contextseek 0.1.0

ContextSeek semantic context substrate for agent systems.

ContextSeek

Semantic context infrastructure for AI agents. 中文文档

Overview

Agent self-evolution is taking shape along two technical paths. One extracts and solidifies experience from runtime behavior (e.g. Hermes, OpenHuman). The other evolves the context infrastructure beneath the agent—organizing, updating, and linking context automatically—without modifying agent execution logic.

ContextSeek focuses on the latter. It turns one-off, task-level gains into compounding value across context lifecycles, so heterogeneous agent systems can share a single semantic layer for retrieval, provenance, and evolution.

Three constraints still stand in the way: heterogeneous integration—Memory, Trace, and related components expose incompatible APIs and semantic conventions; insufficient retention—runtime experience is consumed in the prompt window and rarely becomes reusable capability; missing provenance—outputs lack traceable evidence chains. ContextSeek is a unified semantic context layer between LLMs and agent runtimes, converging these capabilities in a single object model: everything is a ContextItem, retrievable and traceable, with automatic progression through raw → extracted → knowledge → skill.

Quick Start

pip install contextseek

from contextseek import ContextSeek

ctx = ContextSeek.from_settings()  # reads .env or environment variables

# Write
ctx.add(
    "OceanBase is a financial-grade distributed database supporting HTAP workloads",
    scope="acme/db/engineer",
    source="wiki",
)

# Retrieve (ranked SearchHits; L1 summaries by default)
for hit in ctx.retrieve("distributed database", scope="acme/db/engineer", k=10):
    print(f"[{hit.item.stage.value}] score={hit.score:.2f} | {hit.item.summary[:60]}")

Configure via .env (see .env.example) or ContextSeekSettings in code. A storage backend, an embedding provider, and an LLM are the three required pieces.

Documentation

• Getting started (EN) / 快速上手 (ZH): installation, .env setup, and a walkthrough of the core operations.
• Client API reference: full method signatures for add, retrieve, expand, compact, dream, evidence_chain, and more.
• Configuration reference: all environment variables and ContextSeekSettings fields.
• DataPlugs: how to ingest from RAG pipelines, memory stores, execution traces, and skill / tool registries.
• Examples: annotated scripts for common workflows.
• AppWorld eval / τ-bench eval: optional evaluation harnesses with their own setup requirements.

How it works

• Unified object model — all context — memory, knowledge, traces, skills — is a ContextItem. Items carry mandatory Provenance (source type, source id, confidence) and typed Link edges (supports, refutes, derives, supersedes), enabling a full EvidenceChain DAG with confidence propagation.
• Content tiers — L0 (~100 tokens) feeds embedding recall. L1 (~2 k tokens) is the default surface returned by retrieve(). L2 (full body) is available on demand via expand().
• Retrieval orchestrator — keyword + vector hybrid recall, optional LLM reranking, and scope-based routing. Returns ranked SearchHit rows. Exposes tool specs for OpenAI and Anthropic agents via ctx.tools().
• EvolutionEngine — watches for items that can be merged, resolved, advanced in stage, or distilled into skills. Runs incrementally after writes or on an explicit compact() call.
• DreamEngine — idle-time pattern consolidation and cross-cluster hypothesis generation, triggered via dream().
• HTTP + MCP servers — expose the same operations over FastAPI and the Model Context Protocol for remote agent integrations.

Related Projects

• seekvfs — underlying virtual filesystem

License

Apache License 2.0