contextweaver
The MCP context gateway for tool-heavy agents. Drop contextweaver in front of your MCP servers and the model sees a bounded
ChoiceCardshortlist instead of every tool schema, plus an artifact-backed firewall that swaps a huge raw tool result for a compact summary. Deterministic, no model in the loop, and 43-85 % fewer prompt tokens on the committed benchmarks.
Who it's for: anyone whose agent — Claude Desktop, Cursor, VS Code, or a custom loop — keeps tripping over "too many tools" or "a 16 KB tool result blew up my prompt."
uvx contextweaver demo --scenario killer # zero-install trial
# Or install it:
pip install contextweaver
python -c "import contextweaver; print(contextweaver.__version__)"
contextweaver demo --scenario killer # 60-second taste — no API key, no network
contextweaver start # choose gateway, library, routing, or integration
Use it for real → the MCP gateway quickstart (Claude Desktop / Copilot / custom MCP clients), backed by the MCP Context Gateway architecture. Already have a loop and not sure which piece you need? The two engines also work routing-only or firewall-only. For day-to-day operating guidance, see the Daily Driver guide; for deployment boundaries, see the MCP Gateway Security Model and the least-privilege MCP Gateway Security Guide.
1150+ tests passing · minimal core dependencies · deterministic by default · Python 3.10–3.13
More tools ≠ better answers
As an agent's tool catalog grows, a naive "show every schema" route prompt balloons while the right tool gets harder to find — context rot. contextweaver keeps the model-visible surface bounded (5
ChoiceCards, not 1,328 schemas), so the route prompt stays flat and deterministic. Reproduce the curve with no API key:docs/context_rot.md.
📖 Docs · 🎬 Showcase · 🧩 Where it fits · 🗺️ Ecosystem map · ❓ FAQ · 📊 Scorecard · 📈 Adopter benchmark report · 🧭 Which pattern fits? · 🛠 Cookbook · 🍳 Recipes · 📉 Context rot demo · 🎬 Replay demo (.cast)
Part of the Weaver Stack
contextweaver is the context layer of the Weaver Stack — small, deterministic, independently-usable building blocks for tool-using agents. The core request path runs:
contextweaver ─▶ ChainWeaver ─▶ agent-kernel ─▶ agentfence
| Stage | Component | Responsibility |
|---|---|---|
| Context | contextweaver (this repo) | Route a catalog to bounded ChoiceCards, firewall large tool results, compile a budgeted prompt. |
| Execution | ChainWeaver | Run the selected capability as a deterministic tool/flow. |
| Boundary | agent-kernel | Own the execution boundary; hand contextweaver Frames, not raw output. |
| Guardrails | agentfence | Apply output guardrails to the response. |
The contextweaver → ChainWeaver handoff is advisory: contextweaver routes
(it recommends a capability) and ingests results behind its firewall; the
runtime owns authorization and execution. A runnable end-to-end example —
route a catalog of tools + imported ChainWeaver flows, hand the selection to a
(stubbed) ChainWeaver runtime, then ingest the result — lives at
examples/architectures/contextweaver_to_chainweaver/,
and the contract boundary is documented in
docs/weaver_spec_mapping.md.
Adjacent tools: vibeguard (code-diff safety gate), lessonweaver (lesson capture), and skdr-eval (offline evaluation). Every piece works standalone — contextweaver has no hard dependency on any sibling, so you can use it on its own or slot it into the stack. See the Ecosystem Map for how the pieces compose.
The 60-second failure mode
See why a naive tool-using agent loop breaks down — and what contextweaver does about it — in one command (no API keys, no network):
contextweaver demo --scenario killer
An internal ops agent with 100 tools and a running conversation is asked to "find unpaid invoices, check the account notes, and draft a reminder." A naive loop pays for all 100 tool descriptions, the full history, and a huge raw tool result at once:
| Naive | contextweaver | Reduction | |
|---|---|---|---|
| Tools in the route prompt | all 100 (6,326 chars) | 5 ChoiceCards (491 chars) | 92.2% |
| The huge tool result | raw (14,430 chars) | firewalled summary (60 chars) | 99.6% |
| The full answer prompt | everything raw (21,332 chars) | compiled (814 chars) | 96.2% |
Full walkthrough: The 60-second failure mode. For the same story as a runnable, inspectable script, see the catalog showcase architecture.
