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Updating Docs & Packages with 3.0.0 SDK (#356) 

* chore: (docs) Reconcile new SDK conventions in docs

* chore: updating docs references to SDK.

* chore: final edits to docs and packages for SDK and API updates

* chore: (docs) fix typescript context() options method

---------

Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
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ajspig 2026-01-29 17:10:33 -05:00 committed by GitHub
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8
docs/SKILL.md
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@ -50,7 +50,7 @@ Ask how they want to use Honcho context:
- options:
- "Tool call (Recommended)" - "Agent queries Honcho on-demand via function calling"
- "Pre-fetch" - "Fetch user context before each LLM call with predefined queries"
- "get_context()" - "Include conversation history and representations in prompt"
- "context()" - "Include conversation history and representations in prompt"
- "Multiple patterns" - "Combine approaches for different use cases"
#### Question Set 3 - Session Structure
@ -371,7 +371,7 @@ async function getUserContextForPrompt(userId: string): Promise<Record<string, s
### Pattern C: Get Context for LLM Integration
Use `get_context()` for conversation history with built-in LLM formatting.
Use `context()` for conversation history with built-in LLM formatting.
**Python:**
@ -383,7 +383,7 @@ user = honcho.peer("user-123")
assistant = honcho.peer("assistant", config={"observe_me": False})
# Get context formatted for your LLM
context = session.get_context(
context = session.context(
tokens=2000,
peer_target=user.id, # Include representation of this user
summary=True # Include conversation summaries
@ -434,7 +434,7 @@ When integrating Honcho into an existing codebase:
- [ ] Choose integration pattern:
- [ ] Tool call pattern for agentic systems
- [ ] Pre-fetch pattern for simpler integrations
- [ ] get_context() for conversation history
- [ ] context() for conversation history
- [ ] Store messages after each exchange to build user models
## Common Mistakes to Avoid

10
docs/v3/documentation/features/advanced/representation-scopes.mdx
View File

@ -72,9 +72,9 @@ The `target` parameter controls which representation you retrieve:
| Query | Returns |
|-------|---------|
| `get_representation("alice")` | Conclusions from Honcho's representation of Alice (across all sessions) |
| `get_representation("alice", target="bob")` | Conclusions from Alice's representation of Bob (from sessions Alice participated in) |
| `get_representation("alice", target="charlie")` | Conclusions from Alice's representation of Charlie (from sessions Alice participated in) |
| `representation("alice")` | Conclusions from Honcho's representation of Alice (across all sessions) |
| `representation("alice", target="bob")` | Conclusions from Alice's representation of Bob (from sessions Alice participated in) |
| `representation("alice", target="charlie")` | Conclusions from Alice's representation of Charlie (from sessions Alice participated in) |
### Code Examples
@ -104,7 +104,7 @@ session.add_messages([
# Different sessions with different participants
session2 = honcho.session("game-session-2")
session2.add_peers([alice, charlie])
session2.set_peer_config(alice, SessionPeerConfig(observe_others=True))
session2.set_peer_configuration(alice, SessionPeerConfig(observe_others=True))
session2.add_messages([
charlie.message("I didn't have breakfast. I lied to Bob.")
@ -137,7 +137,7 @@ await session.addMessages([
const session2 = await honcho.session("game-session-2");
await session2.addPeers([alice, charlie]);
await session2.setPeerConfig(alice, { observeOthers: true });
await session2.setPeerConfiguration(alice, { observeOthers: true });
await session2.addMessages([
charlie.message("I didn't have breakfast. I lied to Bob.")

4
docs/v3/documentation/features/chat.mdx
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@ -185,7 +185,7 @@ The chat endpoint shines when your LLM decides what it needs to know. This creat
### Use for runtime decisions
Don't just use chat for LLM prompts - use it to drive application logic, routing, and feature flags based on user behavior.
### Combine with get_context()
Use `get_context()` for conversation context and `peer.chat()` for specific insights. They complement each other.
### Combine with context()
Use `context()` for conversation context and `peer.chat()` for specific insights. They complement each other.
For more ideas on using the chat endpoint, see our [guides](/v3/guides/overview).

