Embedded in your product, or standalone across your org — handling support, operations and transactions with the isolation, audit trail and access control enterprise deployments demand.
White-label embed, or self-hosted behind your firewall — built for security and compliance from day one.
In a single conversation it can look up a customer record, search your knowledge base, trigger a transaction, escalate a case, generate a report, and remember the outcome next time — learning how your organization works along the way, scoped strictly to the domains and data you authorize. It's not a chatbot. It's the operating layer for your enterprise's day-to-day work.
Ask it to look up an order and issue a £200 refund and Codexta recognises two separate domains — splitting the work into isolated task frames, each loading only the tools it needs: support tools for the lookup, banking tools for the refund.
Banking tools never enter a support context. Support tools never touch a payment. Switching between domains it already knows costs zero configuration — and when it doesn't know one yet, that's not a code deploy: it's an authoring conversation away (see Self-Authoring, below).
One instruction. Codexta checks the conditions, makes the decision, finds what's needed, and carries out the action — with every step recorded. Conditional logic. Cross-domain chaining. Real outcomes, fully auditable.
This is what separates a conversational assistant from a genuine AI agent.
| Date | Description | Amount |
|---|---|---|
| 06 Jul 2026 | Nike Air Max 270 | -£94.99 |
| 05 Jul 2026 | Monthly Salary | +£3,200.00 |
| 04 Jul 2026 | Groceries — Tesco | -£43.18 |
| 03 Jul 2026 | Netflix Subscription | -£17.99 |
| 02 Jul 2026 | Transfer from savings | +£500.00 |
Ask for trainers — you get a product grid, with images, sizes, prices and a one-click basket. Ask about a customer's account — you get order history, tracking, saved addresses and store credit, each pulled live and formatted, not buried in a paragraph.
Codexta's present_ tools surface the component that fits the context — not bullet points — so every answer arrives in a format that's actually actionable.
And these aren't fixed. If a card is missing a field or a layout isn't quite right, you don't file a ticket and wait for a release — you just ask, and Codexta builds the fix itself (see Self-Authoring, next).
Product prices, availability, account balances, weather — all pulled live from real APIs. If it can't verify it, it won't say it. Your users will trust it, because it earns that trust on every response.
Ask it to write you a song, tell a joke, or chat about the news, and Codexta declines — not because a filter caught a bad word, but because no loaded guideline covers it. Scope is a strict allow-list, not a blocklist: if a domain wasn't authored for this deployment, the request has nowhere to run.
That's the same mechanism that keeps banking tools out of a shopping task — it's just as strict at the edge of the whole system as it is between domains inside it. You decide exactly what your deployment can do; nothing more is reachable by asking nicely.
Codexta learns how your organization likes to work and saves it — globally, or scoped to a specific department like support, finance, or procurement. Preferences persist across sessions and update on request. No settings screen required.
Correct Codexta on a fact — a wrong return window, a stale API version, a step it skipped — and it doesn't just fix the answer in front of you. It writes the correction down as a lesson, tagged to the domain it belongs to, so the next task that touches the same ground finds it before making the same mistake.
If a stored lesson turns out to be stale itself, Codexta corrects the note in place instead of quietly working around it — a memory that's wrong once shouldn't keep being wrong.
"I can't log in" isn't a free-form chat — it's a state machine. Codexta logs the report, searches the knowledge base for a known fix, and resolves it on the spot if one applies. Only when nothing matches does it open a ticket, notify the reporter, and hand off to a support agent.
When getting the customer back in requires a password, the same rule from Security applies here too: the field is masked on screen and encrypted before it ever reaches the model — the agent verifies the login, never the plaintext.
This isn't a personal notepad — it's an enterprise knowledge engine. Codexta searches through gigabytes of stored documents using hybrid keyword and vector retrieval, finds the relevant sections across multiple files, extracts the facts, summarises them, and presents the answer with links back to every source.
Ask "What was Very Group profit over the last two years?" — even when 2024 and 2025 live in separate reports — and Codexta locates both, pulls the profit figures, combines them, and cites very-group-2024-report and very-group-2025-report as sources. No single document needs the full answer. The agent assembles it.
Drop in a PDF, Excel spreadsheet, or PowerPoint deck. Codexta reads every page, chunks each section, and indexes it with hybrid BM25 + vector search — instantly queryable, cited back to the exact page and row.
It doesn't just read once. It stores findings in a persistent knowledge base so documents uploaded today answer questions asked next month — no re-uploading, no re-reading, no re-explaining.