The Problem
Even with 200K-token context windows, dumping everything into the prompt is expensive, slow, and degrades output quality. More context ≠ better answers — context engineering (deciding what the model sees, when, and at what cost) is the lever that actually moves quality and latency.
Imagine a tool-using agent with a 100-tool catalog and a 50-turn conversation history. At each step the agent must answer four questions:
- Route — which tool should I call?
- Call — what arguments?
- Interpret — what did it return?
- Answer — how do I respond to the user?
Naive approach A — concatenate everything:
100 tool schemas (≈50k tokens) + 50 turns (≈30k tokens) = 80k tokens
Cost: $0.48/request at GPT-4o rates · Latency: 3–5s TTFT
Quality: LLM loses focus — needle-in-haystack accuracy drops with context size
Token limit: 8k → 10× overflow
Naive approach B — cherry-pick manually:
Pick 10 tools, last 5 turns → lose dependency chains
Agent hallucinates tool calls, repeats questions, forgets context
contextweaver approach — phase-specific budgeted compilation:
Route phase: 5 tool cards (≈500 tokens), no full schemas
Answer phase: 3 relevant turns + dependency closure (≈2k tokens)
Result: 2.5k tokens, complete context, deterministic
Cost: 42.7 %-85.0 % fewer prompt tokens [^naive-baseline] · Latency: sub-second · Quality: relevant context only
[^naive-baseline]: Measured against the "concatenate all tool schemas + full
conversation history" baseline using tiktoken.cl100k_base on the six
committed benchmark scenarios. Range 42.7 %-85.0 %, average 65.0 %.
Reproducible via make benchmark-matrix && make scorecard — see the
vs. naïve concat baseline section of
benchmarks/scorecard.md and the
methodology in scripts/baseline_naive.py.
See examples/before_after.py for a runnable side-by-side comparison.
How contextweaver Solves It
contextweaver provides two cooperating engines:
┌────────────────────────────┐
Events ──────>│ Context Engine │──> ContextPack (prompt)
│ candidates → closure → │
│ sensitivity → firewall → │
│ score → dedup → select → │
│ render │
└────────────────────────────┘
▲ facts / episodes
┌──────────┴─────────────────┐
Tools ───────>│ Routing Engine │──> ChoiceCards
│ Catalog → TreeBuilder → │
│ ChoiceGraph → Router │
└────────────────────────────┘
Context Engine — eight-stage pipeline:
- generate_candidates — pull phase-relevant events from the log for this request.
- dependency_closure — if a selected item has a
parent_id, include the parent automatically. - sensitivity_filter — drop or redact items at or above the configured sensitivity floor.
- apply_firewall — tool results are stored out-of-band; large outputs are summarized/truncated before prompt assembly.
- score_candidates — rank by recency, tag match, kind priority, and token cost.
- deduplicate_candidates — remove near-duplicates using Jaccard similarity.
- select_and_pack — greedily pack highest-scoring items into the phase token budget.
- render_context — assemble final prompt string with
BuildStatsmetadata.
Routing Engine — four-stage pipeline:
- Catalog — register and manage
SelectableItemobjects. - TreeBuilder — convert a flat catalog into a bounded
ChoiceGraphDAG. - Router — beam-search over the graph; deterministic tie-breaking by ID.
- ChoiceCards — compact, LLM-friendly cards (never includes full schemas).
