lethes 1.0.1

Constraint-driven LLM context management and message orchestration library

lethes

Constraint-driven LLM context management library — a message orchestration engine that treats conversation history as an optimization problem.

lethes is named after the river of oblivion in Greek mythology. It decides what must be remembered, what can be compressed, and what should be forgotten.

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Core Idea

Traditional context management is a sliding window — truncate from the front when you run out of space. lethes takes a fundamentally different approach:

Traditional	lethes
Fit as many messages as possible	Optimize under constraints
All messages are equal	Every message has a dynamic weight
Truncation = deletion	Truncation = selective compression or drop
Fixed strategy	Pluggable algorithms
No user control	In-conversation flag syntax

---

Features

• Dynamic weighting — message weights are real-time relevance scores against the current query, not fixed numbers
• Constraint-driven — maximize context information subject to token-budget and cost-budget constraints
• Pluggable algorithms — greedy, recency-biased, dependency-aware, prefix-cache-optimized, or bring your own
• User-controllable — a !flag syntax lets users adjust orchestration behaviour inline within the conversation
• Multi-level summarization — turn-level, segment-level, and conversation-level compression instead of blunt deletion
• KV-cache optimization — tracks previously sent sequences to maximize prefix hits and lower API cost
• Tool-call aware — full OpenAI tool calls support; automatically binds assistant/tool message pairs so truncation never produces an invalid sequence
• Multimodal-ready — native image_url content blocks (URL and base64); text is extracted for scoring, image blocks are passed through untouched
• Vendor-agnostic — summarization and embedding use any OpenAI-compatible endpoint (Ollama, vLLM, OpenRouter, …)
• Structured observability — structlog-based JSON events; plug in lethes-observer for a real-time web dashboard

---

Installation

pip install lethes                  # core
pip install lethes[redis]           # enable Redis cache
pip install lethes[bm25]            # enable BM25 keyword relevance
pip install lethes[langchain]       # LangChain adapter
pip install lethes[all]             # all optional dependencies

Requirements: Python 3.11+, tiktoken, pydantic ≥ 2, pydantic-settings ≥ 2, httpx ≥ 0.27, structlog ≥ 24

---

Quick Start

Minimal usage

from lethes import ContextOrchestrator, Conversation, TokenBudget

orchestrator = ContextOrchestrator(
    budget=TokenBudget(max_tokens=8000),
)

result = await orchestrator.process(
    Conversation.from_openai_messages(raw_messages)
)

ready_messages = result.conversation.to_openai_messages()
# → standard OpenAI format, ready for any LLM

raw_messages accepts any OpenAI message format:

messages = [
    # Plain text
    {"role": "user", "content": "What's the weather today?"},

    # Tool call (assistant content is null)
    {"role": "assistant", "content": None, "tool_calls": [{
        "id": "call_abc", "type": "function",
        "function": {"name": "get_weather", "arguments": '{"city":"Paris"}'}
    }]},

    # Tool result
    {"role": "tool", "tool_call_id": "call_abc",
     "name": "get_weather", "content": "22°C, partly cloudy"},

    # Multimodal (image + text)
    {"role": "user", "content": [
        {"type": "text", "text": "What's in this image?"},
        {"type": "image_url", "image_url": {
            "url": "data:image/png;base64,iVBOR...",
            "detail": "high"
        }},
    ]},
]

Full configuration

from lethes import ContextOrchestrator, Conversation, TokenBudget
from lethes.algorithms import RecencyBiasedAlgorithm, DependencyAwareAlgorithm
from lethes.weighting import CompositeWeightStrategy, SmartWeightingStrategy
from lethes.weighting.embedding import EmbeddingSimilarityStrategy
from lethes.summarizers import LLMSummarizer, TurnSummarizer
from lethes.cache import RedisCache
from lethes.engine import ConstraintSet

# Weighting: 70% embedding similarity + 30% smart keyword/coherence
weighting = CompositeWeightStrategy([
    (EmbeddingSimilarityStrategy(
        api_base="https://api.openai.com/v1",
        api_key="sk-...",
        model="text-embedding-3-small",
        cache=RedisCache.from_url("redis://localhost:6379/0"),
    ), 0.7),
    (SmartWeightingStrategy(), 0.3),
])

