forge-ai-runtime 0.8.2

Provider-agnostic AI Runtime SDK

AI Runtime

AI Runtime is a provider-agnostic Python runtime for building AI applications and agent workflows.

Features

• Provider abstraction with a unified LLM contract
• Built-in default provider registry and plugin discovery
• Custom provider registration with test doubles
• Conversation management and session-based execution
• Streaming responses with event processing
• Streaming timeout/cancellation boundary handling
• Automatic tool-calling loop (model requests tools → runtime executes → re-invokes)
• Capability-gated request mapping (tools, structured output, vision, metadata)
• Expanded provider contract: chat, stream, embeddings, image, transcription
• Retries via ProviderConfig.max_retries (forwarded to the backend)
• Uniform events: chat() and stream() both emit StreamEvents
• Rich streaming events: text, usage, tool call, tool result, thinking, permission
• Execution engine and pipeline stages
• Provider metadata, capabilities, and request mapping
• Fully tested runtime and provider integration coverage

Agentic capabilities (v0.6.0)

ai_runtime now closes the gap with agentic coding tools (Claude Code, OpenAI Codex, Cursor):

• Plan mode — AgentRunner.plan() / ExecutionEngine.plan() produce a reviewable Plan (read-only, no tools run) before execution.
• Sub-agents — declare SubAgentSpecs on an Agent; the SupervisorStage fans them out in parallel with isolated contexts and aggregates results.
• Permissions — PermissionPolicy + GuardedToolExecutor enforce allow/deny/ask rules (glob-matched) over tool calls, mirroring tiered permission modes.
• Hooks — HookRegistry with PreToolUse / PostToolUse / PreLLM / PostLLM / OnPlan / OnCompact / OnError lifecycle hooks.
• Auto compaction — CompactionStage summarizes or drops old turns when the context window exceeds its token budget.
• Memory consolidation — MemoryConsolidationStage writes durable learnings (LEARNING: ...) back to the agent's MemoryStore.
• MCP client — MCPClient + StdioTransport speak JSON-RPC over stdio; register_mcp_tools() wraps server tools as runtime Tools.
• Background tasks — BackgroundTaskRegistry for resumable async tasks (à la codex resume / Claude /tasks).
• Skill scoping — Skill gains paths / globs / disable_model_invocation.
• Reasoning controls — ProviderConfig.reasoning_effort / thinking_enabled / thinking_budget_tokens, forwarded when the provider supports reasoning.

Agentic workflow types (v0.8.0)

Beyond the single-agent loop, ai_runtime now ships the higher-level agent primitives that modern agentic frameworks (LangGraph, AutoGen, CrewAI, Reflexion) are built from. They compose with the existing Agent, Skill, Command, and pipeline machinery.

New agent types (ai_runtime.agents):

• WorkflowAgent — a declarative DAG of WorkflowSteps. Steps run in dependency order with concurrent layers (bounded by max_concurrency); each step's output is exposed to downstream steps via {step_name} placeholders. Mirrors the graph/orchestration layer of LangGraph/AutoGen.
• RouterAgent — intent-based dispatcher. Routes a message to a specialist Agent via explicit predicates, keyword matching, or an LLM classifier, falling back to a default_agent. Mirrors multi-agent orchestrators (CrewAI routers, AutoGen groups).
• CriticAgent — Reflexion-style actor/critic loop. Produces a candidate with an actor, evaluates it with a critic (or a validator callable), and feeds critiques back for up to max_iterations until approved. Mirrors self-reflection / CriticGPT patterns.

New skill types (ai_runtime.skills):

• RetrievalSkill — RAG-backed skill that injects retrieved context (via a ai_runtime.rag.Retriever) into the prompt before the LLM call.
• GuardrailSkill — validation hook that gates model output via a guardrail callable, with reject / warn / rewrite on-fail policies. ComposedSkills aggregates retrieval context and applies guardrails automatically.

New command types (ai_runtime.commands):

• Categorized slash commands: /review, /explain, /test, /workflow (plus the existing /compact, /context, /clear). Commands carry a category and args and support with_args() binding.

New streaming event:

• WorkflowEvent — surfaces DAG/router/critic progress (queued → running → completed/failed) so clients can render step status, and is recorded on ExecutionContext.metadata["workflow_steps"].

Built-in agents, skills & commands (v0.8.1)

The framework ships ready-to-use presets so you don't have to hand-roll common agents/skills/commands, and it uses them on itself to become more agentic.

Built-in agents (ai_runtime.agents.builtin): reviewer_agent, explainer_agent, tester_agent, summarizer_agent, plus critic_agent (Reflexion loop) and router_agent (intent router with default review/explain/test routes).

Built-in skills (ai_runtime.skills.builtin): self_review_skill, explain_code_skill, generate_tests_skill, summarize_skill, no_secrets_guardrail (secret-blocking GuardrailSkill), retrieval_skill (RAG-backed), and default_builtin_skills().

