contextweaver 0.1.4

Dynamic context management for tool-using AI agents

contextweaver

Dynamic context management for tool-using AI agents.

contextweaver solves the context window problem: as tool catalogs grow and conversations accumulate history, naive concatenation blows past token limits. contextweaver provides phase-specific budgeted context compilation, a context firewall for large tool outputs, result envelopes with structured fact extraction, and bounded-choice routing over large tool catalogs via DAG + beam search.

Features

• Context Engine — seven-stage pipeline that compiles a phase-aware, budget-constrained prompt from the event log.
• Context Firewall — intercepts large tool outputs, stores raw data out-of-band, and injects compact summaries.
• Routing Engine — navigates catalogs of 100+ tools via a bounded DAG so the LLM only sees a focused shortlist.
• Protocol Adapters — first-class adapters for MCP and A2A protocols.
• Zero Dependencies — pure Python ≥ 3.10, stdlib only.
• Deterministic — identical inputs always produce identical outputs.

Installation

pip install contextweaver

Or install from source:

git clone https://github.com/dgenio/contextweaver.git
cd contextweaver
pip install -e ".[dev]"

Quick start

from contextweaver.context.manager import ContextManager
from contextweaver.types import ContextItem, ItemKind, Phase

mgr = ContextManager()
mgr.ingest(ContextItem(id="u1", kind=ItemKind.user_turn, text="How many users?"))
mgr.ingest(ContextItem(id="tc1", kind=ItemKind.tool_call, text="db_query('SELECT COUNT(*) FROM users')", parent_id="u1"))
mgr.ingest(ContextItem(id="tr1", kind=ItemKind.tool_result, text="count: 1042", parent_id="tc1"))

pack = mgr.build_sync(phase=Phase.answer, query="user count")
print(pack.prompt)       # budget-aware compiled context
print(pack.stats)        # what was kept, dropped, deduplicated

Routing large tool catalogs

from contextweaver.routing.catalog import Catalog, load_catalog_json
from contextweaver.routing.tree import TreeBuilder
from contextweaver.routing.router import Router

items = load_catalog_json("catalog.json")
catalog = Catalog()
for item in items:
    catalog.register(item)

graph = TreeBuilder(max_children=10).build(catalog.all())
router = Router(graph, items=catalog.all(), beam_width=3, top_k=5)
result = router.route("send a reminder email about unpaid invoices")
print(result.candidate_ids)

CLI

contextweaver ships with a CLI for quick experimentation:

contextweaver demo                          # end-to-end demonstration
contextweaver init                          # scaffold config + sample catalog
contextweaver build --catalog c.json --out g.json  # build routing graph
contextweaver route --graph g.json --query "send email"
contextweaver print-tree --graph g.json
contextweaver ingest --events session.jsonl --out session.json
contextweaver replay --session session.json --phase answer

Examples

Script	Description
minimal_loop.py	Basic event ingestion → context build
tool_wrapping.py	Context firewall in action
routing_demo.py	Build catalog → route queries → choice cards
before_after.py	Side-by-side token comparison: WITHOUT vs WITH contextweaver
mcp_adapter_demo.py	MCP adapter: tool conversion, session loading, firewall
a2a_adapter_demo.py	A2A adapter: agent cards, multi-agent sessions

Run all examples:

make example

Documentation

• Architecture — package layout, pipeline stages, design principles
• Concepts — ContextItem, phases, firewall, ChoiceGraph, etc.
• MCP Integration — adapter functions, JSONL format, end-to-end example
• A2A Integration — adapter functions, multi-agent sessions

Development

make fmt      # format (ruff)
make lint     # lint (ruff)
make type     # type-check (mypy)
make test     # run tests (pytest)
make example  # run all examples
make demo     # run the built-in demo
make ci       # all of the above

License

Apache-2.0