# Summarizer: any OpenAI-compatible endpoint
summarizer = TurnSummarizer(
    backend=LLMSummarizer(
        api_base="https://api.openai.com/v1",
        api_key="sk-...",
        model="gpt-4o-mini",
    ),
    cache=RedisCache.from_url("redis://localhost:6379/0"),
)

orchestrator = ContextOrchestrator(
    budget=TokenBudget(max_tokens=12000),
    algorithm=DependencyAwareAlgorithm(
        inner=RecencyBiasedAlgorithm(recency_factor=2.0)
    ),
    weighting=weighting,
    turn_summarizer=summarizer,
    constraints=ConstraintSet(require_last_user=True),
)

---

Tool Calls & Multimodal

Tool calls

lethes fully supports the OpenAI tool calls format with no extra configuration:

user message
  ↓
assistant  (content=null, tool_calls=[{id, type, function}])  ← auto-bound as dependency
  ↓
tool       (tool_call_id=..., content="result")               ← auto-bound as dependency
  ↓
assistant  ("Based on the tool result, the answer is…")

Guarantees:

Scenario	lethes behaviour
Tool result kept, assistant tool_calls dropped	ConstraintChecker promotes the assistant to keep
Assistant tool_calls kept, tool result dropped	ConstraintChecker promotes the tool result to keep
Parallel tool calls (multiple in one turn)	All pairs are bound bidirectionally and kept together
A tool message has a cached summary and the algorithm wants to summarize it	Summarization refused; treated as drop (tool pairs are indivisible)

Dependencies are injected automatically by Conversation.from_openai_messages — no manual wiring needed.

Multimodal messages

# URL format
{"type": "image_url", "image_url": {"url": "https://example.com/img.jpg"}}

# Base64 data URI
{"type": "image_url", "image_url": {
    "url": "data:image/png;base64,iVBOR...",
    "detail": "high"   # "low" | "high" | "auto"
}}

• Only type=text blocks are used for weight scoring and summarization
• Image blocks are passed through unchanged
• Token counting covers text content only (image tokens are billed by the API)

---

How Orchestration Works

Message importance score

Each message's final weight is a product of three factors:

weight = base_weight × relevance_score × recency_multiplier

relevance_score is computed by SmartWeightingStrategy:

relevance_score(msg_i) = keyword_score(msg_i, query)
                       × pair_coherence_boost(msg_i)
                       × role_factor(msg_i)

Factor	Formula	Description
keyword_score	BM25 normalised to [floor, 1.0]	Keyword overlap between message text and the current query
pair_coherence_boost	max(kw_score, coherence × prev_user_score)	Assistant replies inherit part of the preceding user message's score — keeps Q&A pairs together
role_factor	tool_penalty if role=tool or has tool_calls, else 1.0	Downweights intermediate tool-call messages

recency_multiplier is applied by RecencyBiasedAlgorithm during selection:

recency_multiplier = 1 + factor × (position_from_oldest / (n - 1))

Oldest message → ×1.0; newest message → ×(1 + factor). Default factor=2.0.

Selection pipeline

All messages (n)
    │
    ▼ WeightingStrategy.score()
    │  keyword scoring, pair coherence boost, tool downweight
    │
    ▼ algorithm.select()
    │  sort by weight → greedy fill within budget
    │  (recency multiplier applied here by RecencyBiasedAlgorithm)
    │
    ▼ ConstraintChecker.repair()
       force-keep last user message
       resolve dependencies → tool pairs never split
       satisfy min_chat_messages

LLM sub-agent analysis (LLMContextAnalyzer)

Beyond keyword relevance, you can enable an LLM sub-agent for semantic importance classification.

Key design: five-label classification, not floating-point scores.

LLMs are poor at calibrated floats (0.73 vs 0.75 means nothing to them) but excellent at classification. Each message gets one label:

Label	Meaning	Weight
K Keep	Must keep — directly answers the current question	1.00
H Helpful	Should keep — useful background context	0.75
M Maybe	Neutral — marginally relevant, keep if budget allows	0.50
S Skip	Likely not needed — old, off-topic, or superseded	0.25
D Drop	Clearly irrelevant to the current question	0.05

Output format (compact, minimal tokens):

{"labels": ["K", "H", "M", "S", "D", ...]}

The parser accepts three fallback formats: JSON object → JSON array → regex letter extraction.

