WeaveScope

Partners

BeamWeaver partner adapters are native Elixir implementations of provider wire formats. They share the core BeamWeaver.Core.ChatModel interface, but each adapter owns its own message translation, tool rendering, streaming lifecycle, model profiles, request validation, and replay/fake transport coverage.

Use this page as the support matrix. The first matrix covers provider API surface. The second matrix covers composed agent capabilities: which model strings are practical choices when the agent needs planning, tools, filesystem-backed work, subagents, structured output, human review, and event streaming.

Capability Matrix

Partner Primary modules Chat Chat Completions Responses API Embeddings Tools Structured output Streaming Token counting
OpenAI BeamWeaver.OpenAI.* Yes Yes Yes Yes Function tools and Responses built-ins JSON schema via Responses or Chat Completions Text deltas, lifecycle events, reconstructed responses Tokenizer/profile based
Anthropic BeamWeaver.Anthropic.* Yes No No No Custom tools and Anthropic server tools Tool/schema strategy through model calls Text deltas, typed events, reconstructed messages Anthropic count-tokens endpoint
Google BeamWeaver.Google.* Yes No No No Function declarations and Gemini built-ins Gemini generation config schema Text deltas, typed events, reconstructed messages Gemini count-tokens endpoint
DeepSeek BeamWeaver.DeepSeek.* Yes Yes Yes No Function tools, hosted web search, and Responses apply_patch JSON object plus local validation in Chat; JSON Schema in Responses Text/reasoning/tool deltas, named Responses events, reconstructed messages Approximate fallback
Moonshot/Kimi BeamWeaver.Moonshot.* Yes Yes No No OpenAI-compatible functions, K3 required/dynamic tools, and legacy Kimi $web_search JSON object/schema request options Text/reasoning/tool-call deltas, choice- or response-level usage, typed events, reconstructed messages Moonshot estimate-token endpoint
xAI BeamWeaver.XAI.* Yes Yes Yes Yes OpenAI-compatible function tools and xAI built-ins JSON schema request options Text deltas, typed events, reconstructed messages Tokenizer/profile or approximate fallback
Z.ai BeamWeaver.ZAI.* Yes Yes No No OpenAI-compatible function tools JSON object mode plus schema instructions and local validation Text/reasoning/tool-call deltas, usage chunks, reconstructed messages Approximate fallback

Composed Agent Model Matrix

Deep-agent behavior is composition in BeamWeaver: TodoList planning, filesystem tools, skills, memory files, subagents, interrupts, context editing, and graph checkpoints are normal agent runtime capabilities. The model adapter determines whether a composed agent can reliably expose tools, parse structured output, stream useful events, and count enough tokens for context management.

Model family Recommended BeamWeaver strings Composed agent fit Tool loop Structured output Streaming and observability Token budget support Notes
OpenAI GPT openai:gpt-5.6-sol, openai:gpt-5.6-terra, openai:gpt-5.6-luna, openai:gpt-5.4-mini, explicit BeamWeaver.OpenAI.* structs Strong default Custom function tools, Responses built-ins, raw Responses tool-result turns Provider-native Responses or Chat Completions schema; tool strategy fallback at agent layer Text, reasoning, tool-call lifecycle, reconstructed streamed responses Tokenizer/profile based with approximate fallback GPT-5.6 adds 1.05M context, max effort, pro mode, and persisted reasoning; hosted multi-agent/programmatic tool calling are not yet wrapped.
Anthropic Claude anthropic:claude-opus-5, anthropic:claude-sonnet-5, anthropic:claude-fable-5, anthropic:claude-mythos-5, anthropic:claude-sonnet-4-6, anthropic:claude-opus-*, anthropic:claude-haiku-* Strong default Custom tools plus Anthropic server tools through provider helpers Anthropic output config plus BeamWeaver parsing/validation Text, typed Anthropic stream envelopes, reconstructed messages Anthropic count-tokens endpoint Opus 5 defaults to adaptive thinking, supports the full effort ladder and server-side refusal fallback, and does not support web fetch; prompt caching and server tools are provider-specific.
Google Gemini google:gemini-3.7-flash, google:gemini-3.6-flash, google:gemini-3.5-flash-lite, other explicit google:gemini-* profiles Supported Function declarations and Gemini built-ins Gemini generation config schema Text, typed Gemini events, reconstructed messages Gemini count-tokens endpoint Gemini identifiers must use the google: prefix; 3.7 Flash is the recommended coding and agentic workhorse; 3.5 Flash Cyber is not a public Gemini API model.
DeepSeek V4 deepseek:deepseek-v4-flash, deepseek:deepseek-v4-pro Supported Function tools; Responses adds hosted web search and apply_patch Chat JSON object plus local validation; Responses JSON Schema Text/reasoning/tool chunks, typed events, reconstructed messages Approximate fallback Explicit deepseek: prefix required. Both models support Chat, Responses, beta prefix/FIM, and Anthropic compatibility.
Moonshot/Kimi moonshot:kimi-k3, moonshot:kimi-k2.7-code, moonshot:kimi-k2.7-code-highspeed, moonshot:kimi-k2.6, moonshot:kimi-k2.5 Supported with Kimi constraints OpenAI-compatible functions; K3 adds required choice and dynamic loading; legacy $web_search only where thinking can be disabled JSON object/schema request options Text, reasoning, tool-call chunks, usage chunks, reconstructed messages Moonshot estimate-token endpoint K3 has 1,048,576-token context/output limits, always reasons with reasoning_effort: "max", and rejects K2 thinking; web search is currently being updated by Kimi.
xAI Grok xai:grok-4.6, xai:grok-4.5, xai:grok-4.3, explicit BeamWeaver.XAI.* structs Supported OpenAI-compatible functions and xAI built-ins JSON schema request options Text, reasoning/citation metadata, typed events, reconstructed messages Tokenizer/profile or approximate fallback Grok 4.6 is the default for coding, agentic work, and Grok-specific reasoning/citation behavior; provider metadata is normalized.
Z.ai GLM zai:glm-5.2, explicit BeamWeaver.ZAI.ChatModel structs Supported OpenAI-compatible functions with tool_stream for streamed arguments JSON object mode plus BeamWeaver schema instructions and local validation Text, reasoning, tool-call chunks, usage chunks, reconstructed messages Approximate fallback GLM identifiers must use zai:; only glm-5.2 is enabled. Usage includes cached-input and reasoning-token details for cost metadata.
Fake chat fake:chat Test only Fixture tool calls Fixture structured responses Fixture text/events Fake or approximate Use for deterministic composed-agent middleware, checkpoint, HITL, and subagent tests.

This matrix is not a benchmark table. BeamWeaver verifies the runtime surfaces locally, but it does not publish cross-model Deep Agents eval scores. Validate model quality against your own tool set, prompts, and latency/cost constraints.

Common Contract

All first-class partner chat models:

  • implement BeamWeaver.Core.ChatModel

  • accept BeamWeaver.Core.Message input

  • return BeamWeaver.Core.Message output with normalized metadata where the provider exposes it

  • route HTTP through BeamWeaver.Transport

  • support fake or replay transports for tests without live credentials

  • expose namespace constructors such as BeamWeaver.OpenAI.chat_model/1

  • participate in BeamWeaver.Models.init_chat_model/2 provider-prefix routing