incontext 0.0.3

incontext is a Hermes Agent plugin that gives each LLM request a viable output budget below Hermes' context-compression boundary. It prevents a fixed, oversized max_tokens value from consuming input space, but also refuses to manufacture tiny completions that are technically valid and operationally useless to an agent.

Algorithm

The policy uses these values:

• W is Hermes' effective compression window, read from the installed ContextCompressor.
• P is the exact number of tokens in the provider-visible prompt.
• R is the minimum viable output reserve. It is configured with INCONTEXT_MIN_OUTPUT_TOKENS and defaults to 4096. An explicit smaller Hermes-wide output cap lowers R, because that cap is an operator decision.
• B is an optional positive output cap on an individual request.
• F is INCONTEXT_FALLBACK_MARGIN_TOKENS, used only when exact tokenization is unavailable.

Before Hermes constructs the main provider request, incontext reports this token pressure to its compression preflight:

preflight_pressure = P + R - 1

Hermes compresses when that pressure is at least W. Therefore compression is requested exactly when W - P < R. The subtraction of one is intentional: a prompt with exactly R tokens of output space remains valid, while a prompt with R - 1 tokens does not.

After the final request has been constructed, the middleware counts it again and computes:

required_output = min(R, B) if B is present else R
remaining       = W - P
max_tokens      = min(remaining, B) if B is present else remaining

The middleware inserts the output cap only when remaining >= required_output. Otherwise it leaves the request unchanged: the preflight path owns main-turn compression, and fail-open behavior is safer for call sites that bypass it than sending a predictably truncated tool call or text fragment. A provider-specific output constraint must also leave at least required_output tokens.

A smaller positive caller cap is preserved and becomes that request's required minimum. This keeps deliberately bounded operations, such as context summaries and generated titles, bounded. Without an explicit smaller cap, incontext never dynamically emits max_tokens below R; in particular it does not turn an exhausted window into max_tokens=1.

Hermes auxiliary calls do not pass through the public llm_request middleware, and Hermes omits max_tokens for most custom providers. The plugin therefore wraps the auxiliary request builder with the same budgeting rule. If exact tokenization fails, both preflight and middleware use Hermes' rough estimate plus F, so they retain the same decision boundary. If both estimators fail, the original request is left unchanged.

Startup rejects F + R >= W. Hermes normalizes a rough prompt estimate to at least one token, so that configuration could never leave R viable output tokens during a tokenizer outage, even after compressing everything else.

This addresses the same output-budget arithmetic discussed in NousResearch/hermes-agent#38652.

Installation

Install the published package from PyPI and enable it using the same plugin name, incontext:

python -m pip install incontext
hermes plugins enable incontext

To use the current development branch instead, install it directly from GitHub:

python -m pip install 'git+https://github.com/pomponchik/incontext.git@develop'
hermes plugins enable incontext

Restart the long-running Hermes gateway after installing or upgrading the Python package. Hermes discovers it through the official hermes_agent.plugins entry-point group; no source file has to be copied into $HERMES_HOME/plugins.

Configuration

The bundled vllm backend is selected by default. Its INCONTEXT_TOKENIZER_URL setting is required and must point to the /tokenize endpoint of the same vLLM model Hermes uses:

export INCONTEXT_BACKEND='vllm'
export INCONTEXT_TOKENIZER_URL='https://inference.example/tokenize'
export INCONTEXT_TOKENIZER_TIMEOUT_SECONDS='30'
export INCONTEXT_FALLBACK_MARGIN_TOKENS='1024'
export INCONTEXT_MIN_OUTPUT_TOKENS='4096'

Environment variables are loaded through typed skelet.Storage fields backed by ordered skelet.EnvSource instances. Primary INCONTEXT_* names take precedence over the supported legacy aliases. Text normalization and blank value rejection are implemented by the fields' native conversion and validation rules.

Hermes' model.default, model.context_length, and compression.threshold remain the source of truth. The plugin constructs Hermes' installed ContextCompressor and uses its resolved threshold_tokens; it does not copy version-sensitive threshold arithmetic.

The optional variables are:

The former HERMES_VLLM_TOKENIZER_* and HERMES_DYNAMIC_BUDGET_FALLBACK_MARGIN_TOKENS names are accepted as migration aliases. New deployments should use the INCONTEXT_* names.

Replacing the inference backend

The budgeting core depends only on the abstract incontext.Backend contract. It has no import or construction dependency on vLLM. A backend supplies its safe diagnostic source, exact count(...), cache invalidation, and an optional output-field normalization hook.

Backend implementations are named pristan plugins in the incontext.backends entry-point group. The generic skelet environment has a typed backend field whose default is vllm. At runtime incontext performs the single named resolution directly:

backend = backends[environment.backend].one()

The incontext distribution itself publishes the vllm entry point. Loading that entry point imports incontext.vllm_provider, whose only responsibility is to construct VllmBackend. All /tokenize payload rules, vLLM response fields, context-length validation, transport settings, and caching live inside that class rather than in the budgeting core.

A third-party distribution can provide another backend without changing incontext. Its implementation subclasses the stable abstract contract and its plugin module registers a provider under a new name:

# acme_backend/plugin.py
from __future__ import annotations

from typing import Any, Dict

from incontext import Backend, backends


class AcmeBackend(Backend):
    @property
    def source(self) -> str:
        return "acme-tokenizer"

    def count(
        self,
        request: Dict[str, Any],
        *,
        context_length: int,
    ) -> int:
        ...

    def clear_cache(self) -> None:
        ...


@backends.plugin("acme")
def provide_acme_backend() -> Backend:
    return AcmeBackend()

The third-party package makes that module discoverable in pyproject.toml:

[project.entry-points."incontext.backends"]
acme = "acme_backend.plugin"

After installing the package, select it through the same typed configuration field and restart the Hermes process:

export INCONTEXT_BACKEND='acme'

Only the selected provider is instantiated. An unknown name fails .one(); the unique slot rejects duplicate providers under the same name while loading entry points. Startup therefore fails instead of choosing a backend implicitly. Each backend owns and validates its backend-specific configuration; the generic settings object contains only the compression-window, viability-reserve, and fallback-budget policy.

Safety properties

• With the bundled backend, vLLM applies its real chat template to messages, tools, and chat_template_kwargs; local tokenizer approximations are not used.
• The returned max_model_len must equal Hermes' configured context length.
• max_tokens, max_completion_tokens, and max_output_tokens are reduced to the smallest positive caller cap while preserving the corresponding provider-selected field name; an implicit cap below the minimum viable output reserve is handed to preflight compression.
• The incoming request is copied and never mutated.
• Exact counts use a bounded, thread-safe cache.
• The exact counter is also used by Hermes' preflight compressor, eliminating the former gap where compression used a rough count but budgeting used an exact one.
• If /tokenize fails, Hermes' own rough estimator is used with an additional safety margin. If both counters fail, the middleware leaves the request unchanged instead of taking Hermes down.
• Logs contain counts and exception types, never prompts, credentials, or raw provider errors.

The tokenizer endpoint sees the prompt content by design. Run it on a trusted network path and use the same access controls as the inference endpoint.