Also works as routing-only or firewall-only
The MCP gateway is the headline, but those are really two cooperating engines you can adopt independently — a context compiler and a tool router. Reach for whichever your agent needs:
| If your agent has... | contextweaver gives you... |
|---|---|
| Too many MCP / FastMCP / Python tools | A bounded ChoiceCard shortlist instead of dumping every schema into the route prompt. |
| Huge JSON, logs, tables, or binary tool results | An artifact-backed context firewall: compact summary in the prompt, raw bytes out of band. |
| Long tool-using conversations | Phase-specific context packs with budgeted selection and dependency closure. |
| Use this when... | Do not use this when... |
|---|---|
| You already have an agent loop and need runtime context control. | You need an agent framework, LLM SDK, or tool executor. |
| Your model-visible tool list or tool-result payloads are getting too large. | Your agent has a handful of tiny tools and no context-budget pressure. |
| You want deterministic prompt budgeting and traceable drops. | You only need long-term memory, RAG, or observability by itself. |
Not sure which piece fits? The which-pattern decision tree maps each symptom (long conversations → full pipeline; 50+ tools → routing-only; huge tool outputs → firewall-only) to one concrete next step.
When not to use contextweaver
contextweaver is a context compiler for tool-using agents — its value scales with the size of the catalog and the length of the conversation. Reach for something simpler when none of that pressure exists:
- Small tool catalogs (≤ 5 tools). Dumping every schema into the prompt costs a few hundred tokens. Building a routing graph and running beam search adds latency and a dependency you don't need.
- Single-shot Q&A or one-turn agents. With no history to compact and no
follow-up phases (
call/interpret/answer), phase-aware budgeting is dead weight — pass the user's message straight to the model. - Tiny tool outputs. If every
tool_resultis comfortably under the configuredfirewall_threshold(default 2000 characters), the context firewall correctly no-ops — you're paying the conceptual cost of the firewall for zero token savings. - Full context is cheaper than the engineering. If your naïve prompt fits comfortably under the model's context window and your token bill is not a concern, the before/after scorecard numbers won't move the needle.
- You need non-deterministic, LLM-driven routing. contextweaver's
routing engine is deterministic by design (tie-break by sorted ID). If
you want an LLM to decide which tool to call from free-form reasoning,
LangGraph or a plain
tool_choice="auto"call is a better fit — seedocs/comparison.mdfor the trade-offs.
If you're not sure, the 10-minute Quickstart below is the cheapest way to find out: a 40-tool catalog and a 50-turn transcript is the smallest scenario where contextweaver clearly pays for itself.
Quickstart
Install
Try the CLI without installing it:
uvx contextweaver demo --scenario killer
Or install it persistently:
pip install contextweaver
contextweaver ships with a minimal, opinionated core: tiktoken,
PyYAML, rank-bm25, mcp, jsonschema, Typer, and Rich. These power
token budgeting, YAML catalog/config files, the default lexical retrieval
backend, the MCP proxy/gateway runtime, schema validation, and the CLI.
Optional capabilities are gated behind extras so the core install stays small:
| Extra | What it adds |
|---|---|
contextweaver[weaver-spec] |
Weaver Stack contract adapters (weaver_contracts) |
contextweaver[fastmcp] |
FastMCP catalog adapter and discovery helpers |
contextweaver[crewai] |
CrewAI runtime integration |
contextweaver[pydantic-ai] |
Pydantic AI runtime integration |
contextweaver[smolagents] |
Hugging Face smolagents runtime integration |
contextweaver[agno] |
Agno runtime integration |
contextweaver[langchain] |
LangChain integration helpers |
contextweaver[voice] |
Pipecat voice-agent integration |
contextweaver[retrieval] |
Fuzzy lexical matching backend (rapidfuzz) |
contextweaver[embeddings] |
Sentence-transformers embedding backend |
contextweaver[tokenizers] |
Tokenizer install contract (core tiktoken today) |
contextweaver[sqlite] |
SQLite store install contract (stdlib-backed today) |
contextweaver[mem0] |
Mem0 external-memory backend |
contextweaver[otel] |
OpenTelemetry tracing + metrics export |
contextweaver[e2e-eval] |
Optional real-model benchmark hook (no dependency today) |
contextweaver[docs] |
MkDocs documentation toolchain |
contextweaver[dev] |
Test, lint, type-check, and fixture toolchain |
contextweaver[all] |
Convenience bundle for broad optional runtime capabilities |
Some extras are install-contract markers rather than dependency bundles:
tokenizers, sqlite, and e2e-eval are intentionally empty today because
the current implementations use core dependencies or the Python standard
library. They stay user-facing so requirements files can opt into the capability
surface without changing when future implementation-specific packages are added.
