · 04/08/2026

AI Queries and Browser AI Phoenix filters now understand English. Type "responses with apologies" and get a real filter expression back: ▎ 'sorry' in output.value or 'apolog' in output.value

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arize-phoenix @arize-phoenix.bsky.social · 03/08/2026

The experiment said the fix works, but experiments aren't production. Real spans, new prompt, zero errors. Will PXI get it right? youtu.be/biMD5LHdXQk

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arize-phoenix @arize-phoenix.bsky.social · 01/08/2026

Balancing cost, speed, and correctness can be a tricky balance. That's why we need good visuals to figure out the right sweet spot!

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arize-phoenix @arize-phoenix.bsky.social · 31/07/2026

Customizable visualizations of your agent traces are here. Track cache hits, online eval degradations, tool call errors, all in real-time so you can quickly identify problems in production. What does your agent operations center look like?

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arize-phoenix @arize-phoenix.bsky.social · 29/07/2026

New Sandbox in Phoenix - Monty (Python)!

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arize-phoenix @arize-phoenix.bsky.social · 28/07/2026

In an agent session, a cache miss re-bills your entire history at full input price. That's why a "continue" after a coffee break can cost more than the model's actual answer. Cache read vs. write isn't a footnote in your bill. Earendril's post is a must read. earendil.com/posts/promp...

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arize-phoenix @arize-phoenix.bsky.social · 27/07/2026

The agent kept writing SQL querying a "products" table but the table is called "catalog." PXI analyzes the problem and proposes a fix all on its own: youtu.be/CN8VwB_4V_Y

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arize-phoenix @arize-phoenix.bsky.social · 25/07/2026

Search -> Select -> Download -> Coding Agent. Feed critical traces back to Agents. No fancy words for this one: this is plain old-fashioned debugging. Just now with Agents.

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arize-phoenix @arize-phoenix.bsky.social · 21/07/2026

Your agent's SQL tool calls start failing with "no such table: products." Before you attempt a fix, take a snapshot of the failures with a dataset. Just ask PXI to do it for you: youtu.be/gb_78l788Ls

youtube.com

Making a Dataset from Failing Traces with Phoenix and PXI

An agent's SQL tool calls start erroring out with "no such table: p...

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arize-phoenix @arize-phoenix.bsky.social · 19/07/2026

phoenix is now is an oauth2 authorization server! `px auth login` opens your browser, you get a short-lived user-scoped token. no long-lived api keys. and admins can audit / revoke every cli and mcp grant from one screen. remote mcp server ships in beta too - more on this soon.

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arize-phoenix @arize-phoenix.bsky.social · 13/07/2026

Phoenix's agent PXI can propose annotation categories, annotate traces en mass, and even suggest fixes based on patterns of failures. Learn how to do it: youtu.be/iF25CqJv4tA

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arize-phoenix @arize-phoenix.bsky.social · 13/07/2026

Our favorite tools are the ones that have maximum customizability. Last week we added customizable charts, command K, and recent searches. this week we've added custom column ordering. Built to help you have the tables and dashboards you need to monitor agents day in and day out.

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arize-phoenix @arize-phoenix.bsky.social · 12/07/2026

What does your agent operation center look like?

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arize-phoenix @arize-phoenix.bsky.social · 11/07/2026

Experiment Baselining and Charts When trying to determine if a new model is up to the task, you need to factor in many dimensions. performance - measured by evals latency - is the model fast enough to give you the right UX tokens - how chatty is the model to achieve the result

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arize-phoenix @arize-phoenix.bsky.social · 11/07/2026

Do you love localfirst development? @nearestnabors.com will be speaking on how to get frontier LLM results on device using Phoenix and prompt engineering techniques on Sunday, July 12, 14:15. See you soon, Berlin! www.localfirstconf.com/

localfirstconf.com

Local-First Conf 2026

Join us for the third edition of Local-First Conf. Connect with a rapidly-growing community in an intimate setting. Berlin 12-14th July 2026.

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arize-phoenix @arize-phoenix.bsky.social · 10/07/2026

Phoenix has an agent built into it now! PXI can help you find the crucial traces you should actually be reading. Short video on how to do use PXI in your daily flow: youtu.be/5lUgdRFf4ZI

youtube.com

Find the traces that matter with Phoenix and PXI

Hundreds of traces and they all look fine? 🤔 Here's how to find the...

