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@cognipeer/agent-sdk

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Lightweight, message-first agent runtime that keeps tool calls transparent, supports provider-native reasoning plus post-tool reflection, automatically summarizes long histories, and ships with planning, multi-agent handoffs, and structured tracing.

  • SDK source: src/
  • Examples: examples/
  • Docs (VitePress): docs/
  • Requires Node.js 18.17+

Table of contents

Overview

@cognipeer/agent-sdk is a zero-graph, TypeScript-first agent loop. Tool calls are persisted as messages, token pressure triggers automatic summarization, and optional planning mode enforces TODO hygiene with the bundled manage_todo_list tool. Multi-agent composition, structured output, and batched tracing are built-in.

Highlights:

  • Message-first design – assistant tool calls and tool responses stay in the transcript.
  • Token-aware summarization – chunked rewriting archives oversized tool outputs while exposing get_tool_response for lossless retrieval.
  • Planning mode – adaptive system prompt + TODO tool supports full plan writes and version-safe partial updates.
  • Unified reasoning surface – one reasoning config controls provider-native reasoning and post-tool plain-text reflections.
  • Structured output – provide a Zod schema and the agent injects a finalize tool to capture JSON deterministically.
  • Multi-agent and handoffs – wrap agents as tools or transfer control mid-run with asTool / asHandoff.
  • Usage + events – normalize provider usage, surface tool_call, plan, summarization, reflection, metadata, and handoff events.
  • Structured tracing – optional per-invoke JSON traces with metadata, payload capture, and pluggable sinks (file, HTTP, Cognipeer, custom).

What’s inside

Path Description
src/ Source for the published package (TypeScript, bundled via tsup).
examples/ End-to-end scripts demonstrating tools, planning, summarization, multi-agent, MCP, structured output, and vision input.
docs/ VitePress documentation site served at cognipeer.github.io/agent-sdk.
dist/ Build output (generated). Contains ESM, CommonJS, and TypeScript definitions.
logs/ Generated trace sessions when tracing.enabled: true. Safe to delete.

Install

Install the SDK and its (optional) LangChain peer dependency:

npm install @cognipeer/agent-sdk zod
# Optional: LangChain bindings (if you want to use fromLangchainModel)
npm install @langchain/core @langchain/openai

The SDK includes a built-in native provider layer that talks directly to OpenAI, Anthropic, Azure, Bedrock, Vertex, and any OpenAI-compatible API — no LangChain required.

You can also bring your own model adapter as long as it exposes invoke(messages[]) and (optionally) bindTools().

Quick start

Native provider (no LangChain)

import { createSmartAgent, createTool, createProvider, fromNativeProvider } from "@cognipeer/agent-sdk";
import { z } from "zod";

const echo = createTool({
  name: "echo",
  description: "Echo back user text",
  schema: z.object({ text: z.string() }),
  func: async ({ text }) => ({ echoed: text }),
});

// Pick any provider – OpenAI, Anthropic, Azure, Bedrock, Vertex, or OpenAI-compatible
const model = fromNativeProvider(
  createProvider({ provider: "openai", apiKey: process.env.OPENAI_API_KEY! }),
  { model: "gpt-4o" },
);

const agent = createSmartAgent({ model, tools: [echo], runtimeProfile: "balanced" });
const result = await agent.invoke({ messages: [{ role: "user", content: "say hi" }] });
console.log(result.content);

Switch providers by changing a single config line:

// Anthropic
createProvider({ provider: "anthropic", apiKey: process.env.ANTHROPIC_API_KEY! })

// Azure OpenAI
createProvider({ provider: "azure", apiKey: "...", endpoint: "https://my-resource.openai.azure.com", deploymentName: "gpt-4o" })

// AWS Bedrock
createProvider({ provider: "bedrock", region: "us-east-1", accessKeyId: "...", secretAccessKey: "..." })

// Google Vertex AI
createProvider({ provider: "vertex", projectId: "my-project", accessToken: process.env.VERTEX_TOKEN })

// Any OpenAI-compatible endpoint (Ollama, Groq, Together, vLLM, …)
createProvider({ provider: "openai-compatible", apiKey: "...", baseURL: "https://custom.endpoint/v1" })