Or from source:
git clone https://github.com/dgenio/contextweaver.git
cd contextweaver
pip install -e ".[dev]"
Adopting in 5 lines from an existing OpenAI / Anthropic / Gemini agent
from contextweaver.adapters.openai_messages import from_openai_messages
from contextweaver.context.manager import ContextManager
from contextweaver.types import Phase
mgr = ContextManager()
from_openai_messages(messages, into=mgr) # also: from_anthropic_messages / from_gemini_contents
pack = mgr.build_sync(phase=Phase.answer, query="...")
See Adopting from an existing chat history
for the full snippet (including the to_* inverse adapters for round-tripping
back into the provider SDK).
10-Minute Quickstart
For a guided setup with prerequisites, three runnable examples, expected output, and next steps, see docs/quickstart.md.
Already have an agent and not sure which piece you need? See Which pattern fits my use case? — a symptom-based decision tree (long conversations → full pipeline; 50+ tools → routing-only; huge tool outputs → firewall-only) that points each branch to one concrete next step.
Minimal agent loop
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
Route a large tool catalog
from contextweaver.routing.catalog import Catalog, load_catalog_json
from contextweaver.routing.tree import TreeBuilder
from contextweaver.routing.router import Router
catalog = Catalog()
for item in load_catalog_json("catalog.json"):
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)
Runtime Loop (4 Phases)
For a complete route -> call -> interpret -> answer reference flow, see:
examples/full_agent_loop.pyfor a runnable end-to-end script.docs/guide_agent_loop.mdfor the flow diagram, pseudo-code, and module map.
The runtime loop example demonstrates:
- Route-phase prompt assembly with ChoiceCards.
- Call-phase prompt assembly with selected tool schema hydration.
- Interpret-phase firewall behavior (large tool output summarized into context).
- Answer-phase context composition with accumulated history and result envelopes.
Framework Integrations
Looking for "where does contextweaver fit alongside my runtime?" — start with the How contextweaver Fits positioning page, then jump into the Cookbook for copy-paste recipes.
The Code adapter column lists the importable adapter module and its
optional extra; its plain-dict conversion path works without the extra
installed. Guide only rows ship a written integration guide but no dedicated
tools → catalog adapter yet (issue #401).
| Framework | Guide | Code adapter | Use Case |
|---|---|---|---|
| MCP | Guide | adapters.mcp |
Tool conversion, session loading, firewall · Security model |
| A2A | Guide | adapters.a2a |
Agent cards, multi-agent sessions |
| FastMCP | Cookbook recipe | adapters.fastmcp · [fastmcp] |
Composed MCP servers → bounded-choice routing |
| LlamaIndex | Guide | Guide only | RAG + tools with budget control |
| OpenAI Agents SDK | Guide | adapters.openai_agents · [openai-agents] |
Swarm hand-offs with unified context |
| Google ADK / Vertex AI | Guide | adapters.google_adk · [google-adk] |
Gemini tool-use with context budgets |
| LangChain + LangGraph | Guide | adapters.langchain · [langchain] |
Chain + graph agents with firewall |
| Pipecat | Guide | Guide only | Real-time voice agents with async context build |
| CrewAI | Guide | adapters.crewai · [crewai] |
Role-based crews with bounded tool shortlists |
| Pydantic AI | Guide | adapters.pydantic_ai · [pydantic-ai] |
Type-safe agents with lossless message round-trip |
| smolagents | Guide | adapters.smolagents · [smolagents] |
Hugging Face CodeAgent / ToolCallingAgent with step-log ingestion |
| Agno | Guide | adapters.agno · [agno] |
Toolkit-routed agents; layers above Agno Memory |
| Microsoft Agent Framework | Guide | adapters.agent_framework · [agent-framework] |
AutoGen / Semantic Kernel tools + thread history |
