Describe an agent. Point it at your data.
Watch it run.
Port of Context is one place to put AI agents into production and keep control of them: any model, your own infrastructure, and a full trace of every run.
A $7B private equity fund runs 50+ custom agents on Port of Context.
deal-sourcing · diligence · portfolio monitoring
Model-agnostic
When a model fails,
your agent keeps going.
The same agent runs on Claude, GPT, Gemini, or open weights, switching between them automatically if one slows or goes down. The model you run is a choice you control, not a vendor that locks you in.
Observability
More than observability: agents that speak up.
Every tool call, retrieval, and reasoning step is captured live: what each agent did, why, and what it cost. Replay any run, compare them side by side, and trace every output to its source. And when a run drifts, pctx doesn't just log it, it tells you. Built on AVP, our open agent-observability spec.
Your agents, for a fraction of the cost.
Run the same agents for a fraction of the cost, and keep it that way as they take on more tools. Up to 98% fewer tokens per run.
Pull every record that needs attention and summarize why.
One program. Context loaded once.
Your agents, your infrastructure.
Deploy Port of Context wherever your data already lives.
- Bring your own models and keys
- You control what leaves your environment
- Open source and MIT-licensed
Built for teams that can't send their data anywhere.
Your operators describe the work. Port of Context runs the agent across every document, call, and system inside your own infrastructure and returns cited, audit-ready output. It runs in your environment, not ours.
Private equity
Data-rich SaaS
Law firms
Map Port of Context to your environment.
We'll look at your infrastructure, models, and compliance constraints, then tell you honestly whether Port of Context fits. A real engineer follows up fast.
Questions, answered.
A self-hosted AI agent platform lets you deploy and run AI agents inside your own infrastructure instead of a vendor’s cloud. Port of Context runs in your cloud, on-prem, or air-gapped environment, so your data and your agents’ tool calls stay in your environment, and you control what leaves.
Yes. The same agent runs on Claude, GPT, Gemini, or open-weight models, and you can switch between them without a rewrite. The model layer is abstracted, so a provider change, outage, or price move never forces you to rebuild your agents.
Yes. Port of Context is built to run entirely inside your own infrastructure, including on-prem and fully air-gapped networks. With local or open-weight models, an agent can run end to end with no external network calls. Nothing leaves the environment. This is why teams in finance, insurance, legal, healthcare, and government deploy it on their own hardware, where sensitive data stays in their environment and you control what leaves.
Every tool call runs in an isolated environment by default, rather than reaching directly into your systems. Nothing touches your data or services unless you allow it, which contains the agent and removes a common path for data leaks.
Every tool call, retrieval, and reasoning step is captured live. You can replay any session, compare runs side by side, and trace each output back to the source it came from, along with what the run did, why, and what it cost. Observability is built on AVP, our open agent-observability spec.
Code mode is how Port of Context runs an agent’s tool calls: as code, with the heavy data handled outside the model’s context. The result is faster, cheaper runs that stay reliable even when an agent uses many tools.
Those tools run agents or workflows in a hosted environment you don’t control. Port of Context is self-hostable, model-agnostic, and isolated by default, with full observability of every run. It is built for teams that need agents in production on their own infrastructure, not for vendor-hosted automation.
No. Operators describe an agent and deploy it without standing up a stack. Engineers can still open every layer when they want to: model choice, tool whitelists, isolation policy, and the eval harness. Same platform, progressive disclosure.