12
docs/v3/documentation/reference/sdk.mdx
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@ -612,13 +612,15 @@ const context = await session.context({ summary: true, tokens: 2000 });
const richContext = await session.context({
tokens: 2000,
peerTarget: "user",
peerPerspective: "assistant",
searchQuery: "What are my preferences?",
peerPerspective: "assistant",
limitToSession: true,
searchTopK: 10,
searchMaxDistance: 0.8,
includeMostFrequent: true,
maxConclusions: 25
representationOptions: {
searchTopK: 10,
searchMaxDistance: 0.8,
includeMostFrequent: true,
maxConclusions: 25
}
});
// Search session content

14
docs/v3/guides/integrations/n8n.mdx
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@ -60,7 +60,7 @@ The workflow has two parts (separated by sticky notes in the canvas):
2. **AI Chat Interface**: Chat Trigger → Agent (with LLM and Honcho tools)
The Agent uses Honcho's `get_context()` endpoint to retrieve relevant information about email conversations, enabling contextual conversations about your email data.
The Agent uses Honcho's `context()` endpoint to retrieve relevant information about email conversations, enabling contextual conversations about your email data.
![Complete workflow overview showing both data ingestion and chat sections](/images/integrations/n8n/complete_workflow.png)
@ -179,7 +179,7 @@ Add a **When chat message received** node (from LangChain nodes) to create an in
```
You are a helpful assistant that retrieves context about email conversations.
Use the Get_Context tool to retrieve session context.
Use the Context tool to retrieve session context.
Today's date: {{ $now }}
```
@ -192,7 +192,7 @@ Add an **OpenAI Chat Model** node (or your preferred LLM) and connect it to the
Create an HTTP Request Tool node for Honcho's context retrieval:
**Get Context Tool:**
**Context Tool:**
- **Method**: `GET`
- **URL**: `https://api.honcho.dev/v3/workspaces/email-test/sessions/new_session/context`
- Returns formatted context for the entire session including all messages and peer interactions
@ -229,7 +229,7 @@ Want to skip the manual setup? Import this workflow directly into n8n. In n8n, g
},
{
"parameters": {
"content": "## AI Chat With Honcho get_context()\nQuery your email data using natural language.\n\n**Run after data ingestion** to chat with the agent.",
"content": "## AI Chat With Honcho context()\nQuery your email data using natural language.\n\n**Run after data ingestion** to chat with the agent.",
"height": 480,
"width": 752
},
@ -265,7 +265,7 @@ Want to skip the manual setup? Import this workflow directly into n8n. In n8n, g
{
"parameters": {
"options": {
"systemMessage": "You are a helpful assistant that retrieves context about email conversations.\n\nUse the Get_Context tool to retrieve session context.\n\nToday's date: {{ $now }}"
"systemMessage": "You are a helpful assistant that retrieves context about email conversations.\n\nUse the Context tool to retrieve session context.\n\nToday's date: {{ $now }}"
}
},
"id": "049c3c19-756c-4755-88d9-94312857d9bb",
@ -550,7 +550,7 @@ Want to skip the manual setup? Import this workflow directly into n8n. In n8n, g
784
],
"id": "095e62d6-aae3-4eb8-9b9c-c473bfe2716d",
"name": "Get_Context",
"name": "Context",
"credentials": {
"httpBearerAuth": {
"id": "NbrkGo1GdYWQY3OX",
@ -693,7 +693,7 @@ Want to skip the manual setup? Import this workflow directly into n8n. In n8n, g
]
]
},
"Get_Context": {
"Context": {
"ai_tool": [
[
{

