The Best AI Meeting Assistants for Claude Cowork and Claude Code (2026)

The real comparison is no longer who writes the prettiest summary. It is where your meeting context lives, how Claude reaches it, and how much manual work remains after the call.

If you use Claude Cowork or Claude Code, you have probably had this thought in the middle of a meeting: Claude could act on all of this - if it could see it.

The requirements, tradeoffs, customer language, ownership decisions, and “let’s ship it this way” moments are exactly the context an agent needs. But most meeting tools were designed around a human opening a dashboard and reading a recap. Their unit of value is the note. An agent’s unit of value is usable context it can retrieve, combine with other evidence, and turn into work.

In 2026, nearly every serious meeting assistant can produce summaries. Several now offer MCP connectors. Botless capture is no longer rare. The useful question is more specific:

When the meeting ends, is the context already where your agent works - or does your agent still have to ask another product for permission to retrieve it?

That distinction is why Earmark is our top recommendation for Claude Code and desktop Cowork workflows, while Granola, Fathom, Fireflies, and Otter remain strong choices for different operating models.

The short answer

• Best for Claude Code and desktop Cowork: Earmark. Every completed meeting can be written automatically to a local Markdown file, while structured artifacts such as PRDs, tickets, decision logs, and engineering specs are generated during the conversation.

• Best notes-first MCP option: Granola. It combines unobtrusive capture and polished notes with a well-documented Claude connector and MCP server.

• Best for customer-facing call libraries: Fathom. It offers an official Claude connector, Claude Code MCP setup, transcript APIs, and strong post-call workflows.

• Best for capture and integration breadth: Fireflies. It supports bots, browser capture, desktop capture, mobile recording, in-person conversations, and a large integration ecosystem.

• Best established transcription workflow: Otter. It now supports botless desktop capture and advertises MCP access, so it is much more agent-compatible than older comparisons suggest.

The important caveat is that “best” changes with the Claude surface you use. If your work happens in Claude Code or Cowork on desktop, local files are unusually powerful. If you use Cowork mainly on the web or mobile, or need Claude to query a centrally permissioned team archive, an MCP-based cloud product may be more convenient.

Why Claude Code and Cowork change the evaluation

Claude Code works directly inside a project directory. It can inspect and edit files, use CLAUDE.md for persistent project instructions, and connect to external systems through MCP. Anthropic also supports the --add-dir flag when you want Claude Code to reach an additional directory outside the current project. Anthropic’s Claude Code documentation describes both the filesystem workflow and MCP, while its memory documentation explains how project files and additional directories enter context.

Cowork uses the same agentic architecture without requiring a terminal, but its current operating model is more nuanced than “Claude runs on your laptop.” Anthropic says Cowork sessions run in the cloud by default. On desktop, Cowork can read and write local files through the Claude Desktop app; if a task needs those files, the app must remain open and connected. Anthropic documents those boundaries explicitly.

MCP solves a different problem. It is an open standard that lets an AI client request data or invoke tools in an external system. That is ideal when meeting history lives in a vendor’s cloud, when permissions should be evaluated at request time, or when users need access from multiple devices. The MCP project describes it as a standard connection between AI applications and external data, tools, and workflows.

MCP is a bridge to context. A local file is possession of context. The better choice depends on whether you value centralized retrieval or durable portability.

How we evaluated the tools

We reviewed current public product pages, help centers, developer documentation, MCP instructions, and data-handling disclosures as of September 8, 2026. We evaluated each product on six questions:

1. Can it capture online and in-person conversations without requiring a visible meeting bot?

2. Can Claude access the transcript directly, or must it retrieve it from a cloud service?

3. Does the product create agent-ready work, or primarily notes and summaries?

4. Is the meeting record portable in a plain, durable format?

5. What happens to the data during capture, storage, deletion, and MCP access?

6. Does the transcript preserve speaker identity well enough for downstream decisions?

We did not run a controlled transcription-accuracy benchmark, so we do not rank vendors by accuracy. Vendor claims about accuracy, time saved, and market adoption are also excluded from the ranking.

Head-to-head comparison

1. Earmark: best for automatic local context and live deliverables

Earmark’s clearest advantage is not another summary template. It is the handoff.

When a meeting ends, the Earmark desktop app automatically writes a Markdown transcript to your machine. The default folder is ~/Documents/Earmark/Meeting Transcripts/, although you can choose another folder inside your home directory. Each file includes YAML frontmatter for the title, date, participants, timestamps, timezone, meeting key, and workspace, followed by the transcript itself. The behavior is enabled by default and applies to standard and temporary meetings. Earmark documents the complete file format and requirements here.

That makes a meeting behave like any other source file Claude can work with. Claude Code can search it, compare it with a spec, use it to update documentation, or trace a decision back to the conversation that produced it. Desktop Cowork can access the same folder without a separate meeting-vendor connector.

The second distinction is when the output is created. Earmark tasks can generate PRDs, Jira or Linear tickets, decision logs, executive updates, engineering specs, and follow-ups. When live sync is enabled, those artifacts regenerate during the meeting and finalize against the full transcript when recording stops. Earmark’s product documentation says live artifacts refresh every 45 seconds.