Ask it to hook up a new API and Codexta writes the tool itself — a real, compiled action, tested live against the endpoint, with credentials pulled from an encrypted vault it never sees in plaintext. Ask for a bespoke result card and it writes the UI component too, styled to match the app's theme automatically.
Every change is versioned. Nothing goes live on its own — a fix is saved as a draft first, and only an explicit "activate" makes it the version real conversations use.
When a user signs in, registers, or authenticates, the password never reaches the language model in plain text. It's encrypted the moment it's entered, held in session-only memory, and used at the real value the model never has access to.
Sealed end-to-end — closing an entire class of prompt-injection and credential-leak risk that affects most agent platforms today. Every action is written to an immutable audit log, access is scoped by role, and — because the model can run self-hosted — nothing has to leave your network boundary.
Most agent frameworks need a frontier model to stay reliable — the reasoning has to compensate for a thin, generic system prompt. Codexta pushes that work the other direction: task-frame isolation, strict guideline gating, and per-domain tool loading are load-bearing engineering, not decoration, so a smaller, cheaper model can drive the same workflow just as reliably.
That's what makes a self-hosted Gemma 4 or DeepSeek deployment realistic in the first place — running on infrastructure you already pay for, with no per-token bill and no data leaving your network, instead of routing every turn through a premium API.
Codexta doesn't have to look like Codexta. Branding — name, logo, colours, domain — is a single config, not a fork, so a retailer like Very can ship it as their own customer-facing assistant: no "powered by" badge, no second product for customers to learn. The same REST API driving this experience is what you wire into your existing site or app, so it becomes a feature of the product your customers already use, not a new destination.
Don't need a customer-facing embed? Run the same engine standalone, behind your firewall, as an internal tool for your own teams — same domain isolation, same audit trail, same access control, no embed required.
LangChain/LangGraph is the closest peer on raw orchestration — its graph model genuinely covers task dependencies well, though that graph is authored by a developer at build time; Codexta's task graph is invented by the model itself, on the fly, per conversation. Claude Code and ChatGPT solve different problems (a coding CLI, a general chat product), so several rows below don't apply to them at all — marked as such rather than scored as a loss for a category they were never built for.
| Capability | Codexta | LangChain / LangGraph | Claude Code | ChatGPT |
|---|---|---|---|---|
| Domain task isolation tools/context scoped per task, banking never leaks into retail |
✓ automatic, no code — inferred per request | ◐ possible, but a developer must hand-design the graph — not automatic | ◐ built-in sub-agents isolate context, but a person must invoke them each time | ✕ single shared thread |
| Structured task graph with blocking dependencies sub-task blocks caller, resumes automatically when unblocked |
✓ automatic — the model invents the graph itself, live, no code | ◐ a true DAG, but a developer must author every node/edge in code beforehand — not automatic | ◐ built-in flat checklist, no dependency blocking — no code needed, just weaker | ✕ no exposed task graph |
| Runtime self-authoring of tools, guidelines & UI compile → test → version → activate, no source edit, no redeploy |
✓ automatic — compiles, tests and versions itself, no developer touches source | ✕ tools are always hand-written code by a developer — no in-conversation authoring | ◐ built-in — it can write/edit files directly, but that's editing real source with no sandboxed draft/version/rollback | ◐ built-in GPT Builder configures tools, but as a separate authoring flow with no version/rollback |
| Per-turn message reconstruction fixed-order state snapshot, not an ever-growing appended log |
✓ automatic every turn | ◐ built-in — LangGraph checkpoints full state per node, a different (also code-free at runtime) model | ✕ append-only transcript + auto-compact | ✕ append-only conversation |
| Context compression with an explicit keep-list evicts low-value data, keeps exact records the task is actively using |
✓ automatic budgeted eviction + stub-and-reread | ◐ summarization memory classes exist, but a developer must wire and configure them | ◐ built-in auto-compact, coarser than a keep-list — no code needed, just less precise | ◐ built-in background summarization — automatic, but not configurable by anyone |
| Long-term recall of exact original data restores the real structured record, not a paraphrase |
✓ automatic — one tool call restores the original structure | ◐ vector retrieval is available, but a developer must assemble and tune the whole RAG pipeline | ✕ no persistent cross-session recall by default | ◐ built-in memory stores discrete facts, not tool-call payloads |