Built-in commands (ai_runtime.commands.builtin): reusable Command factories (review_command, explain_command, test_command, workflow_command, compact_command, context_command, clear_command) consumed by default_commands().

Self-agentic wiring — the runtime turns its own primitives on itself:

• AgentRunner(self_review=True) runs a CriticAgent self-review (reflexion) pass over its own output before returning, in both run() and stream().
• CompactionStage uses the framework's own summarizer_agent as an LLM-backed compaction summarizer (via make_agentic_compaction_summarizer) when a provider is present, instead of naively dropping old turns.

Integration surfaces (v0.7.0)

To embed ai_runtime in a web app, desktop app, VS Code, or CLI like Claude Code / Codex / Cursor / Copilot, the framework now ships:

• Built-in tools — ReadFileTool, WriteFileTool, EditFileTool, GlobTool, GrepTool, BashTool (scoped to a sandbox root via register_builtin_tools). These mirror the file/shell/grep primitives of the four tools.
• Checkpoints / undo — CheckpointManager snapshots files before edits so the UI can roll back (à la Cursor/Claude/Copilot checkpoints).
• Agent config files — load_project_instructions() discovers .github/copilot-instructions.md, AGENTS.md, CLAUDE.md, .cursor/rules from the project root and folds them into the system prompt.
• Transport-agnostic server — AgentServer exposes an Agent over a JSON-line protocol (AgentRequest / AgentResponse / StreamEvent) via serve_stdio() (VS Code / CLI) and serve_http() (web / desktop).
• CLI — ai-runtime console script: ai-runtime "prompt" --model ..., --mode plan|stream, --serve (stdio), --http (HTTP server), --yolo.
• Workspace / project — Project scopes memory, tools, permissions, and checkpoints to a project root (the unit you mount in a client).
• Slash commands — CommandRegistry / default_commands() (/compact, /context, /clear) mirroring Copilot's / menu.
• BYO provider — ProviderConfig.from_env() reads COPILOT_PROVIDER_* vars to point at Ollama / vLLM / any OpenAI-compatible endpoint.

Web application (web/)

A ready-to-run web UI + REST/WebSocket API that exercises the full framework:

export AI_RUNTIME_API_KEY=sk-...      # or AI_RUNTIME_PROVIDER/AI_RUNTIME_BASE_URL
./venv/bin/python web/run.py          # serves http://127.0.0.1:8787

Features exposed in the UI:

• Streaming chat (WebSocket streams StreamEvents: text, tool calls, tool results, thinking, usage, completion)
• Plan mode (/api/chat with mode: plan returns a reviewable Plan)
• Projects — Project scoping with auto-loaded instruction files + sandboxed built-in tools (Read/Write/Edit/Glob/Grep/Bash)
• Checkpoints / undo — snapshot + restore files before agent edits
• Permissions — PermissionPolicy allow/deny/ask rules per tool + params
• Sub-agents — configure SubAgentSpecs; supervisor fans them out
• Background tasks — submit/resume/cancel via BackgroundTaskRegistry
• Slash commands — /compact, /context, /clear, plus categorized agentic commands /review, /explain, /test, /workflow
• MCP — connect a stdio MCP server and register its tools
• Reasoning controls — reasoning_effort / thinking_enabled per request
• Built-in agents panel — run reviewer / explainer / tester / summarizer / router / critic presets from the UI (GET /api/builtin/agents, POST /api/builtin/agents/run)
• Built-in skills panel — compose self_review, explain_code, generate_tests, summarize, no_secrets_guardrail, retrieval skills into a session (GET /api/builtin/skills, POST /api/builtin/skills/apply)
• Built-in command palette — invoke categorized commands from the UI (GET /api/builtin/commands, POST /api/builtin/commands/run). Both the composer slash-autocomplete and the command palette show a worked example for each command (e.g. /review def add(a, b): …).
• Self-review toggle — enable the reflexion self-review pass per session (POST /api/builtin/self-review)

See web/README.md for the full API reference.

Built-in commands

The web UI (and CLI) ship a categorized / command menu. Type / in the composer to see the full list with live examples, or open the command palette (Cmd/Ctrl+K). Every command accepts free-form text after its name:

Command	Category	Example
/compact	general	/compact — summarize the conversation to free context
/context	general	/context — show a token/context breakdown
/clear	general	/clear — clear the conversation history
/review	review	/review def add(a, b):\n return a + b
/explain	explain	/explain the quicksort implementation in sort.py
/test	test	/test def divide(a, b):\n return a / b
/workflow	workflow	/workflow refactor Extract the parser into its own module

• General commands (/compact, /context, /clear) run as side effects against the active session.
• Agentic commands (/review, /explain, /test, /workflow) render their prompt template with the supplied text and run it through the session's own agent runner, streaming the result back into the chat.