| OpenAPI | Guide | adapters.openapi |
Route over REST API operations (no extra) |
| Agent Skills | Guide | adapters.agent_skills |
Route over SKILL.md libraries with lazy body hydration |
Core Concepts
| Concept | Description |
|---|---|
ContextItem |
Atomic event log entry: user turn, agent message, tool call, tool result, fact, plan state. |
Sensitivity |
ContextItem.sensitivity defaults to public; the default policy drops confidential and restricted items before they reach the prompt. |
Phase |
route / call / interpret / answer — each with its own token budget. |
ContextFirewall |
Intercepts tool results: stores raw bytes out-of-band, injects compact summary (with truncation for large outputs). |
ChoiceGraph |
Bounded DAG over the tool catalog. Router beam-searches it; LLM sees only a focused shortlist. |
ResultEnvelope |
Structured tool output: summary + extracted facts + artifact handles + views. |
BuildStats |
Per-build diagnostics: candidate count, included/dropped counts, token usage, drop reasons. |
See docs/concepts.md for the full glossary,
docs/architecture.md for pipeline detail and design rationale,
and docs/troubleshooting.md for common issues, debugging
techniques, and performance optimisation tips.
Why Trust contextweaver?
1. Test Coverage & Reliability
contextweaver is built for production use with comprehensive quality gates:
- Broad regression suite across all modules — context pipeline, routing engine, firewall, adapters, stores, CLI, sensitivity enforcement
- mypy strict type checking — zero errors across all source files
- ruff clean linting — zero warnings
- CI pipeline on every pull request and on pushes to
main(see workflows) - Deterministic by default — tie-break by ID, sorted keys; identical inputs always produce identical outputs. Configurable retrieval backends (TF-IDF, BM25, fuzzy) preserve determinism within each mode.
- Public benchmark scorecard — top-k recall, token savings, and routing latency at
catalog sizes 50 / 83 / 1000, plus context pipeline metrics across six reference
scenarios. See
benchmarks/scorecard.md(regenerate withmake scorecard) and the adopter-facingbenchmark report.
Run the full suite yourself:
git clone https://github.com/dgenio/contextweaver.git
cd contextweaver
pip install -e ".[dev]"
make ci # fmt + lint + type + test + schemas-check + example + demo (all pass)
Most agent libraries fail unpredictably when context exceeds token limits. contextweaver's deterministic design and comprehensive test coverage ensure your agent behaves the same way every time — critical for debugging, testing, and production deployment.
2. Design Rationale
Every architectural choice was made for a reason:
| Decision | Reason |
|---|---|
| Minimal core dependencies | A small, audited set of widely-used deps (tiktoken, PyYAML, rank-bm25, mcp, jsonschema, typer, rich); no heavy ML / cloud-SDK packages pulled in by default. |
| Protocol-based interfaces | EventLog, ArtifactStore, EpisodicStore, FactStore are typing.Protocol — swap backends without forking. |
| Async-first context engine | Async-compatible compilation API for real-time integrations; build_sync() wrappers for synchronous callers, with room for future non-blocking execution. |
| Phase-specific token budgets | Route / call / interpret / answer phases each get their own budget — no one-size-fits-all truncation. |
| Context firewall | Large tool outputs stored out-of-band; only compact summaries reach the prompt. |
| Dependency closure | parent_id chains keep tool results coherent — tool calls are never separated from their results. |
These aren't accidental features. They are design decisions optimized for reliability, extensibility, and production use. A minimal, audited core-dependency set means you can adopt contextweaver without disrupting your existing stack.
See docs/architecture.md for full pipeline detail and design rationale.
