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Documentation Index

Fetch the complete documentation index at: https://www.truefoundry.com/llms.txt

Use this file to discover all available pages before exploring further.

TrueFoundry Agent Harness is available on SaaS only.
Build agents natively on TrueFoundry. You bring the intent — TrueFoundry handles everything else.

Bring Your Own Model

Connect any LLM available through TrueFoundry’s AI Gateway. Switch models without rewriting your agent.

Skills

Attach versioned SKILL.md instructions from the Skills Registry. The agent loads the right skill at runtime — no prompt bloat.

MCP Server Integration

Plug in any MCP server to give your agent tools — web search, APIs, databases, file access, and more.

Sandbox Access

Agents run in a secure execution environment. Code execution, file operations, and external calls are sandboxed by default.
TrueFoundry manages the platform problems so you don’t have to: context window management, tool call orchestration, conversation history, system prompt handling, and multi-turn state. You focus on building and shipping.

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Agent Playground

Navigate to Agents > Playground in the sidebar to open the Playground directly — no registry step required. The Playground is where you build, configure, and test your agent interactively.
Agent Playground with model selection, system prompt, MCP servers, and skills panels
1

Select a model

Choose any model available through AI Gateway.
2

Write your instructions

Add a system prompt that defines the agent’s behavior, persona, and constraints.
3

Connect MCP Servers

Click the + next to MCP Servers to open the tool selection dialog. Search for MCP servers, browse available tools, and select the ones your agent needs.
Tools and MCP Servers selection dialog
4

Add Skills

Click the + next to Skills to open the Skills selector. It lists every skill you have access to, grouped by Repository. Each row has a version dropdown — defaults to the latest, but you can pin any older version.
Skills selector in the Agent Playground listing available skills with version pickers and a preload toggle
Check the skills you want. Each selected skill has a Preload SKILL.md toggle:
ModeBehavior
Off (default)Only the skill’s name and description are exposed upfront. The body is loaded on demand when the agent decides the skill is relevant.
OnThe full SKILL.md body is injected upfront. Higher context cost, but no extra step at runtime.
Turn preload on for short, always-relevant skills. Leave it off for long, situational skills that only fire occasionally.
Two skills cannot share the same name within a single agent, even across different Repositories. The selector greys out conflicting skills and shows a tooltip — pick one or use a differently named copy.
Skills require the agent’s Sandbox to be enabled. Access is inherited from the skill’s parent Repository — end-users invoking a saved agent get implicit access to all skills attached at save time.
5

Run and iterate

Use the chat interface to test the agent. Adjust the prompt, switch models, or add more tools until the agent behaves as expected.

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Saving the Agent

Once you’re satisfied with the agent’s behavior, click Save Agent in the top-right corner. The dropdown gives you two options:
  • Save New Version — Update an existing agent with your changes.
  • Save New Agent — Create a brand-new agent from the current configuration.
Save dropdown with Save New Version and Save New Agent options

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Saved Agent

Once saved, your agent appears in the Agent Registry. Click the agent name to open its detail page.
Agent Registry showing a list of agents with the ask-ai-agent row highlighted
The detail page has three tabs: Overview, Agent Runs, and Use in Code.

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Overview

The Overview tab shows the full agent configuration at a glance — model, model parameters, execution settings (sandbox, iteration limit, response format), system instructions, connected MCP tools, and attached skills.
Agent Overview tab showing model config, instructions, connected tools and attached skills
Click Test Agent to reopen the agent in the Playground for further iteration. To edit the agent’s name, description, tags, or collaborator permissions, click Manage from the registry row.

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Agent Runs

The Agent Runs tab gives you a single place to see every execution of this specific agent. Unlike the global AI Monitoring view, this is scoped to the agent — so you can understand exactly how it is being used and by whom.
Agent Runs tab with a list of executions filtered by last 30 days, showing user, message, turns, and cost
From this view you can:
  • See who is using the agent — each run shows the user or virtual account that invoked it.
  • Filter runs — narrow down by time range, user email, or any custom metadata passed at invocation time.
  • Gauge cost and complexity — turn count and dollar cost are visible per run without opening anything.
Click any row to drill into that run. The detail panel shows the full conversation — every user message and assistant response in sequence — along with total turns, cost, and token usage at the top.
Single agent run detail showing user message, assistant response, turn count, cost, and token usage
Each assistant turn has a View turn trace link that opens the corresponding span in AI Monitoring, so you can debug tool calls, reasoning steps, or latency issues without leaving the context of the run.

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Use in Code

If you want to integrate this agent into your application or automate it from a pipeline, the Use in Code tab has ready-to-copy Python and TypeScript snippets generated for this specific agent.
Use in Code tab showing a Python code snippet using the truefoundry gateway SDK
For the full SDK reference, authentication setup, and streaming guide, see API Reference.