Earmark’s advantage is not that Claude can summarize the meeting later. It is that the handoff already exists before you remember to make one.

There are real limitations. Earmark’s transcript currently does not attach individual names to each utterance. Its documentation says conversational turns are preserved, but there are no “Speaker 1” or named speaker labels. That matters when exact attribution is more important than the decision itself. The limitation is documented in Earmark’s transcription guide.

The local-file feature also requires the Mac or Windows desktop app version 1.4.1 or later. And unlike the other leading tools in this comparison, Earmark does not currently document a public MCP server; its Claude advantage is filesystem-native, not connector-native.

Earmark is also not a local-only transcription product. Audio is captured on the device and streamed to AssemblyAI over an encrypted WebSocket for real-time transcription; Earmark says the audio is not stored after transcription. Standard meeting transcripts remain in the Earmark workspace until deleted. Its security documentation explains that processing and storage model.

For more sensitive meetings, temporary mode automatically soft-deletes the cloud meeting record when the meeting ends and permanently purges the transcript, artifacts, tasks, and participant data after a 30-day recovery period. Earmark documents that lifecycle here. Because automatic local Markdown also works for temporary meetings, the local file remains under your device’s controls unless you delete it yourself.

Best for: product managers, engineering leads, and founders whose next step happens in Claude Code or desktop Cowork—and who want the conversation to become both a durable local source and structured work before the call is over.

2. Granola: best notes-first Claude MCP experience

Granola is the strongest alternative if you prefer a polished notes experience and want Claude to retrieve meeting context from the cloud through MCP.

Its capture model is genuinely unobtrusive: Granola records from the device instead of joining as a participant, and its mobile apps support in-person meetings. Its official MCP connector can search meetings, list folders, retrieve notes, extract decisions, and—on paid plans—retrieve raw transcripts. Claude Code setup is a single MCP command followed by browser authentication.

Granola’s current MCP documentation also makes the boundaries clear: the free plan is limited to personal notes from the last 30 days, transcript retrieval is paid, queries follow the active Granola workspace, and rate limits currently average about 100 requests per minute.

Granola supports Markdown, but the workflow is different from Earmark’s. Users can copy an AI-generated note to the clipboard in Markdown or separately copy the full transcript. Historical exports arrive as CSV files containing titles, summaries, transcripts, and metadata. Those are useful portability features, but they are manual export paths rather than an automatic file written after every meeting. See Granola’s documentation for sharing and Markdown copy and historical CSV export.

Granola is ahead of Earmark on speaker labeling in some environments. Optional speaker tags can identify participants in Google Meet and Zoom, though they require platform-specific setup and cannot always distinguish several people sharing one room device. Granola documents the setup and limitations here.

Its retention controls are also more granular than a simple keep/delete toggle: individual users can configure transcript auto-deletion from one day to one year or leave it off, while enterprise policies can be enforced workspace-wide. Notes remain after the underlying transcript is deleted, although regeneration and chat quality are reduced. Granola’s retention documentation explains the tradeoff.

Granola also says non-enterprise accounts may contribute anonymized data to service improvement by default but can opt out; Enterprise workspaces are opted out. That policy is documented here.

Best for: people who want excellent human-readable notes plus a mature, authenticated MCP path into Claude, especially when centralized cloud access matters more than an automatic local archive.

3. Fathom: best for customer-facing teams with a cloud call library

Fathom is a strong fit when customer conversations, sales workflows, and a shared recording library are the center of gravity.

Fathom now advertises both bot and no-bot capture, so describing it as bot-only is no longer accurate. It also offers an official connector for Claude on the web and desktop. Claude Code users can add Fathom’s remote MCP endpoint directly from the terminal. Fathom’s Claude documentation provides the exact setup.

For teams building their own workflows, Fathom also exposes meeting transcripts through its API and supports webhooks when new meeting content is ready. Transcript responses include speaker display names, matched calendar email addresses, text, and timestamps. The transcript endpoint is documented here.

The tradeoff is architectural, not qualitative. Claude retrieves Fathom context from a cloud service. That is useful for cross-device access and centrally managed meeting libraries, but it is not the same as having every conversation arrive automatically as a plain file beside the work. We found no documented automatic per-meeting Markdown archive in the public materials reviewed for this comparison.

Best for: sales, customer success, and other customer-facing teams that already value Fathom’s shared call library and want to make it queryable from Claude without moving the underlying source of truth.

4. Fireflies: best for capture and integration breadth

Fireflies has the widest capture surface in this group. Its public product documentation lists an auto-joining bot, a Google Meet Chrome extension, a desktop app, mobile apps for in-person conversations, audio and video uploads, dialer integrations, and an API.

It also layers meeting search, speaker recognition, live assistance, and a large catalog of AI skills and business integrations on top of that capture. Fireflies describes those capture modes and workflow features here, with additional detail on in-person mobile capture.

Its MCP server is officially listed in Claude’s connector directory and supports OAuth. The documented MCP tools are focused and read-oriented: retrieve the user profile, retrieve a transcript by ID, and search past transcripts with filters. That makes Fireflies useful for questions across a meeting corpus, but the richer collaboration and analytics experience still lives in Fireflies itself. The Fireflies MCP guide documents both the connector and its available tools.