Smart agent (planning + summarization)

import { createSmartAgent, createTool, fromLangchainModel } from "@cognipeer/agent-sdk";
import { ChatOpenAI } from "@langchain/openai";
import { z } from "zod";

const echo = createTool({
  name: "echo",
  description: "Echo back user text",
  schema: z.object({ text: z.string().min(1) }),
  func: async ({ text }) => ({ echoed: text }),
  maxExecutionsPerRun: null,
});

const model = fromLangchainModel(new ChatOpenAI({
  model: "gpt-4o-mini",
  apiKey: process.env.OPENAI_API_KEY,
}));

const agent = createSmartAgent({
  name: "ResearchHelper",
  model,
  tools: [echo],
  runtimeProfile: "balanced",
  planning: { mode: "todo", replanPolicy: "on_failure" },
  memory: { provider: "inMemory", scope: "session", writePolicy: "auto_important" },
  summarization: { summaryTriggerTokens: 8000, summaryMode: "incremental" },
  context: { policy: "hybrid", lastTurnsToKeep: 8 },
  toolResponses: {
    defaultPolicy: "summarize_archive",
    toolResponseRetentionByTool: { read_skills: "keep_full" },
    maxToolResponseChars: 4000,
    maxToolResponseTokens: 1200,
  },
  limits: { maxToolCalls: 5, maxContextTokens: 12000 },
  tracing: { enabled: true },
});

const result = await agent.invoke({
  messages: [{ role: "user", content: "plan a greeting and send it via the echo tool" }],
  toolHistory: [],
});

console.log(result.content);

Tool-response retention is lazy and summarizer-driven:

  • Tool outputs are stored at full fidelity in state.toolHistory and are never reduced at tool-call time.
  • When the summarizer runs (context limits reached), old tool messages are rewritten in place according to defaultPolicy (default: summarize_archive). The full payload is still recoverable via get_tool_response using the execution id embedded in the placeholder.
  • toolResponseRetentionByTool lets you opt specific tools out of reduction (e.g. read_skills: "keep_full").
  • criticalTools are never reduced. The built-in list covers response, manage_todo_list, and get_tool_response.
  • maxToolResponseChars / maxToolResponseTokens only drive an eager hard-cap truncation when a single tool output is big enough to blow up the very next model call. The truncated head always points at get_tool_response for recovery.

The smart wrapper now supports runtime presets (fast, balanced, deep, research), custom profiles layered on top of a base preset, structured summarization, hybrid context compaction, configurable tool-response retention, in-memory fact storage, delegation limits, and an eval harness via runSmartAgentEvalHarness(...).

You can also define a custom profile by extending a built-in preset and overriding only the knobs you need:

const agent = createSmartAgent({
  name: "CustomPlanner",
  model,
  runtimeProfile: "custom",
  customProfile: {
    extends: "balanced",
    limits: { maxToolCalls: 10, maxContextTokens: 18000 },
    planning: { mode: "todo" },
    context: { lastTurnsToKeep: 10 },
    memory: { writePolicy: "manual" },
  },
});

Reasoning & reflection

Both createAgent(...) and createSmartAgent(...) accept a unified reasoning config:

const agent = createSmartAgent({
  model: fromNativeProvider(
    createProvider({ provider: "openai", apiKey: process.env.OPENAI_API_KEY! }),
    { model: "gpt-5" },
  ),
  tools: [echo],
  reasoning: {
    enabled: true,
    level: "high",
    native: { effort: "high" },
    reflection: {
      cadence: "after_tool",
      mode: "piggyback",
      maxTokens: 450,
      keepLast: 4,
      summarize: false,
    },
  },
});

const result = await agent.invoke({
  messages: [{ role: "user", content: "Research the repo and propose the next implementation step." }],
}, {
  onEvent(event) {
    if (event.type === "reflection") {
      console.log("reflection:", event.text);
    }
  },
});

console.log(result.state?.reflections?.at(-1)?.text);

What this does:

  • reasoning.native passes provider-specific reasoning knobs through the native provider layer.
  • reasoning.reflection adds a short plain-text reflection after qualifying turns without committing that note as a normal assistant message.
  • Reflection notes are persisted on result.state.reflections; only the last keepLast are re-injected into the next prompt as synthetic system context.