from lethes.weighting import CompositeWeightStrategy, SmartWeightingStrategy
from lethes.weighting.llm_analyzer import LLMContextAnalyzer
from lethes.cache import RedisCache

weighting = CompositeWeightStrategy([
    (SmartWeightingStrategy(), 0.4),       # fast keyword signal (no API call)
    (LLMContextAnalyzer(                    # semantic 5-label classification (cached)
        api_base="https://api.openai.com/v1",
        api_key="sk-...",
        model="gpt-4o-mini",
        cache=RedisCache.from_url("redis://..."),
    ), 0.6),
])

LLMContextAnalyzer also supports an optional two-phase entry logic (use_entry_logic=True): it first receives a compact topic-cluster overview, then calls an expand_topic tool to examine specific clusters in detail before labelling — useful for very long conversations where the full message list would overflow the sub-agent's context.

---

Flag Control Syntax

Users control orchestration behaviour inline with a ! prefix:

!key=value,+persistKey=value2,-closeFeature message body

Prefix	Meaning	Example
!key / !key=val	Temporary — current turn only	!nosum
!+key / !+key=val	Persistent — stored in session state	!+pin
!-key	Remove a persistent flag	!-pin

Built-in flags

Truncation / budget control

Flag	Effect
!full	Disable all truncation, pass the full context
!target=N	Token target for this turn (aim for N, not a hard cap)
!context=N	Keep only the most recent N conversation turns
!nosum	Disable summarization; over-budget messages are dropped

Anchoring (force-keep)

Flag	Effect
!pin	Pin the current message — never truncated or summarized
!recent=N	Force-keep the N most recent non-system messages, regardless of weight
!keep_tag=label	Keep all messages with the given tag (use with !+tag=)

Weight overrides

Flag	Effect
!weight=N	Base weight of the current message (default 1.0)
!tool_penalty=F	Override tool-call downweight multiplier for this turn
!pair_coherence=F	Override Q&A coherence factor for this turn (0.0–1.0)

Metadata

Flag	Effect
!tag=label	Attach a tag to the current message (use with !keep_tag=)

Examples:

# Pin important background permanently
!+pin This is critical system context. Always refer to it.

# Force keep last 4 messages + compact token target
!recent=4,target=6000 Answer based on the recent conversation.

# Deprioritize tool call history this turn
!tool_penalty=0.1 Summarize the conversation, ignoring tool-call details.

# Tag a key decision and retrieve it by tag later
!+tag=key_decision We decided to use PostgreSQL as the primary database.
# ... several turns later ...
!keep_tag=key_decision Continue based on our earlier architecture decisions.

---

Architecture

Full orchestration pipeline (9 steps)

Raw message list  (list[dict], OpenAI format)
        │
        ▼  Conversation.from_openai_messages()
           Parses roles, tool_call dependencies, multimodal blocks
        │
        ▼  ContextOrchestrator.process()
        │
  ①  Flag parsing      Extract !flag prefixes → SessionFlags; clean message content
  ②  Budget override   full → unlimited; context=N → turn limit; target=N → soft target
  ③  Anchor pinning    !recent=N and !keep_tag= pin messages before scoring
  ④  Token counting    Fill token_count on each message (tiktoken)
  ⑤  Dynamic weighting WeightingStrategy.score() → message.weight = base × relevance
  ⑥  Algorithm         algorithm.select() → SelectionResult {keep / summarize / drop}
  ⑦  Constraint repair ConstraintChecker promotes messages until all rules satisfied
  ⑧  Summarization     asyncio.gather() compresses all summarize messages (cache-first)
  ⑨  Assembly + tracking  system msgs + summary block + kept msgs (original order);
                           PrefixSequenceTracker records sent sequence for next turn
        │
        ▼  OrchestratorResult
           .conversation.to_openai_messages()  → ready for any LLM
           .run_id          → correlation ID (matches observer log events)
           .token_count     → total tokens in outgoing conversation
           .estimated_cost_usd

Weighting strategies (weighting/)

