FAQ · in plain English
The questions, answered plainly.
What Taksham is, who it's for, and how it works — no jargon you have to already know. If a term is unfamiliar, there's a plain glossary at the bottom. New terms are introduced where they first appear.
Start here.
What is Taksham?
Taksham is the control room for teams building AI agents into their own products. When your software uses an AI model to decide and act — answer a customer, issue a refund, file a ticket, browse a site — Taksham records what it did, checks whether it was right, controls what it's allowed to do, and shows you the bill. One place to watch your agents, debug them, test them, secure them, and keep their cost in line.
The core idea: every action an agent takes is recorded once, on a single timeline we call the spine. Every feature reads from that one record — so nothing about a run is a guess.
Who is it for — and is this just a ChatGPT or Claude subscription?
No — and this is the most common mix-up, so it's worth being blunt. A ChatGPT or Claude subscription is for a human to chat with an AI. Taksham is for the engineers who put AI inside software other people use.
The moment an AI is making decisions in your product — not a person typing into a chat box — a different set of problems shows up: it behaves differently every time so you can't test it normally, it quietly runs up a bill, it can be tricked, and when it breaks at 2am you have no idea why. Taksham is the tooling for that job. It's for builders, not chatters.
The short version: ChatGPT and Claude are tools for your people. You've also got agents in production now — answering customers, touching data, spending money — and nobody is actually managing them. Taksham is HR for your agents: it gives each one an identity, reviews its work, catches it when it goes wrong and makes sure that exact failure can't ship again, and keeps its costs in check. You run your humans; we run your agents.
What problems does it actually solve?
Four that ordinary software tools don't handle well:
- It's non-deterministic. The same question can get different answers, so you can't test it with the usual "assert this equals that." You need evals that grade behavior statistically.
- Cost is opaque. Every model call costs money, agents make lots of calls, and the bill arrives later with no breakdown. You need cost attached to each run.
- It can be attacked. Hidden instructions in an agent's input can hijack it (a "prompt injection"). You need a firewall and red-teaming.
- It's a black box when it breaks. Without a faithful record of what the agent did, a production failure is unreproducible. You need a replayable trace.
What can it do, in one tour?
The features are grouped into five bands. In plain words:
- Core — See every run: Traces, Observe, and a Gateway your model calls route through.
- Reliability — Make it not break: Evals, the incident-to-test Loop, Verify, a Copilot, and Authoring.
- Govern — Stay in control: Govern, Security (red-teaming), the Firewall, Discovery, and self-observability.
- Runtime — Run it well: durable Runtime, cost Optimize, Memory, and offline-first Data.
- Leapfrog — Newer surfaces: Browser, Train, Voice, and Swarm (multi-agent maps).
For the full breakdown, see the product page.
The spine, the loop, and why one beats five.
What is the “spine”?
The spine is a single, append-only record of everything your agents do. Think of a hospital chart: every reading, medication, and note for a patient lands in one chart, in order, and nobody erases an entry — you only add to it. The spine is that chart for an agent. Every model call, tool result, safety check, decision, and dollar is written once, and never changed.
Because there's one honest record, every feature reads the same truth. "Why did it deny that refund?" has an answer in the chart — not a theory stitched from five different logs.
What is the “loop”?
The loop is how a failure becomes a permanent fix. Picture a crash investigation: after an incident, investigators pull the flight recorder, reconstruct exactly what happened, and turn the finding into a rule so that exact failure can't recur.
Taksham does the same for agents. A production failure is replayed faithfully from the spine, automatically turned into a failing test drawn straight from that real run, and wired into your release pipeline as a gate. The next change that would re-introduce the bug won't ship. Reliability becomes a ratchet that only tightens.
How is this different from buying five separate tools?
You could buy tracing from one vendor, evals from another, a gateway from a third, a guardrail from a fourth, a red-team script from a fifth. Each keeps its own data, its own IDs, its own version of events. When something breaks, you export from all five and try to line them up by hand — and still guess.
Taksham is one spine, so the data already connects. Watch → test → fix → govern is a single loop, not five disconnected products that were never meant to agree. That's the whole point: the failure is explainable because every piece of evidence is already in the same place.
If it's built on open standards, what's actually proprietary?
Fair question — and the honest answer names what isn't special. The storage engine and the trace format are mature open pieces we build on, not things we reinvent. Plenty of tools store traces.
What's ours is the integration. Taksham doesn't just store traces — it lands your model calls, safety checks, agent-identity decisions, eval scores, and costs as one connected timeline that every feature reads and writes. Because we own the whole run end-to-end, we can replay it faithfully and turn a failure into a permanent test. A product stitched together from separate point tools can't replay a run it doesn't fully own. The moat isn't "we store traces" — it's the loop that only a single, owned spine makes possible.