Use this only with models/endpoints that actually support provider-native reasoning. Unsupported models usually return a provider error rather than silently degrading.

Base agent (minimal loop)

Prefer a tiny core without system prompt or summarization? Use createAgent:

import { createAgent, createTool, fromLangchainModel } from "@cognipeer/agent-sdk";
import { ChatOpenAI } from "@langchain/openai";
import { z } from "zod";

const echo = createTool({
  name: "echo",
  description: "Echo back",
  schema: z.object({ text: z.string().min(1) }),
  func: async ({ text }) => ({ echoed: text }),
});

const model = fromLangchainModel(new ChatOpenAI({ model: "gpt-4o-mini", apiKey: process.env.OPENAI_API_KEY }));

const agent = createAgent({
  model,
  tools: [echo],
  limits: { maxToolCalls: 3, maxParallelTools: 2 },
});

const res = await agent.invoke({ messages: [{ role: "user", content: "say hi via echo" }] });
console.log(res.content);

Key capabilities

  • Native provider layer – call OpenAI, Anthropic, Azure, Bedrock, Vertex, and any OpenAI-compatible API directly. No LangChain required. Unified ChatCompletionRequest / ChatCompletionResponse schema with per-provider wire format conversion.
  • Full token tracking – every response surfaces inputTokens, outputTokens, cachedInputTokens, cachedWriteTokens, and reasoningTokens for all six providers.
  • Unified reasoning controls – enable provider-native reasoning (reasoning.native) and post-tool reflection (reasoning.reflection) from one config surface.
  • Summarization pipeline – automatic chunking keeps tool call history within contextTokenLimit / summaryTokenLimit, archiving originals so get_tool_response can fetch them later.
  • Retention controls – tool outputs can be kept full, reduced to structured previews, archived, or dropped based on size tiers, critical-tool fallback, per-tool overrides, and recent-response pinning.
  • Planning discipline – when planning is enabled the system prompt distinguishes full plan writes from incremental plan updates and emits plan events as todos change.
  • Structured output – supply outputSchema and the framework adds a hidden response finalize tool; parsed JSON is returned as result.output.
  • Multi-agent orchestration – reuse agents via agent.asTool({ toolName }) or perform handoffs that swap runtimes mid-execution.
  • MCP + LangChain tools – any object satisfying the minimal tool interface works; LangChain’s Tool implementations plug in directly.
  • Vision input – message parts accept base64 or URL images for multimodal requests.
  • Observability hooks – config.onEvent surfaces tool lifecycle, summarization, reflection, metadata, and final answer events for streaming UIs or CLIs.

Examples

Examples live under examples/ with per-folder READMEs. Build the package first (npm run build or npm run preexample:<name>).

Folder Focus
basic/ Minimal tool call run with real model.
tools/ Multiple tools, Tavily search integration, onEvent usage.
tool-limit/ Hitting the global tool-call cap and finalize behavior.
todo-planning/ Smart planning workflow with enforced TODO updates.
summarization/ Token-threshold summarization walkthrough.
summarize-context/ Summaries + get_tool_response raw retrieval.
structured-output/ Zod schema finalize tool and parsed outputs.
rewrite-summary/ Continue conversations after summaries are injected.
multi-agent/ Delegating between agents via asTool.
handoff/ Explicit runtime handoffs.
mcp-tavily/ MCP remote tool discovery.
vision/ Text + image input using LangChain’s OpenAI bindings.

To run examples:

# Install root dependencies
npm install

# Install example dependencies
cd examples
npm install

# Run an example from the examples directory
npm run example:basic
npm run example:tools
npm run example:multi-agent

Or run directly with tsx:

# From examples directory
OPENAI_API_KEY=... npx tsx basic/basic.ts

Architecture snapshot

The agent is a deterministic while-loop – no external graph runtime. Each turn flows through:

  1. resolver – normalize state (messages, counters, limits).
  2. contextSummarize (optional) – when token estimates exceed the active summarization threshold, archive heavy tool outputs.
  3. agent – invoke the model (binding tools when supported).
  4. tools – execute proposed tool calls with configurable parallelism.
  5. reflect (optional) – append a plain-text reflection after tool turns when reasoning.reflection is enabled.
  6. toolLimitFinalize – if tool-call cap is hit, inject a system notice so the next assistant turn must answer directly.