Strategy	Speed	Accuracy	Requires
StaticWeightStrategy	Instant	—	Nothing (score = 1.0 for all)
KeywordRelevanceStrategy	Fast	Moderate	rank_bm25 (optional) or built-in TF-IDF
SmartWeightingStrategy	Fast	Good	Nothing (keyword + Q&A coherence + tool penalty)
EmbeddingSimilarityStrategy	Medium	High	Any embedding API + cache recommended
LLMContextAnalyzer	Slow	Highest	Any chat API; results cached (30 min TTL)
CompositeWeightStrategy	Depends	Configurable	Union of inner strategies

All strategies implement the same protocol:

async def score(
    messages: list[Message],
    query: str,
    conversation: Conversation,
    context: dict | None = None,
) -> dict[str, float]:   # {message_id: relevance_score}

Selection algorithms (algorithms/)

def select(
    conversation: Conversation,
    budget: Budget,
    constraints: ConstraintSet,
    token_counter: TokenCounter,
) -> SelectionResult:

Algorithm	Description
GreedyByWeightAlgorithm	Sort by weight descending, greedily fill budget; prefer summarize over drop (prefer_summarize=True)
RecencyBiasedAlgorithm	Apply a linear recency multiplier (default factor=2.0) then delegate to greedy
DependencyAwareAlgorithm	Decorator: ensure kept messages' dependency chains are recursively kept
PrefixCacheOptimizedAlgorithm	Lock the longest common prefix from the previous turn to maximize KV-cache hits, then greedy-fill the remainder

Summarizers (summarizers/)

TurnSummarizer          single turn (user+assistant) → summary string  [cached, TTL=24 h]
      ↓
SegmentSummarizer       N turns → segment summary  (two-pass: per-turn then aggregate)
      ↓
ConversationSummarizer  full conversation → high-level overview

LLMSummarizer calls /v1/chat/completions directly via httpx — no SDK dependency, works with any OpenAI-compatible service. Supports extra_body for provider-specific parameters.

Cache layer (cache/)

class CacheBackend(Protocol):
    async def get(self, key: str) -> str | None: ...
    async def set(self, key: str, value: str, ttl: int | None = None) -> None: ...
    async def delete(self, key: str) -> None: ...
    async def exists(self, key: str) -> bool: ...

Backend	Import	Notes
InMemoryCache	from lethes.cache import InMemoryCache	Default; zero dependencies
RedisCache	from lethes.cache import RedisCache	RedisCache.from_url("redis://...") requires lethes[redis]

Cache keys are derived from SHA-256 hashes of message content — identical contexts share cache entries.

Budget types (models/budget.py)

Type	Key parameter	Behaviour
TokenBudget(max_tokens=N)	max_tokens (0 = unlimited)	Hard token cap
TokenTargetBudget(target_tokens=N)	target_tokens, overshoot=150	Aim to fill to ~N tokens (soft target)
CostBudget(max_cost_usd=N)	max_cost_usd (0 = unlimited)	Soft USD cost cap
CompositeBudget(token_budget, cost_budget)	Both must be satisfied	CompositeBudget.unlimited() for pass-through

---

Observability

lethes emits structured JSON log events via structlog, keyed by run_id per process() call. Configure once at startup:

from lethes import configure_logging
import logging

configure_logging(
    level="DEBUG",   # DEBUG | INFO | WARNING | ERROR
    fmt="json",      # "json" (default) | "console"
    handlers=[...],  # optional: provide your own logging.Handler list
)

Each handler with no formatter set receives the structlog JSON formatter automatically.

lethes-observer

lethes-observer is a companion real-time dashboard that receives log events over HTTP and provides an in-browser view of pipeline runs, message dispositions, weights, and sub-agent calls.

from lethes import configure_logging
from lethes_observer.handler import LethesObserverHandler  # from lethes_observer/

h = LethesObserverHandler("http://localhost:7456")
configure_logging(level="DEBUG", handlers=[h])

Start the observer server:

cd lethes_observer
uvicorn server:app --port 7456

---

Open WebUI Integration

Installed package

from lethes.integrations.open_webui import OpenWebUIFilter as Filter

Core Valves (no observer / live pricing):