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arize-phoenix @arize-phoenix.bsky.social · 09/07/2026

⌘K is in Phoenix. jump to any project, dataset, prompt, or experiment. No mouse required.

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arize-phoenix @arize-phoenix.bsky.social · 07/07/2026

Agent traces and trajectories are growing increasingly longer and more complex. We've seen some traces 1000s of spans deep. That's why we've added trace search. Search across a trace and the UI will now show you the call stack to the spans you are looking for across workflows and sub-agent calls.

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arize-phoenix @arize-phoenix.bsky.social · 07/07/2026

The OSS team has a mantra: 3 clicks to glory. We're not there yet but we're getting closer!

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arize-phoenix @arize-phoenix.bsky.social · 04/07/2026

Fable 5 might be amazing, but it can't tend the grill 🍔 Happy Fourth of July from our team to yours. We hope you are getting to spend the day with friends, family, and loved ones.

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arize-phoenix @arize-phoenix.bsky.social · 01/07/2026

PXI now supports sub-agent streaming so you can inspect the execution of the sub-agents you kick off. We've started moving much the skills and tools available in the main agent into the sub-agents to empower interesting delegation patterns.

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arize-phoenix @arize-phoenix.bsky.social · 30/06/2026

PXI (Phoenix Intelligence) now runs in your terminal! You can now use PXI without leaving your terminal. It's the same agent that powers the in-browser experience, now available as an interactive chat in your shell. npm install -g @arizeai/phoenix-cli@latest > pxi

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arize-phoenix @arize-phoenix.bsky.social · 24/06/2026

📦🏷️🔖🔑 This week in Phoenix: - server-side bash for PXI subagents, - label management on list pages, - trace-level annotations everywhere - and OAuth2 role-override preservation. arize.com/docs/phoeni...

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arize-phoenix @arize-phoenix.bsky.social · 19/06/2026

Meet PXI (pronounced "pixie") 🎉 the AI engineering agent we built into Phoenix. Hand it the investigation instead of scrolling through traces by hand, and it works through your telemetry the way a coding agent works through code. The full story of how we built it 👉 arize.com/blog/meet-pxi/

arize.com

Meet PXI: the AI engineering agent inside Phoenix

PXI is the open-source AI engineering agent built into Phoenix. Hand it a failing trace, an evaluator, or a prompt, and it investigates your telemetry for you.

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arize-phoenix @arize-phoenix.bsky.social · 17/06/2026

📊 Phoenix 17.7.0 makes your token usage legible. New token detail charts break prompt + completion tokens into their parts, over time: • Prompt → input, cache read, cache write, audio • Completion → output, reasoning, audio

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arize-phoenix @arize-phoenix.bsky.social · 08/06/2026

Phoenix just hit 10,000 GitHub stars! Three years ago, Phoenix didn't exist. Arize was a closed-source company. A small team was asked to change that. Catch the full interview with the team who made it happen and where AI observability is going next: arize.com/phoenix-10k

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arize-phoenix @arize-phoenix.bsky.social · 26/05/2026

"Don't trust. Evaluate." @nearestnabors.com set out to replace Sonnet with Gemma. The evals showed a quantifiably better option. Full walkthrough: capability evals + prompt engineering to ship a local 3B that matches Sonnet, 2x faster, $0/call. Built with Phoenix. arize.com/blog/how-to...

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arize-phoenix @arize-phoenix.bsky.social · 21/05/2026

Phoenix now lets you compose evaluation strategies in code. Most eval tooling hands you a fixed menu of judge templates. Real evaluation is rarely that tidy.

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arize-phoenix @arize-phoenix.bsky.social · 19/05/2026

The Arize DevRel team wants to connect with Phoenix users like you. What you're tracing, what's working, what's rough? Schedule time with the team here: cal.com/team/arize-...

cal.com

Phoenix and AX Free Weekly User Interviews | Arize Dev Rel | Cal.com

Phoenix and AX Free Weekly User Interviews

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arize-phoenix @arize-phoenix.bsky.social · 15/05/2026

Something we’ve been playing with and liking a lot: Give every coding agent its own observability stack.

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