A useful model still needs tools, an execution environment and a way to hand over its work. Allfurdfr brings those pieces into one product, with results you can inspect.
Capabilities / Agents / Execution
01
// Discover
6
// Templates
3 min
// Run limit
3
// Free runs
A model can explain a plan without carrying it out. Useful agent work needs a connection to real data, a place to execute and an output that survives the conversation. That is the gap Allfurdfr is built to close.
01 / The diagnosis
The friction is often in the connections. A research tool lives in one place, instructions in another, and generated files disappear with an environment. We bring these pieces together without asking you to assemble a platform first.
02 / The response
Start with a capability you can call. Combine tools inside an agent, give it a bounded runtime, and inspect the result before you reuse it. The same building blocks work from the browser or your own application.
03 / The product today
Allfurdfr is a workspace for research, analysis and engineering agents. The catalog lists what is connected. Every run records what it received, what it did and what it produced. Outputs remain available after the sandbox shuts down.
6
// Reusable agent templates
3
// Free starter runs
3 min
// Sandbox run limit
1
// Key for API access
We want AI to be as unremarkable inside a business as a database or a payment rail: dependable, owned, monitored and boring in the best sense. That means building systems people trust enough to stop thinking about.
Useful automation should be accessible without a dedicated infrastructure team. We want the path from a specific question to a finished deliverable to be clear: choose the tools, run the job, inspect the evidence and keep the result.
The product follows the work. Each step gives you something concrete to inspect before moving to the next.
Browse the catalog and choose a model, API or template that matches the job. Read its inputs and limits before running it.
Write a clear objective, add useful context and specify the deliverable. Save the instructions to reuse them.
Watch the agent use tools and execute commands in its own sandbox. Check the output before connecting it to another workflow.
Read the trace and generated files. Keep useful outputs and revise the instructions when a run does not meet your expectations.
Call the agent from your application or return to the console for the next task. Each execution gets its own record.
These principles shape what we build, what we expose in the interface and what we ask you to review.
A progress animation is not proof of work. Runs should expose real tool calls, command results and downloadable outputs.
01 / 04Your results should remain useful outside the platform. Download the files and integrate them into the tools you already use.
02 / 04Capability descriptions should match the connected implementation. We distinguish a successful run from an answer that only sounds convincing.
03 / 04Review agent outputs before making consequential decisions. Bounded runtime and visible traces help you understand what happened; they do not make every answer correct.
04 / 04The details that matter are practical: clear inputs, reliable execution, honest failure states and files that remain available when the run ends.
Small, explicit jobs are easier to test and reuse. Describe the expected output and split large projects into focused runs.
Use a workspace for a public repository or start from an empty environment. The run retains the instructions alongside its outputs.
Ask agents to record assumptions and sources in their deliverables. A useful result explains both what it found and what remains uncertain.
When a run fails, the workspace shows its status and returns its credit. You can inspect the trace, narrow the task and try again.
Agent templates for the tasks you repeat. Adapt the instructions and inspect a real run before relying on them.
Public sources, evidence and summaries
Sources, summaries and briefs
Calculations, tables and validation
Tables, calculations and insights
Code inspection and concrete findings
Repositories, evidence and fixes
Readable guides and useful handovers
Instructions, guides and reports
Executable tests and real command output
Checks, test suites and results
Connected APIs and structured responses
Public data, sources and context
Bring a task you can describe and a result you can recognize. Start with a template and make the workflow yours.
// Three free runs. No credit card required.
