BINETEX is the open-source platform for running AI agents securely at scale — deterministic guardrails, a governed tool-server registry, and built-in retrieval, all on infrastructure you control.
BINETEX is the open-source platform for running AI agents securely at scale across your organization. It's the infrastructure layer that turns experimental agents into systems you can actually put in front of real data.
Connect agents to the tools and knowledge they need through the Model Context Protocol, keep every action inside guardrails you define, and run the whole thing on container infrastructure you already own — self-hosted, at no license cost. One platform for technical and non-technical staff alike, from a private tool registry to built-in retrieval to cost controls and observability.
Explore the platformTechnical and non-technical staff get the same simple chat. Behind it: a private registry of approved tool servers, built-in retrieval over your own knowledge, your choice of model, and deterministic guardrails on every action.
"onboarding" changed:Q-current → 6 chunks · rerankedlabel=onboarding state=open → 3 ticketsThree docs changed: Access Requests, Device Setup, and First Week. Two open tickets reference the old access-request flow — linked below.
A recreation of the BINETEX workspace. Every tool call runs against an approved server from your private registry — and every one is traced, costed, and guarded.
Every agent action passes through guardrails that are deterministic, not probabilistic. They are built to stop agents from leaking sensitive data, corrupting systems, or following instructions smuggled in through the content they read — so a clever prompt can't talk your agent into misbehaving.
When an agent can read private data, process untrusted content, and reach the outside world all at once, that combination becomes a path for prompt-injection attacks that exfiltrate data. BINETEX is designed to break that chain, keeping injected instructions from ever turning into real, harmful actions.
A single chat interface serves both technical and non-technical staff. Anyone can connect to an approved tool server from the private registry with one click, pull from the shared company prompt library, and get useful work done without touching a line of code.
Capture the prompts that actually work and make them company property. A shared, curated library means the best way to ask is one click away for the whole organization — and improvements roll out to everyone instead of living in one person's notes.
Work with autonomous agents where conversations already happen. Reach them through the common workplace messaging channels your teams use every day, so an agent is just another participant — kicking off tasks, answering questions, and reporting back in the flow of work.
Connect agents to your internal knowledge so they answer from your own data. The full retrieval stack — chunking, embedding, hybrid search, and reranking — runs inside the platform. There's no external vector database to stand up, secure, or pay for.
Use multiple AI models from the major commercial providers alongside open-source models, side by side. Pick the right model for each job, swap them without rewriting your agents, and avoid getting locked into a single vendor's roadmap or pricing.
Maintain a private registry of tool servers — self-hosted or remote, self-built or third-party. Every server is versioned with full version control and rollback, so you can promote a new release with confidence and revert instantly if something looks wrong.
Govern who can use what with granular, team-based access management and compliance controls built for multi-team environments. Approve servers centrally, scope them to the teams that should have them, and keep the whole estate inside policy.
Run tool servers with enterprise-grade isolation so one team's workload can't reach into another's. Each server operates inside its own boundary, with automatic scaling and health checks keeping it responsive without manual babysitting.
Keep credentials out of prompts and config files. Centralized secrets management injects what each server needs at runtime and rotates keys automatically, so access stays tight and a leaked credential has a short, controlled life.
Monitor and cap spend per team, per agent, and across the whole organization. A dynamic optimizer routes simpler tasks to cheaper models automatically, trimming the bill without anyone hand-tuning which model handles which request.
See what your agents are actually doing. Exported metrics, distributed tracing, and prebuilt dashboards track token usage, latency, and blocked tool calls out of the box — so you can debug a slow run or spot a spike in refused actions at a glance.
Stand the platform up with a single command and provision it as infrastructure-as-code, so environments are reproducible and reviewable. It runs on standard container infrastructure with low reported latency, ready for production rather than just demos.
Start from a curated catalog of several hundred evaluated tool servers for agents. Browse what's already been vetted, add it to your registry, and extend it with your own servers — self-hosting and community contributions are part of the design.
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Through the Model Context Protocol, agents reach the tools and data they need as governed tool servers — self-hosted or remote, first-party or third-party. You approve them, version them, and scope them to teams.
Relational databasesDocument storesData warehousesVector & search indexesObject storageInternal wikisKnowledge basesTicketing & issuesSource controlCI/CD pipelinesCalendars & emailSpreadsheetsAnalytics & BICRM recordsMessage queuesWorkplace channelsSecrets vaultsObservability backendsWeb & HTTP fetchFilesystemsDocument parsingGeospatial data …and many more in the catalog.
This is the failure mode that keeps agents out of production. The moment an agent can read your private data, process content you don't control, and communicate with the outside world at the same time, it becomes vulnerable to prompt-injection attacks that quietly exfiltrate that data.
Hidden instructions in a document, a ticket, or a web page can hijack a well-meaning agent and turn its own access against you. Probabilistic pleading with the model — "please don't do anything bad" — is not a control.
BINETEX breaks the chain — deterministically, on every action.
The same platform that makes agents easy to use makes them safe to operate at scale. Everything an enterprise needs to govern, budget, and watch its agents ships in the box.
A private registry of tool servers with version control and rollback, granular team-based access, and compliance controls for multi-team environments.
Monitor and limit spend per team, per agent, and organization-wide. A dynamic optimizer routes simpler tasks to cheaper models to keep costs down automatically.
Exported metrics, distributed tracing, and prebuilt dashboards track token usage, latency, and blocked tool calls — so issues surface before they become incidents.
BINETEX runs on standard container infrastructure with low reported latency, scales automatically with health checks, and goes from zero to running with a single command — provisioned as infrastructure-as-code.
Self-host BINETEX free, request an enterprise demonstration, or talk through a security review. Tell us what you're building and we'll point you at the fastest path.
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