Which AI models does it work with?
It's provider-agnostic. Your model calls go through Taksham's gateway, which speaks the same API shape as OpenAI — so most apps and SDKs point at it by changing one setting (the base URL). From there you can route to OpenAI, Anthropic, or other providers without changing your code.
If you don't set any provider keys, a small built-in stand-in answers so the platform still runs end-to-end for evaluation.
Trying it, and wiring in your own app.
How do I try it or deploy it?
One command, on your own machine. With Docker installed, this pulls a prebuilt image, generates the secrets it needs, starts on an embedded database, and prints the URL:
curl -fsSL https://raw.githubusercontent.com/utkarshparcel/taksham/master/install.sh | shPrefer to build it yourself from a checkout of the repo? Run TAKSHAM_BUILD=1 ./install.sh instead. Either way it self-seeds, so all the surfaces are live the moment it's up. The demo login is demo@taksham.dev / taksham-demo.
Deploying with an AI agent or want the full operator runbook? See the agent guide and GO-LIVE.md in the repo.
How do I connect my own app to it?
There are three on-ramps, and you can use one or all:
- Send it your runs. Point an OpenTelemetry exporter (an open standard for traces), or the Taksham SDK, at
/v1/traces. Now your agents' activity lands on the spine. - Route your model calls through it. Use the gateway at
/api/gatewayas an OpenAI-compatible base URL. Every call gets recorded, routed, cached, and budgeted. - Drop the firewall in front. Send risky text to
/v1/guardto screen inputs and outputs inline.
There are SDKs for TypeScript (@taksham/sdk) and Python (taksham_sdk).
Where your data lives.
Does my data leave my environment?
No — not if you self-host, which is the default way to run it. Taksham runs on your own servers. The spine and every feature live inside your perimeter, so your traces and your AI traffic never leave your environment unless you explicitly decide they should.
That's a setting, not a promise: the whole platform is first-party code you can run yourself. The one thing that does leave — only if you choose it — is the call to an external model provider, and you control which provider that is (or none).
How does it keep agents from doing the wrong thing?
Two layers. The firewall inspects what goes into and out of an agent in real time and blocks unsafe content or attempts to hijack it (a prompt injection). And every agent has its own identity, so you can grant it least-privilege access, see exactly what it was allowed to do, and revoke it — all recorded on the spine.
You can also red-team your own agents: run attacks against them through the gateway to find weaknesses before someone else does, and feed what you learn back into the firewall.
What it takes to run, and how ready it is.
What does it take to run?
If you self-host, it runs in your own environment — from a single small server on an embedded database (DuckDB) that needs no external services — so for a trial or a small workload, all you need is that one machine. The heavier scale substrates (a columnar database, an external Postgres, and so on) are optional, for when you outgrow a single box.
Want us to help you stand it up, or a hosted option? Talk to us — we'll get you running in your own perimeter.
Is it production-ready?
Here's the honest answer. Taksham is a comprehensive first-party beta. The full platform is built and runs end-to-end today on the embedded stack — every surface is live on a seeded spine, and the control plane is proven.
What's still in progress is real-peer certification: proving each capability against live systems at scale (a production-grade columnar database, real identity providers, real telephony, and so on) rather than the embedded defaults. We hold a strict bar — a capability counts as done only when it's proven against a real peer, not a mock — and that work is ongoing. We don't claim customers, uptime guarantees, or compliance certifications we haven't earned.
You run on an embedded database by default — does it actually scale?
Two honest halves. The unified part — one spine every feature reads — is real and runs end-to-end today on the embedded database, tested all the way from the SDK to the query. Swapping in the heavy-duty scale database is a configuration change behind the same interface; the feature screens don't change.
The at-scale part is exactly what real-peer certification is for: the production columnar database is wired and structurally complete, but we haven't yet certified it against a live cluster under load — so we don't claim a scale number we haven't earned. Today Taksham is go-live-ready for a single-tenant self-host pilot; multi-tenant-at-scale is the next gate. Past demo, not yet GA — and we'd rather say so than oversell it.
Deploying with an AI agent?
There's a companion page written for an AI assistant a human points at Taksham — with the exact commands, environment variables, and endpoints it needs to deploy and operate it.
The words, defined plainly.
Still curious?
See it run on your own machine.
No account, no credit card. Clone it, run it, and open the spine — every surface is live on a seeded run the moment it boots.