prellm 0.3.10

preLLM — One function for small LLM preprocessing before large LLM execution. Like litellm.completion() but with decomposition.

preLLM

One function for small LLM preprocessing before large LLM execution. Like litellm.completion() — but with a smart preprocessing layer.

from prellm import preprocess_and_execute

result = await preprocess_and_execute(
    query="Deploy app to production",
    small_llm="ollama/qwen2.5:3b",     # local, fast, cheap
    large_llm="anthropic/claude-sonnet-4-20250514",  # cloud, powerful
)
print(result.content)

Install & Run in 60 Seconds

pip install prellm

# CLI — zero config
prellm query "Zdeployuj apkę na prod" --small ollama/qwen2.5:3b --large gpt-4o-mini

# With strategy
prellm query "Refaktoryzuj kod" --strategy structure --json

# Two-agent pipeline (v0.3)
prellm query "Deploy app" --pipeline dual_agent_full

# Docker
docker run prellm/prellm query "Deploy app" --small ollama/qwen2.5:3b --large gpt-4o-mini

How It Works

User Query
  → Small LLM (≤24B, local)    → classify / structure / enrich    → optimized prompt
    Qwen2.5 / Phi3 / Gemma       PromptPipeline (YAML)
  → Large LLM (cloud)          → execute with full context        → validated response
    GPT-4 / Claude / Llama       ResponseValidator (YAML schema)

Result: 70–80% token savings + enterprise-quality output for the price of a small LLM call.

---

Python API

One Function — Two Execution Paths

from prellm import preprocess_and_execute

# PATH A: Strategy-based (v0.2, default)
result = await preprocess_and_execute(
    query="Deploy app to production",
    small_llm="ollama/qwen2.5:3b",
    large_llm="anthropic/claude-sonnet-4-20250514",
    strategy="structure",                 # classify|structure|split|enrich|passthrough
    user_context="gdansk_embedded_python",
)

# PATH B: Pipeline-based two-agent (v0.3)
result = await preprocess_and_execute(
    query="Deploy app to production",
    small_llm="ollama/qwen2.5:3b",
    large_llm="anthropic/claude-sonnet-4-20250514",
    pipeline="dual_agent_full",           # any pipeline from pipelines.yaml
)

print(result.content)              # Large LLM response
print(result.decomposition)        # Small LLM analysis
print(result.model_used)           # Which large model answered
print(result.small_model_used)     # Which small model preprocessed

Sync Version

from prellm import preprocess_and_execute_sync

result = preprocess_and_execute_sync("Deploy app", large_llm="gpt-4o-mini")
# Works exactly the same, just blocking

Zero-Config

# Defaults: small=ollama/qwen2.5:3b, large=claude-sonnet, strategy=classify
result = await preprocess_and_execute("Refaktoryzuj kod")

---

LLM Provider Examples

preLLM uses LiteLLM under the hood, so any model string supported by LiteLLM works.

Ollama (local, free)

# Start Ollama: ollama serve
# Pull model:   ollama pull qwen2.5:3b

result = await preprocess_and_execute(
    query="Explain Kubernetes pods",
    small_llm="ollama/qwen2.5:3b",       # local small model
    large_llm="ollama/llama3:70b",        # local large model
)
# Cost: $0.00 — both models run locally

Ollama + OpenAI (hybrid)

result = await preprocess_and_execute(
    query="Review my Python code",
    small_llm="ollama/qwen2.5:3b",       # local preprocessing
    large_llm="gpt-4o-mini",             # OpenAI execution
)
# Cost: $0.00 (local) + ~$0.15 (OpenAI) = $0.15

Ollama + Anthropic (hybrid)

result = await preprocess_and_execute(
    query="Deploy microservices to K8s",
    small_llm="ollama/phi3:mini",         # local preprocessing
    large_llm="anthropic/claude-sonnet-4-20250514",  # Anthropic execution
)

OpenAI only

result = await preprocess_and_execute(
    query="Analyze sales data",
    small_llm="gpt-4o-mini",             # cheap OpenAI preprocessing
    large_llm="gpt-4o",                  # powerful OpenAI execution
)

Anthropic only

result = await preprocess_and_execute(
    query="Write a compliance report",
    small_llm="anthropic/claude-haiku",
    large_llm="anthropic/claude-sonnet-4-20250514",
)

Groq (fast inference)

result = await preprocess_and_execute(
    query="Summarize meeting notes",
    small_llm="groq/llama-3.1-8b-instant",   # fast Groq preprocessing
    large_llm="groq/llama-3.3-70b-versatile", # fast Groq execution
)

Mistral

result = await preprocess_and_execute(
    query="Translate technical docs",
    small_llm="mistral/mistral-small-latest",
    large_llm="mistral/mistral-large-latest",
)

Azure OpenAI

result = await preprocess_and_execute(
    query="Generate quarterly report",
    small_llm="azure/gpt-4o-mini-deployment",
    large_llm="azure/gpt-4o-deployment",
)

AWS Bedrock

result = await preprocess_and_execute(
    query="Optimize Lambda function",
    small_llm="bedrock/anthropic.claude-haiku",
    large_llm="bedrock/anthropic.claude-sonnet",
)

Full provider list: See LiteLLM docs — preLLM supports all 100+ providers.