| Automatic lessons-learned capture & self-correction a user correction is written down, tagged, and fixed if it later goes stale |
✓ automatic, tagged, self-correcting — no user action needed | ✕ no built-in equivalent — a developer would have to build this from scratch | ◐ CLAUDE.md persists notes, but a person must write and maintain it — nothing is auto-captured | ◐ built-in memory can save a stated correction, but it's not a tagged, self-correcting store |
| Per-domain scoped preferences "always ask before >£100" for banking, "always °C" for weather — independently |
✓ automatic, global or per-domain | ✕ no built-in preference system — a developer would build one | ◐ CLAUDE.md is project-scoped and manually written, not per-capability | ✕ memory is global/flat, not domain-scoped |
| Knowledge base correlating facts across many documents answers a question no single stored document can answer alone |
✓ automatic — hybrid search, persists & grows across sessions | ◐ RAG is possible, but a developer must assemble the entire pipeline (chunking, embeddings, store, retrieval) | ◐ built-in — strong within a repo's files via grep/read, but not a persistent cross-session KB | ◐ built-in file search cites sources, but only within a session's uploads |
| Adaptive, runtime-authorable UI components product grids, tables, custom cards the agent can also edit on request |
✓ automatic — present_* components, editable at runtime, no code | — different category (backend framework, no UI layer) | — different category (terminal output, not a UI runtime) | ◐ built-in canvas/rich responses, not an extensible component system |
| Zero-trust credential handling a login password never reaches the model in plaintext |
✓ automatic — encrypted client-side, substituted only inside the tool call | ✕ no framework guarantee at all — entirely up to how a developer wires each tool | — different category (not a login-flow agent) | ✓ built-in — Actions can use OAuth, token held by the platform (requires configuring an Action) |
| Self-hosted / open-weight model deployment no vendor lock-in to one model provider |
✓ automatic — any OpenAI-compatible endpoint, no code | ✓ genuinely model-agnostic too (swapping providers still means updating your integration code) | ✕ tied to Claude models | ✕ tied to OpenAI models |
| Embeddable into an existing site e.g. a retailer bolts it onto their own e-commerce site |
◐ the same REST API this app runs on can be wired into any site, but a developer has to build that integration — no drop-in widget yet | ◐ same story — it's a backend library, a developer builds the API + front-end widget from scratch | — different category (a local CLI/IDE tool, not a website embed) | ◐ the Assistants/Chat API can be embedded, but again a developer builds the widget — no first-party "drop into your site" product |
| White-label branding your name, your colours, your domain — no "powered by" badge |
✓ config-driven — one file controls name, colours & domain, so the whole surface ships under your brand | ◐ it's your own frontend code either way, so branding is entirely up to what a developer builds | — different category (a local CLI/IDE tool, not a customer-facing product) | ✕ ships as OpenAI/ChatGPT-branded, no white-label option |
✓ built-in and automatic, no code required · ◐ read the cell — some are built-in but coarser/manual (no code needed, just weaker), others explicitly say "a developer must" (custom engineering required to get there at all) · ✕ no equivalent · — different product category, not a fair comparison. Reflects each product's default, out-of-the-box behavior as of writing (Aug 2026) — LangChain, Claude Code, and ChatGPT are trademarks of their respective owners and ship new capabilities frequently; treat this as a snapshot, not a permanent verdict.
Codexta splits every request into isolated task frames. Each frame loads only its domain tools and the exact context that step requires — then passes a single result back. No bloat, no bleed-through, no contamination between domains.
Most agent frameworks keep one transcript and replay the whole thing every turn — every tool call that ever ran stays in there, and "the current state" is whatever the model can infer from the last matching mention buried in it. Codexta doesn't work that way. Every turn it rebuilds the message from scratch, in a fixed order — guidelines, then preferences, then context, then the turn-by-turn trace — and a tool call that changes something doesn't add a new line to a growing history. It overwrites the one live snapshot for that entity, in place.
The turn-by-turn trace still records that the three calls happened — but not their payload. Each shows up as an empty marker; the data itself lives exactly once, in the snapshot above.
Rebuilding the message every turn sounds like it should cost more, not less — but the layout is deliberate: guidelines and preferences (rarely change) sit at the top, context (changes slowly) in the middle, the live trace (changes every turn) at the bottom. Everything above the line that just moved is byte-for-byte identical to the previous turn, so the model's prompt cache still serves it — 95%+ of input tokens hit cache in practice, not just on the first reply of a session.