Installation

From PyPI

pip install ai-runtime

From source

git clone https://github.com/KiritVaghela/ai-runtime.git
cd ai-runtime
pip install -e .

Quick Start

import os

from ai_runtime import AgentRuntime
from ai_runtime.conversation import ChatMessage
from ai_runtime.providers.enums import ProviderType

runtime = AgentRuntime.from_provider(
    provider=ProviderType.GROQ,
    model="groq/llama-3.3-70b-versatile",
    api_key=os.getenv("GROQ_API_KEY"),
)

session = runtime.create_session()

response = await session.chat(
    ChatMessage.user("Hello!")
)

print(response.message.content)

Streaming

from ai_runtime.conversation import ChatMessage
from ai_runtime.streaming import TextDeltaEvent

async for event in session.stream(
    ChatMessage.user("Write a haiku.")
):
    if isinstance(event, TextDeltaEvent):
        print(event.delta, end="")

Streaming with timeout

The runtime supports stream timeout propagation through ChatRequest.timeout. If a provider stream stalls, an ErrorEvent is emitted and the stream stops.

from ai_runtime.conversation import ChatMessage, ChatRequest
from ai_runtime.streaming import ErrorEvent, TextDeltaEvent

request = ChatRequest(
    messages=[ChatMessage.user("Generate a poem.")],
    timeout=10.0,
)

async for event in session.stream(request):
    if isinstance(event, TextDeltaEvent):
        print(event.delta, end="")
    elif isinstance(event, ErrorEvent):
        print("\nStream timed out or failed:", event.message)

Provider registry

The runtime uses a provider registry so you can register custom providers or replace the default provider implementation during tests.

from ai_runtime import AgentRuntime
from ai_runtime.providers import ProviderRegistry
from ai_runtime.providers.enums import ProviderType

registry = ProviderRegistry()
registry.register(ProviderType.OPENAI, MyCustomProvider)

runtime = AgentRuntime.from_provider(
    provider=ProviderType.OPENAI,
    model="gpt-4.1",
    api_key=os.getenv("OPENAI_API_KEY"),
    registry=registry,
)

Architecture

AgentRuntime
    └── Session
          └── ExecutionEngine
                └── ExecutionPipeline
                      ├── RequestBuilderStage
                      ├── LLMStage
                      └── ToolLoopStage
                            └── EventProcessor

Tools

Register tools with a ToolRegistry, wrap it in a ToolExecutor, and attach it to the session context. When the model requests a tool, the runtime executes it and feeds the result back automatically.

from ai_runtime.tools import ToolRegistry, ToolExecutor, FunctionTool
from ai_runtime.conversation import ChatMessage

registry = ToolRegistry()
registry.register(
    FunctionTool("get_weather", lambda ctx, inp: f"Weather in {inp['city']}: sunny")
)

session.context.tool_executor = ToolExecutor(registry)

response = await session.chat(
    ChatMessage.user("What is the weather in Paris?")
)
print(response.message.content)

Agents

An Agent bundles a provider, system prompt, tools, memory, and skills. AgentRunner drives execution (including the automatic tool-call loop) and persists conversation memory across turns.

from ai_runtime import AgentRuntime, Agent, AgentRunner
from ai_runtime.tools import ToolRegistry, ToolExecutor, FunctionTool

runtime = AgentRuntime.from_provider(
    provider=ProviderType.GROQ,
    model="llama-3.3-70b-versatile",
    api_key=os.getenv("GROQ_API_KEY"),
)

registry = ToolRegistry()
registry.register(FunctionTool("ping", lambda ctx, inp: "pong"))

agent = runtime.create_agent(
    name="helper",
    system_prompt="You are a helpful assistant.",
    tool_registry=registry,
)

runner = AgentRunner(agent)
response = await runner.run("Ping the tool for me.")
print(response.message.content)

Memory, RAG & Skills

• ai_runtime.memory — MemoryStore, ConversationMemory, SemanticMemory.
• ai_runtime.rag — Document, VectorStore, Retriever for retrieval.
• ai_runtime.skills — Skill + SkillRegistry for composable behaviors.
• ai_runtime.context — ContextWindow for token budgeting/truncation.

Documentation

• Migration Guide
• Adapter Tutorial
• Examples
• Release Checklist

See CHANGELOG.md for version history.

Testing

pytest

Current status:

• Provider registry with custom provider registration
• Streaming response support with timeouts and completion events
• Session-based execution and conversation accumulation
• Provider integration coverage with test doubles

Roadmap

v0.3.x

• Tool registry
• Tool execution
• Permission manager
• Filesystem tools
• Bash tools

Future

• MCP support
• Multi-agent workflows
• Desktop and terminal integrations
• AI Factory integration

Publishing to PyPI

1. Update version in pyproject.toml.
2. Build:

python -m build

3. Check:

twine check dist/*

4. Upload to TestPyPI:

twine upload --repository testpypi dist/*

5. Upload to PyPI:

twine upload dist/*