---

Integration with Existing LiteLLM Projects

Drop-in Enhancement

If you already use LiteLLM, preLLM adds preprocessing with one line change:

# BEFORE — direct litellm call
import litellm
response = await litellm.acompletion(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Deploy app to production"}],
)

# AFTER — preLLM preprocessing + same litellm execution
from prellm import preprocess_and_execute
result = await preprocess_and_execute(
    query="Deploy app to production",
    large_llm="gpt-4o",  # same model, now with preprocessing
)
# result.content == same quality, but with structured decomposition

Use Your Existing .env

preLLM reads the same environment variables as LiteLLM:

# .env — works with both litellm and prellm
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GROQ_API_KEY=gsk_...

# preLLM-specific (optional)
PRELLM_SMALL_MODEL=ollama/qwen2.5:3b
PRELLM_LARGE_MODEL=anthropic/claude-sonnet-4-20250514
PRELLM_STRATEGY=classify

LiteLLM Proxy Integration

If you run a LiteLLM proxy, point preLLM at it:

import os
os.environ["OPENAI_API_BASE"] = "http://localhost:4000"  # your litellm proxy

result = await preprocess_and_execute(
    query="Deploy app",
    small_llm="openai/small-model",   # routed through litellm proxy
    large_llm="openai/large-model",   # routed through litellm proxy
)

OpenAI SDK-Compatible Server

preLLM ships an OpenAI-compatible proxy — use it from any OpenAI SDK client:

# Start preLLM server
prellm serve --port 8080 --small ollama/qwen2.5:3b --large gpt-4o-mini

# Use from OpenAI Python SDK
import openai
client = openai.OpenAI(base_url="http://localhost:8080/v1", api_key="any")
response = client.chat.completions.create(
    model="prellm:default",
    messages=[{"role": "user", "content": "Deploy app to production"}],
)

# Use from curl
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"prellm:qwen→claude","messages":[{"role":"user","content":"Deploy app"}]}'

# Use v0.3 pipeline via API
curl http://localhost:8080/v1/chat/completions \
  -d '{"model":"prellm:default","messages":[{"role":"user","content":"Deploy app"}],"prellm":{"pipeline":"dual_agent_full"}}'

---

Two-Agent Architecture (v0.3)

The pipeline= parameter activates the new two-agent architecture:

USER QUERY
    │
    ▼
┌─────────────────────────────────────┐
│  PREPROCESSOR AGENT (small LLM)     │
│  PromptRegistry (YAML prompts)      │
│  PromptPipeline (YAML steps)        │
│  → classify → structure → compose   │
│  → IntermediateValidator            │
└──────────────┬──────────────────────┘
               │ structured executor_input
               ▼
┌─────────────────────────────────────┐
│  EXECUTOR AGENT (large LLM)         │
│  → execute with full context        │
│  → ResponseValidator (YAML schema)  │
│  → PreLLMResponse (typed)           │
└─────────────────────────────────────┘

Custom Pipelines (YAML)

Define your own preprocessing pipeline — no Python code changes needed:

# configs/pipelines.yaml
pipelines:
  my_pipeline:
    description: "Custom 3-step pipeline"
    steps:
      - name: classify
        prompt: classify          # from configs/prompts.yaml
        output: classification
      - name: extract
        prompt: structure
        output: fields
      - name: compose
        prompt: compose
        input: [query, classification, fields]
        output: composed_prompt

result = await preprocess_and_execute(
    query="Deploy app",
    pipeline="my_pipeline",  # uses your custom YAML pipeline
)

Available Pipelines

Pipeline	Steps	Best for
classify	classify	Quick intent routing
structure	classify → structure → compose	DevOps, API calls
split	classify → split → compose	Complex multi-part queries
enrich	classify → enrich	Incomplete prompts
dual_agent_full	context → decompose → optimize → format	Maximum quality
passthrough	(none)	Direct forwarding

Custom Prompts (YAML)

All system prompts are in configs/prompts.yaml with Jinja2 templating:

# configs/prompts.yaml
prompts:
  classify:
    system: |
      You are a query classifier.
      Intents: {{ intents | default("deploy, query, create, delete") }}
      Respond ONLY with JSON: {"intent": "...", "confidence": 0.0-1.0}
    max_tokens: 256
    temperature: 0.1

Response Validation (YAML)

Validate LLM outputs with schemas — no code changes:

# configs/response_schemas.yaml
schemas:
  classification:
    required_fields: [intent, confidence]
    types:
      intent: string
      confidence: float
    constraints:
      confidence: {min: 0.0, max: 1.0}
      intent: {enum: [deploy, query, create, delete, other]}

