Automation
Reusable agents
Save instructions for repeatable research, analysis and engineering jobs. Test them in the console and call them by API.
The platform
Real capabilities. One connected workspace.
Find a model or API, build a reusable agent and run it in a bounded sandbox. Keep the tools, execution trace and deliverables together.
Capabilities, agents and execution
3 min
// Median latency
1
// API key
6
// Agent templates
Linux
// Sandbox runtime
Platform capabilities
Everything needed to turn a question into work.
A connected set of building blocks. Browse the live catalog for supported inputs, limits and working examples.
Intelligence
Language models
Generate text through the connected model capability. The catalog identifies the supported model and the cost of each run.
Data
Public data APIs
Fetch public research, repository and package data through validated capability inputs. Results preserve the original source URLs.
Assurance
Executable checks
Ask your agent to test its calculations or generated code. Inspect the actual command, exit code and output in the run trace.
Intelligence
Live research
Retrieve current public data when your task needs it. Provider responses are attached to the run instead of replaced with sample data.
Operations
Credit accounting
One run reserves one credit. Failed and cancelled runs return it once. See your balance and run history in the workspace.
Automation
Saved instructions
Name an agent and save its task instructions. Run it again with new context or update it when the job changes.
Infrastructure
Cloud sandbox
Give each agent run a fresh Linux sandbox with Node and Python. Collect the generated files before the environment stops.
Operations
Run inspection
Read the submitted input, tool trace, generated files and final status. Download the deliverables as an archive.
// WORKFLOWS IN PRACTICEA workflow is where
STEP 01 STEP 02 STEP 03 STEP 04 STEP 05 Try this workflow
A workflow is where
an answer becomes work.
Start with a working pattern. Save the instructions, test a run and inspect what the agent actually did.
Research
Turn a question into a brief with sources.
Query
Fetch
Analyze
Write
Inspect
// INTELLIGENCE WITH CONTEXTGive the model
Give the model
something to work with.
Connect language reasoning to public APIs, executable code and a filesystem. Keep the result grounded in the work your agent actually performed.
Connected models
Use the supported language capability from the catalog.
Live sources
Query public APIs with explicit, validated inputs.
A bounded runtime
Every sandbox run has a timeout and a clear end.
Human review
Inspect the evidence before relying on an output.
// CONNECTED TO THE WORKReal tools.
Real tools.
Useful connections.
Connected capabilities for language, public research, repositories and packages. Browse the catalog for the actual inputs and limits.
OpenAIGitHubCrossrefnpmUSGSPythonNode.jsHTTPS APIMCP
Browse capabilities// RUNS AND ACTIVITYEvery run
Open activity
Every run
leaves a record.
Your input, the tools used, actual command output and the final result. Inspect the work before it becomes part of your workflow.
Input
The submitted request
Trace
Tools and command results
Output
Files and deliverables
Usage
Credits and model tokens
// CHOOSE YOUR INTERFACEThree ways in.
Three ways in.
The same useful work.
Console
Build and test your agents in a visual workspace.
Get startedAPI
Submit runs and retrieve results from your application.
Get startedAI tools
Connect your local assistant through MCP.
Get started// WHERE IT FITSFour kinds of work.
01 02 03 04
Four kinds of work.
One execution layer.
Research
Build source-backed briefs from public data.
Explore agentsEngineering
Generate code, run checks and download files.
Explore agentsOperations
Analyze small datasets and prepare reports.
Explore agentsApplications
Call capabilities and agents through an API.
Explore agentsPlatform questions
What teams ask before they commit.
Deployment model, ownership, evaluation and what happens on a bad day.
Your account and run records are stored in our managed database. Agent execution uses a hosted sandbox and connected model providers. Downloaded artifacts remain yours to use. Do not submit secrets or data you are not authorized to process.
Create an account and use your three starter credits. Choose a capability or describe a small task. A sandbox run has up to three minutes to complete; the workspace shows progress and the final result.
Yes. Create a revocable API key and follow the working requests in the documentation. You can submit a capability or agent run, inspect its status and retrieve its output from your application.
You can read and download the files generated by your runs. Review their contents and any dependency licenses before using them. Model and provider services remain subject to their own terms.
Each accepted run reserves one credit. Failed and cancelled runs return that credit once. Three starter credits are included. The six monthly plans list their credit allocations on the pricing page.
Inspect the trace and verify the result before relying on it. Agents can make mistakes. Narrow the task, add missing context and rerun when needed. A completed run is evidence of execution, not a guarantee that every conclusion is correct.
The live catalog identifies the models and APIs currently connected to Allfurdfr. Agent runs use the configured language model with sandbox tools. We do not list unsupported providers as available integrations.
Save your instructions as an agent and run it again with new context. Active subscribers can enable daily or weekly schedules. API access lets your own applications submit work and read its results.
See it running
Bring a real task and watch it run.
Start with a concrete deliverable. Follow the tool trace, inspect the generated files and decide what to reuse from the result.
// Three free runs. No credit card required.