3. Standardization via Protocol Support
contextweaver supports both emerging agentic protocols out of the box:
MCP (Model Context Protocol) — convert tool definitions and results into native contextweaver types:
- Compatible with any MCP server (Claude Desktop, VS Code, custom servers)
- Structured content, output schemas, binary artifacts, and per-part annotations all handled
ingest_mcp_result()for one-call result ingestion with automatic artifact persistence
A2A (Agent-to-Agent) — multi-agent session management with unified context:
- Agent cards converted to
SelectableItemfor routing - Cross-agent session loading via
load_a2a_session_jsonl() - A2A results stored in
ResultEnvelopewith facts and artifact handles
weaver-spec — canonical contracts for the Weaver Stack (contextweaver, ChainWeaver, agent-kernel):
- Lossless
to_weaver_*/from_weaver_*round-trips forSelectableItem,ChoiceCard,RoutingDecision, andFrame(viaResultEnvelope) weaver_contractsis an opt-in dep —pip install 'contextweaver[weaver-spec]'- Validated in CI on every PR against the JSON Schemas at
raw.githubusercontent.com/dgenio/weaver-spec/main/contracts/json/(the source the gate fetches; the same documents are also published athttps://weaver-spec.dev/contracts/v0/)
contextweaver is designed as a protocol-friendly context management layer for tool-using agents. Supporting MCP, A2A, and weaver-spec keeps the integration boundary explicit as these protocols mature.
4. Framework Agnostic
contextweaver works with any LLM provider and any agent framework:
- LLM providers: OpenAI, Anthropic, Google, open-source models — no API keys required by contextweaver itself
- Agent frameworks: LlamaIndex, LangChain, LangGraph, OpenAI Agents SDK, Google ADK, Pipecat, custom loops
- No vendor lock-in: stdlib-only core; no cloud dependencies; runs anywhere Python 3.10+ runs
See the Framework Integrations table and the interop guide for supported adapters and positioning.
You are not locked into a specific framework or LLM provider. contextweaver is a layer beneath frameworks — context management as a composable primitive.
5. Stability & Compatibility
contextweaver is currently Alpha in package metadata because the project is still clarifying its pre-1.0 stability boundary. The core context/routing APIs are intentionally deterministic and heavily tested; newer runtime surfaces such as gateway commands, adapters, and extras are called out as experimental where appropriate. See the detailed stability and 1.0 readiness checklist.
| Surface | Current promise | Notes |
|---|---|---|
| Documented public APIs | Change deliberately, with changelog/migration notes when behavior changes. | Dataclasses, stores, context/routing APIs, and documented adapters. |
| Experimental runtime surfaces | May change before 1.0. | MCP gateway/proxy commands, newer optional extras, and reference architecture variants. |
| Internal modules | No compatibility promise. | Modules beginning with _ and test helpers are implementation details. |
| Python support | Python 3.10–3.13 (inclusive). | Every version is exercised as a gating CI matrix cell; dependencies use library-grade lower-bound-only constraints by default, with a few documented exceptions (a floor-deps CI job proves the floors). 3.14 is pending upstream adapter-ecosystem support. |
Adopting a library is a long-term commitment. The stability page makes the Alpha/Beta/1.0 line explicit so teams can decide which surfaces are ready for their risk tolerance.
Weaver Spec Compatibility
contextweaver implements weaver_contracts >= 0.2.0, < 1.0 (canonical
contracts for the Weaver Stack — see
weaver-spec).
| Invariant | Status | Where enforced |
|---|---|---|
| I-03 — Routing presents bounded choices, not full schema catalogs | ✅ Satisfied | ChoiceCard strips args_schema; routing returns ≤ top_k cards. See src/contextweaver/routing/cards.py and docs/gateway_spec.md. |
| I-05 — contextweaver receives Frames, not raw output | ⚠️ Canonical path shipped; cross-repo mirror pending | The canonical seam is ContextManager.ingest_envelope(): the execution boundary firewalls and hands over a ResultEnvelope (the native preimage of a spec Frame, mapped via adapters/weaver_contracts.py); contextweaver does budgeted selection without re-deriving firewalling. The raw-output APIs (ContextManager.ingest_tool_result(raw_output=...), ingest_mcp_result(...)) remain for standalone use but are non-canonical for spec compliance. See the firewall boundary doc for who firewalls what; the matching I-05 statements in weaver-spec and agent-kernel still need to be mirrored. |
Contract adapters (pip install 'contextweaver[weaver-spec]'):
from contextweaver.adapters.weaver_contracts import (
to_weaver_routing_decision,
from_weaver_routing_decision,
to_weaver_frame,
from_weaver_frame,
)
Round-trips are lossless via a reserved metadata["_contextweaver"] payload;
see docs/weaver_spec_mapping.md for the full
mapping table.
