Praxos creates a team chat that preserves the reasoning behind every shipped feature
Small teams using AI tools often struggle with context switching—founders discuss customer needs while engineers build features, and by the time someone else picks up the work, the original reasoning is lost across Slack threads, GitHub PRs, and coding sessions. The key details vanish when team members are away or move on, leaving others to reconstruct decisions from scattered notes.
Praxos addresses this by adding a searchable memory layer to team communication. Unlike traditional chat apps, it automatically connects external sources—GitHub pull requests, email threads, recorded calls, and even full coding agent session logs—to the relevant discussions. When an engineer or AI agent revisits a task, the full history, including why changes were made and which alternatives were considered, remains accessible.
The system captures:
- Customer interactions, such as call transcripts, email exchanges, and meeting notes tied to specific features
- Full logs of agent conversations (e.g., Claude or Cursor sessions), not just the final output
- Code-related context, like PR discussions, commit messages, and review comments linked to original requests
- Decision rationales, including who influenced choices and what trade-offs were evaluated
A standout use case is onboarding. New engineers instantly access the reasoning behind legacy systems—not just the code, but the context behind design decisions and past agent interactions. For example, if a founder later asks why a webhook system was built a certain way, Praxos retrieves the original customer discussion, internal debates, and agent-generated insights in a single view, eliminating the need to reconstruct the history manually.
The agent integration is particularly powerful. Coding agents don’t just analyze the current file; they inherit the same organizational memory as the team. When prompted to implement a feature like ACME’s webhook retry logic, the agent recalls past customer requirements, such as service-level agreements discussed months earlier, directly from the system’s records.
Praxos is available as a desktop app, with native calls and mobile support in development. All communications—chat, threads, and external references—live in one searchable timeline. Teams of 2–10 people, where half focus on customer work and the other half build with AI, qualify for six months free. Interested teams can contact [email protected].
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Curious about the WhatsApp integration. Did you run into any blocking issues with their API? I found that when an engineer or an LLM agent picks up a task, the full history travels with it, so any API changes would be instantly visible in the context.
Losing three days to a forgotten database swap is a nightmare—especially when the reasoning behind it was buried in a Slack thread or a quick Cursor session. To avoid this, capture every external touchpoint (like PR discussions, commit messages, or even your coding agent logs) in a single queryable memory layer, so the full context travels with the task. How do you ensure these decisions don’t vanish when someone’s out of office?
Worried about those merge conflicts in the context docs. Is there a way to resolve them manually?
The memory layer in Praxos ingests every external touchpoint — like GitHub PRs, email threads, and recorded calls — linking and making them queryable. This way, when an engineer picks up a task, the full history travels with it, including why the auth flow changed or what ACME Corp asked for, even if someone goes on vacation. ## How Praxos Solves the Context-Switching Tax Praxos attacks this directly. It's a team chat app (channels, threads, DMs — the basics work) but the differentiator is the memory layer. Every external touchpoint — GitHub PRs, email threads, recorded calls, even your coding agent conversations — gets ingested, linked, and made queryable. When an engineer or an LLM agent picks up a task, the full history travels with it. What the memory layer actually captures - Customer conversations — call transcripts, email threads, meeting notes tied to the relevant feature work - Agent interactions — full Claude/Codex/Cursor session logs, not just the final diff - Code context — PR discussions, commit messages, review comments linked to the original request - Decision trails — why a technical choice was made, who weighed in, what alternatives were considered ## Does the Memory Layer Improve Onboarding? Where it clicks in practice A new engineer joins and inherits instant context on a legacy service — not just the code, but the why behind every module, plus the agent conversations that shaped it. Three weeks later when the founder asks "wait, why did we handle webhooks that way?", Praxos surfaces the relevant Slack thread, the email with ACME's feedback, and the actual Cursor session where the change was implemented and discussed — all in one place. No more hunting through three months of messages. ## Does Praxos actually work? The Praxos team uses it to run the whole company — product, sales, engineering — and the memory layer is the difference between a chaotic Slack free-for-all and a coherent, searchable record of every decision and interaction. The context-switching tax on small AI-assisted teams is brutal. One founder talks to customers, another ships code with Claude, and two weeks later nobody remembers why the auth flow changed or what ACME Corp actually asked for. The reasoning lives in a customer call, three Slack threads, and a handful of Cursor sessions — scattered, unsearchable, and lost the moment someone goes on vacation.
Curious if this locks sections or just flags overlaps like a Git conflict—but imagine if it also automatically indexed every Slack thread, customer call transcript, or agent session (like Claude’s chat logs) into a single searchable knowledge base so you could pull up the full context of a change in seconds. That way, when someone asks why the auth flow was tweaked, you’d just query the system instead of digging through fragmented notes.