Experiential is an open-source model gateway and router, meaning a service that gives agents one API for hosted, user-provided, local, and custom language models. It is for teams that need to choose models, control access and spending, and route production requests according to quality, speed, or cost. The catalogue entries provide agent skills and instructions for operating or configuring the gateway.
Borrowing it
Nothing to install: this file belongs to experientiallabs/experiential. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/experientiallabs/experiential/main/AGENTS.mdgit clone --depth 1 https://github.com/experientiallabs/experientialWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/experientiallabs/experiential/agents-md)<a href="https://agentmods.dev/instructions/experientiallabs/experiential/agents-md"><img src="https://agentmods.dev/badge/instructions/experientiallabs/experiential/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/experientiallabs/experiential/agents-md"><img src="https://agentmods.dev/badge/instructions/experientiallabs/experiential/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.07424 | $0.07424 |
| Opus 5.5 | $0.02970 | $0.02970 |
| Sonnet 5.5 | $0.01485 | $0.01485 |
| Haiku 4.5 | $0.00742 | $0.00742 |
Grade A, and why
experiential AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 445 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent guide — experiential
Experiential builds immutable task evidence from agent traces, composes and fits frozen model routers,
runs those routers on loopback, and executes bounded SFT from persisted datasets. All importable
code lives under exp/; benchmark data arrives as a dependency (see rule 6).
Product and repository boundaries
Make open-source Experiential excellent for individual users. Keep enterprise operations, organization management, billing, and deployment orchestration in other repos. Enterprise builders can build that layer themselves, but this boundary must never compromise our gateway's performance, security, or maintainability.
Public code, documentation, commit messages, PR descriptions, and comments must describe the standalone open-source behavior. Keep private repository links, internal deployment details, customer information, and private operational or experiment evidence in private repos or local records. Limit disclosure cleanup to sensitive cases; preserve legitimate public interfaces and ordinary technical terminology. Keep shared interfaces small, performant, and reliable.
Toolchain
Managed with uv; lint/format with ruff; type-check with ty.
uv sync --extra dev
uv run ruff check .
uv run ruff format --check .
uv run ty check
uv run pytest -q
Repository checks
- Every new or rewritten hand-authored source, configuration, and documentation file with a
covered suffix stays below 1,000 physical lines. The executable limit is 999 lines and counts
comments and blank lines. Test modules named
*_test.pyare exempt so cohesive tests are not split solely to satisfy a line count. Generated lock files are excluded. Generated code belongs in an explicitly namedgenerated/directory and is never edited by hand. - Full-repository Ruff check, Ruff format check, and ty check are required on every change. No pre-existing lint or type failures are grandfathered.
- Production imports follow the approved dependency direction: common may not import runtime, simulation, optimize, or cli; runtime may not import simulation, optimize, or cli; simulation may not import optimize or cli; optimize may not import cli. Optimize owns application orchestration and may depend inward on common, runtime, and simulation. The AST gate rejects every current forbidden edge directly and proves that the package graph is acyclic.
- The root CLI command set is exact:
build,capture,config,eval,login,optimize, andrun. An invocation without a subcommand opens the default gateway home screen.exp/cli/app_test.pyand the release tests enforce the current command and distribution shape.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +16 lines · +303 tokens per session 9e84e20623ac
- 3d ago Changed · +39 lines · +823 tokens per session 4e2e4de0bb43
- 22d ago Changed · +1 lines · +22 tokens per session 34cb7e683d95
- 1mo ago Changed · +6 lines · +102 tokens per session b2d305ed22dd
- 1mo ago Changed · +3 lines · +69 tokens per session 12ab8fed5f55
- 1mo ago Changed · +12 lines · +174 tokens per session 516764219385
- 1mo ago Changed · +4 lines · +73 tokens per session 9dc18a52c253
- 1mo ago Changed · +9 lines · +192 tokens per session f83234e4077b
- 2mo ago First seen · 355 lines · 5,666 tokens per session scan A 1527a15aa911
experiential AGENTS.md is an instructions file published in the GitHub repository experientiallabs/experiential (13,670 stars, last pushed today), licensed Apache-2.0. It adds 7,424 tokens to every session, about $0.0297 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
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Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.