CI conformance — every PR runs scripts/weaver_spec_conformance.py,
which does both a Python round-trip (cw → spec → cw == cw) and JSON-Schema
validation. CI fetches the schemas from
raw.githubusercontent.com/dgenio/weaver-spec/main/contracts/json/, which
mirrors the published documents at https://weaver-spec.dev/contracts/v0/
(same content, different host). Run locally with make weaver-conformance.
6. Roadmap & Community
Current package version: 0.17.0.
Recent milestones:
| Milestone | Status | Highlights |
|---|---|---|
| v0.8 | ✅ complete | CrewAI adapter Phase 1, Mem0 external-memory backend, provider-SDK-leak tests |
| v0.9 | ✅ complete | Provider message adapters, cache-stable routing, launch polish |
| v0.10 | ✅ complete | contextweaver mcp serve, MCP Context Gateway architecture, gateway benchmark suite, route/context explanations |
| v0.11 | ✅ complete | Memory-source adapter interface, session-handoff context pack, "when not to use" guidance |
| v0.15.0 | ✅ complete | Selection contracts, framework adapters, CI gate consolidation, routing cache, sidecar API |
| v0.16.0 | ✅ complete | Memory consolidation, cross-primitive identity, stable error codes, deprecation machinery, resource/prompt gateway |
| v0.17.0 | ✅ current (v0.17.0) | MCP gateway platform-maturity cluster (catalog governance, observability, model assist, transport/lifecycle, interop tooling), knowledge-bundle adapters, secret-scrubbing parity, onboarding wizard |
| Beta readiness | 🚧 in progress | Provider-adapter render fix, community standards, adopter benchmark report, stability checklist |
| v1.0 | 📋 planned | API freeze, documented deprecation policy, long-term compatibility window |
Community:
- GitHub Discussions — ask questions, share patterns
- GitHub Issues — report bugs, request features
- CHANGELOG — track every release
contextweaver is under active development with a clear roadmap. The core is usable today; the project remains Alpha until the Beta readiness checklist is complete or intentionally revised.
7. Comparison
Snapshot of the launch landscape as of 2026-05-31 — see footnotes for the versions referenced and the evidence behind each non-trivial claim. Will be refreshed each minor release.
| Approach | Tool routing | History compaction | Sensitivity firewall | Deterministic | MCP-native |
|---|---|---|---|---|---|
| contextweaver (this repo, v0.17.0) | ✅ Bounded DAG + beam search · per-phase ChoiceCards [^cw-route] |
✅ Phase-aware budgeted compilation · 43-85 % token reduction vs naïve [^cw-bench] | ✅ Built-in (size-gated, with ArtifactRef drilldown) [^cw-fire] |
✅ By default — tie-break by sorted IDs [^cw-det] | ✅ Native proxy + gateway runtimes per docs/gateway_spec.md [^cw-mcp] |
| Naïve concat-everything | ❌ No router · prompt carries every tool schema | ❌ No compaction · prompt grows with turn count | ❌ Raw outputs in the prompt | ⚠️ Only if the upstream LLM is | ⚠️ Compatible but no shaping |
| LangGraph memory (0.6.x) | ❌ Out of scope — LangGraph routes state, not tools | ⚠️ Optional via ConversationSummaryMemory (LLM-based, non-deterministic) [^lg-mem] |
❌ Not provided | ⚠️ Workflow yes; memory summarizer no | ⚠️ Possible via custom adapter, not first-class |
| LlamaIndex retrievers ([0.11.x](https://gi |
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