4
examples/crewai/python/pyproject.toml
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@ -1,6 +1,6 @@
[project]
name = "honcho-crewai"
version = "0.1.0"
version = "0.2.0"
description = "CrewAI integration with Honcho for persistent agent memory"
readme = "README.md"
requires-python = ">=3.10"
@ -34,7 +34,7 @@ classifiers = [
]
dependencies = [
"crewai>=0.134.0",
"honcho-ai>=0.2.0",
"honcho-ai>=2.0.0",
"openai>=1.0.0",
"python-dotenv>=1.0.0",
]

2
examples/crewai/python/src/honcho_crewai/__init__.py
View File

@ -53,7 +53,7 @@ from honcho_crewai.tools import (
HonchoSearchTool,
)
__version__ = "0.1.0"
__version__ = "0.2.0"
__all__ = [
"HonchoDependencyError",
"HonchoDialecticTool",

6
examples/crewai/python/src/honcho_crewai/tools.py
View File

@ -17,7 +17,7 @@ logger = logging.getLogger(__name__)
# Input Schemas
class GetContextInput(BaseModel):
"""Input schema for get_context tool."""
"""Input schema for context tool."""
tokens: Optional[int] = Field(
default=None, gt=0, description="Maximum number of tokens to include in the context"
@ -84,7 +84,7 @@ class HonchoGetContextTool(BaseTool):
def __init__(self, honcho: Honcho, session_id: str, peer_id: str) -> None:
"""
Initialize the get_context tool.
Initialize the context tool.
Args:
honcho: Honcho client instance
@ -105,7 +105,7 @@ class HonchoGetContextTool(BaseTool):
peer_perspective: Optional[str] = None,
) -> str:
"""
Execute get_context and format results.
Execute context retrieval and format results.
Args:
tokens: Maximum tokens to include

2681
examples/crewai/python/uv.lock
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2
examples/langgraph/python/pyproject.toml
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@ -5,7 +5,7 @@ description = "LangGraph integration with Honcho for stateful conversational AI"
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"honcho-ai",
"honcho-ai>=2.0.0",
"langchain-core>=1.0.6",
"langgraph>=1.0.3",
"openai>=1.99.7",

331
examples/langgraph/python/uv.lock
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@ -180,7 +161,7 @@ dependencies = [
[package.metadata]
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{ name = "langchain-core", specifier = ">=1.0.6" },
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{ name = "openai", specifier = ">=1.99.7" },
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[[package]]

116
examples/langgraph/typescript/bun.lock
View File

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@ -144,36 +82,32 @@
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}
}

2
examples/langgraph/typescript/package.json
View File

@ -18,7 +18,7 @@
"author": "",
"license": "MIT",
"dependencies": {
"@honcho-ai/sdk": "^1.5.0",
"@honcho-ai/sdk": "^2.0.0",
"@langchain/langgraph": "^1.0.2",
"dotenv": "^17.2.3",
"openai": "^6.9.1",

8
examples/n8n/n8n.json
View File

@ -18,7 +18,7 @@
},
{
"parameters": {
"content": "## AI Chat With Honcho get_context()\nQuery your email data using natural language.\n\n**Run after data ingestion** to chat with the agent.",
"content": "## AI Chat With Honcho context()\nQuery your email data using natural language.\n\n**Run after data ingestion** to chat with the agent.",
"height": 480,
"width": 752
},
@ -54,7 +54,7 @@
{
"parameters": {
"options": {
"systemMessage": "You are a helpful assistant that retrieves context about email conversations.\n\nUse the Get_Context tool to retrieve session context.\n\nToday's date: {{ $now }}"
"systemMessage": "You are a helpful assistant that retrieves context about email conversations.\n\nUse the Context tool to retrieve session context.\n\nToday's date: {{ $now }}"
}
},
"id": "049c3c19-756c-4755-88d9-94312857d9bb",
@ -339,7 +339,7 @@
784
],
"id": "095e62d6-aae3-4eb8-9b9c-c473bfe2716d",
"name": "Get_Context",
"name": "Context",
"credentials": {
"httpBearerAuth": {
"id": "NbrkGo1GdYWQY3OX",
@ -482,7 +482,7 @@
]
]
},
"Get_Context": {
"Context": {
"ai_tool": [
[
{