Fireflies also makes an important security caveat unusually explicit: its Zero-Day Retention commitments for core subprocessors do not apply once data is sent through an external connector such as Claude. At that point, the external platform’s own processing and retention terms govern the data. That is not a reason to avoid MCP, but it is a reason not to describe any MCP workflow as if data never crosses systems.

Best for: organizations that want maximum capture flexibility, broad CRM and workflow integrations, and cross-meeting analytics—with Claude as one interface into a larger Fireflies system.

5. Otter: best established live-transcription workflow

Otter should no longer be described as the least agent-native option in this group.

Its current product supports bot-free recording from Mac and Windows, browser capture, mobile capture, live transcription, speaker recognition, summaries, action items, shared Channels, and connected workflows. Otter also now advertises an MCP server that lets Claude and other AI clients access meeting knowledge. Those capabilities are listed on Otter’s current product page.

Otter remains cloud-first. Its value is a searchable, collaborative conversation system with live visibility and a large set of integrations—not an automatic local Markdown pipeline. If your primary goal is to keep a team’s meeting knowledge centralized and query it from Claude, that may be exactly the right architecture. If your goal is for every meeting to become a durable, agent-neutral local file without an export step, it is a different fit.

Best for: teams that already rely on Otter for live transcription and collaborative meeting knowledge and want to extend that archive into Claude through MCP.

Local files versus MCP: which is actually better?

Neither architecture wins every workflow.

Local Markdown is strongest when you want durable ownership and composability. Files can be searched with ordinary tools, referenced by Claude Code, indexed by Obsidian, backed up, versioned, moved between agents, or processed by a script without calling the meeting vendor again. Once the file exists, reading it does not depend on the vendor’s API availability, OAuth state, plan entitlements, or rate limits.

MCP is strongest when you want live access to a centrally managed source. It can preserve vendor-side permissions, expose shared meeting history without copying it onto every laptop, and work from Claude surfaces that cannot directly reach your filesystem. For Cowork on the web or mobile, MCP is generally more natural. For desktop Cowork, both models are viable.

The best long-term architecture may be hybrid: a shared, permission-aware cloud corpus for organization-wide discovery and a plain local record for individual ownership, agent work, and portability.

A connector makes your meetings available to an agent. A plain file makes them available to any agent—including the one you have not chosen yet.

A practical Claude Code setup with Earmark

Earmark writes transcripts outside most code repositories by default. Claude Code can reach that folder by starting a session with an additional directory:

claude --add-dir "$HOME/Documents/Earmark/Meeting Transcripts"

claude --add-dir "$HOME/Documents/Earmark/Meeting Transcripts"

claude --add-dir "$HOME/Documents/Earmark/Meeting Transcripts"

Then ask Claude to retrieve only the relevant meeting context:

Find the most recent architecture review in my Earmark transcripts.
Extract the agreed API behavior, unresolved risks, and acceptance criteria.
Compare those decisions with the current implementation and propose a plan

Find the most recent architecture review in my Earmark transcripts.
Extract the agreed API behavior, unresolved risks, and acceptance criteria.
Compare those decisions with the current implementation and propose a plan

Find the most recent architecture review in my Earmark transcripts.
Extract the agreed API behavior, unresolved risks, and acceptance criteria.
Compare those decisions with the current implementation and propose a plan

You can also add a short instruction to your project’s CLAUDE.md telling Claude where meeting transcripts are available and when it should consult them. Avoid importing an entire growing transcript archive into every session; point Claude to the directory and let it search only when the task requires it.

For Cowork, grant the desktop app access to the transcript folder and keep the app open when a task needs local files. If you use Cowork from the web or mobile and want remote meeting access, choose one of the MCP-based tools or move selected files into a Cowork project.

The bottom line

The AI meeting-assistant category was built around the recap. Claude Code and Cowork move the value downstream: from remembering what happened to acting on what happened.

Botless capture still matters, but it is becoming table stakes. MCP access matters, but it is increasingly available across vendors. The more durable differentiation is whether a meeting becomes portable context, whether that context reaches your agent without another manual loop, and whether useful work starts before the meeting ends.

For a notes-first cloud workflow, Granola is excellent. For customer call libraries, Fathom is strong. For maximum integration breadth, Fireflies stands out. For established live transcription and shared conversation knowledge, Otter has become a credible Claude option.

For Claude Code and desktop Cowork users who want every conversation to arrive automatically as local Markdown—and want PRDs, tickets, decisions, and specs taking shape while they are still talking—Earmark is the strongest fit.

The meeting assistant for the agent era should not make you export your own conversation before your agent can use it.

Download Earmark

Disclosure and methodology: Earmark publishes this comparison. We reviewed current public documentation from Anthropic and every vendor listed above on September 8, 2026. We did not accept vendor claims about transcription accuracy or productivity gains as independently verified evidence. Product capabilities and plan limits change; verify security, retention, and pricing requirements directly with each vendor before purchase.