astro-anchor 0.1.1

Context engineering toolkit for AI applications

Context engineering toolkit for AI applications.

Docs · Quickstart · Cookbook · API Reference · Issues

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Stop duct-taping RAG, memory, and tools together. anchor gives you a single, token-aware pipeline that assembles context for any LLM — with smart budgets, hybrid retrieval, and provider-agnostic formatting out of the box.

Why anchor?

Feature	LangChain	LlamaIndex	mem0	anchor
Hybrid RAG (Dense + BM25 + RRF)	partial	yes	no	yes
Token-aware Memory	partial	no	yes	yes
Token Budget Management	no	no	no	yes
Provider-agnostic Formatting	no	no	no	yes
Protocol-based Plugins (PEP 544)	no	partial	no	yes
Zero-config Defaults	no	no	yes	yes
2000+ tests, 94% coverage	—	—	—	yes

Quick Install

pip install astro-anchor

Optional extras

pip install astro-anchor[bm25]        # BM25 sparse retrieval
pip install astro-anchor[anthropic]   # Anthropic Claude support
pip install astro-anchor[cli]         # CLI tools (typer + rich)
pip install astro-anchor[flashrank]   # FlashRank reranking
pip install astro-anchor[otlp]        # OpenTelemetry tracing
pip install astro-anchor[all]         # Everything

30 Seconds to Your First Pipeline

from anchor import ContextPipeline, MemoryManager, AnthropicFormatter

pipeline = (
    ContextPipeline(max_tokens=8192)
    .with_memory(MemoryManager(conversation_tokens=4096))
    .with_formatter(AnthropicFormatter())
    .add_system_prompt("You are a helpful assistant.")
)

result = pipeline.build("What is context engineering?")
print(result.formatted_output)   # Ready for Claude API
print(result.diagnostics)        # Token usage, timing, overflow info

Features

• Hybrid RAG — Dense embeddings + BM25 sparse retrieval with Reciprocal Rank Fusion
• Smart Memory — Token-aware sliding window with automatic eviction
• Token Budgets — Priority-ranked context assembly that never exceeds your window
• Provider Agnostic — Format output for Anthropic, OpenAI, or plain text
• Protocol-Based — Plug in any vector store, tokenizer, or retriever via PEP 544 Protocols
• Type-Safe — Pydantic v2 models throughout, full py.typed support
• Agent Framework — Build tool-calling agents with @tool decorator and skills
• Full Observability — OpenTelemetry tracing, cost tracking, per-step diagnostics

Hybrid Retrieval Example

import math
from anchor import (
    ContextPipeline, ContextItem, QueryBundle, SourceType,
    DenseRetriever, InMemoryContextStore, InMemoryVectorStore,
    retriever_step,
)

def my_embed_fn(text: str) -> list[float]:
    seed = sum(ord(c) for c in text) % 10000
    raw = [math.sin(seed * 1000 + i) for i in range(64)]
    norm = math.sqrt(sum(x * x for x in raw))
    return [x / norm for x in raw] if norm else raw

dense = DenseRetriever(
    vector_store=InMemoryVectorStore(),
    context_store=InMemoryContextStore(),
    embed_fn=my_embed_fn,
)

items = [
    ContextItem(content="Python is great for data science.", source=SourceType.RETRIEVAL),
    ContextItem(content="RAG combines retrieval with generation.", source=SourceType.RETRIEVAL),
]
dense.index(items)

pipeline = ContextPipeline(max_tokens=8192).add_step(retriever_step("search", dense, top_k=5))
query = QueryBundle(query_str="How does RAG work?", embedding=my_embed_fn("How does RAG work?"))
result = pipeline.build(query)

Memory Management Example

from anchor import ContextPipeline, QueryBundle, MemoryManager

memory = MemoryManager(conversation_tokens=4096)
memory.add_user_message("Help me migrate from MySQL to Postgres")
memory.add_assistant_message("Sure! What MySQL version are you using?")
memory.add_user_message("MySQL 8.0, about 50GB")

pipeline = ContextPipeline(max_tokens=8192).with_memory(memory)
result = pipeline.build(QueryBundle(query_str="What are the risks?"))
# Memory is automatically included, oldest turns evicted if over budget

Decorator API Example

from anchor import ContextPipeline, ContextItem, QueryBundle

pipeline = ContextPipeline(max_tokens=8192)

@pipeline.step
def boost_recent(items: list[ContextItem], query: QueryBundle) -> list[ContextItem]:
    return [
        item.model_copy(update={"score": min(1.0, item.score * 1.5)})
        if item.metadata.get("recent") else item
        for item in items
    ]

result = pipeline.build("What is context engineering?")

Provider Formatting

from anchor import AnthropicFormatter, OpenAIFormatter

# Anthropic: {"system": "...", "messages": [...]}
result = pipeline.with_formatter(AnthropicFormatter()).build(query)

# OpenAI: {"messages": [{"role": "system", ...}, ...]}
result = pipeline.with_formatter(OpenAIFormatter()).build(query)

Architecture

ContextPipeline
  │
  ├── System Prompts (priority=10)
  ├── Memory Manager (priority=7)
  ├── Pipeline Steps
  │     ├── Retriever Steps (append items)
  │     ├── PostProcessor Steps (transform items)
  │     └── Filter Steps (filter items)
  │
  ▼
ContextWindow (token-aware, priority-ranked)
  │
  ▼
Formatter (Anthropic / OpenAI / Generic)
  │
  ▼
ContextResult (formatted output + diagnostics)

Token Budgets

from anchor import ContextPipeline, default_chat_budget

budget = default_chat_budget(max_tokens=8192)
pipeline = ContextPipeline(max_tokens=8192).with_budget(budget)

Three presets available: default_chat_budget (conversational), default_rag_budget (retrieval-heavy), default_agent_budget (balanced).

Development

git clone https://github.com/artcgranja/anchor.git
cd anchor
uv sync
uv run pytest     # 2000+ tests
uv run ruff check src/ tests/

Roadmap

• v0.1.0 (current) — Hybrid RAG, Memory, Pipeline, Formatters, Async, Decorator API, Agent Framework, Full docs
• v0.2.0 — MCP Bridge, progressive summarization, persistent storage backends
• v0.3.0 — GraphRAG, multi-modal context, LangChain/LlamaIndex adapters
• v1.0.0 — Production-grade APIs, plugin ecosystem

License

MIT — see LICENSE for details.