The loop stops when the assistant produces a message without tool calls, a structured output finalize signal is observed, or a handoff transfers control. See docs/architecture/README.md for diagrams and heuristics.

API surface

Exported helpers (agent-sdk/src/index.ts):

Agent factories:

  • createSmartAgent(options)
  • createAgent(options)
  • createTool({ name, description?, schema, func, needsApproval?, approvalPrompt?, approvalDefaults?, maxExecutionsPerRun? })

Native providers (no LangChain):

  • createProvider(config) – factory for all six providers
  • fromNativeProvider(provider, options?) – wraps a provider as a BaseChatModel
  • OpenAIProvider, AnthropicProvider, AzureProvider, OpenAICompatibleProvider, BedrockProvider, VertexProvider
  • Types: ChatCompletionRequest, ChatCompletionResponse, TokenUsage, ProviderConfig, ReasoningConfig, ReflectionRecord, ReflectionEvent

LangChain adapters (optional):

  • fromLangchainModel(model)
  • withTools(model, tools)
  • fromLangchainTools(tools)

Utilities:

  • buildSystemPrompt(extra?, planning?, name?)
  • Node factories (nodes/*), context helpers, token utilities, and full TypeScript types (SmartAgentOptions, SmartState, AgentInvokeResult, etc.).

SmartAgentOptions accepts the usual suspects (model, tools, limits, runtimeProfile, customProfile, useTodoList, summarization, reasoning, usageConverter, tracing). See docs/api/ for detailed type references.

Tools can also declare maxExecutionsPerRun to cap successful executions for that tool within a single agent run. Leave it unset or set it to null for unlimited usage. This is separate from global limits such as limits.maxToolCalls and limits.maxParallelTools.

Tracing & observability

Enable tracing by passing tracing: { enabled: true }. Each invocation writes trace.session.json into logs/<SESSION_ID>/ detailing:

  • Model/provider, agent name/version, limits, and timing metadata
  • Structured events for model calls, tool executions, summaries, reflections, and errors
  • Optional payload captures (request/response/tool bodies) when logData is true
  • Aggregated token usage, byte counts, and error summaries for dashboards

You can disable payload capture with logData: false to keep only metrics, or configure sinks such as httpSink(url, headers?), cognipeerSink(apiKey, url?), otlpSink(endpoint, headers?), or customSink({ onEvent, onSession }) to forward traces after each run. Sensitive headers/callbacks remain in-memory and are never written alongside the trace.

Each session/event also carries OTel-compatible correlation identifiers (traceId, rootSpanId, spanId, parentSpanId) so you can stitch agent traces into distributed telemetry pipelines.

Development

Install dependencies and build the package:

cd agent-sdk
npm install
npm run build

From the repo root you can run npm run build (delegates to the package) or use npm run example:<name> scripts defined in package.json.

Publishing

Only publish agent-sdk/:

cd agent-sdk
npm version <patch|minor|major>
npm publish --access public

prepublishOnly ensures a fresh build before publishing.

Troubleshooting

  • Missing tool calls – ensure your model supports bindTools. If not, wrap with withTools(model, tools) to provide best-effort behavior.
  • Summaries too aggressive – adjust limits.maxToken, contextTokenLimit, and summaryTokenLimit, or disable with summarization: false.
  • Large tool responses – return structured payloads and rely on get_tool_response for raw data instead of dumping megabytes inline.
  • Usage missing – some providers do not report usage; customize usageConverter to normalize proprietary shapes.

Documentation

  • Live site: https://cognipeer.github.io/agent-sdk/
  • Key guides within this repo:
    • docs/getting-started/
    • docs/core-concepts/
    • docs/architecture/
    • docs/api/
    • docs/tools/
    • docs/examples/
    • docs/debugging/
    • docs/limits-tokens/
    • docs/tool-development/
    • docs/faq/

Contributions welcome! Open issues or PRs against main with reproduction details when reporting bugs.

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🤖 Lightweight, message-first agent runtime

cognipeer.github.io/agent-sdk/

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nodejs javascript agent ai llm llm-agent agentic llm-tools llm-framework llm-agents agentic-framework agentic-rag agentic-ai cognipeer

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