Valve	Default	Description
max_tokens	10000	Token limit (0 = unlimited)
algorithm	recency_biased	greedy_by_weight / recency_biased / dependency_aware
recency_factor	1.5	Recency bias strength
weighting	smart	static / keyword / smart
tool_penalty	0.5	Weight multiplier for tool-call messages
pair_coherence	0.8	Score fraction inherited by assistant replies
llm_analysis	false	Enable LLM sub-agent context analysis
llm_analysis_weight	0.6	LLM analysis fraction in composite weighting
summary_api_base	OpenAI	OpenAI-compatible API for summarization / LLM analysis
summary_api_key	—	API key
summary_model	gpt-4o-mini	Model for summarization / LLM analysis
summary_target_ratio	0.3	Summarization compression ratio
retry_attempts	3	Retry attempts for summarization calls
nosum_by_default	false	Globally disable summarization
cache_backend	memory	memory / redis
redis_url	redis://redis:6379/0	Redis URL
pricing_config_path	—	Path to a custom pricing JSON file; leave empty to use OpenRouter live pricing
openrouter_pricing	true	Fetch live model pricing from OpenRouter at startup; disable in offline/air-gapped environments

Standalone filter (lethes_observer/helper/open_webui_filter.py)

The standalone file can be dropped directly into your Open WebUI functions directory — no package install needed. It includes all base Valves plus:

Valve	Default	Description
use_openrouter_pricing	true	Fetch live model pricing from OpenRouter API
pricing_cache_ttl_hours	24.0	How long to cache OpenRouter pricing data
model_aliases	{}	JSON map of raw model IDs → canonical names, applied before prefix-stripping. Example: {"poe.gemini-3-flash": "gemini-3-flash-preview"}
observer_url	—	lethes-observer server URL (e.g. http://localhost:7456)
observer_log_level	DEBUG	Log level forwarded to the observer

The standalone filter also:

• Reads actual token counts and cost from the LLM response body (outlet()) rather than always estimating
• Logs a pipeline.outlet event so the observer can display real vs estimated cost

---

Generic Middleware

from lethes.integrations import LethesMiddleware

middleware = LethesMiddleware(orchestrator=my_orchestrator)

# Direct call
processed = await middleware(raw_messages, model_id="gpt-4o")

# As a decorator
@middleware.wrap
async def call_llm(messages, **kwargs):
    return await client.chat.completions.create(
        model="gpt-4o", messages=messages, **kwargs
    )

---

Custom Extensions

All extension points are Protocol types — no base class required:

# Custom weighting strategy
class MyWeightingStrategy:
    async def score(
        self,
        messages: list[Message],
        query: str,
        conversation: Conversation,
        context: dict | None = None,
    ) -> dict[str, float]:   # {message_id: relevance_score}
        ...
    def name(self) -> str: return "my_strategy"

# Custom selection algorithm
class MyAlgorithm:
    def select(
        self,
        conversation: Conversation,
        budget: Budget,
        constraints: ConstraintSet,
        token_counter: TokenCounter,
    ) -> SelectionResult:
        ...
    def name(self) -> str: return "my_algo"

# Custom summarization backend
class MySummarizer:
    async def summarize(
        self,
        messages: list[Message],
        *,
        target_ratio: float = 0.3,
        context_messages: list[Message] | None = None,
    ) -> str:
        ...
    def name(self) -> str: return "my_summarizer"

# Custom cache backend
class MyCache:
    async def get(self, key: str) -> str | None: ...
    async def set(self, key: str, value: str, ttl: int | None = None) -> None: ...
    async def delete(self, key: str) -> None: ...
    async def exists(self, key: str) -> bool: ...

---

Project Structure

src/lethes/
├── models/           Messages, conversations, budgets, pricing models
├── flags/            Flag syntax parser and session state
├── weighting/        Dynamic relevance scoring strategies
│   ├── static.py     StaticWeightStrategy
│   ├── keyword.py    KeywordRelevanceStrategy
│   ├── smart.py      SmartWeightingStrategy
│   ├── embedding.py  EmbeddingSimilarityStrategy
│   ├── llm_analyzer.py  LLMContextAnalyzer
│   └── composite.py  CompositeWeightStrategy
├── algorithms/       Context selection algorithms
├── cache/            Cache backends + prefix sequence tracker
├── summarizers/      LLMSummarizer, TurnSummarizer, SegmentSummarizer, ConversationSummarizer
├── engine/           ConstraintChecker, ContextPlan, ContextOrchestrator
├── integrations/     OpenWebUIFilter, LethesMiddleware
├── observability.py  configure_logging, get_logger, make_formatter
└── utils/            Token counting, content extraction, ID generation

---

License

MIT