---

5 Decomposition Strategies (v0.2)

Strategy	What it does	Best for
classify	Classify intent + domain	General queries, routing
structure	Extract action, target, params	DevOps commands, API calls
split	Break into sub-queries	Complex multi-part requests
enrich	Add missing context	Incomplete prompts, safety
passthrough	No preprocessing	Simple/direct queries

With Domain Rules

result = await preprocess_and_execute(
    query="Usuń bazę danych klientów",
    small_llm="ollama/qwen2.5:3b",
    large_llm="gpt-4o-mini",
    domain_rules=[{
        "name": "destructive_db",
        "keywords": ["delete", "drop", "usuń"],
        "required_fields": ["target_database", "backup_confirmed"],
        "severity": "critical",
    }],
)
print(result.decomposition.missing_fields)  # ["target_database", "backup_confirmed"]

---

Use Cases

1. Code Refactoring

result = await preprocess_and_execute(
    query="Popraw mój projekt z hardcode'em",
    small_llm="ollama/qwen2.5:3b",
    large_llm="anthropic/claude-sonnet-4-20250514",
    strategy="structure",
    user_context="gdansk_embedded_python",
)
# Small LLM: classify intent, extract structure, compose prompt
# Large LLM: complete refactored code with tests
# Cost: $0.01 + $0.45 = $0.46

2. Kubernetes Diagnostics

result = await preprocess_and_execute(
    query="Zdiagnozuj problem z K8s podami",
    small_llm="ollama/qwen2.5:3b",
    large_llm="gpt-4o-mini",
    pipeline="structure",
    user_context={"cluster": "k8s-prod", "namespace": "backend"},
)
# Preprocessor: parse context, identify missing fields, compose prompt
# Executor: root cause + K8s manifests + Prometheus rules
# Cost: $0.02 + $0.38 = $0.40

3. Business Automation

result = await preprocess_and_execute(
    query="Zautomatyzuj kalkulację leasingu dla camper van",
    small_llm="ollama/qwen2.5:3b",
    large_llm="anthropic/claude-sonnet-4-20250514",
    pipeline="enrich",
    user_context="PL_automotive_leasing",
)
# Preprocessor: domain=automotive, locale=PL, required=[VAT, WIBOR]
# Executor: Python calculator + Excel generator + PDF templates
# Cost: $0.015 + $0.52 = $0.535

---

Configuration (YAML)

# configs/prellm_config.yaml
small_model:
  model: "ollama/qwen2.5:3b"
  fallback: ["phi3:mini"]
  max_tokens: 512

large_model:
  model: "gpt-4o-mini"
  fallback: ["llama3", "mistral"]
  max_tokens: 2048

default_strategy: classify

domain_rules:
  - name: production_deploy
    keywords: ["deploy", "push", "release"]
    required_fields: ["environment", "version"]
    severity: critical
    strategy: structure

Per-Domain Defaults

Ready-to-use configs in configs/defaults/:

Domain	File	Covers
DevOps	configs/defaults/devops.yaml	deploy, K8s, monitoring, CI/CD
Coding	configs/defaults/coding.yaml	refactoring, review, debugging
Business	configs/defaults/business.yaml	leasing, invoicing, compliance
Embedded	configs/defaults/embedded.yaml	RPi, ESP32, sensors, IoT

---

Process Chains (DevOps Workflows)

from prellm import PreLLM, ProcessChain

engine = PreLLM("configs/prellm_config.yaml")
chain = ProcessChain("configs/deploy.yaml", engine=engine)
result = await chain.execute(env="production", dry_run=True)

for step in result.steps:
    print(f"{step.step_name}: {step.status}")

---

Architecture

preprocess_and_execute(query, small_llm, large_llm, strategy= | pipeline=)
    │
    ├── [strategy path — v0.2]
    │   ├── ContextEngine (env/git/system)
    │   ├── QueryDecomposer (small LLM)
    │   │   └── classify → structure → split → enrich → compose
    │   └── LLMProvider (large LLM via litellm)
    │
    ├── [pipeline path — v0.3]
    │   ├── PreprocessorAgent
    │   │   ├── PromptRegistry (YAML, Jinja2)
    │   │   ├── PromptPipeline (YAML-configurable steps)
    │   │   │   ├── LLM steps (small LLM calls)
    │   │   │   └── Algorithmic steps (validation, formatting)
    │   │   └── ContextEngine + UserMemory (SQLite)
    │   ├── ExecutorAgent
    │   │   ├── LLMProvider (large LLM via litellm)
    │   │   └── ResponseValidator (YAML schemas)
    │   └── 100+ models via LiteLLM
    │
    └── PreLLMResponse (Pydantic v2 validated)

Development

git clone https://github.com/wronai/prellm
cd prellm
poetry install
poetry run pytest                 # 219+ tests
poetry run pytest --cov           # coverage report
poetry run ruff check prellm/     # linting

Roadmap

See ROADMAP.md for the full plan.

License

Apache License 2.0 - see LICENSE for details.

Author

Created by Tom Sapletta - [e-mail]