Parlant is a framework for controlling how customer-facing AI agents use conversational context, rules, knowledge, and tools. It is for teams building consistent, compliant, traceable interactions in consumer and sensitive business settings.
Borrowing it
Nothing to install: this file belongs to emcie-co/parlant. 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/emcie-co/parlant/develop/CLAUDE.mdgit clone --depth 1 https://github.com/emcie-co/parlantWrote 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/emcie-co/parlant/claude-md)<a href="https://agentmods.dev/instructions/emcie-co/parlant/claude-md"><img src="https://agentmods.dev/badge/instructions/emcie-co/parlant/claude-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/emcie-co/parlant/claude-md"><img src="https://agentmods.dev/badge/instructions/emcie-co/parlant/claude-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 | $0.00611 | $0.00611 |
| Opus 5 | $0.00305 | $0.00305 |
| Sonnet 5 | $0.00122 | $0.00122 |
| Haiku 4.5 | $0.00061 | $0.00061 |
Grade A, and why
parlant CLAUDE.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 9d ago.
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.
What it actually says
This is the main repo of Parlant (https://parlant.io).
Parlant is a Python based agent framework. Its core strengths:
- It allows you to create compliant and controlled AI agents for customer-facing use cases
- It provides many conversational management features out of the box
- It's built for enterprise, large-scale use cases, where SLAs, stability and security are paramount
The repo's structure follows the Hexagonal Architecture (Ports and Adapters) approach.
- src/parlant
- core: Core framework code
- adapters: Implementations of interfaces using 3rd party tools
- api: REST API layer using FastAPI. Uses modules from core/
- tests: all tests for the project. Structure strives to mirror that which is under src/parlant.
General Coding Instructions:
- Always ensure you stick to Hexagonal Architecture patterns in line with how they're used in this codebase.
- Every time you add something, look for similar things in the codebase and ensure you follow the coding style.
- We use MyPy on strict mode. Every parameter needs to be type-annotated. Every function's result too.
- If you need to add a test for something, first say where you plan to add it and ask for confirmation.
- We follow TDD. When you make a change, first create a failing test. Once it fails, implement just enough so it passes.
- If you need to test classes/methods in sdk.py (or generally to test things that relate to engine behavior) make sure you inherit from SDKTest and understand how it works and how to use it.
- Test names should go "testthat..." using clear names that explain the context, what is executed, and what is the expected result.
- You can run tests using pytest. Make sure you run "uv run pytest tests/path/to/test/file.py" while also specifying the test name that you need to run.
Always follow this plan when asked to code a feature or fix a bug:
- Consider the codebase's structure
- Describe your implementation plan, including: a. What tests you will write (test names + files they would live in) b. Why do you think the tests would initially fail c. Where you would plan to implement the code that would make the tests pass
- Ask for plan confirmation. If you get feedback, revise your plan and ask for confirmation again until you get it.
- Implement the tests first. Ask for confirmation and code review.
- Once tests are approved, once again suggest your implementation plan for making them pass, and get plan review until confirmation.
- Once your implementation plan is confirmed, go ahead with implementing the code to pass them.
- Make sure to format all of the files you changed using ruff (it is installed in the environment).
- Run
uv run python scripts/lint.py --mypy --ruffto ensure your code has no lint issues.
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.
- 9d ago First seen · 41 lines · 611 tokens per session scan A 3d1abc742592
parlant CLAUDE.md is an instructions file published in the GitHub repository emcie-co/parlant (18,283 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 611 tokens to every session, about $0.0031 per session on Opus 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.
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